Thalesians Obrazy z Thalesians z całego świata w ciągu ostatnich 6 lat The Thalesians są think tankiem dedykowanych profesjonalistów zainteresowanych finansami ilościowymi, ekonomią, matematyką, fizyką i informatyką, niekoniecznie w tej kolejności. Blog Zobacz nasz nowy blog Thalesians Zarezerwuj Kup naszą nową książkę. Trading Thalesians - Czego pradawny świat może nas nauczyć na temat handlu dzisiaj (Palgrave Macmillan) przez współzałożyciela Thalesians, Saeed Amen przedmowa założyciela, Paul Bilokon Founding Grupa została założona we wrześniu 2008 r. Przez Paula Bilokona (wówczas analityka ilościowego w Lehman Brothers specjalizującej się w wymianie zagranicznej i w niepełnym wymiarze czasu pracy w Imperial College) oraz dwóch jego przyjaciół i współpracowników: Matthew Dixona (wówczas analityka ilościowego w Deutsche Bank) i Saeed Amen (wówczas stratega ilościowego w Lehman Brothers) . Otwarcie poziomu 39 w 2017 roku przez burmistrza Borisa Johnsona The Thalesians są teraz również członkiem poziomu 39 - największego w Europie akceleratora technologii dla finansów, handlu detalicznego, cyberbezpieczeństwa i przyszłych miast firm technologicznych Wydarzenia Badania Consulting Wydarzenia The Thalesians pierwotnie z siedzibą w Londynie, w Wielkiej Brytanii . W styczniu 2017 roku organizacja stała się prawdziwie globalna, kiedy Matthew Dixon przywiózł ją do Stanów Zjednoczonych, gdzie prowadzi seminaria Thalesians NYC z New York Leader Harvey Stein. Attila Agod to Budapeszt przewodniczący naszych seminariów w Thalesians Budapest. Obecnie jesteśmy w trakcie rozszerzania naszych seminariów do Pragi i prowadzenia kolejnych warsztatów. Badania Pod koniec 2017 roku rozpoczęliśmy publikowanie przełomowych notatek dotyczących strategii ilościowych. Nasz wysiłek jest kierowany przez Saeed Amen, wykorzystując prawie dziesięć lat jego doświadczenia, zarówno tworzenia, jak i późniejszego handlu systemowymi modelami transakcyjnymi w FX w dużych bankach inwestycyjnych. Odwiedź Research, aby uzyskać więcej informacji. Doradztwo W 2017 r. Zaczęliśmy oferować usługi konsultingu ilościowego na zamówienie, podpisując kontrakt z naszym pierwszym klientem, dużym amerykańskim funduszem hedgingowym i RavenPack, głównym dostawcą danych. Nasze usługi obejmują tworzenie dostosowanych na zamówienie systematycznych modeli handlowych i innych analiz ilościowych rynków finansowych, takich jak hedging walutowy i analiza kosztów transakcji walutowych (TCA). Odwiedź Consulting, aby uzyskać więcej informacji. Nasza filozofia Nazywamy się Thales of Miletus (), przedsoborowym greckim filozofem, który żył w ca. 624 pne - ca. 546 p. n.e. Thales był matematykiem i jest znany wielu uczniom szkół średnich z jednego z jego twierdzeń dotyczących geometrii. Ale co ważniejsze dla nas, był jednym z pierwszych użytkowników opcji: Thales, więc historia się toczy, ponieważ jego bieda była wyśmiewana bezużytecznością filozofii, ale z jego wiedzy o astronomii, którą zaobserwował, gdy jeszcze była zima, miał być duży zbiór oliwek, więc zebrał niewielką sumę pieniędzy i wpłacił depozyty zaokrąglające za wszystkie prasy oliwne w Miletus i Chios, które wynajął na niskim czynszu, ponieważ nikt go nie prowadził i kiedy nadszedł sezon, nagle pojawił się nagły popyt na kilka maszyn, a wypuszczając je na warunki, które mu się podobały, uświadomił sobie dużą sumę pieniędzy, udowadniając, że filozofom łatwo jest być bogatym, jeśli wybierz, ale nie na tym im zależy. Arystoteles, Polityka, 1259a. Morale tej anegdoty polega na tym, że filozofom łatwo jest być bogatym, jeśli wybiorą słynnego Milesa i udowodnią to. My, Talesowie. podziwiam go za to. Ale my również dzielimy wiele jego wartości, na przykład jego podstawową wiarę, że szczęśliwy człowiek jest zdefiniowany jako jeden, (który jest zdrowy w ciele, zaradny w duszy i łatwo nauczalnej naturze). To wiki zostało stworzone, aby służyć jako źródło informacji na temat finansowania ilościowego, zestawiać odniesienia do różnych powiązanych zasobów i służyć jako punkt konwergencji dla Thalesian. nasi koledzy i współpracownicy. Wyrosło z wiki finansów Paula Bilokona, którą założył w lutym 2007 roku. Wierzymy, że tajemnica i wierność są ważne w świecie finansów. Ale uznajemy także siłę wymiany informacji w otwartych społeczeństwach. Niech twoja logika biznesowa pozostanie ściśle strzeżoną tajemnicą. Ale uwolnij wszystko inne do domeny publicznej. To, co się kręci, obejdzie się w ten sposób, co ostatecznie pozwoli ci zreformować koło. Więcej naszych prelegentów na wydarzeniach w Thalesians w ciągu ostatnich 6 lat Nadchodzące wydarzenia Śro, 22 lutego: Saeed Amen Wed, 29 marca: TBD Śro, 26 kwietnia: TBD śr., 24 maja: TBD Seminarium Thalesians (Londyn) 8212 Saeed Amen 8212 Korzystanie z Pythona analizować rynki finansowe Rejestracja Jednym z popularnych sposobów modelowania dynamiki portfela zleceń dla najlepszej oferty i pytania na poziomie 1 jest stosowanie przybliżonych przybliżeń dyfuzji zredukowanej. Powszechnie wiadomo, że największym czynnikiem wpływającym na ruch cen jest brak równowagi w najlepszej ofercie i pytanie. Zbadamy dane z książek o ograniczeniach poziomu 1 koszyka zapasów i zbadamy dowody liczbowe dryfu, korelacji, zmienności i ich zależności od nierównowagi. W oparciu o odkrycia numeryczne opracowujemy nieparametryczny dyskretny model dla dynamiki najlepszej oferty i pytania. Model ten można zobrazować za pomocą modelu uproszczonego z analityczną łatwopalnością, która może jednocześnie dopasować dane empiryczne korelacji, zmienności i prawdopodobieństwa zmiany ceny. (Wspólna praca z Tzu-Wei Yang) Lingjiong Zhu dorastał w Szanghaju i wyjechał na studia do Anglii, gdzie otrzymał licencjat na Uniwersytecie Cambridge w 2008 roku. Następnie przeniósł się do Stanów Zjednoczonych i otrzymał tytuł doktora na Uniwersytecie Nowojorskim w 2017 roku. Po pracy w Morgan Stanley, poszedł do pracy na University of Minnesota jako adiunkt w Dunham Jackson, a następnie dołączył do wydziału na Florida State University jako adiunkt w 2018 roku. W wolnym czasie lubi czytać, podróżować i chodzić do wystaw sztuki, muzeów i koncertów muzyki klasycznej. IAQF-Thalesians Seminaria Seria seminariów IAQF-Thalesians to wspólny wysiłek IAQF (wcześniej IAFE) i Thalesians. Celem serii jest stworzenie forum wymiany nowych pomysłów i wyników związanych z obszarem finansów ilościowych. Cel ten realizowany jest poprzez organizowanie seminariów, na których wiodący praktycy i pracownicy akademiccy przedstawiają nowe prace, a także po seminariach z odbiorem w celu ułatwienia dalszej interakcji i dyskusji. Seria seminariów jest ograniczona wyłącznie do członków IAQF i Thalesians. Seminarium IAQF-Thalesians (Nowy Jork) 8212 Dr Sebastian Jaimungal 8212 Algorytmy handlowe z uczeniem się w utajonych modelach alfa Poniedziałek, 15 maja 2017: NYU Kimmel Center. Pokój 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Rejestracja Sygnały alfa dla strategii arbitrażu statystycznego często są powodowane czynnikami ukrytymi. W tym artykule przeanalizowano, jak optymalnie handlować z ukrytymi czynnikami, które powodują, że ceny przeskakują i rozpraszają się. Ponadto uwzględniamy wpływ działań handlowców na ceny notowane i ceny, które otrzymują z obrotu. Przy dość ogólnych założeniach pokazujemy, w jaki sposób przedsiębiorca może nauczyć się rozkładu wstecznego w stanach ukrytych i wyraźnie rozwiązać problem optymalnego handlu ukrytego w modzie online. Ponadto opracowujemy algorytm forward-backward oparty na maksymalizacji oczekiwań w celu skalibrowania modelu z czystym skokiem do danych historycznych, zilustrowania skuteczności optymalnej strategii poprzez symulacje i porównania ze strategiami, które ignorują uczenie się w ukrytych czynnikach. (Wspólna praca z Philippe Casgrain, U. Toronto) Dr Sebastian Jaimungal jest profesorem zwyczajnym na Wydziale Nauk Statystycznych Uniwersytetu w Toronto, gdzie jest dyrektorem programu Masters of Financial Insurance, uczy w Masters of Mathematical Program finansowy i program doktorancki. Sebastian jest obecnym przewodniczącym (i byłym zastępcą dyrektora programowego) w SIAM Financial Mathematics and Engineering (SIAGFMampE), jest współautorem książki "High-Frequency and Algorithmic Trading" wydanej przez Cambridge University Press (2018) i działa w redakcji wielu czasopism naukowych i branżowych, w tym: SIAM Journal w zakresie matematyki finansowej (SIFIN), International Journal of Theoretical and Applied Finance (IJTAF), High Frequency. Journal of Risks and Argo. Sebastian jest także członkiem założycielem Stowarzyszenia rynków towarowych i energii. IAQF-Thalesians Seminaria Seria seminariów IAQF-Thalesians to wspólny wysiłek IAQF (wcześniej IAFE) i Thalesians. Celem serii jest stworzenie forum wymiany nowych pomysłów i wyników związanych z obszarem finansów ilościowych. Cel ten realizowany jest poprzez organizowanie seminariów, na których wiodący praktycy i pracownicy akademiccy przedstawiają nowe prace, a także po seminariach z odbiorem w celu ułatwienia dalszej interakcji i dyskusji. Seria seminariów jest ograniczona wyłącznie do członków IAQF i Thalesians. Ostatnie wydarzenia Seminarium IAQF-Thalesians (New York) 8212 Dr. Alan Moreira 8212 Zmienność zarządzanych portfeli Środa, 15 lutego 2017 r .: NYU Kimmel Center. Pokój 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Rejestracja Zarządzane portfele, które podejmują mniejsze ryzyko, gdy zmienność jest wysoka, wytwarzają duże wartości alfa, zwiększają współczynniki Sharpe'a i generują duże zyski dla inwestorów o średniej odmienności. Dokumentujemy to dla rynku, wartości, pędu, rentowności, zwrotu z kapitału własnego i czynników inwestycyjnych, a także handlu walutami. Zmienność czasu zwiększa współczynniki Sharpe'a, ponieważ zmiany zmienności nie są kompensowane przez proporcjonalne zmiany oczekiwanych zwrotów. Nasza strategia jest sprzeczna z konwencjonalną mądrością, ponieważ w recesji wymaga względnie mniejszego ryzyka, ale nadal przynosi wysokie średnie zyski. Wyklucza to typowe wyjaśnienia oparte na ryzyku i stanowi wyzwanie dla strukturalnych modeli oczekiwanych zwrotów w czasie. Alan Moreira jest adiunktem w Wyższej Szkole Zarządzania Uniwersytetu Yale. Pochodzi z Rio de Janeiro w Brazylii, otrzymał tytuł licencjata na Uniwersytecie Federalnym w Rio de Janeiro (UFRJ) i doktorat z ekonomii finansowej na Uniwersytecie w Chicago. Badania dr Moreiras badają, w jaki sposób pośrednictwo finansowe kształtuje realną gospodarkę oraz przyczyny i konsekwencje fluktuacji niepewności. Jego badania zostały opublikowane w najlepszych czasopismach, w tym w Journal of Financial Economics i Journal of Finance. Oprócz nauczania zarządzania ryzykiem w programie MBA w Yale School of Management, dr Moreira uczy wartości aktywów na poziomie dr. W wolnym czasie lubi jeździć na rowerze, podróżować i spędzać czas z rodziną. Alan Moreira, Adiunkt Finansowy, Yale School of Management 1 IAQF-Seminaria Thalesian Seria seminariów IAQF-Thalesians to wspólny wysiłek IAQF (wcześniej IAFE) i Thalesians. Celem serii jest stworzenie forum wymiany nowych pomysłów i wyników związanych z obszarem finansów ilościowych. Cel ten realizowany jest poprzez organizowanie seminariów, na których wiodący praktycy i pracownicy akademiccy przedstawiają nowe prace, a także po seminariach z odbiorem w celu ułatwienia dalszej interakcji i dyskusji. Seria seminariów jest ograniczona wyłącznie do członków IAQF i Thalesians. Seminarium Thalesians (Londyn) 8212 Oskar Mencer 8212 Wieloskalowy przepływ danych Obliczenia ryzyka w chmurze hybrydowej Data i godzina 7:30 pm w środę 25 stycznia 2017 Rejestracja Natychmiastowa zmienność logarytmicznego powrotu w lognormalnym ułamkowym modelu SABR jest napędzana przez potęgowanie skorelowanego ułamkowego ruchu Browna. Ze względu na mieszany charakter napędzania ruchów Browna i ułamkowego Browna, gęstość prawdopodobieństwa dla takich modeli jest mniej znana w literaturze. Prezentujemy w tym wystąpieniu reprezentację mostu dla wspólnej gęstości lognormalnego ułamkowego modelu SABR w przestrzeni Fouriera. Ocena reprezentacji mostu wzdłuż właściwie wybranej ścieżki deterministycznej daje styl Edgewortha rozszerzania gęstości prawdopodobieństwa dla ułamkowego modelu SABR. Bezpośrednie uogólnienie reprezentacji do gęstości połączeń w wielu momentach prowadzi do heurystycznego wyprowadzenia zasady dużego odchylenia dla gęstości połączenia w małym czasie. Aproksymację zmienności implikowanej można łatwo uzyskać, stosując formułę asymptotyczną Laplace'a do wywołania lub ceny i porównując współczynniki. Prezentacja opiera się na wspólnej pracy z Jiro Akahori i Xiaoming Song. Tai-Ho Wang jest profesorem matematyki w Baruch College, City University of New York od 2017 roku. Jego badania w zakresie finansów ilościowych obejmują implikowane asymptotyki zmienności w krótkim czasie, arbitraż statyczny, darmowe ograniczenia opcji koszyka, optymalną likwidację i wykonanie w modelach wpływu na rynek , a ostatnio dynamika informacji na rynku finansowym. IAQF-Thalesians Seminaria Seria seminariów IAQF-Thalesians to wspólny wysiłek IAQF (wcześniej IAFE) i Thalesians. Celem serii jest stworzenie forum wymiany nowych pomysłów i wyników związanych z obszarem finansów ilościowych. Cel ten realizowany jest poprzez organizowanie seminariów, na których wiodący praktycy i pracownicy akademiccy przedstawiają nowe prace, a także po seminariach z odbiorem w celu ułatwienia dalszej interakcji i dyskusji. Seria seminariów jest ograniczona wyłącznie do członków IAQF i Thalesians. Seminarium IAQF-Thalesians (Nowy Jork) 8212 Dr Hongzhong Zhang 8212 Podpisywanie rynku na rynku z nocnymi kosztami zapasów Czwartek, 14 grudnia 2018 r .: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Rejestracja Udział w rynku przeprowadzanym przez firmy o wysokiej częstotliwości (HFT) stale rośnie. Cechą wyróżniającą HFT jest to, że handlują intraday, kończąc dzień na płasko. Aby rzucić światło na ekonomię HFT i odejść od istniejących teorii rynku, modelujemy HFT, który ma dostęp do nieograniczonej dźwigni finansowej w ciągu dnia, ale musi sfinansować wszelkie zapasy na koniec dnia z egzogenicznie ustalonymi kosztami. Mimo że koszty zapasów występują tylko pod koniec dnia, mają wpływ na dynamikę cen w ciągu dnia i płynność. Prowadzi to do endogenicznego mechanizmu wpływu cen na rynku dnia bieżącego. Z czasem zbliża się koniec dnia handlowego, nasila się wrażliwość cen na poziomy zapasów, zwiększając wpływ cen i zwiększając spready kupna i sprzedaży. Ponadto brak równowagi w zamówieniach typu "kupuj i sprzedaj" może katalizować podwyżki i spadki cen, nawet w przypadku stałych funkcji związanych z podażą i popytem. Empirycznie pokazujemy, że prognozy te są potwierdzane na rynku skarbowym w USA, gdzie spready między cenami kupna i sprzedaży mają tendencję do wzrostu pod koniec dnia. Ponadto zmiany cen są ujemnie skorelowane ze zmianami poziomu zapasów mierzonymi skumulowanym obrotem netto. (Wspólna praca z Tobiaszem Adrianem, Agostino Capponi i Erikiem Vogtem) Hongzhong Zhang jest adiunktem na Columbia University. Jego badania koncentrują się na szerokim zakresie zastosowanego prawdopodobieństwa z zastosowaniami inżynierskimi, finansowymi i ubezpieczeniowymi. W szczególności, niektóre z jego obecnych zainteresowań badawczych obejmują asymptotykę, wypłaty, optymalne zatrzymywanie i wykrywanie zmian w reżimie. IAQF-Thalesians Seminaria Seria seminariów IAQF-Thalesians to wspólny wysiłek IAQF (wcześniej IAFE) i Thalesians. Celem serii jest stworzenie forum wymiany nowych pomysłów i wyników związanych z obszarem finansów ilościowych. Cel ten realizowany jest poprzez organizowanie seminariów, na których wiodący praktycy i pracownicy akademiccy przedstawiają nowe prace, a także po seminariach z odbiorem w celu ułatwienia dalszej interakcji i dyskusji. Seria seminariów jest ograniczona wyłącznie do członków IAQF i Thalesians. Thalesians Xmas Party (Londyn) 8212 Iain Clark 8212 Domniemane wypłaty z ryzyka walutowego - odwrotności i prognozy dotyczące wyniku głosowania Brexit i wyborów Trumpa Chcielibyśmy zaprosić Cię na nasze seminarium świąteczne w Thalesians w Londynie, gdzie Iain Clark będzie prezentował Następnie odbędzie się nasza świąteczna impreza w barze GampTea w hotelu Marriott w Canary Wharf, gdzie będziemy podawać napoje i kanapki. Cena biletu obejmuje zarówno rozmowę, jak i imprezę (pierwsze kanapki z napojami). Wybór canape będzie obejmował niektóre z następujących produktów: Aubergine and haloumi wrap Brie i szynka parma brioche Crudits i hummus shot glasses Otwarta twarz wędzona bajgiel z łososia Mini burgery Lamb samosa Spring rolls Krewetki z ziemniaków muszle Data i godzina 7:30 pm w poniedziałek 12 grudnia 2018 r. Ginger Room, a następnie napoje i wzmacniacze w barze GampTea, Marriott Hotel, Canary Wharf, Londyn, Wielka Brytania, Meetup W maju 2018 r. w prezentacji QampA po prezentacji przez prelegenta zaobserwowano odwrócenie ryzyka GBPUSD wykazywali bardzo nietypowe zachowanie - mianowicie skrajne pochylenie w krótkich okresach, ale stosunkowo płaskie uśmiechy później. Jest to najbardziej nietypowy podpis zmienności i natychmiast nawiązano połączenie z nadchodzącym głosowaniem referendum w sprawie Brexitu. Mówca, w trybie pilnym, biorąc pod uwagę aktualny charakter rynku pre-Brexit, przeprowadził analizę ze swoim współautorem na temat implikowanej dystrybucji dla oczekiwań rynku dla GBPUSD wokół daty referendum (23 czerwca 2018 r.), Z przewidywaniami na miejscu odtąd. Dokument został przesłany do SSRN (ssrnabstract2794888) w dniu 13 czerwca, w którym zidentyfikowaliśmy dowody empiryczne w zmienności zmienności dla spadku GBPUSD z 1,4390 do zakresu 1,10 do 1,30 w przypadku oddania głosu - ruch w dół 0,14 do 0,34. Analiza ta, nietypowo dla badań ilościowych, otrzymała pokrycia w FT i Sunday Telegraph, a nasze prognozy zostały potwierdzone, gdy ogłoszono wynik referendum, a funt szterling spadł z 1,50 do 1,33, co oznacza spadek o 0,17 w ciągu kilku godzin. Po tej analizie zastosowaliśmy podobne metody do meksykańskiego peso w stosunku do dolara amerykańskiego (USDMXN) tuż przed wyborami w USA w 2018 r. I byliśmy w stanie przewidzieć dewaluację peso w zakresie 20-24 pesos za dolara w przypadku Trump zwycięstwo, które zostało potwierdzone przez kolejne wydarzenia. Podczas tego wykładu przejdę przez naszą analizę informacji zawartych w skośności zmienności i podstawę naszej analizy prognostycznej. Iain J. Clark (MIMA CMath, MInstP CPhys, CStat, FRAS) ma ponad 14-letnie doświadczenie jako front office quant. Pracował jako kierownik analizy ilościowej FX i towarów w banku standardowym, jako szef analizy ilościowej FX w Unicredit i Dresdner Kleinwort oraz w Lehman Brothers, BNP Paribas i JP Morgan. Iain ma doktorat z matematyki stosowanej na Queensland University oraz tytuł magistra matematyki finansowej z uniwersytetów w Edynburgu i Heriot-Watt. Jego główne zainteresowania badawcze dotyczą opcji egzotycznych, modeli stochastycznych dla walut i towarów oraz metod numerycznych wyceny opcji. Często uczestniczy w konferencjach branżowych, szkoleniach i wykłada na różnych uniwersytetach. Jego pierwsza książka "Kurs wymiany walut: przewodnik dla praktyków" została opublikowana w listopadzie 2017 r. Przez Wiley Finance, a jego druga książka "Cennik opcji cenowych: Przewodnik dla praktyków" ma pojawić się na początku 2017 r. (Również z Wiley Finance). Seminarium Thalesians (Londyn) 8212 Vlasios Voudouris 8212 Elastyczna nauka maszynowa dla finansów Data i godzina 7:30 pm w środę 23 listopada 2018 Ginger Room, Marriott Hotel, Canary Wharf, Londyn, UK. Spotkanie Wraz z szybkimi zmianami w technologii obliczeniowej i dużym wiekiem danych, dziedzina nauki o danych jest stale kwestionowana. Zadaniem naukowców zajmujących się danymi jest zrozumienie ogromnej ilości danych: wyodrębnienie ważnych wzorców i trendów oraz zrozumienie danych. Wyzwania związane z uczeniem się na podstawie danych doprowadziły do rewolucji w technikach uczenia maszynowego. Zestaw narzędzi GAMLSS w naszej próbie uczenia się na danych finansowych. GAMLSS jest obecnie szeroko stosowany w analizach predykcyjnych i kwantyfikacji ryzyka (np. Utrata wartości domyślnej). Ze względu na elastyczność modeli GAMLSS możemy scharakteryzować następujące cechy danych: Charakterystyki rozłożonych danych o dużej elastyczności lub jasności. Oznacza to, że prawdopodobieństwo rzadkich zdarzeń (np. Wartość odstająca) występuje z wyższym lub niższym prawdopodobieństwem w porównaniu z rozkładem normalnym. Ponadto prawdopodobieństwo wystąpienia wartości odstającej może zmienić się w zależności od wartości objaśniających. Skośność zmiennej odpowiedzi, która może zmieniać się w zależności od zmiennych objaśniających. Nieliniowa lub płynna relacja między zmienną docelową a zmiennymi objaśniającymi. W oparciu o naszą książkę "Elastyczna regresja i wygładzanie": użycie GAMLSS w R, wykład zawiera dużą liczbę praktycznych przykładów (na przykład przewidywań i kwantyfikacji ryzyka), które odzwierciedlają zakres problemów poruszanych przez modele GAMLSS. Oznacza to również, że przykłady stanowią praktyczną ilustrację procesu korzystania z modeli GAMLSS do uczenia maszynowego. Vlasios Voudouris jest ekspertem ds. Danych z doświadczeniem w zakresie analizy predykcyjnej opartej na danych i kwantyfikacji ryzyka na rynkach finansowych. Jego główne badania koncentrują się na: i) półparametrycznych modelach uczenia maszynowego ii) innowacyjnych procesach selekcji modeli oraz iii) solidnej diagnostyce dla systematycznego handlu i kwantyfikacji ryzyka. Jest współautorem książki "Elastyczna regresja i wygładzenie": używanie GAMLSS w R i związanego z nim oprogramowania w R i Javie. GAMLSS (uogólnione modele addytywne dla skali i kształtu lokalizacji) polega na uczeniu się z danych przy użyciu semi-parametrycznych nadzorowanych algorytmów uczenia maszynowego. Ponadto Vlasios opracował oparte na danych modele agentowe do scenariuszy testów warunków skrajnych (z naciskiem na rynki towarowe). Jego modele i narzędzia są używane przez wiele organizacji. W ramach dwóch konkretnych przykładów: 1) MFW wykorzystał GAMLSS do testów warunków skrajnych w systemie finansowym USA 2) Vlasios i jego współpracownicy przedstawili zestaw modeli GAMLSS dla Banku Anglii (BoE). Korzystając z GAMLSS, Vlasios opracował systematyczny model transakcyjny dla WTI Crude Oil (NYMEX). Vlasios posiada doktorat z City, University of London. Seminarium IAQF-Thalesians (Nowy Jork) 8212 Dr Michael Imerman 8212 Wgląd w analizę opartą na danych na temat ryzyka zmienności ryzyka Czwartek, 17 listopada 2018 r .: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Rejestracja Większość tych rozmów będzie pochodzić ze wspólnej pracy, którą zrobiłem z Jianqing Fan w Princeton i Wei Dai w Dimensional Fund Advisors. Postanowiliśmy przeprowadzić czysto opartą na danych analizę premii za ryzyko zmienności, korzystając z narzędzi z zakresu finansowania o wysokiej częstotliwości i analizy Big Data. Twierdzimy, że premię za ryzyko zmienności, luźno zdefiniowaną jako różnicę pomiędzy zmiennością zrealizowaną a implikowaną, najlepiej można zrozumieć, gdy postrzega się ją jako systematyczną odchylenie cenowe. Najpierw korzystamy z danych transakcyjnych