Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information

Frédéric Godin

IAQF/Thalesians seminar

Tuesda, December 3rd, 6:00 pm

Reception at 7:30 pm

Fordham University, McNally Amphitheater, 140 West 62nd Street, New York, NY 10023

• IAQF registration
• Thalesians 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.