Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
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Authors: François, P., Gauthier, G., Godin, F., Pérez Mendoza, C. O.
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.
About Geneviève Gauthier
Geneviève Gauthier is a professor in the Department of Decision Sciences at HEC Montréal and holds a PhD in Mathematics from Carleton University in Ottawa. She specializes in financial engineering, with a particular interest in financial econometrics and derivative products. Her research focuses on the development of advanced mathematical models for financial risk management and market modeling. Renowned for her academic contributions, she also collaborates with the financial sector.