o bardzo wysokiej częstotliwości dla SPDR oraz nowatorskiego podejścia do szacowania zmienności zintegrowanej w dziedzinie częstotliwości w celu obliczenia zmienności realizowanej. Od tego momentu odejmujemy codziennie VIX, naszą miarę zmienności implikowanej, aby skonstruować szereg czasowy premii za ryzyko zmienności. Aby zidentyfikować czynniki odpowiedzialne za premię za ryzyko zmienności jako kosztowną stronniczość, rozłożymy ją na wielkość i kierunek. Mamy przekonujące dowody, że wielkość odchylenia realizowanej zmienności od implikowanej zmienności reprezentuje nierównowagę podaży i popytu na rynku w celu zabezpieczenia ryzyka ogółu. Trudno jednoznacznie zaakceptować hipotezę, że kierunek lub znak premii za ryzyko zmienności odzwierciedla oczekiwania dotyczące przyszłych poziomów zmienności. Jednak dowody potwierdzają hipotezę, że znak premii za ryzyko zmienności wskazuje na zyski lub straty portfela zabezpieczonego w delcie zgodnie z Bakshi i Kapadią (2003). Jako ktoś, kto pochodził z wykształcenia finansowego, ale rozwinął skłonność do nauki o danych i analiz, spędzę trochę czasu na końcu mojego przemówienia na temat moich przemyśleń na temat tego, w jaki sposób obejmuje się naukę o danych (w pewnym sensie i w innych) przez ilościową społeczność finansową. Michael B. Imerman jest Theodore A. Lauer, wybitny profesor inwestycji i adiunkt w Wydziale Finansów Perella na Uniwersytecie Lehigh. Dr Imermans poprzednie wizyty były w Princeton w Departamencie ORFE i Rutgers Business School, skąd otrzymał tytuł doktora. Przed przyjazdem na uniwersytet Imerman pracował jako analityk w firmie Lehman Brothers, obsługując wysokiej jakości platformy kredytowe i kredytowe instrumenty pochodne. W Lehigh profesor Imerman uczy pochodnych i zarządzania ryzykiem zarówno na poziomie licencjackim, jak i magisterskim. Jego głównym obszarem badań jest modelowanie ryzyka kredytowego z aplikacjami do bankowości, zarządzaniem ryzykiem i regulacjami finansowymi. Ostatnio był aktywnie zaangażowany w integrację technik informatycznych w zakresie oceny ryzyka na rynku sekurytyzowanych kredytów hipotecznych. IAQF-Thalesians Seminaria Seria seminariów IAQF-Thalesians to wspólny wysiłek IAQF (wcześniej IAFE) i Thalesians. Celem serii jest stworzenie forum wymiany nowych pomysłów i wyników związanych z obszarem finansów ilościowych. Cel ten realizowany jest poprzez organizowanie seminariów, na których wiodący praktycy i pracownicy akademiccy przedstawiają nowe prace, a także po seminariach z odbiorem w celu ułatwienia dalszej interakcji i dyskusji. Seria seminariów jest ograniczona wyłącznie do członków IAQF i Thalesians. Seminarium Thalesians (Londyn) 8212 Prof David Hand 8212 Zasada nieprawdopodobieństwa: dlaczego spotykają się przypadki, cuda i rzadkie zdarzenia Codziennie Rejestracja daty i czasu Sprzedawcy swapów z wariancją otrzymują premię za ryzyko różniące się w czasie dla ich ekspozycji na zrealizowaną wariancję, poziom wariancji stawki swapowe i nachylenie krzywej zamiany wariancji. Aby zmierzyć wariancję długoterminową, szacujemy model dynamicznej struktury terminowej, w której ceny wariancji zmieniają się w USA, Wielkiej Brytanii, Europie i Japonii. Model rozkłada krzywą swapu wariancji na struktury terminowe premii za ryzyko i oczekiwanych wielkości ryzyka. Empirycznie dokumentujemy silną strukturę czynników w globalnych stopach swapów z wariancją i stwierdzamy, że premie z tytułu wariancji są ujemnie skorelowane z bogactwem sektora pośrednictwa finansowego. Nasze wyniki potwierdzają hipotezę, że pośrednicy finansowi są krańcowym inwestorem na rynku wymiany wariancji. Erik Vogt jest ekonomistą finansowym w funkcji rynków kapitałowych Banku Rezerw Federalnych w Nowym Jorku. Jego główne zainteresowania badawcze dotyczą wyceny aktywów, ekonometrii finansowej, zmienności i ryzyka płynności oraz danych o wysokiej częstotliwości w różnych klasach aktywów, w tym akcjach, obligacjach skarbowych, instrumentach pochodnych i obligacjach przedsiębiorstw. Jego badania dotyczące płynności rynku i pośredników w handlu brokerami otrzymały między innymi informacje w mediach Bloomberg, Reuters i Yahoo Finance, a także cytowane w zeznaniach Senatu USA przed Podkomitet ds. Papierów Wartościowych, Ubezpieczeń i Inwestycji oraz Podkomitet ds. Polityki Gospodarczej , Komisja ds. Bankowości, mieszkalnictwa i spraw miejskich. Erik aktywnie służy jako recenzent w wielu recenzowanych czasopismach, w tym w przeglądzie badań finansowych, w czasopiśmie Econometrics, w Journal of Empirical Finance, Journal of Econometrics finansowych i Quantitative Finance. Erik dołączył do nowojorskiego Fed w lipcu 2017 r. I posiada tytuł doktora. i M. A. w dziedzinie ekonomii na Duke University i licencjata. z matematyki i ekonomii z London School of Economics. Przed ukończeniem szkoły średniej pracował jako Associate Economist w Federal Reserve Bank of Chicago. IAQF-Thalesians Seminaria Seria seminariów IAQF-Thalesians to wspólny wysiłek IAQF (wcześniej IAFE) i Thalesians. Celem serii jest stworzenie forum wymiany nowych pomysłów i wyników związanych z obszarem finansów ilościowych. Cel ten realizowany jest poprzez organizowanie seminariów, na których wiodący praktycy i pracownicy akademiccy przedstawiają nowe prace, a także po seminariach z odbiorem w celu ułatwienia dalszej interakcji i dyskusji. Seria seminariów jest ograniczona wyłącznie do członków IAQF i Thalesians. Seminarium Thalesians (Londyn) 8212 Nick Baltas 8212 Strategie przenoszenia wielu zasobów Data i godzina 7:30 pm. w środę 28 września 2018 Ginger Room, Marriott Hotel, Canary Wharf, Londyn, UK. Strategie Meetup Carry były przede wszystkim badane i eksplorowane na rynkach walutowych, gdzie, w przeciwieństwie do niezabezpieczonego parytetu stóp procentowych, pożyczki z kraju o niskim oprocentowaniu i inwestowanie w kraj o wysokim oprocentowaniu historycznie dawały pozytywne i statystycznie znaczące zyski. Ta prezentacja rozszerza pojęcie przenoszenia do różnych klas aktywów, patrząc na kontrakty terminowe na towary, indeksy akcji i obligacje rządowe. Zbadamy opłacalność wariantów przekrojowych i szeregów czasowych strategii carry w ramach każdej klasy aktywów, ale przede wszystkim badamy korzyści wynikające z budowy strategii przenoszenia aktywów o wielu aktywach po prawidłowym uwzględnieniu struktury kowariancji całego wszechświata. Nick Baltas jest dyrektorem wykonawczym w ramach grupy Global Quantitative Research w UBS. Jego zainteresowania badawcze obejmują systematyczne strategie dotyczące wielu aktywów, budowę portfela, analizę ryzyka i ocenę wyników. Nick dołączył do UBS w lutym 2017 r. I od tego czasu utrzymuje także stanowiska akademickie w Imperial College Business School i Queen Mary University of London. Jego badania zostały nagrodzone licznymi grantami i nagrodami oraz cytowane przez prasę finansową. Przed objęciem obecnej funkcji Nick spędził dwa lata jako wykładowca finansów w Imperial College Business School, gdy przez dwa lata był nagradzany nagrodą Star Teacher of the Year w uznaniu za jego nauczanie, a prawie rok jako menedżer ds. Ryzyka w Londynie oparty na funduszu hedgingowym. Ma tytuł DEng w dziedzinie inżynierii elektrycznej i komputerowej na Narodowym Uniwersytecie Technicznym w Atenach, magistra w dziedzinie przetwarzania sygnału wzmacniacza komunikacyjnego w Imperial College London oraz doktorat z finansów w Imperial College Business School. IAQF-Thalesians Seminarium (Nowy Jork) 8212 Dr Arun Verma 8212 Arbitraż statystyczny wykorzystujący strategie handlu ilościowego oparte na wiadomościach i nastrojach społecznych Czwartek, 15 września 2018 r .: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Rejestracja W celu poznania wartości osadzonej w danych News Amplituda Sentymentu Społecznego, budujemy trzy rodzaje strategii inwestowania w akcje oparte na danych dotyczących nastrojów i pokazujemy, że strategie oparte na sentymentach przewyższają znacząco indeksy porównawcze. Arun Verma dołączył do grupy Bloomberg Quantitative Research w 2003 roku. Wcześniej zdobył doktorat z Cornell University w dziedzinie matematyki stosowanej w dziedzinie informatyki. W firmie Bloomberg dr Vermas początkowo pracował nad modelami zmienności stochastycznej dla wyceny instrumentów EquityFX i Exotics, np. Arbitrage free Volatility interpolation, Variance Swaps and VIX FuturesOptions pricing and Cross Currency Volatility Surface construction. More recently, he has enjoyed working at the intersection of such areas as data science, innovative quantitative techniques and interactive visualizations for help reveal embedded signals in financial data, e. g. building quant trading strategies for statistical arbitrage. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians Seminar (London) 8212 Scott Cogswell 8212 Initial Margin Model and Regulation for Uncleared Derivatives Date and Time 7:30 p. m. on Wednesday 20th July 2018 Meetup Deep Learning has experienced explosive growth over the last few years with applications in diverse areas such as biomedicine, language processing and self-driving cars. The goal of this talk is to give an introduction to Deep Learning from the perspective of learning patterns in sequences, with an emphasis on understanding the core principles behind the algorithms. We will review the latest advances in Recurrent Neural Networks and discuss applications of RNNs to learning patterns in market data. Steve Hutt is a consultant in Deep Learning and Financial Risk, currently working for CME Group. He has previously been head quant for credit at UBS and Morgan Stanley, and before that a mathematician doing stuff in an obscure branch of topology. IAQF-Thalesians Seminar (New York) 8212 Dr. Tobias Adrian 8212 Nonlinearity and Flight-to-Safety in the Risk-Return Tradeoff for Stocks and Bonds Thursday, June 16, 2018: NYU Kimmel Center. Room 905907, Kimmel Center, 60 Washington Square South, NY 10012, NY Registration We document a highly significant, strongly nonlinear dependence of stock and bond returns on past equity-market volatility as measured by the VIX. We propose a new estimator for the shape of the nonlinear forecasting relationship that exploits additional variation in the cross section of returns. The nonlinearities are mirror images for stocks and bonds, revealing flight to safety: Expected returns increase for stocks when volatility increases from moderate to high levels, while they decline for Treasuries. We further demonstrate that these findings are evidence of dynamic asset pricing theories where the time variation of the price of risk is a function of the level of the VIX. Tobias Adrian is a Senior Vice President of the Federal Reserve Bank of New York and the Associate Director of Research and Statistics Group. His research covers asset pricing, financial intermediation, and macroeconomics, with a focus on the aggregate implications of capital market developments. He has contributed to the NY Feds financial stability policy and to its monetary policy briefings. Tobias Adrian holds a Ph. D. from MIT and a MSc from LSE. He has taught at MIT, Princeton University, and NYU. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians Seminar (Zurich) 8212 Felix Zumstein - Python in Quantitative Finance Date and Time 7:00 p. m. on Thursday, 9 June, 2018 Examining the electronic trading business from a practitioners perspective. This business has undergone many changes in recent years due to the emergence of new hardware and software products, the development of new quantitative and computational techniques, and changes in market structure and regulations. A market maker needs to be agile in order to remain competitive. This synoptic talk briefly considers the various factors that come into a market makers business calculus. Paul A. Bilokon is Director at Deutsche Bank, where he runs the global credit and core quant teams, part of Markets Electronic Trading (MET) group. He is one of the pioneers of electronic trading in credit, including indices, single names, and cash, and has worked in e-trading, derivatives pricing, and quantitative finance at bulge bracket institutions, including Morgan Stanley, Lehman Brothers, Nomura, and Citigroup. His more than a decade-long career spans many asset classes: equities, FX spot and options, rates and credit. Paul was educated at Christ Church, Oxford, and Imperial College. The domain-theoretic framework for continuous-time stochastic processes, developed with Prof. Abbas Edalat, earned him a PhD degree and a prestigious LICS paper. Pauls other academic interests include stochastic filtering and machine learning. He is an expert developer in C, Java, Python, and kdbq, with a special interest in high performance scientific computing. His interests in philosophy and finance led him to formulate the vision for and found Thalesians, a think tank of dedicated professionals working in quant finance, economics, mathematics, physics and computer science, the focal point of a community with over 1,500 members worldwide. He serves as its CEO, and runs it with two of his friends and colleagues, Saeed Amen and Matthew Dixon, as fellow Directors. Dr. Bilokon is a joint winner of the Donald Davis Prize (2005), winner of the British Computing Society Award for the Student Making the Best Use of IT (World Leadership Forums SET award, 2005), Ward Foley Memorial Scholarship (2001), two University of London High Achiever Awards (in mathematics and physics, 1999) a Member of the British Computer Society, Institution of Engineering and Technology, and European Complex Systems Society Associate of the Securities and Investment Institute, and Royal College of Science and a frequent speaker at premier conferences such as Global Derivatives, alphascope, LICS, and Domains. IAQF-Thalesians Seminar (New York) 8212 Dr. Luis Seco 8212 Hedge funds: are negative fees in the horizon An option pricing perspective Thursday, May 12, 2018: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Registration The growth of the hedge fund sector is creating a difficult environment for start-ups, which is creating a climate that favors innovative fee structures. In this talk we will review some of them, and will propose a costbenefit analysis using Black-Scholes option pricing which will show that in some situations, the manager will pay the investor. Luis Seco is a Professor of Mathematics at the University of Toronto, where he also directs the Mathematical Finance Program and the RiskLab, a research laboratory that specializes in risk management research. He is the President and CEO of Sigma Analysis amp Management, an asset management firm that provides hedge fund investment products that employ managed account structures to obtain unique transparency, analytics and liquidity services. He holds a PhD in Mathematics from Princeton and was a Bateman Instructor at the California Institute of Technology. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. ThalesiansQuant Finance Group Germany (Frankfurt) 8212 Thomas Wiecki 8212 Predicting out-of-sample performance and building multi-strategy portfolios using Random Forests Date and Time 7:30 p. m. on Wednesday 11th May 2018 PPI AG Office, Wilhelm-Leuschner-Strae 79, Frankfurt Am Main Meetup FREE event, kindly hosted by PPI Thanks for Jochen Papenbrock and Adrian Zymolka for organising and for PPI for hosting. The question of how predictive a backtest is of out-of-sample performance is at the heart of algorithmic trading. Using a unique dataset of 888 algorithmic trading strategies developed and backtested on the Quantopian platform with at least 6 months of out-of-sample performance, we study the prevalence and impact of backtest overfitting. Specifically, we find that commonly reported backtest evaluation metrics like the Sharpe ratio offer little value in predicting out of sample performance (R lt 0.025). However, we show that by training a Random Forest regressor on a variety of features that describe backtest behavior, out-of-sample performance can be predicted at a much higher accuracy (R 0.17) on hold-out data compared to using linear, univariate features. We then show that we can construct a multi-strategy portfolio based on predictions by the Random Forest which performed significantly better out-of-sample than other alternatives. Thomas Wiecki is the Data Science Lead at Quantopian focusing Bayesian models to evaluate trading algorithms. Previously, he was a Quantitative Researcher at Quantopian developing an open-source trading simulator as well as optimization methods for trading algorithms. Thomas holds a PhD from Brown University. Global Derivatives (Budapest - External Event) 8212 Speakers including Carr amp Hull 8212 Trading and risk management Thalesians Workshop Date and Time 9th - 13th May, 2018 Hotel Intercontinental, Budapest, Hungary To sign up You can register for this event and pay online at the Global Derivatives Europe website: icbi-derivativesFKN2466TH - Members of the Thalesians receive a 15 discount (click on the link to activate) The Worlds Largest Quant Finance Conference Join 500 Quants amp Traders From Around The World Over 130 Sessions Covering 5 Full Days Of Content 120 Expert Speakers Buy-Side Summit: Quantitative Investment amp Portfolio Strategies Fintech amp Disruptive Innovation Summit Unmissable speakers for 2018 Peter Carr, Global Head of Market Modelling, Morgan Stanley John Hull, Professor Of Derivatives amp Risk Management, University of Toronto Zoltan Eisler, Co-Head of Execution, Capital Fund Management Fabrizio Anfuso, Head of Collateralized Exposure Modelling, Credit Suisse Th alesians Workshop on ElectronicSystematic Trading at Global Derivatives The Thalesians will be running a workshop at Global Derivatives, which will be led by Saeed Amen and Paul Bilokon, who have a combined experience of two decades in this field. Topics to be discussed include market microstructure and an interactive Python session on systematic trading strategies. Introduction to algorithmic trading and market microstructure models Foundations of linear filtering with applications Foundations of nonlinear filtering with applications How can we define beta in FX and how can we make it smarter Trading with Big Data: Creating systematic trading strategies in FX and fixed income, using new forms of data, with a focus on central bank communications, alpha capture amp news analytics Trading Strategy Focus: How to build a CTAtrend following fund Python amp PyThalesians: Going from systematic trading ideas to backtesting in Python (with tutorial) Author Talk: Trading Thalesians What the ancient world can teach us about trading today (Palgrave Macmillan) External: Emerging Quant Managers (Chicago) 8212 Euan Sinclair 8212 Systematic Vol Trading Date and Time 3:30 p. m. on Friday 6th May 2018 In this talk, we investigate whether we can improve the risk adjusted returns of a traditional, directional (CTA style) trend following strategy by employing systematic option trading strategies. We shall be looking at several markets including FX and equities. Jacob Bartram has extensive experience in trading at both banks and hedge funds. His background includes FX option and volatility trading, along with trading system design and development. He has presented at numerous industry conferences, including Global Derivatives and TradeTech FX. IAQF-Thalesians Seminar (New York) 8212 Dr. Lawrence R. Glosten 8212 Strategic Foundation for the Tail Expectation in Limit Order Book Markets Thursday, April 14, 2018: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Registration We analyze the strategic interactions of liquidity suppliers quoting on a limit order book. In an environment with noise traders and informed traders trading on news we show that there is an equilibrium that feature quoters using mixed strategies each offering the same quantity of shares at random prices (and, of course, random bid prices). These random prices with the associated quantities form the market quotes and the depth of book, or price schedule. There are equilibria with a smaller number of quoters quoting a larger number of shares and equilibria with a larger number of quoters quoting a smaller number of shares. Considering a sequence of equilibria with the number of quoters getting large, we establish that the stochastic equilibrium price schedule converges to the zero profit deterministic competitive price schedule. An offer (or bid) is characterized as the expectation of the future value conditional on the offer being picked off by a larger buy (or sell) order. Lawrence R. Glosten is the S. Sloan Colt Professor of Banking and International Finance at Columbia Business School. He is also co-director (with Merritt Fox and Ed Greene) of the Program in the Law and Economics of Capital Markets at Columbia Law School and Columbia Business School and is an adjunct faculty member at the Law School. He has been at Columbia since 1989, before which he taught at the Kellogg Graduate School of Management at Northwestern University, and has held visiting appointments at the University of Chicago and the University of Minnesota. He has published articles on the microstructure and industrial organization of securities markets the relationship between venture capitalists and entrepreneurs evaluating the performance of portfolio managers asset pricing and more recently exploration of the law and economics of capital market regulation. His work on electronic exchanges in the Journal of Finance won a Smith Breeden Distinguished Paper Prize. He has served as an editor of the Review of Financial Studies, associate editor of the Journal of Finance and serves on several other editorial boards. He has been a consultant for the New York Stock Exchange, Justice Department, and SEC and has served on the NASDAQ Economic Advisory Board. He received his AB from Occidental College (1973) and his Ph. D. in managerial economics from Northwestern University (1980). IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians Seminar (London) 8212 Robin Hanson 8212 Economics when robots rule the Earth (Book) Date and Time 7:30 p. m. on Monday, 21 March, 2018 Level39, One Canada Square, Canary Wharf, London, E14, UK Meetup FREE event - kindly sponsored by the Level39 - fintech accelerator - level39.co Full title: The Age of Em: Work, Love and Life when Robots Rule the Earth (Amazon pre-order book here ) Robots may one day rule the world, but what is a robot-ruled Earth like Many think the first truly smart robots will be brain emulations or ems. Scan a human brain, then run a model with the same connections on a fast computer, and you have a robot brain, but recognizably human. Train an em to do some job and copy it a million times: an army of workers is at your disposal. When they can be made cheaply, within perhaps a century, ems will displace humans in most jobs. In this new economic era, the world economy may double in size every few weeks. Some say we cant know the future, especially following such a disruptive new technology, but Professor Robin Hanson sets out to prove them wrong. Applying decades of expertise in physics, computer science, and economics, he uses standard