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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

    • 03 Dec 2024
    • 6:00 PM
    • 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 present a dynamic hedging scheme for S&P 500 options, where rebalancing decisions are enhanced by integrating information about the implied volatility surface dynamics. The optimal hedging strategy is obtained through a deep policy gradient-type reinforcement learning algorithm, with a novel hybrid neural network architecture improving the training performance. The favorable inclusion of forward-looking information embedded in the volatility surface allows our procedure to outperform several conventional benchmarks such as practitioner and smiled-implied delta hedging procedures, both in simulation and backtesting experiments.


    Bio:

    Frédéric Godin is an Associate Professor at Concordia University (Montreal, Canada) in the Department of Mathematics and Statistics. His areas of research are financial engineering, risk management, actuarial science, reinforcement learning and energy markets. He also holds the Fellow of the Society of Actuaries (FSA) and Fellow of the Canadian Institute of Actuaries (FCIA) designations.

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