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Events / Thalesians Series

About The Series

The IAQF's Thalesians Seminar Series is a joint effort on the part of the IAQF (www.iaqf.org) and the Thalesians (www.thalesians.com).  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.

Call For Speakers

If you are interested in speaking at one of the upcoming seminars, please email info@iaqf.org

Past Seminars

About The Organizer

Harvey Stein is a senior VP in the Labs group at Two Sigma. From 1993 to 2022, Dr. Stein was at Bloomberg, where he served as the head of several departments including Quantitative Risk Analytics, Counterparty and Credit Risk, Interest Rates Derivatives, and Quantitative Finance R&D. Harvey is well known in the industry, having published and lectured on credit risk modeling, financial regulation, interest rate and FX modeling, CVA calculations, mortgage backed security valuation, COVID-19 data analysis, and other subjects.

Dr. Stein is on the board of directors of the IAQF, a board member of the Rutgers University Mathematical Finance program, an adjunct professor at Columbia University, and organizer of the IAQF/Thalesians financial seminar series. He's also worked as a quant researcher on the Bloomberg for President campaign.

Dr. Stein holds a Ph.D. in Mathematics from the University of California, Berkeley (1991) and a B.S. in Mathematics from Worcester Polytechnic Institute (1982).

 



Upcoming Seminars

    • 05 Mar 2024
    • 6:00 PM (EST)
    • Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023
    Register


    6:00 PM Seminar Begins

    7:30 PM Reception


    Hybrid Event:

    Fordham University

    McNally Amphitheater

    140 West 62nd Street

    New York, NY 10023


    Free Registration!


    For Virtual Attendees: Please select Virtual instead of member type upon registration.

    Abstract:

    This presentation explains the Expectile, Conditional Value-at-Risk (CVaR)/Superquantile, Value-at-Risk (VaR)/Quantile and other risk measures in the framework of the Fundamental Risk Quadrangle (FRQ) theory. According to the FRQ, a Quadrangle includes four stochastic functions: Error, Regret, Risk, and Deviation. These functions are interconnected through a Statistic stochastic function. Risk is used in stochastic optimization, and Error is employed in the statistical estimation of the corresponding Statistic. The Expectile, similar to VaR and CVaR, is used in risk management. Quadrangles based on VaR and CVaR statistics are well-established. The Errors in these Quadrangles are minimized for VaR and CVaR estimation. We focus on the recently proposed Quadrangles based on Expectile. We examine properties of these Expectile Quadrangles, with a particular emphasis on the new Quadrangle, where the Expectile is both Statistic and Risk. This Expectile Quadrangle is based on the new Piecewise Linear Error, which is an alternative to the standard Asymmetric Variance Error. We have demonstrated the equivalence of Expectile regression with these two Errors. Linear regression with the Piecewise Linear Error is reduced to linear programming. Optimization of Expectile is reduced to convex and linear programming using the FRQ Regret Theorem. Moreover, the Kusuoka representation of Expectile, together with the FRQ Regression Theorem implies the equivalence of Expectile and Quantile regressions. Theoretical findings are validated with several case studies.

    Bio:

    Stan Uryasev is Professor and Frey Family Endowed Chair of Quantitative Finance at the Stony Brook University.

    He received his M.S. in Applied Mathematics from the Moscow Institute of Physics and Technology (MIPT), Russia, in 1979 and Ph.D. in Applied Mathematics from the Glushkov Institute of Cybernetics, Kiev, Ukraine in 1983. From 1979 to 1987 he held a research position at the Glushkov Institute. From 1988 to 1992 he was a Research Scholar at the International Institute for Applied System Analysis, Luxenburg, Austria. From 1992 to 1998 he held the Scientist position at the Risk and Reliability Group, Brookhaven National Laboratory, Upton, NY. From 1998 to 2019 he was the George and Rolande Willis Endowed Professor at the University of Florida, and the director of the Risk Management and Financial Engineering Lab.