theories to paint a detailed picture of a world dominated by ems. While human lives dont change greatly in the em era, em lives are as different from ours as our lives are from those of our farmer and forager ancestors. Ems make us question common assumptions of moral progress, because they reject many of the values we hold dear. Read about em mind speeds, body sizes, job training and career paths, energy use and cooling infrastructure, virtual reality, aging and retirement, death and immortality, security, wealth inequality, religion, teleportation, identity, cities, politics, law, war, status, friendship and love. This book shows you just how strange your descendants may be, though ems are no stranger than we would appear to our ancestors. To most ems, it seems good to be an em. Robin Dale Hanson is an associate professor of economics at George Mason University and a research associate at the Future of Humanity Institute of Oxford University. He is known as an expert on idea futures and markets, and he was involved in the creation of the Foresight Exchange and DARPAs FutureMAP project. He invented market scoring rules like LMSR (Logarithmic Market Scoring Rule)used by prediction markets such as Consensus Point (where Hanson is Chief Scientist), and has conducted research on signaling. MathFinance 2018 (Frankfurt - External Event) 8212 Speakers including Wystup amp Dupire 8212 Quant event Date and Time 21-22st March 2018 Frankfurt School of Finance amp Management To sign up You can find out more about this event and register and pay online at the MathFinance website: mathfinanceconference. html In the past 16 years the MathFinance Conference became to one of the top quant events tailored to the European Finance Community. The conference is intended for practitioners in the areas of trading, quantitative or derivative research, risk and asset management, insurance as well as for academics studying or researching in the field of financial mathematics or finance in general. The Conference talks are given by both industry experts and top academics. A wide range of subjects is covered, from state-of-the-art approaches to key issues faced in industry and academia to IT implementation and pricing software. There will be enough time for questions and discussions after each talk and additional breaks provide you the opportunity to build networks within the quantitative finance community. Many speakers who have also spoken at the Thalesians will be speaking, including Uwe Wystup and Attilio Meucci. Many other well known figures such as Bruno Dupire will also be addressing the conference. IAQF-Thalesians Seminar (New York) 8212 Dr. Alexander Lipton 8212 Modern Monetary Circuit Theory Tuesday, March 15, 2018: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Registration A modern version of Monetary Circuit Theory with a particular emphasis on stochastic underpinning mechanisms is developed. It is explained how money is created by the banking system as a whole and by individual banks. The role of central banks as system stabilizers and liquidity providers is elucidated. Both the Chicago Plan and the Free Banking Proposal are discussed. It is shown how in the process of money creation, banks become naturally interconnected. A novel Extended Structural Default Model describing the stability of the Interconnected Banking Network is proposed. The purpose of bank capital and liquidity is explained. A multi-period constrained optimization problem for a banks balance sheet is formulated and solved in a simple case. Both theoretical and practical aspects are covered. Alexander Lipton is a Managing Director, Quantitative Solutions Executive at Bank of America, Visiting Professor of Quantitative Finance at University of Oxford and Advisory Board member at the Oxford-Man Institute. Prior to his current role, he was a Managing Director, Co-head of the Global Quantitative Group at Bank of America Merrill Lynch and a Visiting Professor of Mathematics at Imperial College London. Earlier, he was a Managing Director and Head of Capital Structure Quantitative Research at Citadel Investment Group in Chicago he has also worked for Credit Suisse, Deutsche Bank and Bankers Trust. Before switching to finance, Alex was a Full Professor of Mathematics at the University of Illinois and a Consultant at Los Alamos National Laboratory. He received his undergraduate and graduate degrees in pure mathematics from Moscow State University. Liptons interests encompass all aspects of financial engineering, including large-scale bank balance sheet modeling and optimization, enterprise-wide holistic risk management and stress testing, CCPs, electronic trading, trading strategies, payment systems, theory of monetary circuit, as well as hydrodynamics, magnetohydrodynamics, and astrophysics. Lipton authored two books, and edited five books, including, most recently, Risk Quant of the Year Award, Risk Books, London, 2017, and The Oxford Handbook of Credit Derivatives, Oxford University Press, Oxford, 2017 (with Andrew Rennie). He published more than a hundred scientific papers on a variety of topics in applied mathematics and financial engineering. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians Seminar (London) 8212 Prof Jessica James 8212 FX Option Trading (Book) Date and Time 7:30 p. m. on Monday, 29 February, 2018 Ginger Room, Marriott Hotel, Canary Wharf, London, UK. Meetup Full title: FX Option Performance - An Analysis of the Value Delivered by FX Options Since the Start of the Market (The Wiley Finance Series) (Amazon book order here ) Get the little known yet crucial facts about FX options Daily turnover in FX options is an estimated U. S. 207 billion, but many fundamental facts about this huge and liquid market are generally unknown. FX Option Performance provides the information practitioners need to be more effective in the market, with detailed, specific guidance. This book is a unique and practical guide to option trading, with the courage to report how much these contracts have really made or lost. Breaking free from the typical focus on theories and generalities, this book gets specific travelling back in history to show exactly how options performed in different markets and thereby helping investors and hedgers alike make more informed decisions. Not overly technical, the rigorous approach remains accessible to anyone with an interest in the area, showing investors where to look for value and helping corporations hedge their FX exposures. FX Option Performance begins with a quick and practical introduction to the FX option market, then provides specific advice toward structures, performance, rate fluctuation, and trading strategies. Examine the historical payoffs to the most popular and liquidly traded options Learn which options are overvalued and which are undervalued Discover surprising, generally unpublished facts about emerging markets Examine systemic option trading strategies to find what works and what doesnt On average, do options result in profit, loss, or breaking even How can corporations more costeffectively hedge their exposure to emerging markets Are cheap outofthemoney options worth it Professor Jessica James is Senior Quantitative Researcher at Commerzbank in London. She joined Commerzbank from Citigroup where she held a number of FX roles, latterly as Global Head of the Quantitative Investor Solutions Group. Prior to this she was the Head of Risk Advisory and Currency Overlay for Bank One. Before her career in finance, James lectured in physics at Trinity College, Oxford. Her significant publications include the Handbook of Foreign Exchange (Wiley), Interest Rate Modelling (Wiley), and Currency Management (Risk books). Her new book FX Option Performance was published in May 2018. She has been closely associated with the development of currency as an asset class, being one of the first to create overlay and currency alpha products. Jessica is a Managing Editor for the Journal of Quantitative Finance, and is a Visiting Professor both at UCL and at Cass Business School. Apart from her financial appointments, she is a Fellow of the Institute of Physics and has been a member of their governing body and of their Industry and Business Board. IAQF-Thalesians Seminar (New York) 8212 Dr. Harry Mamaysky 8212 Does Unusual News Forecast Market Stress Meetup How to build a CTA - Creating a trend following fund (Saeed Amen) - In this talk we explain how to create trend following strategies which CTA-style funds typically follow. We shall also give a step by step demo of implementing an FX trend following strategy in PyThalesians - open source Python library for analysing markets - githubthalesianspythalesians Pair trading strategies (Delaney Granizo-Mackenzie) - Pairs trading is a form of mean reversion that has a distinct advantage in always being hedged against market movements. It is generally a high alpha strategy when backed up by some rigorous statistics. Delaney Granizo-Mackenzie will review some general principles for pairs trading, and then dive into the statistics behind the strategy during this talk. What is cointegration How to test for cointegration What is pairs trading How to find cointegrated pairs How to generate a tradeable signal This talk is part of The Quantopian Lecture Series. All lecture materials can be found at: quantopianlectures. Saeed is the founder of Cuemacro and is a co-founder of the Thalesians. Over the past decade, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. Independently, he is also a systematic FX trader, running a proprietary trading book trading liquid G10 FX, which has had a Sharpe ratio over 1.5 since 2017. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan). Delaney Granizo-Mackenzie is an engineer at Quantopian who focuses on how Quantopian can be used as a teaching tool. After studying computer science at Princeton, Delaney joined Quantopian in 2017. Since then he has led successful course integrations at MIT Sloan and Stanford, and is working with over 20 courses for this fall. Delaney is using his experience and feedback from professors to build a quantitative finance curriculum focusing on best statistical practices to be offered for free. Delaneys background includes 7 years of academic research at a bioinformatics lab, and a strong focus on statistics and machine learning. Thalesians Sance (Budapest) 8212 Robin Hanson amp Panel 8212 Economics when robots rule the Earth A very special thanks to Attila Agod for organising this talk Our goal is to create a social convergence point for the quantitative financial professionals in Hungary with quarterly events Date and Time 7:00 p. m. on Fri 29th January, 2018 7:00 p. m. - Welcome drinks, 8:00 p. m. - Robin Hanson presentation 9:00 p. m. - Discussion panel 12.00 a. m. - Next pub Palack Borbr, Szent Gellrt sqr 3, Budapest Meetup At the 8th Thalesians Sance, Robin Hanson will present us a thought experiment about the life and economics of our society after the singularity. Robin is the author of the Age of Em - Work, Love and Life when Robots Rule the Earth (ageofem ). Members of the panel: - Attila Agod - Mark Horvath (Causality) - Saeed Amen (The Thalesians) Robin Dale Hanson is an associate professor of economics at George Mason University and a research associate at the Future of Humanity Institute of Oxford University. He is known as an expert on idea futures and markets, and he was involved in the creation of the Foresight Exchange and DARPAs FutureMAP project. He invented market scoring rules like LMSR (Logarithmic Market Scoring Rule)used by prediction markets such as Consensus Point (where Hanson is Chief Scientist), and has conducted research on signaling. Thalesians Seminar (London) 8212 Nick Firoozye 8212 Managing Uncertainty, Mitigating Risk (Book) Date and Time 7:30 p. m. on Wednesday, 20 January, 2018 Ginger Room, Marriott Hotel, Canary Wharf, London, UK. Meetup Financial risk management started in a period when academic finance was wedded to probability. Risk and its transferability was the focus and uncertainty was sidelined. After the recent financial crisis, uncertainty and its consequences have become a major concern for many prominent academics, yet practitioners are constrained by probability-based tools and regulatory mandates. Managing Uncertainty, Mitigating Risk offers a liberated perspective on uncertainty in banking and finance. The book stresses that uncertainty must be confronted by using a broader range of inputs, employing methods outside conventional probability. More often than not, systemic risks are not completely unforeseeable and a range of likely risk scenarios can be fleshed out, quantified and largely mitigated. We can accomplish this only if we widen our knowledgebase to include qualitative data and judgment. Probability and historical data alone cannot sufficiently model game-changing and catastrophic one-off situations such as Eurozone exit and breakup, US debt ceiling, and Brexit. This book presents a robust foundation and a novel and practical method for incorporating uncertainty into existing risk frameworks. It takes the reader beyond the realms of probability in modern finance, into imprecise probability the mathematics of uncertainty. We introduce uncertain value-at-risk (UVaR), a measure which takes the VaR engine and enhances it using credal nets, an imprecise extension of Bayesian nets. Unlike the unjustified precision of probability-based models, UVaR helps to assesses uncertainty by incorporating expert insight through priors, with more extensive datasets. By combining a solid quantitative method with an implementation framework and cases, this book allows the reader to not only understand the solution for managing uncertain one-offs, but also to see the end-product. This is a starting point for risk practitioners to go beyond regulatory-initiated tools in order to employ their own approaches towards recognizing and managing uncertainty. Nick Firoozye is a Managing Director at Nomura International and heads a global team in cross-product derivatives research. He has many years of experience in a variety of research and trading roles in both buy-side and sell-side firms including Goldman Sachs, Deutsche Bank, Citadel, Sanford Bernstein and Lehman Brothers. Known for his work in Quantitative Strategy, Nicks area of expertise ranges from asset allocation models and macro-financial forecasting to systematic and RV trading. Previously, he was Head of European Rates Strategy, and covered the Eurozone crisis, rescue packages and possible break-up, working closely with the risk management and legal teams. Dr Firoozye was an Assistant Professor at the University of Illinois, and holds a PhD in Applied Mathematics from Courant Institute, New York University. He speaks and writes frequently on financial markets and economics issues. His team was recently awarded Global Capitals Derivatives Research House of 2018, and he was co-author of one of five papers shortlisted for the 2017 Wolfson Economics Prize on the breakup of the Eurozone. IAQF-Thalesians Seminar (New York) 8212 Dr. Nick Costanzino 8212 Pricing and Hedging Recovery Risk with Structural and Reduced Form Models Tuesday, January 12, 2018: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Registration The fixed-income literature attempts to explain credit spreads though a decomposition into different risk premia. The most commonly analyzed risk premia are default and liquidity risk. Recovery risk has not received much attention most likely because of the pervasive practice of assuming constant recovery in most credit models. However, assuming a constant recovery has two major effects. The first is we have inconsistent pricing (if recovery is a known constant, what is the price of a recovery swap) and the second is over - or underpricing the default risk portion of the credit spread. In this talk I will present recent work on isolating the recovery risk premium in corporate bond and CDS spreads using both structural and hazard rate models. This allows us to isolate the recovery risk premium from the default risk premium, as well as provide a consistent pricing framework for all recovery linked products including bonds, CDS and recovery swaps. Finally, we discuss some trading opportunities that can be exploited using framework. Nick Costanzino received his PhD in Applied Mathematics in 2006 from Brown University in Providence R. I. His thesis combined tools from pseudodifferential operators and dynamical systems to prove multidimensional stability of certain nonlinear wave structures in fluids. He later moved to the Penn State University Math Department as a Chowla Assistant Professor where he was introduced to quantitative finance and helped developed their Mathematical Finance program. After a brief tenure at Wilfrid Laurier University in Canada he then moved to the finance industry working in various credit roles including risk manager for the CDS and corporate bond trading desk at Scotiabank. He is interested in all areas of quantitative finance, but particularly those which lead to improvements in understanding the credit and equity markets. Nick is currently in the Investment Analytics group at AIG in New York and is a member of RiskLab at the University of Toronto. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. External (London) 8212 International Conference on Computational Finance (ICCF2018) University of Greenwich Date and Time Registration We present a liquidity factor IML, the return on illiquid-minus-liquid stock portfolios. The IML, adjusted for the common risk factors, measures the illiquidity premium whose annual alpha is about 4 over the period 1950-2017. I then test whether the systematic risk () of IML is priced in a multi-factor CAPM. The model allows for a conditional of IML that rises with observable funding illiquidity and adverse market conditions. The conditional IML is positively and significantly priced, and remains so after controlling for the beta of illiquidity shocks. Yakov Amihud is Ira Rennert Professor of Entrepreneurial Finance at the Stern School of Business, New York University. He is the coauthor of Market Liquidity: Asset Pricing, Risk and Crises (Cambridge University Press, 2017). His research focuses on the effects of asset liquidity on value and expected return, and on the design and evaluation of securities markets trading methods. On these topics, Amihud has done consulting work for the NYSE, AMEX, CBOE, CBOT, and other securities markets. He has published more than seventy research articles in professional journals and in books, and edited and co-edited five books on topics such as LBOs, bank MampAs, international finance, and securities market design. His research also includes the evaluation of corporate financial policies, mergers and acquisitions, initial public offerings, objectives of corporate managers, dividend policy, and law and finance. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians SeminarXmas Dinner (London) 8212 Matthew Dixon 8212 Machine Learning in Trading: Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi Date and Time 6.30p. m. on Monday, 14 December, 2018 La Tasca, West India Quay, Canary Wharf, London E14 4AE Meetup Talk amp Dinner We invite you to our 2018 Thalesians LDN Xmas seminar amp dinner by Matthew Dixon on Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi followed by dinner at La Tasca in Canary Wharf. The presentation begins at 6.30pm, followed by dinner at 7.30pm (menu below). On Arrival - A Glass of Sangra Tradicional To Start - Tabla Espanola (to share) - Traditional Spanish cured meats with mixed olives, Manchego cheese, bread and oil. Christmas Albndigas (Madrid) - Turkey amp pork meatballs, in a rich, sherry and cranberry sauce. Pulpo Gratin Y Queso GF (Galicia) - A medley of potatoes and octopus baked in a creamy lobster sauce and gratinated with Manchego cheese. Pollo Marbella GF (Malaga) - Chicken breast, cooked with chorizo in a white wine amp cream sauce. La Tasca House Green Salad GF V (Navarra) Patatas Bravas con Alioli (Espaa) - Fried potato, with spicy tomato sauce and roasted garlic mayonnaise. Paella de Carne GF (Valencia) - With chicken breast and chorizo. Paella Verduras GF V (Valencia) - With seasonal vegetables. To Finish - Churros - Doughnut twists, served with fresh strawberries and marshmallows, plus a rich chocolate sauce Deep neural networks (DNN) have demonstrated their power in areas such as vision (think Google image search) and speech recognition (think Siri). Some financial firms are beginning to apply these techniques to market data and other information important for trading and investing. But training DNNs (that is, setting them to work to develop models) is extremely compute intensive. In this talk, Matthew will describe a DNN model for predicting price movements from time series data, then explain techniques that enable this model to exploit the parallel computing capacity of the Intel Xeon Phi processor in conjunction with multi-core CPUs. Matthew Dixon is a Managing Director and Head of Americas at Thalesians Ltd. He is also an Assistant Professor of Finance in the Stuart Business School at the Illinois Institute of Technology. His research focuses on the application of advanced computational techniques to financial modeling and data analysis especially where high performance and scalability are critical for practical application. Matthews research is currently funded by Intel Corporation. He has contributed to the R package repository and published around twenty peer-reviewed technical articles. He has taught financial econometrics, derivatives, machine learning and text mining at the University of San Francisco and held visiting appointments in CSMath at Stanford University and UC Davis. Prior to joining academia, he has held industry appointments as a quant at banks such as Lehman Brothers, the Bank for International Settlements and binary options Capital. He chairs the workshop on computational finance at the annual SuperComputing conference and serves on the program committee of HPC and on the editorial board of the Journal of Financial Innovation. Matthew holds a MEng in Civil Engineering from Imperial College London, a MSc in Parallel and Scientific Computation (with distinction) from the University of Reading, and a PhD in Applied Math from Imperial College London. He became a chartered financial risk manager in 2017. Thalesians Panel (London) 8212 CudmoreBurroughs amp more 8212 Global macro panel Registration The structural default model of Lipton and Sepp, 2009 is generalized for a set of banks with mutual interbank liabilities whose assets are driven by correlated Levy processes with idiosyncratic and common components. The multi-dimensional problem is made tractable via a novel computational method, which generalizes the one-dimensional fractional partial differential equation method of Itkin, 2017 to the two - and three-dimensional cases. This method is unconditionally stable and of the second order of approximation in space and time in addition, for many popular Levy models it has linear complexity in each dimension. Marginal and joint survival probabilities for two and three banks with mutual liabilities are computed. The effects of mutual liabilities are discussed, and numerical examples are given to illustrate these effects. Dr. Andrey Itkin is an Adjunct Professor at NYU, Department of Risk and Financial Engineering and Director, Senior Research Associate at Bank of America. He received his PhD in physics of liquids, gases and plasma, and degree of Doctor of Science in computational molecular physics. During his academic carrier he published few books and multiple papers on chemical and theoretical physics and astrophysics, and later on computational and mathematical finance. Andrey occupied various research and managerial positions in financial industry and also is a member of multiple professional associations in finance and physics. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians Seminar (London) 8212 Robert Carver 8212 Lessons from Systematic Trading Date and Time 7:30 p. m. on Wednesday, 21 October, 2018 Ginger Room, Marriott Hotel, Canary Wharf, London, UK. Meetup Its my belief that successful systematic trading is not about finding some deep hidden source of alpha, but about avoiding stupid mistakes. In this talk I share some of the mistakes Ive made, and seen others make, whilst designing and managing systematic trading systems for both a multi billion hedge fund and a retail trading account. This is a wide ranging talk which provocatively questions many commonly held beliefs about the business of managing money systematically. Robert Carver is an independent systematic trader, and writer. He trades his own capital with a fully automated system of 40 futures markets, using a proprietary system written in python. Robert is the author of Systematic Trading, a forthcoming book to be published by Harriman House in October 2018. He regularly blogs on the subject of trading, finance and investment. Robert, who has bachelors and masters degrees in Economics, began his city career trading exotic derivative products for binary options Capital. He then worked as a portfolio manager for AHL. one of the worlds largest systematic hedge funds before, during and after the global financial meltdown of 2008. Robert was responsible for the creation of AHLs fundamental cross asset global macro strategy, and then managed the funds multi billion dollar fixed income portfolio. He retired from the industry in 2017. IAQF-Thalesians Seminar (New York) 8212 Dr. Dan Pirjol 8212 Can one price Eurodollar futures in the Black-Derman-Toy model Wednesday, October 14, 2018: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Registration Interest rates models with log-normally distributed rates in continuous time are known to display singular behavior. For example, Eurodollar futures prices are infinite in the Dothan and Black-Karasinski models, as shown in 1998 by Hogan and Weintraub. These singularities are usually assumed to disappear when the models are simulated in discrete time. Using a precise simulation of the BDT model, we demonstrate that this is true only for sufficiently low volatilities. Eurodollar futures prices explode for volatilities above a critical value. The explosion is due to contributions from a region in state space which corresponds to very large interest rates and is truncated off in usual simulation methods such as trees and finite difference methods. In the limit of a very small simulation time step the explosion appears for any volatility, and reproduces the Hogan-Weintraub singularity of the continuous time model. Dan Pirjol works in the Model Risk Group at JP Morgan, covering valuation models in commodities. Previously he was with Markit and Merrill Lynch in various roles in modeling and model risk, after doing research in theoretical high energy physics. He is interested in applications of methods from mathematical physics and probability to problems in mathematical finance. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians Sance (Budapest) 8212 Taylor Spears amp Panel 8212 The Sociology of CVA A very special thanks to Attila Agod for organising this talk Our goal is to create a social convergence point for the quantitative financial professionals in Hungary with quarterly events Date and Time 7:00 p. m. on Fri 9th October, 2018 7:00 p. m. - Welcome drinks, 8:00 p. m. - Taylor Spears presentation 9:00 p. m. - Discussion panel 12.00 a. m. - Next pub Palack Borbr, Szent Gellrt sqr 3, Budapest Meetup At the 7th Thalesians Sance Taylor Spears from the Sociology Department of The University Edinburgh will introduce the evolution of Credit Valuation Adjustment (CVA) from a sociologists point of view. After Taylors talk a panel of practitioners will challenge his ideas. Members of the panel: - Andras Bohak (MSCI, Counterparty credit researcher) - Daniel Homolya (Mol Group, Financial risk management team lead) - Balazs Palosi-Nemeth (ING, Architect) - Gabor Salamon (Morgan Stanley, CVA team lead) Dr Taylor Spears is a research fellow in the Sociology of Financial Modelling at the School of Social and Political Science in the University of Edinburgh. Thalesians Seminar (New York) 8212 Creating trend following fund: How to build a CTA interactive Python PyThalesians demo Date and Time 6:00 p. m. on Thursday, 1 October, 2018 Shark Tank, Grind Broadway, 22nd Floor, 1412 Broadway, New York, NY Meetup In this talk, we shall be discussing CTAs and giving some background about the industry. We shall give a brief overview of the types of strategies CTAs use to trade markets, creating a generic proxy for a typical CTA fund. We shall also be discussing how CTA strategies can be used to improve the risk adjusted returns of long only equity and bond investors. Later, there will also be an interactive Python demo showing how to use the PyThalesians Python code library (partially open sourced on GitHub ). Amongst other things we shall investigate the properties of intraday FX volatility, where well be accessing live market data via Bloomberg and also creating customised plots using Matplotlib. Selected Bios Saeed is the founder of Cuemacro and is a co-founder of the Thalesians. Over the past decade, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. Independently, he is also a systematic FX trader, running a proprietary trading book trading liquid G10 FX, which has had a Sharpe ratio over 1.5 since 2017. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan). Thalesians Seminar (London) 8212 Stephen Pulman 8212 Multi-Dimensional Sentiment Analysis Date and Time 7:30 p. m. on Wednesday, 23 September, 2018 Ginger Room, Marriott Hotel, Canary Wharf, London, UK. Meetup All sentiment analysis systems can deliver positive negativeneutral classifications. But there are many other useful signals in text: emotion, intent, speculation, risk, etc. This talk will present a survey of the state of the art in recognising these other dimensions of sentiment in text and describe some practical applications in finance and elsewhere. Stephen Pulman is Professor of Computational Linguistics at the Department of Computer Science, Oxford University. He is a Professorial Fellow of Somerville College, Oxford, and a Fellow of the British Academy. He has also held visiting professorships at the Institut fr Maschinelle Sprachverarbeitung, University of Stuttgart and at Copenhagen Business School. He is a co-founder of TheySay Ltd. Previous positions include Professor of General Linguistics at Oxford University, Assistant Professor (Reader) at the University of Cambridge Computer Laboratory, and Director of SRI Internationals Cambridge. IAQF-Thalesians Seminar (New York) 8212 Dr. Agostino Capponi 8212 Arbitrage-Free Pricing of XVA Monday, September 21, 2018: NYU Kimmel Center. Room 914, Kimmel Center, 60 Washington Square South, NY 10012, NY Registration The recent financial crisis has highlighted the importance to account for counterparty risk and funding costs in the valuation of over-the-counter portfolios of derivatives. When managing their portfolios, traders face costs for maintaining the hedge of the position, posting collateral resources, and servicing their collateral requests. Due to the interdependencies between these operations, such costs cannot be separated and attributed to different business units (CVA, DVA and FVA desks). In this talk, we introduce a unified framework for computing the total costs, referred to as XVA, of an European style derivative transaction traded between two risky counterparties. We use no-arbitrage arguments to derive the nonlinear backward stochastic differential equations (BSDEs) associated with the portfolios which replicate long and short positions in the claim. This leads to defining buyers and sellers XVAs which in turn identify a no-arbitrage band. When borrowing and lending rates coincide, our framework recovers a generalized version of Piterbargs model. In this case, we provide a fully explicit expression for the uniquely determined price of XVA. When they differ, we derive the semi-linear partial differential equations (PDEs) associated with the non-linear BSDEs and show that they admit a unique classical solution. We use these solutions to conduct a numerical analysis showing high sensitivity of the no-arbitrage band and replicating strategies to funding spreads and collateral levels. Agostino Capponi is an assistant professor in the IEOR Department at Columbia University, where he is also a member of the Institute for Data Science and Engineering. Agostino received his Master and Ph. D. Degree in Computer Science and Applied and Computational Mathematics from the California Institute of Technology, respectively in 2006 and 2009. His main research interests are in the area of networks, with a special focus on systemic risk, contagion, and control. In the context of financial networks, the outcome of his research contributes to a better understanding of risk management practices, and to assess the impact of regulatory policies aimed at controlling financial markets. He has been awarded a grant from the Institute for New Economic Thinking for his research on dynamic contagion mechanisms. His work on systemic risk dynamics under central clearing done in collaboration with the Department of Treasury has obtained press coverage from major organizations such as Bloomberg and Reuters. His research has been published in top-tier journals of Financial Mathematics, Operations Research, and Engineering. His work has also been published in leading practitioner journals and invited book chapters. Agostino holds a world patent for a target tracking methodology in military networks. IAQF-Thalesians Seminars The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only. Thalesians Seminar (San Francisco) 8212 Steven Pav - Portfolio Inference and Portfolio Overfit Date and Time amp Schedule 6:00 p. m. on Thursday, 10 September, 2018 6pm: Reception in Julias Lounge 7pm: Talk in the Members Lounge 8pm: Networking(Part of Master Thesis: Kocyigit, Eren, The Use Of Retail Structured Products And Their Applications In Turkey, Istanbul Bilgi University, 2017) 3. STRUCTURED PRODUCTS 3.1. Definition of Structured Products Structured products are tailor-made products which aim to provide the best solution to the investors with this tailoring process (Kat 2001). There is not a single definition for structured products. Different definitions can be found in different sources. Definitions of structured products from web Web site (wikipedia. org) defined structured product as A structured product is generally a pre-packaged investment strategy based on derivatives, such as a single security, a basket of securities, options, indices, commodities, debt issuances andor foreign currencies, and to a lesser extent, swaps. Web site (hedgefund-index) defined structured product as structured products are synthetic investment instruments specially created to meet specific needs that cannot be met from the standardized financial instruments available in the markets. Definitions of structured products from well-known institutions Definition of SSPA (Swiss Structured Products Association) is structured products are investment products available to the public whose repayment value derives from the development of one or several underlying assets. Definition of (structuredretailproducts ) is structured products are investment products that generate a pre-defined return linked to one or more underlying financial prices, rates or indices. Definitions of structured products from books Structured products are defined as structured products refer to combinations of individual financial instruments, such as bonds, stocks and derivatives. by Oesterreichische Nationalbank (Structured Products Handbook 2004) According to Chorafas D. N (2007) Structured products are securities that provide investors with a redemption amount, which may be with either full or partial capital protection, and a certain type of return. Das (2000) defined them as combinations of derivatives and underlying financial instruments which exhibit structures with special riskreturn profiles. As it can be realized from all these definitions although there is not a single definition for structured products, there are some certain features that can be mentioned for the structured products like Mostly they consist of at least 2 products a common bond or a deposit, plus a derivative. The payoffs of the structured products depend on one or more underlying assets. According to these features it can be told that Structured Products are tailor made financial instruments that are composed of mostly more than one product and have a performance depending on one or more underlying assets. 3.2. Composition amp Design of Structured Products Most of the structured products consist of 2 components basic financial instrument (BFI) component and derivative component (See figure 3.1). The payoff, risk-level and general characteristics of a structured product can be determined from these 2 components. Figure 3.1: Composition of a Basic Structured Product In BFI component, products like bonds, notes and deposits can be located. This component generates a fixed return to the structured product in most types of structured products. In derivative component mostly options with different kinds of underlying instruments and different strategies are located. Options in this component can be linked to different instruments like equities (stocks, indices), commodities, foreign exchanges and interest rates. They can be linked to a single type of instrument or more than one instrument as a hybrid design. Options in derivative component can be in different types like call amp put, vanilla amp exotic options (barrier, lookback, asian and etc8230 (See Appendix A for most popular types of exotic options that are used in structured products) and these options can be in different strategies like bearish, bullish and neutral. All the characteristics of a structured product like payoff, maturity, underlying instrument and its risk level can be determined according to The type, maturity and payoff features of the instrument(s) that is located in BFI component. (e. g. if it is a bond type of this bond (government or corporate amp zero or coupon). its interest rate and maturity affects the structured products payoff amp risk level) The type, maturity and payoff features of the instrument(s) that is located in derivative component. (e. g. if it is an option, its underlying asset (fx, equity, interest rate, commodity or hybrid), its type (call amp put, vanilla amp exotic), its strategy (bearish, bull ish, neutral) and its maturity affects the structured products payoff amp risk level) The weight of these components. Figure 3.2 illustrates structured products on risk-return graph. As it can be realized from that graph a typical structured product locates between bonds and options on risk-return graph. In other words structured product is more risky than government bonds and less risky then options. According to the types, characteristics and ratios of structured products components, risk level of the structured products can move between the risk level of government bonds and options. In order to move a structured products risk level between the risk level of government bonds and options, components and their weight should be re-structured. With the identical components of 2 different structured products the weight of these components affects the risk level of these structured products. Greater weight of bond component moves the structured product through government bond risk level, greater weight of derivative component moves the structured product through options risk level. With the same weight and identical BFI components of 2 different structured products, the risk level of derivative components effect the risk level of these structured products. Riskier option component moves the structured product through options risk level, less risky option component moves the structured product through government bond risk level. With the same weight and identical option components of 2 different structured products, the risk level of BFI components affect the risk level of these structured products. Riskier BFI component moves the structured product through options risk level, less risky BFI component moves the structured product through government bond risk level. 3.3. Types of Structured Products Until this section we analyzed the definition and the composition of the structured products. In this section types of the structured products will be analyzed according to The market that they bought amp sold Their underlying asset Their risk level 3.3.1. Structured Products and Their Market Structured products are financial instruments so they should have a market to be bought or sold. According to their trading markets they can be divided into 2 categories 3.3.1.1. Structured Products in OTC markets These types of structured products dont have an organized market and mostly traded between two different parties. There is not an organizer of these transactions and the parties are responsible to each other for all the liabilities that come along with the product. For example Dual Currency Deposit (DCD) which will be analyzed in Chapter 5, can be count as a structured product whose transaction occurs in OTC markets. In the transaction of a DCD product there are two different parties and one structured product whose specifications (maturity, strike price, underlying asset and etc8230) are determined and fixed by these two parties. The possible outcomes and liabilities will be faced by these two parties only not by another third party. 3.3.1.2. Structured Products in Organized Markets These kind of structured products are bought and sold in organized markets, that is to say transactions of the products occur under the control of an authority and its set of rules and regulations. The best examples of this category are the structured products which are traded in Scoach1. Unlike the structured products traded on OTC markets, these kinds of products bought and sold in an organized market. 3.3.2. Structured Products and Their Underlying Instrument Another categorization of the structured products is according to their underlying instrument. Although there are unlimited strategies to compose a structured product there are some main instruments that can be considered as underlying assets when we analyze structured products. Foreign Exchange (FX) Linked Structured Products: The payoffs of these kinds of structured products depend on the performance of the currency which is underlying. Products can be linked to one single currency pair or a basket of currency pairs. Most popular types of currency pairs can be formed between USD, JPY, EUR, CHF, GBP, CAD and AUD (Wystup 2006) Commodity Linked Structured Products: The underlying assets of these kinds of structured products are commodities. Underlying asset can be a single commodity or a basket of commodities. Some popular types of commodities are gold, silver, oil and etc8230 Equity-Linked Structured Products: They are promoted as an alternative to directly investing in equities since the underlying assets of these kinds of structured products are equities (Chorafas 2007). There are 2 main types of equity linked structured products: Share linked and Index linked structured products. Share linked structured products can be composed of a single share or a basket of shares. Some popular types of shares are Deutsche Bank, Allianz, Bayer and etc8230 Index linked structured products can be composed of a single index or a basket of indices. Some popular types of indices are DJ Eurostoxx50, DAX, FTSE100 and etc8230 Interest Rate Linked Structured Products: The underlying assets of these kinds of structured products are interest rates. Products can be linked to one single interest rate or a basket of interest rates. Some popular types of interest rates are LIBOR, EURIBOR and etc8230 Hybrid-Linked Structured Products: In these kind, different types of underlying instruments comes together in a structured product. For example if a product has currency, commodity and equity as underlying at the same time, this structured product can be evaluated as hybrid-linked structured product. These 5 types of underlying instruments can be classified as the major types, despite them there are other types of underlying instruments such as Credit Linked Structured Products: In this kind, the underlying asset is mostly a pool of debt instruments. So the performance of the structured product linked to these debt instruments. (e. g. Asset Backed Securities, Credit Default Obligations and etc8230) Fund Linked Structured Products: In this kind the product and its payoff is linked to the performance of a fund. Most popular type is hedge fund linked products. They provide their investors, easy and less costly participation to the hedge funds. Inflation Linked Structured Products: In this kind, the product is linked to a single inflation rate of a specific country or a zone or basket of inflation rates. Most of them target to protect investors from resurgence in inflation (Chorafas 2007). 