    His research is focused on efficient computer modeling and optimization techniques and their applications in finance and DOD projects. He published four books (two monographs and two edited volumes) and more than 130 research papers. He is a co-inventor of the Conditional Value-at-Risk and the Conditional Drawdown-at-Risk optimization methodologies. He developed optimization software in risk management area, including Drawdown and Credit Risk minimization.

    His joint paper with Prof. Rockafellar on Optimization of Conditional Value-At-Risk in The Journal of Risk, Vol. 2, No. 3, 2000 is among the 100 most cited papers in Finance. Many risk management/optimization packages implemented the approach suggested in this paper (MATLAB implemented a toolbox).​

    Stan Uryasev is a frequent speaker at academic and professional conferences. He has delivered seminars on the topics of risk management and stochastic optimization. He is on the editorial board of a number of research journals and is Editor Emeritus and Chairman of the Editorial Board of the Journal of Risk.

    • 09 Apr 2024
    • 6:00 PM (EDT)
    • Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023
    Register


    6:00 PM Seminar Begins

    7:30 PM Reception


    Hybrid Event:

    Fordham University

    McNally Amphitheater

    140 West 62nd Street

    New York, NY 10023


    Free Registration!


    For Virtual Attendees: Please select Virtual instead of member type upon registration.

    Abstract:

    We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional word-based methods, e.g., bag-of-words or word vectors, the contextualized representation captures both the syntax and semantics of text, thus providing a more comprehensive understanding of its meaning. Notably, word-based approaches are more susceptible to errors when negation words are present in news articles. Our study includes data from 16 international equity markets and news articles in 13 different languages, providing polyglot evidence of news-induced return predictability. We observe that information in newswires is incorporated into prices with an inefficient delay that aligns with the limits-to-arbitrage, yet can still be exploited in real-time trading strategies. Additionally, we find that a trading strategy that capitalizes on fresh news alerts results in even higher Sharpe ratios.

    Bio:

    Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He has served as Co-Editor for the Journal of Financial Econometrics and has been on the editorial board as an Associate Editor for many prestigious journals, including the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, and Management Science. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. He has been recognized as one of Poets & Quants’ Best 40-under-40 Business School Professors of 2023. At Booth, he teaches a variety of courses related to FinTech, Big Data, and Statistical Inference to MBA, college, and PhD students. Xiu earned his PhD and MA in applied mathematics from Princeton University.

    • 07 May 2024
    • 6:00 PM (EDT)
    • Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023
    Register


    6:00 PM Seminar Begins

    7:30 PM Reception


    Hybrid Event:

    Fordham University

    McNally Amphitheater

    140 West 62nd Street

    New York, NY 10023


    Free Registration!


    For Virtual Attendees: Please select Virtual instead of member type upon registration.

    Abstract:

    Topological Data Analysis (TDA) has emerged as a powerful methodology in time-series analysis and signal processing. TDA is able to provide detailed descriptions of complex data which complements statistical methods. Recent applications include detection of critical transitions in financial time series, particularly of financial bubbles. The methodology relies on time-delay coordinate embedding, which is used to construct, from the time-series, a point-cloud in some space. The dynamics on the point-cloud unveils patterns in the time-series. Most of the evidence so far on the adeptness of TDA to detect financial bubbles has been empirical. We present, for the first time, a heuristic argument for why TDA can detect financial bubbles. We use models from economics that assert that the time series exhibit certain oscillatory patterns when approaching a tipping point. These oscillations determine holes in the point-clouds, which can be quantified by TDA. When approaching the tipping point of a bubble, there are significant changes in the nature of the oscillations, and consequently in the TDA output. These changes can be captured via persistence homology and yield early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin price.

    Bio:

    Marian Gidea is a professor of mathematics at Yeshiva University in New York City. He held previous appointments at the Mathematical Sciences Research Institute in Berkeley, the Institute for Advanced Study in Princeton, Centre de Recerca Matemàtica in Barcelona, Northeastern Illinois University, Northwestern University, and Loyola University Chicago. He also served at the National Science Foundation as a program director in the Mathematical Sciences Division. His research interests include Dynamical Systems, Topological Data Analysis, and Financial Mathematics.

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