3.3.3. Structured Products and Their Risk Level In this section structured products will be categorized according to their risk levels. Figure 3.3 shows main types of structured products when their risk levels are considered Figure 3.3: Structured Products and Their Risk Levels As can be seen from figure 3.3 there are 4 main types of structured products according to their risk levels and they can be lined up from less risky to the riskier as following: Capital protected products Yield enhancement products Participation products Leverage products At this section the categorization of structured products are made according to the classification of structured products made by Swiss Structured Products Association (SSPA) and European Structured Investment Products Association (EUSIPA). Within the following sections most popular types of products will be analyzed. (See Appendix C for all types of structured products according to SSPA and EUSIPA structured products categorization model.) Capital protected products are structured products which protect the initial investment at the maturity. These products can also generate a return to their investors above their initial investment. These kinds of products mostly consist of a traditional bond (the part which protects the initial investment at the maturity), plus a derivative (the part which may generate a return above initial investment mostly option.) In these kinds of products return of the product above the initial investment is determined by options performance multiplied by participation rate. Participation rate is determined by dividing the remaining capital after investing in traditional bond to the price of derivative that is used to structure this capital protected product. In capital protected products mostly issuers set the capital protection level at 100, but they can also set it higher or lower. In Europe todays low interest rates make it harder to provide 100 capital protection to the investors while structuring attractive products thats why 70 8211 80 capitalprotection started to used in most of European capital protected products. (Marray 2009) Figure 3.4 shows composition and operating process of a basic capital protected product. Figure 3.4: Composition of Capital Protected Products In Figure 3.4 Y refers to value of a derivative, X refers to value of a bond. At the maturity the value of bond will reach the initial investment which is XY. So the capital is protected by this way. At this example Z refers to an extra yield that is generated by the option. At the maturity XY generated independent from the options performance. On the contrary, Z which represents the yield above the initial investment is dependent to the options performance and participation rate. Figure 3.5 shows a numerical example of a capital protected product from JP Morgan Structured Investments Solution Catalogue (2007). In this figure return of a 1000 investment to a capital protected product linked to the SampP 500 Index with a 90 participation rate is shown in 2 different scenarios. Figure 3.5: A Capital Protected Product Example In the first scenario if the SampP 500 Index rises to 20, then the investors receives at the maturity initial investment SampP performance participation rate 1000 0,20 0,90 180 In the second scenario if the SampP 500 Index falls to 20, then the investor receives at the maturity hisher initial investment which is equal to 1000. In this scenario rather than incurring a 200 loss in the initial investment, investor receives hisher principal back at the maturity. Capital protected products can be considered as transition products for the structured products market. As can be seen their place on risk return graph from Figure 3.3, retail investors who didnt invest in structured products before mostly choose these kinds of products if they want to try investing in structured products for the first time. In other words, it can be told that capital protected products are the middle term between the phrases of being a conservative investor and sophisticated investor. Because of this reason as Roger (2008) reported in his study most of the banks offer their customers these types of structured products by assuming that most of their customers are loss averse investors. Before investing in more risky structured products, loss averse investors firstly chooses capital protected structured products. Yield Enhancement products are kind of structured products which are desirable to the investors when markets are stable or moving sideways. They offer returns above the traditional bonds if the underlying assets prices move sideways or go up (Barlocher 2009). If investors invest in these types of products their yield can be above market however, their capital may be at risk. In these kinds of products the risk of the investor occurs when the prices of underlying assets go down. Mostly these products have a predetermined limit on the return (cap). Investors who invest in yield enhancement products mostly have following market expectations (SSPA Swiss Derivative Map 2009) Sideways market (flat market prices of instruments are moving sideways) of underlying Falling volatility. There are two main types of yield enhancement products Discount Certificates and Reverse Convertibles. Reverse convertibles have coupon payments which are above the coupon payments of traditional bonds however, discount certificates have no coupon payments, they are sold at prices below their underlying assets market price (Barlocher 2009). a. Discount Certificates Discount Certificates (DCs) are structured products which allow investors to invest to an index, basket of securities or a certain security with a price which is lower than the market price. (Thats why they known as discount certificates). This discount is given to the investor in exchange for a fixed maximum return which should be accepted by the investor. This fixed maximum return is known as predetermined cap. Each DC has its own underlying security and a maximum price which is called cap strike. At the certificates maturity, if the price of the underlying is lower than the cap, the investor receives physical form of the underlying (if the underlying is a share). Instead of physical delivery of the underlying, for DCs that are consisted of non-traded assets like indices, cash settlement is also possible (Wilkens, Erner and Roder 2003). On the other hand, if the price of the underlying is higher or equal to the cap, the investor receives the maximum amount which is equivalent to the cap. In Figure 3.6 an example from Deutsche Bank AG Discount Certificates Product Brochure (2006) can be found. In Figure 3.6 x axis shows the prices of underlying ABC share and y axis of the graph shows the payoff amount of discount certificate at different prices. Figure 3.6: A Discount Certificate Example In this example it is assumed that ABC share is trading at 5. A 1- month Discount Certificate on ABC with a cap strike at 4.86 costs 4.76. Investors may get the share at a discount of 4.8 (of the initial share price) but in return, they have to accept a maximum payout of 4.86 which will give a maximum return of 2.1. Till maturity DCs price will be depended on the ABC share but not reflect precisely. The DC tracks upward movements to the cap strike, on the other hand when the price of the share falls, the DC price also falls. At the maturity such possible scenarios could occur: Scenario 1: The underlying share is trading at or above the cap strike. Then the DC pays the cap strike of 4.86. Scenario 2: The underlying share is trading below the cap strike and higher than the original invested amount. Then the investor will get one underlying share. In total investor receives a return since current price of the share is higher than the original investment amount. Scenario 3: The underlying share is trading below the cap strike and the original invested amount. The investor will get one underlying share. The investor receives a loss in total since current price of the share is lower than the original investment amount. But this loss will always be lower than a direct investment on the underlying share. b. Reverse Convertibles Reverse Convertibles (RCs) are securities that are linked to an underlying stock and pay above market coupons. In return for this coupon, there is no guarantee that investors will recover the full amount of invested capital and unlike direct investment in a stock or bond, upside potential of a RC is limited to this coupon amount. (JP Morgan Structured Investments Solution Series Volume III: Reverse Exchangeables 2007). They are also known in the market as Reverse Exchangeable Securities (RES) and described by Benet, Giannetti and Pissaris (2003) as interest-paying, non - principal-protected structured products, offering a fixed interest rate that is higher than conventional debt securities. At maturity the price of underlying is compared to the price at the time of issue. So the investor gets coupon payment principal investment if the price at the maturity is equal to or greater than the initial price of the underlying. If the price at the maturity is less than the price at the time of issue then the investor gets number of shares that is found by dividing the principal investment by the share price at the time of issue. Figure 3.7 shows payoff graph of a RC. In this figure the line which is thin represents the price change of underlying asset and the line which is thick represents the payoff of a discount certificate according to the price changes of the underlying asset. x axis shows the prices of underlying asset and y axis of the graph shows the payoff amount of RC at different prices. Figure 3.7: Payoff Graph of a Reverse Convertible As can be seen from Figure 3.7 at the prices that are above the strike price, RC pays a fixed amount of return to the investor. In the figure this amount is shown as cap. At the prices that are below the strike price, payoff of RC will be affected 1 to 1 by the performance of underlying share. In other words if underlying share price will fall a, the price of RC that is linked to this share will fall a. To explain how RCs work following example is given: Lets assume an investor purchases 1,000 of a one-year reverse convertible linked to the price of XYZ share, price of XYZ share at issuance is 10 (strike amp initial price) and the coupon rate is 10 At the maturity If the price of XYZ share is 10 or greater investor will receive 100 (cap) in interest and the return of his principal, for a total of 1,100. If the price of XYZ share is less than 10 lets say 5, the investor receives 100 interest plus 100 shares (1,000 divided by 10) of XYZ share. To compare investing in XYZ share directly and investing RC linked to XYZ, Table 3.2 is designed. In this table 4 different scenarios are considered as the XYZ shares price will be EUR 12, 10.8, 9 or 7 at the maturity. According to these scenarios comparisons are made in order to show the advantages amp disadvantages of investing in RC in different prices at the maturity. Table 3.1: Direct Investment 8211 Reverse Convertible Comparison As can be seen from Table 3.1 In scenario 1 at the maturity the price of XYZ share is EUR 12, direct investment to the XYZ share will lead a 20 profit for investor while investing in RC linked to XYZ share will lead a profit of 10 that is equal to the coupon amount. In scenario 2 at the maturity the price of XYZ share is EUR 10.8, direct investment to the XYZ share will lead a 8 profit for investor while investing in RC linked to XYZ share will lead a profit of 10 that is equal to the coupon amount. In scenario 3 at the maturity the price of XYZ share is EUR 9, direct investment to the XYZ share will lead a 10 loss for investor while investing in RC linked to XYZ share will not lead any profit or loss to the investor. In scenario 4 at the maturity the price of XYZ share is EUR 7, direct investment to the XYZ share will lead a 30 loss for investor while investing in RC linked to XYZ share will lead 20 loss to the investor. Participation products are kind of structured products can be one to one with the prices of the underlying assets, or with some leverage and certain discontinuities. The basic difference between participation products and yield enhancement amp capital protected products is there is not a cap level in participation products (Barlocher 2009). Most common types of participation products that are traded in the market are Open End Certificates, Outperformance Certificates, Bonus Certificates and Outperformance Bonus Certificates. a. Open End Certificates They are also known as Tracker Certificates in the market. Open End Certificates (OECs) are suitable for investors who want to benefit from the performance of an index, a sector, a commodity or interest rates. OECs dont have fixed expiry dates, meaning that investors can pursue an investment goal of their choice for as long as they please. According to their underlying instrument they can be classified as Open End Index Certificates, Open End Commodity Certificates and Open End Interest Rate Certificates. According to their design and market expectation they can be classified as Bull Certificates (long-tracker certificates: they are suitable for the investors whose market expectations are rising underlying) and Bear Certificates (short-tracker certificates: they are suitable for the investors whose market expectations are falling underlying.) Figure 3.8 shows the payoff graph of an OEC. In this figure the line which is thin represents the price change of underlying asset and the lines which are thick represent the payoff of Bear and Bull Certificates. x axis shows the prices of underlying asset and y axis of the graph shows the payoff amount of Bear and Bull Certificates at different prices. Figure 3.8: Payoff Graph of an Open End Certificate As can be seen from Figure 3.8, payoffs of Bear and Bull Certificates are affected 1 to 1 (without leverage) from the underlying assets price movements. Also these certificates provide 100 and unlimited participation to the underlying assets price movements both in upside and downside. Unlike discount certificates they are traded without any discount. To explain how OECs work following example is given Most OECs have an exchange ratio which converts the index level into an OEC price. In this example it is assumed that exchange ratio is 0,01 thats why current price of the OEC is 12 USD when the SampP 500 Index level is 1.200. Since OEC reflects the performance of underlying index 1 to 1 At maturity the price of the OEC will be determined by (Index Level at maturity) (Exchange Ratio) Holder of OEC will gain or lose (Current Index Level Index Level at maturity) (Exchange Ratio) If at maturity SampP 500 Index closes at 1.320 (representing a 10 increase), holder of this OEC will gain (1.320 1.200) (0,01) USD 1,2 and gets USD13,2 at maturity (a return on investment of 10) If at maturity SampP 500 Index closes at 1.080 (representing a 10 increase), holder of this OEC will lose (1.200 8211 1080) (0,01) USD 1,2 and gets USD 10,8 at maturity (a loss on investment of 10) The question is why investors invest in OECs instead of investing the underlying instrument directly although they dont have capital protection and they reflect the underlying instruments performance 1 to 1 The answer is OECs provide investors spreading the risks by investing inexpensively in a broadly diversified product. In other words they provide easy access for the investors to a large variety of alternative investments. For example it can be so expensive to invest in SampP 500 by purchasing each stock individually, but by investing to an open-end certificate which is linked to SampP 500 index, investor can reach SampP 500 indexs performance with low transaction costs and transparent fees. Also if an investor tries to invest in each SampP 500 stock individually it will be hard to follow the performance of the whole portfolio. But by investing in an OEC which is linked to SampP 500 provides the investor to track the performance of the investment anytime. The risks that can be mentioned about these types of products are comparable to a direct investment in the underlying. In other words if the underlying of the OEC decreases, the value of the OEC decreases. As the worst scenario, the investor can lose their entire investment. Also the OECs that are issued on international currency carry currency risk. However, there are some types of certificates that are called Quanto Certificates which enable the investor to participate the product in hisher own local currency. When the certificates are Quanto, investors dont participate in risks or opportunities that are arising from exchange rate movements between the currencies of underlying and certificate. (Goldman Sachs 2 Year Quanto SGD 100 Capital Protected Certificate on an Asian FX Basket Product Brochure 2009) Unlike OECs Quanto Certificates do have a participation ratio and provide the investors a pre-determined participation rate to the underlying performance. b. Outperformance Certificates Outperformance Certificates (OCs) are also known in the market as Sprint Certificates, Accelerator Certificates, or Speeders. Their payoffs are depending on tracking the underlying instrument. Unlike OECs, OCs tracks the underlying 1 to 1 till the strike price and disproportional on the prices that are above the strike price. In other words on the prices that are above the strike price OCs offers disproportional participation to the underlyings performance. This proportional participation is determined by pre-specified multiple (known as performance factor) times the return on the underlying asset (Hernandez, Lee amp Liu 2007). This performance factors is always above 100 thats why these products are known as outperformance certificates. Figure 3.9 shows the payoff graph of an OC. In this figure the line which is thin represents the price change of underlying asset and the line which is thick represents the payoff of an OC according to the price changes of the underlying asset. x axis shows the prices of underlying asset and y axis of the graph shows the payoff amount of OC at different prices. Figure 3.9: Payoff Graph of an Outperformance Certificate As can be seen from the graph till strike price holder of OC will participate to the underlying assets performance 100 and at the prices above the strike price participation of the OC holder to the asset performance will be above 100. To explain how OCs work following example is given Black amp Scholes model for equity-index options In Black amp Scholes model for pricing equity-index options, (S) which represents the current price of underlying is changed as (Se-qt) where q represents dividend payment value that is obtained from the entire index. The Black amp Scholes Model is formulated as S Spot exchange rate that the domestic currency is converted to foreign currency r Risk free interest rate for domestic currency r Risk free interest rate for foreign currency When an option pricing model used in a structured product firstly the options underlying and option type that is embedded to this structured product is determined then the pricing of structured product is formulated according to this option types pricing. For example pricing of a structured product that has an exotic option that is written on an equity-index will be different then pricing of another structured product that has a European call option that is written on a currency. 3.4.2. Pricing a Structured Product with Black amp Scholes Model As it is told in this study several times while pricing a structured product components of this structured product and their valuation is taken into account to formulate a single model for pricing of this particular structured product. In order to formulate the valuation method of a structured product, it is better to obtain this structured product payoff profile. Payoff profile of a structured product can be obtained by the combinations of the payoff profiles of its components. To value a structured product it is better to determine the positions that the issuer will hold by structuring this structured product. Following example valuations that are made for reverse convertibles, discount certificates and outperformance certificates are made according to the positions that are being held by the issuer by structuring these products. 3.4.2.1. Pricing a Reverse Convertible (Exchangeable) The payoff for an investment in one (plain vanilla) reverse exchangeable with face value 1000, C coupon payment, strike price of I0, and a term to maturity T, is exactly the same as the payoff for holding the following three positions (Hernandez, Lee amp Liu 2007) Long position in one zero coupon bond with face value equal to 1,000 and same maturity with reverse exchangeable. Long position in zero coupon bonds that have the face values same as the reverse exchangeables coupons payments and have the same maturity dates with the reverse exchangeable coupon payment dates. A short position in put option with an exercise price of I0, term to maturity of T, (same strike price, same time to maturity with the reverse exchangeable) and number of options of 1,000I0. Reverse exchangeables payoff profile can be obtained from the payoff profiles of these 3 positions. 3.4.2.2. Pricing a Discount Certificate Discount certificate can be considered as a special case of reverse exchangeable with a zero coupon bond and only one embedded put option (Hernandez, Lee amp Liu 2007). So the valuation of it can be formulated as (In this formulation X is used as strike price in order to cover all possible cases) 3.4.2.3. Pricing a Outperformance Certificate The payoff for an investment in one (uncapped) outperformance certificate with a strike price of I0, term to maturity T, and a performance factor of PF is exactly the same as the payoff for holding the following three positions (Hernandez, Lee amp Liu 2007) Long position in the underlying asset. Short position in zero coupon bonds of which face values are the cash dividends to be paid by the underlying asset and have the same maturity dates with the exdividend dates of cash dividends A long position in call options on the underlying asset with an exercise price of I0, term to maturity of T, (same strike price, same time to maturity with the outperformance certificate). Number of options can be determined by deducting 1 from performance factor and is known as additional performance factor (APF) As can be seen from these 3 pricing examples of different structured products it can be realized that structured products with different components and payoff profiles have different pricing formulas. In order to value a structured product firstly this products components should be priced. Since Black amp Scholes model is very popular in the financial markets to value options, in this study Black amp Scholes method is used while pricing the option component of different structured products. In every kind of structured product, as can be seen from pricing examples of reverse exchangeable, discount certificate and outperformance certificate after obtaining each components value of a specific structured product, these values should be combined in order to derive a single pricing formula for this specific structured product. 3.5. Advantages and Disadvantages (Risks) of Structured Products Before this section, at the section 3.3 most common types of structured products, their categories, features, and characteristics were analyzed product by product. However, at this section advantages (by the way attractions) and disadvantages (in other words risks) of structured products will be analyzed as a whole. 3.5.1. Advantages of Structured Products Structured products became so popular within the retail investors in most of the markets. Main reason of this development is structured products are attracting the retail investors with their advantages. Main advantages provided by structured products to their investors are Higher return: Depending on the risk level of a structured product, it is possible to have a higher return than traditional bonds or deposits by investing in a structured product. (Structured products with higher risk levels have higher earning potential) Capital protection: Some of the structured products provide full or partial capital protection. This feature attracts mostly risk averse investors to invest in structured products. The popularity of these products increases when the volatility increases in the market since the investors seek opportunities to reduce risks (Chorafas 2007). These products are mostly popular in the slightly developing structured products markets like Turkey because they provide a transition period for the markets from traditional investment products to structured products. After financial crisis, products with capital protection become more popular among retail investors. Easy access: As can be seen from the section 3.3, structured products can be linked to many different assets. Investing directly to these assets may not be easy or investing in them can be costly for retail investors. Structured products solve this problem by providing customers easy investment at a moderate charge to these kinds of products whose direct investment is hard and costly for retail investors. Tailor-made: Structured products are designed as tailor-made products in order to meet investors specific demands. In other words, the payoffs of the products can be tailored according to different requirements of the investors. This feature of structured products provides the investors flexibility in their investment decisions and provides diversity of products in the financial markets. Diversification: The ability to customize a variety of assumptions into one instrument is one of the principle attractions of structured products for retail investors because that provides attractive diversification properties to the investors (Lamb 2007). Also combining different types of products in a specific product provides investors spreading the market risk. In other words an investor can participate in a diversified portfolio by buying a structured product that is consisting of many different instruments. According to Hernandez, Lee and Liu (2007) this combination of different instruments in structured products enhanced the capital market efficiency which also leads a reduction in transaction costs. Tax benefits: Some of the structured products are designed in order to provide tax benefits to the investors. Especially the structured products which are tailored to private banking customers have this feature. Transparency of Portfolio Management: Investing in structured products is mostly more efficient than investing in a mutual fund when transparency is the issue. While investing in a mutual fund, all the investment decisions are left to the manager of the mutual fund and the performance of the fund can be tracked in total. However, investing in a structured product provides investors to track the performance of the structured product as a whole, or each component separately. Thats why it can be told that investing in structured product provides a more transparent portfolio management than investing a mutual fund. 3.5.2. Disadvantages amp Risks of Structured Products First of all most of the structured products are designed in unique and complex forms when they are compared to other traditional instruments. Thats why their compositions, payoff profile and other unique characteristics cannot be easily understood by ordinary investors. The biggest risk that leads a lot of disadvantages is the meeting of ordinary investors and extraordinary instruments when structured products are the issue. Other risks that should be considered about structured products are Liquidity Risk: Since structured products are tailor-made, they are found in the market as customized products. Most of them are traded in OTC markets so they are lack of secondary markets. Thats also because most of the structured products are seem as buy-and-hold investment vehicles to the investors (Lamb 2007). Addition to that, most of the structured products have longer maturities and this also leads liquidity risk to their investors. Credit Risk: Structured products are issued by financial institutions. Although their payoff depend on their underlying instrument, their issuer and this issuers creditworthiness is also important. Thats because as a result of the default of the issuer, investor could get nothing from the structured product heshe invested although underlying instrument of that product did well. Pricing Risk: Another consideration is pricing risk in other words pricing transparency risk. Since there is not a uniform standard for pricing it is hard to determine the right price for a specific product. According to Katrina Lamb (2007) most of the structured products issuers are using their own pricing models and thats why there is not an explicit fee or other expense to the investor. As can be seen from Chapter 2 lots of the academic works are made about the pricing subject of the structured products in order to test the pricing models of the different issuers. The aims of these academic papers were in order to find whether the products are fairly priced or not. In most of the studies authors concluded as the structured products arent fairly priced and the pricing is on the disadvantage of the investor. Most of these studies showed that complexity of the products leads complex valuation methods and these methods resulted as unfavorable prices for the investors. Higher complexity of products leads higher margins in the prices of structured products on investors disadvantage (Wilkens, Erner and Roder 2003). Especially after the banking crisis, retail investors started to worry about return of their capital so complexity in pricing became a problem for them. Retail investors started to seek products that can be easily understood by them (Wright 2008). Entrop, Scholz and Wilkens (2008) are expecting this unfair pricing will be eliminated in the future with the increase in competition between issuers. Since the market depth of secondary markets is not sufficient of it is difficult for the investors to determine the best price for structured products. Addition to the risks that are mentioned above there are some disadvantages of the structured products such as If an investor invests in X share heshe can get dividend payments of that share, but if shehe invests in a structured product whose underlying is that X share, heshe cannot get any dividend payments. Most of the structured products fees may be much higher than the standard instruments such as mutual funds, bonds and shares. Also their cost and fee rates may be much complex then regular instruments. For example if an investor invests in X share, heshe can easily understand the commission rate because it is paid by the transaction occurs. But if heshe invests in a capital protected structured product whose underlying instrument is that X share, then the commission rate can be differ if the investor buys and holds till the maturity date or sells before the maturity date. Because of all these risks and disadvantages, these products are subject to different regulations in different countries depending on the products characteristics, risk level and tolerance of the countrys regulatory agency. Each country has a regulatory agency or agencies which determine specific regulations about each kind of structured products. Regulation levels change for the same type of structured products among different countries or for different types of structured products in the same country. For example Switzerland has limited regulatory restrictions for structured products compared to other countries and this leads a considerable freedom to the structurers in Swiss market (Yumusak 2007). Especially just after Credit Crunch Crises, regulation of these products is tightened in most of the countries and these products were started to be questioning by the regulatory authorities. Even some countries thought to ban these products. Instead of banning these products as Hens and Rieger (2008) implied in their study understanding of investors about these products should be improved to solve the complexity and other related problems. When advantages, disadvantages and risks of structured products are considered it can be told that a structured product can bring lots of advantages to an investor although the same structured product can bring lots of disadvantages to another investor. Here the key term for the investors is analyzing all the features and possible risks of the products in detail and choosing the right product for their needs. That means if the match of the investor and the product is right then it is possible talk about lots of advantages of this togetherness, but if not then disadvantages arise. Derivatives are essentially innocent for the right purposes (Chambers 2008), since these derivatives are created structured products same statement can be valid for also structured products. Share this entry erenkocyigitwp-contentuploads201802structured-products8.jpg 145 348 Z. Eren Koyiit erenkocyigitwp-contentuploads201806eren-kocyigit-2.png Z. Eren Koyiit 2018-02-12 23:26:20 2018-02-12 23:26:20 Structured Products Part 3: All About Structured Products You might also like2018-08-10 1. 2. AQR AQR 2018-08-10 1. 2. AQR AQRimplementability 3. 1. your alpha is some sort of beta2. there is some alpha not captured by current factor model implementreturn anomaly alpha , Asset Pricing Models - Alpha - Special Issue Research from AQR Monkey Performance Measures 1. CAPM: 1.1 Markowitz, H. (1952). Portfolio selection. The journal of finance . 7 (1), 77-91. PORTFOLIO SELECTION 1.2 Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The review of economics and statistics . 13-37. The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets on JSTOR 1.3 Lintner, J. (1965). Security Prices, Risk, and Maximal Gains from Diversification. The Journal of Finance . 20 (4), 587-615. SECURITY PRICES, RISK, AND MAXIMAL GAINS FROM DIVERSIFICATION 1.4 Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance . 19 (3), 425-442. CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK 1.5 Sharpe, W. F. (1963). A simplified model for portfolio analysis. Management science . 9 (2), 277-293. Management Science: INFORMS 2. Black CAPM: 2.1 Jensen M C, Black F, Scholes M S. The capital asset pricing model: Some empirical testsJ. 1972. papers. ssrnsol3pa pers. cfmabstractid908569 3. Merton ICAPM: Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica: Journal of the Econometric Society . 867-887. An Intertemporal Capital Asset Pricing Model on JSTOR 4. Fama French 3 Factor Model: 4.1 Fama, E. F. amp French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of financial economics . 33 (1), 3-56. Common risk factors in the returns on stocks and bonds 4.2 Fama, E. F. amp French, K. R. (1992). The crosssection of expected stock returns. the Journal of Finance . 47 (2), 427-465. The Cross-Section of Expected Stock Returns 5. Carhart 4 Factor Model: Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of finance . 52 (1), 57-82. On Persistence in Mutual Fund Performance 6. Fama French 5 Factor Model: Fama, E. F. amp French, K. R. (2018). A five-factor asset pricing model. Journal of Financial Economics . 116 (1), 1-22. A five-factor asset pricing model AQRfactor QMJ 7. Good Literature Reviews: Harvey, C. R. Liu, Y. amp Zhu, H. (2017). . And the cross-section of expected returns (No. w20592). National Bureau of Economic Research. . and the Cross-Section of Expected Returns 300factormake sense 1. AQR 1.1 Quality Minus Junk: Asness, C. Frazzini, A. amp Pedersen, L. H. (2017). Quality minus junk. Available at SSRN . 1.2 Buffets Alpha: Frazzini, A. Kabiller, D. amp Pedersen, L. H. (2017). Buffetts Alpha (No. w19681). National Bureau of Economic Research. 1.3 Time Series Momentum: Moskowitz, T. J. Ooi, Y. H. amp Pedersen, L. H. (2017). Time series momentum. Journal of Financial Economics . 104 (2), 228-250. 1.4 Value Momentum everywhere: Asness, C. S. Moskowitz, T. J. amp Pedersen, L. H. (2017). Value and momentum everywhere. The Journal of Finance . 68 (3), 929-985. IMPORTANT NOTICE 14dinner my strategieseven monkey can make millions Glasserman Monkey Institutional Investor Journals: Home iijournalsJournal of Portfolio Management, Journal of Fixed Income, Journal of DerivativesJournal of Portfolio Management Whaley papers. ssrnsol3pa pers. cfmabstractid2261387 retail investorVIXETFETF SSRNseeking alphashort VIXbuy and hold inverse VIX ETFETNXIV XIVliquidETNBampH XIVXIVinstitutional ownership But, be cautious risk exposureattributefactor modelfactorcarry traderetail investor VIXETF 1. Whaley, R. E. (2017). Trading volatility: At what cost. Journal of Portfolio Management . 40 (1), 95-108. 2. Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. The Journal of Portfolio Management . 31 (3), 92-100. Relationships Between Implied Volatility Indexes and Stock Index Returns: The Journal of Portfolio Management 3. Whaley, R. E. (2000). The investor fear gauge. The Journal of Portfolio Management . 26 (3), 12-17. The Investor Fear Gauge: The Journal of Portfolio Management 3.2 monkeys strategy for deep-in-the-money-option Numerical Method IncMarco AvellanedaAndrew Lo General An Introduction to High-Frequency Finance. Ramazan Gen Numerical Method IncMarco AvellanedaAndrew Lo General An Introduction to High-Frequency Finance. Ramazan Genay, Michel Dacorogna, Ulrich A. Muller, Olivier Pictet, Richard Olsen. Academic Press. 2001. Advanced Trading Rules, Second Edition. Emmanual Acar (Editor), Stephen Satchell (Editor). Butterworth-Heinemann 2nd edition (June 19, 2002). Pairs Trading Statistical Arbitrage in the U. S. Equities Market. Marco Avellaneda and Jeong-Hyun Lee. July 11, 2008. A New Approach to Modeling and Estimation for Pairs Trading, Binh Do, Robert Faff, Kais Hamza, Working Paper, May 29, 2006. Pairs Trading A Cointegration Approach. Arlen David Schmidt, Finance Honors Thesis, University of Sydney, November 2008, Pages 1130. Does Simple Pairs Trading Still Work Binh Do. Robert Faff. Financial Analysts Journal. JulyAugust 2017, Vol. 66, No. 4, pp: 8395. Implementation of Pairs Trading Strategies. yvind Foshaug. Faculty of Science. Koortweg - de Vries Institute for Mathematics. Master of Science Thesis. 2017. Pairs trading. Elliott, van der Hoek, and Malcolm. Quantitative Finance, 2005. Optimal Pairs Trading: A Stochastic Control Approach. Mudchanatongsuk, S. Primbs, J. A. Wong, W. Dept. of Manage. Sci. amp Eng. Stanford Univ. Stanford, CA. Mean Reversion Identifying small mean-reverting portfolios. Alexandre DAspremont. Quantitative Finance, Volume 11 Issue 3 2017. Arbitrage Under Power. Michael Boguslavsky, Elena Boguslavskaya. 2004. Identifying Small Mean Reverting Portfolios. Alexandre dAspremont. 2008. Markov Models Algorithmic Trading: Hidden Markov Models on Foreign Exchange Data. Patrik Idvall, Conny Jonsson. University essay from Linkpings universitetMatematiska institutionen Linkpings universitetMatematiska institutionen. 2008. Markov Switching Regimes in a Monetary Exchange Rate Model, Frmmel, Michael, MacDonald, Ronald, Menkhoff, Lukas, Economic Modelling, Vol. 22 (2005), 3, Pages 485502. Bayesian Bayesian Adaptive Trading with a Daily Cycle. Robert Almgren, Julian Lorenz. The Journal of Trading. Fall 2006, Vol. 1, No. 4: pp. 38-46. On the short-term predictability of exchange rates: A BVAR time-varying parameters approach. Nicholas Sarantis. Journal of Banking amp Finance, Volume 30, Issue 8, August 2006, Pages 2257-2279 . Time Series Analysis Time Series Technical Analysis via New Fast Estimation Methods: A Preliminary Study in Mathematical Finance. Michel Fliess. Cdric Join. Published Presented, IAR-ACD08 (23rd IAR Workshop on Advanced Control and Diagnosis), 2008, Coventry, United Kingdom. A Trading Strategy Based on the Lead-Lag Relationship between the FTSE 100 Spot Index and the LIFFE Traded FTSE Futures Contract. Brooks, C. A. G. Rew and S. Ritson. International Journal of Forecasting 17, 31-44. 2001. Basket trading under co-integration with the logistic mixture autoregressive model. Xixin Cheng, Philip L. H. Yu, W. K. Li. Quantitative Finance, 1469-7696. First published on 09 December 2017. Towards a non-linear trading strategy for financial time series. Fernanda Strozzia, and Jos-Manuel Zaldvar Comenges. Chaos, Solitons amp Fractals. Volume 28, Issue 3, May 2006, Pages 601-615. Trend FollowingMomentum A Test of Momentum Trading Strategies in Foreign Exchange Markets: Evidence from the G7, Robert J. Bianchi, Michael E. Drew, and John Polichronis, Global Business and Economics Review, Vol. 7 (2005), 2-3, Pages 155179. A Momentum Trading Strategy Based on the Low Frequency Component of the Exchange Rate, Richard D. F. Harris and Fatih Yilmaz, Journal of Banking and Finance, 33 (2009), 9, Pages 15751585. Thou shalt buy and hold. Albert Shiryaeva, Zuoquan Xu, Xun Yu Zhoubc. Quantitative Finance. Volume 8, Issue 8 December 2008. pages 765 776. Optimal Trend Following Trading Rules. Min Dai, Qing Zhang, Qiji Jim Zhu. 2017. Technical Indicators A dynamic analysis of moving average rules. Carl Chiarella, Xue-Zhong He, and Cars Hommes. Journal of Economic Dynamics and Control, Volume 30, Issues 9-10, September-October 2006, Pages 1729-1753. Do the technical indicators reward chartists A study on the stock markets of China, Hong Kong and Taiwan. Wing-Keung Wong, Jun Du, Terence Tai-Leung Chong. 2005. A comparison of MA and RSI returns with exchange rate intervention. Thomas C. Shik, Terence Tai-Leung Chong. Applied Economics Letters, Volume 14, Issue 4 6 April 2007. pages 371 383. News amp Announcements Does beta react to market conditions Estimates of bull and bear betas using a nonlinear market model with an endogenous threshold parameter. George Woodward, Heather Anderson, 2009. Journal of Quantitative Finance. March, 2009. Short-term market reaction after extreme price changes of liquid stocks. Adm G. Zawadowski, Gyrgy Andor, Jnos Kertsz, 2007. Journal of Quantitative Finance. May, 2007. Misc Modeling and Forecasting Stock Return Volatility Using a Random Level Shift Model. Yang K. Lu, Pierre Perron. 2009. Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156. When are contrarian profits due to stock market overreaction Andrew W. Lo and A. Craig MacKinlay. Review of Financial Studies 3(1990), 175206. Predictability of nonlinear trading rules in the U. S. stock market. Terence Tai-Leung Chonga, Tau-Hing Lama. 2017. Journal of Quantitative Finance. Issue 9, Volume 10, 2017. A Reality Check for Data Snooping. Halbert White. 2000. Econometrica. Issue 5, Volume 68, 2000. A Test for Superior Predictive Ability. Peter Reinhard Hansen. 2005. Brown Univ. Dept. of Economics Working Paper No. 01-06. Risk Management Extreme Value Theory and Fat Tails in Equity Markets. Blake LeBaron and Ritirupa Samanta. May, 2004. 1.Harry Markowitz. Portfolio selection. 19522.Sharpe, w.f. Capital asset prices:a theory of market equilibrium under conditions of risk. 1964 CAPM3.Fama. Efficient Ca 1.Harry Markowitz. Portfolio selection. 1952 2.Sharpe, w.f. Capital asset prices:a theory of market equilibrium under conditions of risk. 1964 CAPM 3.Fama. Efficient Capital Market. 1991 4. The Pricing of Opitions and Corporate Liabilities. 5.Ross, Stephen. The arbitrage theory of capital asset pricing.1976 6.Narasimhan Jegadeesh, Sheridan Titman. Returns to Buying Winners and Selling Losers:Implications for Stock Market Efficiency. 1993 7. Commmon risk factors in the returns on stocks and bonds.1993 8.Fischer Black. Robert Litterman, Global portfolio optimization,1992 9. Engle. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.1982 10. Fama, E and French, K.The cross-section of expected stock returns.1992 11.Asness. The Interaction of Value and Momentum Strategies.1997 12.Mehra, Rajnish, Edward C. Prescott. The Equity Premium:A Puzzle.1985 13.Robert D, Arnott. Quantitative management in the coming decade.1994 14.Robert D, Arnott. The future for quantitative investment products.2017 15.LaBaron B. Nonlinear dynamics ang economic.1990 16.Farmer, Sidorowich. Yapunov and stock prediction.1990 17.Angelini Eliana. The Quantitative ETF:the new frontier of the investment between innovation and dynamism.2009 18.White H. Economic prediction using neural networks:the case of IBM dailystock returns.1988 19.Jennifer Conrad, Gautam Kaul. An anatomy of trading strategies.1998 20.A. Hameed, K.Yuan to. Momentum Strategies:Evidence from Pacific Basin Stock Markets.2000 21.B. Lev, S.R. Thiagaraian. Fundamental Information Analysis.1993 22.R. Cont. Empirical properties of asset returns: stylized facts and statistical issues.2002 quant : : ft5w : : eajh -- 20180811 Anomalies - The Equity Premium Puzzle-SiegelampThaler, JEP1997(). quant : : ft5w : : eajh -- 20180811 Anomalies - The Equity Premium Puzzle-SiegelampThaler, JEP1997().pdf Capital Asset Prices - A Theory of Market Equilibrium under Conditions of Risk-Sharpe, JF1964(CAPM).pdf Common risk factors in the returns on stocks and bonds-FamaampFrench, JFE1993().pdf Efficient Capital Markets - A Review of Theory and Empirical Work-Fama, JF1970(EMH).pdf Equilibrium in a Capital Asset Market-Mossin, Econometrica1966(CAPM).pdf Existence of an Equilibrium for a Competitive Economy-ArrowampDebreu, Econometrica1954(Arrow-Debreu).pdf Market efficiency, long-term returns, and behavioral finance-Fama, JFE1998(EMH).pdf Multifactor Explanations of Asset Pricing Anomalies-FamaampFrench, JF1996().pdf Portfolio Selection-Markowitz, JF1952(-).pdf Proof that properly anticipated prices fluctuate randomly-Samuelson, Industrial Management Review1965(EMH).pdf Security Prices, Risk, and Maximal Gains From Diversification-Lintner, JF1965(CAPM).pdf The Arbitrage Theory of Capital Asset Pri cing-Ross, JET1976(APT).pdf The Cost of Capital, Corporation Finance and the Theory of Investment-ModiglianiampMiller, AER1958(MM).pdf The Pricing of Options and Corporate Liabilities-BlackampScholes, JPE1973(B-S).pdf Theory of Rational Option Pricing-Merton, Bell JEMS1973(B-S).pdf -- ABN-AMRO A Breathrough in Synthetic Credit Investments. pdf Adelson amp Jacob Consulting The Need to See Past the Data. pdf Advances in Futures and Options Research, Barone-Adesi On the Valuation of American Put Options on Dividend-Paying Stocks. pdf Agder University College, Koekebakker Electricity Term Structure Modelling. pdf Aite Group Trends in OTC Equity Derivatives - Where do we go from here. pdf Amen Introduction To Foreign Exchange. ppt Andrew Davidson amp Co An Implied Prepayment Model for MBS. pdf Andrew Davidson amp Co Divide and Conquer - Exploring New OAS Horizons. pdf Andrew Davidson amp Co Fixed-Rate Agency MBS Prepayments and Model Enhancements. pdf Andrew Davidson amp Co Interest Rate Modeling - A Conscientious Choice. pdf Andrew Davidson amp Co LOANDYNAMICS - ADampCOs Approach to Modeling Credit Risk. pdf Andrew Davidson amp Co The Relationship Between the Yield Curve and Mortgage Current Coupon. pdf Applied Mathematical Finance, Chung Pricing Quanto Equity Swaps in a Stochastic Interest Rate Economy. pdf Applied Mathematical Finance, Hagan Interpolation Methods for Curve Construction. pdf Applied Mathematical Finance, West Calibration of the SABR Model in Illiquid Markets. pdf Applied Spectroscopy, Lodder Quantile Analysis - A Method for Characterizing Data Distributions. pdf ASX Australian Exchange A Guide to the Pricing Conventions of SFE Interest Rate Products. pdf AVT Initial Estimating and Refining Volatility. pdf AXA Investment Why the Implied Correlation of Dispersion Has to be Higher Than the Correlation Swap Strike. pdf Bae Managing Global Financial Risk Using Currency Futures and Currency Options. pdf Bank of America, Andersen Efficient Simulation of the Heston Stochastic Vola tility Model. pdf Bank of America-Merrill Lynch The Big Bang - A Guide to the Standardized CDS Contract. pdf Bank of America An Introduction to Agency MBS Derivatives. pdf Bank of America Credit Strategy - Monolines - A Potential CDS Settlement Disaster. pdf Bank of America Fixed-Rate IO Mortgages. pdf Bank of America Guide to Credit Default Swaptions. pdf Bank of America Hybrid ARM MBS - Valuation and Risk Measures. pdf Bank of America Introduction to Agency CMO Structures. pdf Bank of America Introduction to Cross Currency Swaps. pdf Bank of America Option Prices Imply a Dividend Yield - Examining Recent Trading in JPM. pdf Bank of America Outlook for the RMBS Market in 2007.pdf Bank of America Prepayments on Agency Hybrid ARM MBS. pdf Bank of America Pricing Mortgage-back Securities. pdf Bank of America Residential Mortgages - Prepayments and Prepayment Modeling. pdf Bank of America The Agency ARM MBS Sector. pdf Bank of America Trust IO-PO Market. pdf Bank of America Understanding Mortgage Dollar Rolls. pdf Bank of Canada, Bolder Yield Curve Modelling at the Bank of Canada. pdf Bank of Canada, Ron A Practical Guide to Swap Curve Construction. pdf binary options BESA South Africa Government Inflation-linked Bond Index Guide. pdf binary options CDS Curve Trading Handbook 2008.pdf binary options Convertible Bonds - A Technical Introduction. pdf binary options Correlation Modelling - From Vanilla to Exotic. pdf binary options Dividend Swap Indices - Access to Equity Income Streams Made Easy. pdf binary options European Alpha Anticipator - Decoding the Fed and Monolines. pdf binary options Forward Starting Equity. pdf binary options Global Inflation-Linked Products - A Users Guide. pdf binary options Inflation Derivatives - A Users Guide. pdf binary options The binary options Capital Guide to Cash Flow Collaterialized Debt Obligations. pdf Barra Global Equity - Risk Model Handbook. pdf Barra Single Country Equity - Risk Model Handbook. pdf Bear Stearns Across the Curve in Rates and Structured Products and Across the Grade in Credit Products Outlook 2007.pdf B ear Stearns Bear Stearns Quick Guide to Non-Agency Mortgage-Back Securities. pdf Bear Stearns Introduction to Asset-Backed CDS. pdf Bear Stearns RMBS Residuals - A Primer. pdf Bear Stearns SampP 500 Index Variance - Buying Earnings Volatility. pdf Bear Stearns The Outlook for Fixed Income 2007.pdf Bear Stearns Understanding CMO Toggle Floaters. pdf Bear Stearns Variance Swaps - An Introduction. pdf Benth Analytical Approximation for the Price Dynamics of Spark Spread Options. pdf Bloomberg Magazine, Berger Modeling Future Interest Rates - Taming the Unknownable. pdf Bloomberg Magazine, Carr The Innovator. pdf Bloomberg Magazine, Carr The Value of Volatiliity. pdf Bloomberg, Baver Variance Gamma Option Model. pdf Bloomberg, Berger Modeling Interest Rates - Fundamental Issues. pdf Bloomberg, Berger Stochastic Interest Rates - A Crucial Correlation. pdf Bloomberg, Carr Hedging Variance Options on Continuous Semimartingales. pdf Bloomberg, Dupire Modelling Volatility Skews. ppt Bloomberg, Konikov Basket Default Swaps. pdf Bloomberg, Stein FX Market Behavior and Valuation. pdf Bloomberg, Stein Mortgage Backed Valuation. pdf Bloomberg, Stein Valuation of Exotic Interest Rate Derivatives - Bermudans and Range Accruals. pdf Bloomberg, Yekutieli Implementation of the Hestom Model for the Pricing of FX Options. pdf Bloomberg Credit Default Swaps. pdf BNP Paribas, Atlan Hybrid Equity-Credit Modelling. pdf BNP Paribas Conditions et tarifs - Produits et services pour les particuliers. pdf BNP Paribas Corridor Variance Swaps - A Cheaper Way to Buy Volatility. pdf BNP Paribas DivDax. Trade 2009-2017 dividend swap. pdf BNP Paribas European Volatility Tracker - Feb 2006.pdf BNP Paribas Guide to Structured Products. pdf BNP Paribas Index Variance Arbitrage. pdf BNP Paribas Inflation Linked Bond Markets - 2009 Real Rate amp Curve Modeling. pdf BNP Paribas Quantitative Option Strategy. pdf BNP Paribas Smile Trading. pdf BNP Paribas Structured Retail Products. pdf BNP Paribas The Bermuda Triangle of Super Senior Risk. pdf BNP Paribas The High Yield Handbook, Part 1.pdf BNP Paribas The High Yield Handbook, Part 2.pdf BNP Paribas Understanding Credit Derivatives Vol. 1 - Market Overview. pdf BNP Paribas Understanding Credit Derivatives Vol. 2 - CDS Basics. pdf BNP Paribas Understanding Credit Derivatives Vol. 4 - CDS Pricing. pdf BNP Paribas Understanding Credit Derivatives Vol. 5 - First-to-Default Baskets. pdf BNP Paribas US Index Option Strategies. pdf BNP Paribas Volatility Investing Handbook. pdf BNP Paribas What Future for Dividends in Europe. pdf Bond Exchange of South Africa Bond Pricing Formula - Specifications. pdf Bond Market Association An Analysis and Description of Pricing and Information Sources in the Securitized and Structured Finance Markets. pdf Booz Allen Hamilton The MampA Collar Handbook - How to Manage Equity Risk. pdf Borak FFT Based Option Pricing. pdf Borovkova Analysis and Modelling of Electricity Futures Prices. pdf Bowling Green State University, Bae Managing Global Financial Risk Using Currency Futures and Currency Options. pdf CARR Futures, Burghardt The Convexity Bias in Eurodollar Futures. pdf Carr Futures, Burghardt The Convexity Bias in Eurodollar Futures. pdf. bc Carr Futures, Panos Trading the Unemployment Report. pdf CBOT CBOT Electricity Futures and Options Reference and Applications Guide. pdf CBOT CBOT Soybean Crush Reference Guide. pd f Center for Futures Education The Fundamentals and Techniques of Trading Commodity Spreads. pdf CFA Institute Global Investment Performance Standards (GIPS) - Corrections. pdf CFA Institute Global Investment Performance Standards (GIPS).pdf Chris Market Risk for Volatility and Variance Swaps. pdf Citibank A General Review of CDO Valuation Methods. pdf Citibank Convertible Bonds - A Guide. pdf Citibank Correlation Trading Strategies. pdf Citibank CPDOs - The New Best Seller. pdf Citibank General Review of CDO Valuation Methods. pdf Citibank Guide to Mortgage-Back Securities. pdf Citibank Index-Linked Investment Products. pdf Citibank Interest Rates Workbook. pdf Citibank Introducing the Experimental Prepayment Model. pdf Citibank Latin America Training and Development Center - Asset Backed Finance. pdf Citibank Latin America Training and Development Center - Basic Corporate Finance. pdf Citibank Latin America Training and Development Center - Basic Treasury. pdf Citibank Latin America Training and De velopment Center - Basics of Trade Services and Trade Finance. pdf Citibank Latin America Training and Development Center - Debt Financing. pdf Citibank Latin America Training and Development Center - Equity Financing. pdf Citibank Latin America Training and Development Center - Financial Statement Analysis. pdf Citibank Latin America Training and Development Center - Futures. pdf Citibank Latin America Training and Development Center - Interest Rates. pdf Citibank Latin America Training and Development Center - Introduction to Risk Management. pdf Citibank Mortgage Basics Overview. ppt Citibank Total Rate of Return Indexes - April 2005 Performance. pdf Citibank Using Asset Swap Spreads to Identify Goverment Bond Relative-Value. pdf Citibank Valuing Fixed-Rate IO Mortgages. pdf City Credit Capital, Patten An Introduction to Contracts for Difference. pdf CK Locke and Partners CFD Trading Manual. pdf CME Interest Rate Products - Advanced Topics. pdf Columbia University, Derman Trading Volatility as an Asset Class. pdf Columbia University, Zhao Bayesian Adaptive Portfolio Optimization. pdf Commodities Now, Sikorski EU Emissions Trading - What Does It Mean for an Electricity Generator. pdf Computing, Spath Exponential Spline Interpolation. pdf Convertible Bonds, Berger Valuing Options on Dividend-Paying Stocks. pdf Copenhagen Business School, Nielsen Dividends in the Theory of Derivative Securities Pricing. pdf Cotton Stochastic Volatility Corrections for Interest Rate Derivatives. pdf Courant Institute, Carr Trading Autocorrelation. pdf Courant Institute, Friz Valuation of Volatility Derivatives as an Inverse Problem. pdf Creative Computing, Stineman A Consistently Well-Behaved Method of Interpolation. pdf Credit Suisse CFBSs Starter Kit for Non-Agency Residential Mortgage-Backed Securities. pdf Credit Suisse Credit Portfolio Modeling Handbook. pdf Credit Suisse Credit Suisses Guide to Global Fixed Income Indices. pdf Credit Suisse Fixed-Rate Alt-A MBS - Commonly Asked Questions Answered. pdf Cre dit Suisse Institutional Considerations - The next move on the MBS chessboard. pdf Credit Suisse Institutional Considerations in the MBS Markets. pdf Credit Suisse Option Market Feedback - What can the option markets tell investors and modelers. pdf CSMA CMBS Total Rate of Return Swaps. pdf DerivativeFitch Considerations for Rating Commodities-Linked Credit Obligations. pdf DerivativeFitch First Generation CPDO - Case Study on Performance and Ratings. pdf Derivatives Consulting Group Introduction to Equity Derivatives. pdf Derivatives Strategy, Leib The Art of Option Writing - August 2000.pdf Derivatives Week Variance Swap Volatility and Option Strategies. pdf Deutsche Bank Asset Valuation amp Allocation Models. pdf Deutsche Bank Credit Derivatives - Issues amp Trends. pdf Deutsche Bank Credit Derivatives and Structured Credit. pdf Deutsche Bank Depositary Receipts Handbook. pdf Deutsche Bank FAS 133 Amendments. pdf Deutsche Bank High-Yield Credit Derivatives. pdf Deutsche Bank Modeling Variance Swa p Curves - Theory and Application. pdf Deutsche Bank Pricing Exotic FX amp Equity Derivatives. pdf Deutsche Bank Quantitative Credit Strategy - Aug, 25 2006.pdf Deutsche Bank The Arbitrage CDO Market. pdf Deutsche Borse Group Guide to the Volatility Indices of Deutsche Borse. pdf Diko Risk Premia in Electricity Forward Prices. pdf Dresdner Kleinwort Wasserstein Structured Products Vicious Circle - How Structured Products Exaggerate Long-Dated Implied Volume Moves. pdf Dresdner Kleinwort, Bossu A New Approach for Modelling and Pricing Correlation Swaps. pdf Dresdner Kleinwort, Bossu Equity Correlation Swaps - A New Approach for Modelling amp Pricing. pdf Dresdner Kleinwort, Bossu Introduction to Volatility Trading and Variance Swaps. pdf Dresdner Kleinwort, Clark Numerical Methods for Stochastic Volatility - Fourier Methods, PDEs and Monte Carlo. pdf Dresdner Kleinwort A New Approach For Modeling and Pricing Correlation Swaps. pdf Dubai International Financial Centre A Guide to Islamic Finance In or From the DIFC. pdf Duff amp Phelps Credit Rating Co DCR Rates Asian Diversified Funding Bond CBO. pdf Duff amp Phelps Credit Rating Co DCR Rates First-Ever Weather-Linked Notes. pdf Duff amp Phelps Credit Rating Co DCRs Criteria for Rating Cash Flow CDOs. pdf Econometrica, Cox A Theory of the Term Structure of Interest Rates. pdf Econometrica, Heath Bond Pricing and the Term Structure of Interest Rates - A New Methodology for Contingent Claims Valuation. pdf Econometrica, Phillips Optimal Inference in Cointegrated Systems. pdf Economic Modeling, Johansen Modelling of Cointegration in the Vector Autoregressive Model. pdf EDHEC Risk and Asset Management Research Centre The Amaranth Collapse - What Happened and What Have We Learned Thus Far. pdf Egar Technology, Ioffe Variance Swap Pricing. pdf Egar Technology How to Extend Modern Portfolio Theory to Make Money from Trading Equity Options. pdf Egar Technology Weather Derivatives. pdf Erasmus University, Hallerback An Improved Estimator for Black-S choles-Merton Implied Volatility. pdf Eurex Interest Rate Derivatives - Fixed Income Trading Strategies. pdf Eurex Volatility and its Measurements - The Design of a Volatility Index and the Execution of its Historical Time Series at the Deutsche Borse AG. pdf European Central Bank The Euro Bond Market Study - December 2004.pdf European Securitisation Forum European Securitisation - A Resource Guide. pdf FEA Power Price Simulation using Hybrid Models. pdf FEA Valuing Generation Assets and Tolling Agreements using the Power Sector Model. pdf Federal Reserve Bank of Alanta, Fernndez-Villaverde A, B, Cs (and Ds) for Understanding VARs. pdf Federal Reserve Bank of Chicago Structured Notes. pdf Federal Reserve Bank of Clevland, Haubrich Swaps and the Swaps Yield Curve. pdf Federal Reserve Bank of New York, Fernald The Pricing and Hedging of Index Amortizing Rate Swaps. pdf Federal Reserve Bank of New York, Fleming Repurchase Agreements with Negative Interest Rates. pdf Federal Reserve Bank of New York, Kambhu Trading Risk and Volatility in Interest Rate Swap Spreads. pdf Federal Reserve Bank of San Fransico, Poole Using T-Bill Futures to Gauge Interest-Rate Expectations. pdf Federal Reserve Board, Gurkaynak The US Treasury Yield Curve - 1961 to the Present. pdf Finance and Stochastics, Fusai An Exact Analytical Soltion for Discrete Barrier Options. pdf FitchRatings Asset-Backed Commercial Paper Explained. pdf FitchRatings Credit Policy - 2006 European SF Outlook Chart. pdf FitchRatings Hybrid Securities - An Emperical View. pdf FitchRatings Rating Securitizations Above the Sovereign Ceiling. pdf FitchRatings Structured Finance in Latin Americas Domestic Markets. pdf FitchRatings Synthetic Overview for CMBS Investors. pdf FitchRatings UK Non-Conforming RMBS - Catching a Cold. pdf FOW, Smith Adding a Floor to Equity Cliquets. pdf Frankfurt MathFinance Institute, Kuhn Israeli Options as Composite Exotic Options. pdf Futures Magazine, Gould Comparing Price, Volume amp Open Interest. pdf Ganatra Imple mentation of Variance Swaps in Dispersion Trading Strategies. pdf Glass Fourier Transform Techniques in Stochastic Volatility BGM. pdf Glenwood Capital Investments Variance Swaps and Non-Constant Vega. pdf Global Derivatives 2005, Dupire Exploring Volatility Derivatives - New Advances in Modelling. pdf Goldman Sachs, Black Fixed Income Research - Global Asset Allocation with Equities, Bonds, and Currencies. pdf Goldman Sachs A Mortgage Product Primer. pdf Goldman Sachs Alt-A Market - An Introduction. pdf Goldman Sachs Dividends and Dividend Swaps. pdf Goldman Sachs Fixed Income Research - The Investment Implications of an Inverted Yield Curve. pdf Goldman Sachs Hedge Funds - Have You Missed the Boat. pdf Goldman Sachs How to Value and Hedge Options on Foreign Indexes. pdf Goldman Sachs Introduction to Mortgage-Backed Securities and Other Securitized Assets. pdf Goldman Sachs Speculators, Index Investors, and Commodity Prices. pdf Goldman Sachs Understanding US Economic Statistics. pdf Goldman Sachs Valuing Convertible Bonds as Derivatives. pdf Goteborg University, Kjaer On the Pricing of Cliquet Options with Global Floor and Cap. pdf Hagan Credit Derivatives. pdf Harvard Business School, Donahue Note On Commodity Futures. pdf Harvard Business School Note on Commodity Futures. pdf Henrard Bonds Futures and Their Options - More than the Cheapest-to-Deliver Quality Option and Marginning. pdf HSBC European Meltdown - Europe Fiddles as Rome Burns. pdf HumboldtUniversity, Molgedey Extracting Factors for Interest Rate Scenarios. pdf HVB Group Credit Derivatives Accounting. pdf HVB Group Trading the DAX in CDS Format and Playing Equity versus Debt. pdf IBM Research Report, Glasserman Importance Sampling in the Heath-Jarrow-Morton Framework. pdf IEEE Transactions on Power Systems, Denton Managing Market Risk in Energy. pdf IMF Staff Papers, Sarno Purchasing Power Parity and the Real Exchange Rate. pdf Imperial College, Albanese Pricing Equity Default Swaps. pdf Investopedia Advanced Bond Concepts. pdf I SDA, Altman Analyzing and Explaining Default Recovery Rates. pdf ISDA 2002 ISDA Equity Derivatives Definitions. pdf ISDA 2003 ISDA Credit Derivative Definitions. pdf ISDA EMU and Market Conventions - Recent Developments. pdf Islamic Development Bank Understanding Islamic Finance - A Study of the Securities Market in an Islamic Framework. pdf Islamic Research and Training Institute, Mannan Understanding Islamic Finance - A Study of the Securities Market in an Islamic Framework. pdf ISMA Centre, Alexander Principal Component Analysis of Volatility Smiles and Skews. pdf ITO33, Henrotte Variance Swaps. pdf Jackel Stochastic Volatility Models - Past, Present and Future. pdf Journal of Applied Corporate Finance, Arzac Percs, Decs, and Other Mandatory Convertibles. pdf Journal of Applied Corporate Finance, Black How to Use the Holes in Black-Scholes. pdf Journal of Applied Mathematics and Decision Sciences, Francesco Analysis of an Uncertain Volatility Model. pdf Journal of Banking Finance, Corrado A not e on a simple, accurate formula to compute implied standard deviations. pdf Journal of Computational Finance, Sepp Pricing Options on Realized Variance in Heston Model with Jumps in Returns and Volatility. pdf Journal of Derivatives, Broadie Pricing and Hedging Volatility Derivatives. pdf Journal of Derivatives, Hull The Valuation of Credit Default Swap Options. pdf Journal of Derivatives, Hull Valuation of a CDO and an nth to Default CDS without Monte Carlo Simulation. pdf Journal of Derivatives, Kjaer Fast Pricing of Cliquet Options with Global Floor. pdf Journal of Derivatives, Milevsky A Closed-Form Approximation for Valuing Basket Options. pdf Journal of Discrete Algorithms, Gerbessiotis An Architecture Independent Study of Parallel Segment Trees. pdf Journal of Econometrics, Phillips Understanding Spurious Regression in Econometrics. pdf Journal of Econometrics, Phillips Understanding Spurious Regressions in Econometics. pdf Journal of Economic Development, Islam The Purchasing Power Parit y Relationship - Causality and Cointegration Tests Using Korea-US Exchange Rate and Prices. pdf Journal of Finance, Barone-Adesi Efficient Analytic Approximation of American Option Values. pdf Journal of Finance, Black Interest Rates as Options. pdf Journal of Financial Economics, Geske The Valuation of Compound Options. pdf Journal of Financial Economics, Lettau Expected Returns and Expected Dividend Growth. pdf Journal of Financial Economics, Vasicek An Equilibrium Characterization of the Term Structure. pdf Journal of Fixed Income, Bieri Riding the Yield Curve - A Variety of Strategies. pdf Journal of Hydrologic Engineering, Genest Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask. pdf Journal of International Money and Finance, Levy Pricing European Average Rate Currency Options. pdf Journal of International Money and Finance, Zivot Cointegration and forward and spot exchange rate regressions. pdf Journal of Portfolio Management, Neuberger The Log Contract - A New Instrument to Hedge Volatility. pdf Journal of Portfolio Management, Neuberger The Log Contract. pdf Journal of Risk, Rebonato Evolving Yield Curves in the Real-World Measures - A Semi-Parametric Approach. pdf JP Morgan, Bossu Arbitrage Pricing of Equity Correlation Swaps. pdf JP Morgan, Bossu Fundamental Relationship Between an Indexs Volatility and the Correlation and Average Volatility of Its Components. pdf JP Morgan, Matytsin Modelling Volatility and Volatility Derivatives. pdf JP Morgan, Sim Agency Hybrid ARM Prepayment Model. pdf JP Morgan A Framework for Valuing Financial Hybrids. pdf JP Morgan Abritrage Pricing of Equity Correlation Swaps. pdf JP Morgan Agency Hybrid ARM Prepayment Model. pdf JP Morgan All You Ever Wanted to Know About Corporate Hybrids But Were Afraid to Ask. pdf JP Morgan An Introduction to CFXOs (Foreign Exchange L inked Credit Obligations).pdf JP Morgan CDO Handbook. pdf JP Morgan Corporate Quantitative Weekly. pdf JP Morgan Correlation Vechicles - Techniques for Trading Equity Correlation. pdf JP Morgan Credit Correlation - A Guide. pdf JP Morgan Depositary Receipts Reference Guide. pdf JP Morgan Exploring the TUI Hybrid. pdf JP Morgan Fixed Income Correlation Trading using Swaptions. pdf JP Morgan Fundamental Relationship Between an Indexs Volatility and the Correlation and Average Volaility of its Components. pdf JP Morgan Global Data Watch - August 2006.pdf JP Morgan Hybrid Capital - Moodys Proposes a New Methodology for Hybrids - A non-event for most hybrids and Tier I. pdf JP Morgan Hybrid Primer. pdf JP Morgan Institutional Hedging Activity. pdf JP Morgan Introducing the JPMorgan Cross Sectional Volatility Model amp Report. pdf JP Morgan Just What You Need to Know About Variance Swaps. pdf JP Morgan MBS Primer. pdf JP Morgan Now You See It, Now You Dont - What Happened to US Heating Oil Stocks and Why It Doesnt Matter. pdf JP Morgan Oil amp Gas Basics. pdf JP Morgan Option Trading and Variance Swaps. pdf JP Morgan Par Credit Default Swap Spread Approx imation from Default Probabilities. pdf JP Morgan Profiting from Market Signals. pdf JP Morgan Relative Value Single Stock Volatility. pdf JP Morgan RiskMetrics - Technical Document. pdf JP Morgan The JP Morgan Guide to Credit Derivatives. pdf JP Morgan The JP Morgan Prepayment Model - Its All About Economics. pdf JP Morgan The Price of Credit. pdf JP Morgan Variance Swaps. pdf JP Morgan VDAX-NEW, VSTOXX and VSMI Futures. pdf JP Morgan Volatility, Leverage, and Returns. pdf JP Morgan Which Trade - Choosing Tactical Positions Across Asset Classes. pdf JPMorgan Credit Derivatives - A Primer (1998 Edition).pdf JPMorgan Credit Derivatives - A Primer (2005 Edition).pdf JPMorgan Credit Derivatives 2003 - Advanced Credit Derivatives Valuation - Bridging Credit Default Swaps and Corporate Bonds. pdf JPMorgan Introducing Base Correlations. pdf JPMorgan Introducing Standard First to Default Baskets. pdf Kellogg Graduate School of Management, Andersen (Understanding, Optimizing, Using and Forecasting) Relalize d Volatility and Correlation. pdf Kings College, Shaw Differential Equations for Monte Carlo Recycling and a GPU-Optimized Normal Quantile. pdf Kings College, Shaw Eco-mputational Finance - Differential Equations for Monte Carlo Recycling. pdf Klassen Pricing Variance Swaps with Cash Dividends. pdf Leger Monte Carlo for the Newbies. pdf Lehman Brothers, Harmstone Investing in Implied Volatility. pdf Lehman Brothers, Johnston Callable Securities - An Introduction. pdf Lehman Brothers, Modukuri Mortgage Convexity Risk. pdf Lehman Brothers, OKane Credit Spreads Explained. pdf Lehman Brothers, OKane Introduction to Default Swaps. pdf Lehman Brothers, Pedersen Explaining the Lehman Brothers Option Adjusted Spread of a Corporate Bond. pdf Lehman Brothers, Reddy An Introduction to Floating Rate CMOs. pdf Lehman Brothers, Tuckman Interest Rate Parity, Money Market Baisis Swaps, and Cross-Currency Basis Swaps. pdf Lehman Brothers, Vankudre Treasury Inflation-Protection Securities - Opportunities and Risks. p df Lehman Brothers, Zhou The Swap Curve. pdf Lehman Brothers A Guide to the Lehman Global Family of Fixed Income Indices. pdf Lehman Brothers ABS Outlook 2007 - The Path of Divergence. pdf Lehman Brothers An Introduction to the Non-Agency CMO market. pdf Lehman Brothers Base Correlation Explained. pdf Lehman Brothers Changes to TBA Deliverable. pdf Lehman Brothers CMBS Outlook 2007 - At Both Ends of the Risk-Reward Spectrum. pdf Lehman Brothers Credit Derivatives Explained - Market, Products, and Regulations. pdf Lehman Brothers Credit Derivatives Primer. pdf Lehman Brothers Currency Hedging in Fixed Income Portfolios. pdf Lehman Brothers Defining the TBA Deliverable. pdf Lehman Brothers Equity-Linked Notes - An Introduction. pdf Lehman Brothers Estimating Implied Default Probabilities from Credit Bond Prices. pdf Lehman Brothers Focus - Israel Back to Basics. pdf Lehman Brothers Guide to Agency and Government-Related Securities. pdf Lehman Brothers Guide to Exotic Credit Derivatives. pdf Lehman Broth ers Hybrid ARM Handbook. pdf Lehman Brothers Hybrid ARMS - Unlocking Value in the New Index. pdf Lehman Brothers Interest Rate Futures. pdf Lehman Brothers Interest Rate Parity, Money Market Basis Swaps, and Cross-Currency Basis Swaps. pdf Lehman Brothers Introduction to Asset Swaps, pdf. pdf Lehman Brothers Introduction to Bond Math. pdf Lehman Brothers Introduction to Catastrophe-Linked Securities. pdf Lehman Brothers Introduction to Investment Banking. pdf Lehman Brothers Introduction to Variable Rate Financing. pdf Lehman Brothers Modelling Credit - Theory and Practice. pdf Lehman Brothers Mortgage Convexity Risk. pdf Lehman Brothers Mortgage Options - A Primer. pdf Lehman Brothers Mortgage Outlook for 2007 - Bracing for a Credit Downturn. pdf Lehman Brothers Non-Agency Hybrids - A Primer. pdf Lehman Brothers Optionalising Carry Trades. pdf Lehman Brothers Quantitative Credit Research Quarterly - Quarter 1 2007.pdf Lehman Brothers Quantitative Credit Research Quarterly - Quarter 3 2001.pdf Lehman Brothers Securitized Products Outlook 2007 - Bracing for a Credit Downturn. pdf Lehman Brothers Securitized Products Outlook for 2007 - Bracing for a Credit Downturn (Presentation).pdf Lehman Brothers Structured Credit Strategy - Annual 2004.pdf Lehman Brothers The Hybrid ARM Handbook. pdf Lehman Brothers The Restructuring Clause in Credit Default Swap Contracts. pdf Lehman Brothers The Shape of Implied Loss Distributions. pdf Lehman Brothers The Specified Pool Handbook. pdf Lehman Brothers Trading the Cash-CDS Basis in the Current Environment. pdf Lehman Brothers Treasury Inflation-Protection Securities - Opportunities and Risks. pdf Lehman Brothers Understanding Hedge Fund Performance. pdf Lehman Brothers Valuation of Credit Default Swaps. pdf Leiden University, Pietersz The LIBOR Market Model Masters Thesis. pdf LIFFE LIFFE Options - A Guide to Trading Strategies. pdf London Business School, Bunn Forecasting Electricity Prices. pdf Longstaff Electricity Forward Prices - A High Frequency Empiric al Analysis. pdf MacKenzie Risk, Financial Crises, and Globalization - Long-Term Capital Management and the Sociology of Arbitrage. pdf Marketing Science, Morton Modelling Retail Customer Behavior at Merrill Lynch. pdf Mathematical Finance, Gallucio Theory and Calibration of Swap Market Models. pdf MathFinance AG, Wystup Foreign Exchange Symmetries. pdf Merrill Lynch, Balland Forward Smile. pdf Merrill Lynch, Gatheral Consistent Modeling of SPX and VIX Options. pdf Merrill Lynch, Youssfi Convexity Adjustment for Volatility Swaps. ppt Merrill Lynch CDO Rating Methodologies Review. pdf Merrill Lynch CDS Physical Settlement. pdf Merrill Lynch Concepts in Technical Analysis - A Handbook on the Basics. pdf Merrill Lynch Correlation Trading. pdf Merrill Lynch Credit Derivative Handbook 2003.pdf Merrill Lynch Credit Derivatives Handbook 2000.pdf Merrill Lynch Credit Derivatives Handbook 2006 - Volume 1.pdf Merrill Lynch Credit Derivatives Handbook 2006 - Volume 2.pdf Merrill Lynch Currency Forecasting - Theory amp Practice. pdf Merrill Lynch Icelandic Banks - Not What You Are Thinking. pdf Merrill Lynch Industry Overview - A weaker Q2 for Rates Businesses. pdf Merrill Lynch Introduction to Securitisation. pdf Merrill Lynch Pricing Cancellable LCDS. pdf Merrill Lynch Size and Structure of the World Bond Market 2002.pdf Merrill Lynch The B2B Market Maker Book. pdf Merrill Lynch The Merrill Lynch Guide to Understanding Financial Reports. pdf Merrill Lynch The Mortgage Investor - Year Ahead 2007.pdf Misiorek Point and Interval Forecasting of Spot Electricity Prices - Linear vs. Non-Linear Time Series Models. pdf Moodys, Park The Impact of Subprime Residential Mortgage-Backed Securities on Moodys-Rated Structured Finance CDOs - A Preliminary Review. pdf Moodys Bank-Loan Loss Given Default. pdf Moodys Corporate Default and Recovery Rates, 1920-2007.pdf Moodys Modeling Default Risk. pdf Moodys Moodys Approach to Rating ith-to-Default Basket Credit-Linked Notes. pdf Moodys Piercing the Country Ceiling - An Update. pdf Moodys Rating Preferred Stock and Hybrid Securities. pdf Moodys The Binomial Expansion Method Applied to CBO-CLO Analysis. pdf Moodys The Relative Stability of Cash-Flow vs. Market-Value CDO Ratings. pdf Moodys Understanding the Risks in Credit Default Swaps. pdf Morgan Stanley, Carr Towards a Theory of Volatility Trading. pdf Morgan Stanley CDO Market Insights - Ratings Actions - Something Had to Give. pdf Morgan Stanley CDO Market Insights - Sub-Prime in Prime Time. pdf Morgan Stanley Credit Derivatives Insights - Single Name Instruments amp Strategies, 3rd Ed. pdf Morgan Stanley Credit Derivatives Strategy - Successors and the Case of the Missing Deliverables. pdf Morgan Stanley Structured Credit Insights 2006.pdf Morgan Stanley Swaps. pdf Morgan Stanley The Laymans Guide to Implied Correlation. pdf Morgan Stanley Whay Hedge Funds Make Sense. pdf Mount Lucas Management The Mechanics of the Commodity Futures Markets - What They Are and How They Function. pdf NASDAQ OMX Corporate Act ions Practice Guide. pdf National Chiao Tung University, Dai An Ingenious, Piecewise Linear Interpolation Algorithm for Pricing Arithmetic Average Options. pdf NERC NERC Operating Manual - June 2004.pdf New York University Credit Seminar, Levi A Relationship Between Default Probability and Equity Volatility. pdf New York University, Avellaneda Pricing and Hedging Derivative Securities in Markets with Uncertain Volatilities. pdf New York University, Avellaneda Reconstructing Volatility - New Techniques for Understanding the Implied Volatility of Multi-asset Options. pdf New York University, Avellaneda Weighted Monte-Carlo Methods for Multi-asset Equity Derivatives - Theory and Practice. pdf Nielsen Pricing Asian Options. pdf NIKHEF Theory Group, Weinzierl Introduction to Monte Carlo Methods. pdf Nomura A Journey to the Alt-A Zone - A Brief Primer on Alt-A Mortgage Loans. pdf Nomura ABS Credit Migrations 2004.pdf Nomura ABS Credit Migrations. pdf Nomura ABS Gold Coast Report - Coverage of Selected Sessions of ABS East 2003.pdf Nomura ABX Index - The Constituent Breakdown. pdf Nomura Basel II and Banks - Key aspects and likely market impact. pdf Nomura CDO-CDS Update 01-09-2007.pdf Nomura CDO-CDS Update 02-21-2006.pdf Nomura Constant Maturity CDS (CMCDS) - A Guide. pdf Nomura Correlation Primer. pdf Nomura Credit Default Swap (CDS) Primer. pdf Nomura Economics in Focus - December 2005.pdf Nomura Holiday Special - December 2008.pdf Nomura Home Equity ABS Basics. pdf Nomura How the Events of 9-11 Affect Thinking about Risk. pdf Nomura Jumbo MBS - Wheres the Credit Enhancement. pdf Nomura Jumbo MBS Credit Enhancement - More of the Same, or Less. pdf Nomura MBS Basics. pdf Nomura Model Risk Update - Margins of Error and Scenario Analysis. pdf Nomura One Reason Why CDOs and ABS Backed bby Aircraft, Franchise Loans and 12b-1 Fees Performed Poorly in 2002.pdf Nomura Oops They Did It Again - Jumbo MBS Credit Enhancement Levels Keep Falling. pdf Nomura Report from Boca Raton 2005 - Coverage of Selec ted Sessions of ABS East 2005.pdf Nomura Report from Orlando 2006 - Coverage of Selected Sessions of ABS East 2006.pdf Nomura Report from Orlando 2007 - Coverage of Selected Sessions of ABS East 2007.pdf Nomura Report from Paradise Island - Coverage of Selected Sessions of ABS East 2002.pdf Nomura Sub-prime Suprise. Not. pdf Nomura Synthetic ABS Nuances. pdf Nomura Synthetic CMBS Primer. pdf Nomura Temporal Aspects of CMBS Downgrades and Surveillance. pdf Nomura Tranching Credit Risk - Examples with CDOs and the iTraxx Index. pdf Nordic Risk Summer 2008, Soklakov Information Derivatives. pdf Norma Fixed Income Research Synthetic CMBS Primer. pdf Northwestern University, Watson Vector Autoregressions and Cointegration. pdf NYBOT The US Dollar Index Futures Contract. pdf NYMEX Crack Spread Handbook. pdf Odegaard Financial Numerical Recipes in C. pdf Oesterreichische NationalBank Financial Instruments - Structed Products Handbook. pdf Penn State University, Shapiro Soft Computing and Financial Engineering. pdf Piterbarg EuroDollar Futures Convexity Adjustments in Stochastic Volatlity Models. pdf Plunkett Research, Plunkett Plunketts Energy Industry Almanac. pdf Proceedings of the 2004 Winter Simulation Conference, LEcuyer Quasi-Monte Carlo Methods in Finance. pdf Proceedings of the 2004 Winter Simulation Conference, Lemieux Randomized Quasi-Monte Carlo - A Tool for Improving the Efficiency of Simulations in Finance. pdf Proceedings of the 2004 Winter Simulation Conference, Staum Efficent Simulations for Option Pricing. pdf Prudential Financial Research Stock Valuation Models. pdf Prudential Securities Forward Rates - What Are They and Why Should I Care. pdf Quantitative Finance, Carr Optimal Positioning in Derivative Securities. pdf Quantitative Finance, Cont Dynamics of Implied Volatility Surfaces. pdf Quantitative Finance, Fouque Variance Reduction for Monte Carlo Simulation in a Stochastic Volatility Environment. pdf RBS Greenwich Capital 2007 MBS Outlook. pdf RBS Greenwich Capital U. S. Government 2007 Outlook. pdf Risk Magazine, Andersen All Your Hedges in One Basket. pdf Risk Magazine, Bergomi Smile Dynamics II. pdf Risk Magazine, Bergomi Smile Dynamics III. pdf Risk Magazine, Bergomi Smile Dynamics. pdf Risk Magazine, Burghardt One Good Turn. pdf Risk Magazine, Cardenas Monte Carlo within a Day. pdf Risk Magazine, Carr Introducing the Covariance Swap. pdf Risk Magazine, Castagna The Vanna-Volga Method for Implied Volatilities. pdf Risk Magazine, Derman Finding a Job in Finance. pdf Risk Magazine, Foster Trees from History. pdf Risk Magazine, Frishling A Discrete Question. pdf Risk Magazine, Fruchard Basis for Change. pdf Risk Magazine, Little A Finite-Difference Method for the Valuation of Variance Swaps. pdf Risk Magazine, Overhaus Himalaya Options. pdf Risk Magazine, Quessette New Products, New Risks. pdf Risk Magazine, Ren Calibrating and Pricing with Embedded Local Volatility Models. pdf Risk Magazine, Rubinstein Unscrambling the Binary Code. pdf Risk Magazine, Sepp Variance Swaps Under No Conditions. pdf RiskMetrics Group CreditGrades Technical Document. pdf Salomon Brothers Anatomy of Prepayments - The Salomon Brothers Prepayment Model. pdf Salomon Brothers Understanding the Yield Curve, Part 1 - Overview of Forward Rate Analysis. pdf Salomon Brothers Understanding the Yield Curve, Part 2 - Mar kets Rate Expectation and Forward Rates. pdf Salomon Brothers Understanding the Yield Curve, Part 3 - Does Duration Extension Enhance Long-Term Expected Returns. pdf Salomon Brothers Understanding the Yield Curve, Part 4 - Forecasting US Bond Returns. pdf Salomon Brothers Understanding the Yield Curve, Part 5 - Convexity Bias and the Yield Curve. pdf Salomon Brothers Understanding the Yield Curve, Part 6 - A Framework for Analysing Yield Curve Trades. pdf Salomon Brothers Understanding the Yield Curve, Part 7 - The Dynamic of the Shape of the Yield Curve. pdf Salomon Smith Barney An Introduction to CMO Cashflow Structures. pdf Salomon Smith Barney Exotic Equity Derivatives Manual. pdf Salomon Smith Barney Introductory Guide to Equity Options. pdf Salomon Smith Barney Principles of Principal Components - A Fresh Look at Risk, Hedging and Relative Value. pdf Sapient Derivatives Consulting Group The DCG Quick Reference Guide to Credit Event Terminology. pdf Schoutens Moment Swaps. pdf Serletis Measu ring and Testing Natural Gas and Electricity Markets Volatility - Evidence from Albertas Deregulated Markets. pdf Societe Generale, Sooben Fitting Linkers into a Portfolio. pdf Societe Generale Explanatory Note About the Exceptional Fraud - January 2008.pdf Societe Generale Pricing and Hedging Correlation Products. pdf Societe Generale Quantitative Strategy - Looking for Value in the Sub-Insurance Market. pdf Societe Generale Quantitative Strategy - Pricing Bespoke CDOs - Latest Developments. pdf Socit Gnrale Investment in Power Generation - A Bankers Perspective. pdf Standard amp Poors A Guide to the Loan Market. pdf Standard amp Poors Annual Global Corporate Default Study - Corporate Defaults Poised to Rise in 2005.pdf Standard amp Poors CDO Spotlight - Overview of Modeling Methodology for Commodity CDO Structures. pdf Standard amp Poors CMBS Property Evaluation Criteria. pdf Standard amp Poors Trade Receivable Criteria. pdf Stanford University, Lee Robust Replication of Volatility Derivatives. pdf Stevenson Risk Management and the Role of Spot Price Predictions in the Australian Retail Electricity Market. pdf STOXX Dow Jones STOXX Index Guide - Version 13.pdf Sungard Guidelines for Pricing and Risk Managing Credit Derivatives. pdf Super Computer Consulting, Nelken Weather Derivatives - Pricing and Hedging. pdf SwiftStandards Category 1 - Customer Payments amp Cheques (MT100 - MT199).pdf SwiftStandards Category 2 - Financial Insitution Transfers (MT200 - MT299).pdf SwiftStandards Category 3 - Treasury Markets Foreign Exchange, Money Markets amp Derivatives (MT300 - MT341) Volume 1.pdf SwiftStandards Category 3 - Treasury Markets Foreign Exchange, Money Markets amp Derivatives (MT350 - MT399) Volume 2.pdf SwiftStandards Category 4 - Collections amp Cash Letters. pdf SwiftStandards Category 5 - Securities Markets (MT500 - MT518) Volume 1.pdf SwiftStandards Category 5 - Securities Markets (MT519 - MT543) Volume 2.pdf SwiftStandards Category 5 - Securities Markets (MT544 - MT567) Vo lume 3.pdf SwiftStandards Category 5 - Securities Markets (MT568 - MT599) Volume 4.pdf SwiftStandards Category 6 - Treasury Markets Precious Metals (MT600 - MT699).pdf SwiftStandards Category 6 - Treasury Markets Syndications (MT643 - MT699).pdf SwiftStandards Category 7 - Documetary Credits amp Guarantees (MT700 - MT799).pdf SwiftStandards Category 8 - Travellers Cheques (MT800 - MT899).pdf SwiftStandards Category 9 - Cash Management amp Customer Status (MT900 - MT999).pdf SwiftStandards Category n - Common Group Messages (MTn90 - MTn99).pdf SWX Swiss Exchange Accrued Interest amp Yield Calculations and Determination of Holiday Calendars. pdf Technische Universitat Chemnitz, Kluge Pricing Derivatives in Stochastic Volatility Models using the Finite Difference Method. pdf Technische Universiteit Eindhoven, Kreuk Trading the Difference Between Realised and Implied Volatility. pdf The Bell Journal of Economics and Management Science, Merton Theory of Rational Option Pricing. pdf The Bond Mar ket Association An Investors Guide to Collateralized Mortgage Obligations. pdf The Bond Market Association An Investors Guide to Pass-Through and Collateralized Mortgage Securities. pdf The Bond Market Association The Asset-Backed Market in 1999 and the Outlook for 2000.pdf The Canadian Journal of Economics, Johnson Cointegration, Error, and Purchasing Power Parity between Canada and the United States. pdf The Journal of Derivatives, Hull Efficent Procedures for Valuing European and American Path-Dependent Options. pdf The Journal of Derivatives, Hull Numerical Procedures for Implementing Term Structure Models I - Single-Factor Models. pdf The Journal of Derivatives, Hull Numerical Procedures for Implementing Term Structure Models II - Two-Factor Models. pdf The Journal of Futures Markets, Gray Canonical Valuation of Options in the Presense of Stochastic Volatility. pdf The Journal of Political Economy, Black The Pricing of Options and Corporate Liabilities. pdf The Review of Economics and Sta tistics, Enders Arima and Cointegration Tests of PPP under Fixed and Flexible Exchange Rate Regimes. pdf The Review of Financial Studies, Heston A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. pdf UBS Investment Bank UBS Bloomberg Constant Maturity Commodity Index (CMCI) Family. pdf UBS Investment Bank Understanding the Inflation Derivatives Market Dynamics - Practical Trading Insights. pdf UBS Investment CDPO an Asset Class on its Own or a Glorified Bearish Rated Equity. pdf UBS Warburg CDO Insight. pdf Universidad de Montevideo, Ruibal Forecasting the Mean and the Variance of Electricity Prices in Deregulated Markets. pdf Universidad de Valencia, Lucia Electricity Prices and Power Derivatives - Evidence from the Nordic Power Exchange. pdf Universidad Torcuato Di Tella, Merener Swap Rate Variance Swaps. pdf Universitat Berlin, Buhler Volatility Markets - Consistent Modeling, Hedging, and Practical Implementation. pdf University of Ca lgary, Ware The Valuation of Swing Options in Electricity Markets. pdf University of California, Evans An Introduction to Stochastic Differential Equations - Version 1.2.pdf University of California, Silverman Solution of the Black Scholes Equation using the Greens Function of the Diffusion Equation. pdf University of California, Stoft Primer on Electricity Futures and Other Derivatives. pdf University of Chicago, Lee Corridor Variance Swap. pdf University of Chicago, Lee Gamma Swap. pdf University of Chicago, Lee Weighted Variance Swap. pdf University of Cyprus, Charalambous Artificial Neural Networs for Valuation of Financial Derivatives and Customized Option Embedded Contracts. pdf University of Essex, Liu Realized Volatility Fixings - Why They are Different. pdf University of Frankfurt, Vilkov Variance Risk Premium Demystified. pdf University of Freiburg, Eberlein Sato Processes and the Valuation of Structured Products. pdf University of Ibadan, Ugbebor Testing the Purchasing Power Parity Hy pothesis for the Nigerian Foreign Exchange Markets. pdf University of Illinois, Deng Volatility Dispersion Trading. pdf University of London, Jacquier Variance Dispersion and Correlation Swaps. pdf University of London, Jacquier Volatility Seminar - Some notes on Variance Swaps and Volatility Derivatives. pdf University of Manitoba, Barua Fast Fourier Transform for Option Pricing - Improved Mathematical Modeling and Design of an Efficient Parallel Algorithm. pdf University of Minho, Areal FTSE-100 Implied Volatility Index. pdf University of Otago, Tamagushiku Heath, Jarrow and Morton Interest Rate Modelling Using Principal Component Analysis. pdf University of Pittsburgh, Ruibal On the Variance of Electricity Prices in Deregulated Markets. pdf University of Pittsburgh, Ruibal On the Variance of Electricity Prices in Deregulated Markets. ppt University of Texas, Wiley A UNIX Device Driver for a TransLink II Transputer Board. pdf University of the Witwatersrand, Mahomed Pricing of Himalaya Options. pdf University of the Witwatersrand, Majmin Local and Stochastic Volatility Models - An Investigation into the Pricing of Exotic Equity Options. pdf University of the Witwatersrand, Sheppard Pricing Equity Derivatives under Stochastic Volatility - A Partial Differential Equation Approach. pdf University of Tokyo, Osajima The Asymptotic Expansion Formula of Implied Volatility for Dynamic SABR Model and FX Hybrid Model. pdf University of Toronto, Surkov Parallel Option Pricing with Fourier Space Time-stepping Method on Graphics Processing Units. pdf University of Twente, Vellekoop Cash Dividends and Futures Prices on Discontinuous Filtrations. pdf University of Waterloo, Forsyth Numerical Methods and Volatility Models for Valuing Cliquet Options. pdf University of Waterloo, Windcliff Pricing Methods and Hedging Strategies for Volatility Derivatives. pdf University of Wisconsin-Madison, Shalizi CSSS 2000-2001 Math Review Lectures - Probability, Statistics, and Stochastic Processes. pdf Universit y of Wollongong, Zhu An Exact and Explicit Solution for the Value of American Put and its Optimal Exercise Boundary. pdf Universit del Piemonte Orientale, Marazzina Interest Rate Modelling - A MATLAB Implementation. pdf Unversity Paris IX Dauphine, Geman Towards a European Market of Electricity - Spot and Derivatives Trading. pdf US Navy Mathematics, Basic Math, and Algebra. pdf Vienna University, Redl Modeling Electricity Futures. pdf VMAC A Comprehensive Solution to Counterparty Credit and Cash Demands in Energy Markets. pdf Wachovia Bank, Kramin A Multi-Factor Markovian HJM Model for Pricing Exotic Interest Rate Derivatives. pdf Wall Street Journal, Slater When Hedge Funds Meet Islamic Finance. pdf Weierstrab-Institut, Wystup Efficient Computation of Option Price Sensitivities. pdf Worchester Polytechic Institute, Acheampong Pricing Mortgage-Back Securities using Prepayment. pdf Workshop on Computational Methods for Pricing and Hedging Exotic Options, Dixon Calibrating Spread Options using a Seasonal Forward Model. pdf Yale University, Welch A First Course in Corporate Finance. pdf YieldCurve CDO-Note - Synthetic CDO Note Pricing Model Fact Sheet. pdf York University, Swishchuk Modeling of Variance and Volatility Swaps for Financial Markets with Stochastic Volatility. pdf York University, Swishchuk Modeling of Variance and Volatility Swaps for Financial Markets with Stochastic Volatility. ppt 1300 2N 3alpha-beta 1 2 3 4Bollinger Bands 5 1 2 3 4Bollinger Bands 5Rate of ChangesROC 6Aroon 2 380-20 1AdaBoost 2hurst 3 4ONeil 1 2 35 490 1MFI 2MACD 3OBV 4CMO 5ROC 6DMI 7RSI 8KDJ 1KDJ 2KAMAEMA 3CMOEMA 4CMO 5CCICMO 6EMA 7ROC 8KDJEMA 9EMA 10DMIROC 11DMI 12DMIEMA 13DMIRSI 1DMI 2 3 4 5 raquant raquantqaindex. phpqa215ampqa -2018-05-13-by-piyejingjing raquantqaindex. phpqa216ampqa -20180513-by-martin 150 A 1 902 B 100 90200 C 10 9010 150 A 1 902 B 100 90200 C 10 9010 15 1.51.8 1.5 C Elton, Gruber, Brown and Goetzmann (2003) Ch. 10 Utility Analysis, Modern portfolio theory and investment analysis. 2 3 4 W opac. library. usyd. edu. au:80 recordb3632103 Kritzman, M. (2017) The graceful aging of mean-variance optimization, Journal of Portfolio Management, Vol. 37 Issue 2, pp. 3-5. opac. library. usyd. edu. au:80 recordb4152264 S4 Michaud, R. O. (1989) The Markowitz Optimization Enigma: Is Optimized Optimal, Financial Analysts Journal, Jan-Feb, pp. 31-42. opac. library. usyd. edu. au:80 recordb4152266
No comments:
Post a Comment