Hedging Beyond the Mean: A Distributional Reinforcement Learning Perspective for Hedging Portfolios with Structured Products

15 Pages Posted: 12 Feb 2024

See all articles by Jaesun Noh

Jaesun Noh

Korea Advanced Institute of Science and Technology (KAIST) - Graduate School of Finance

Date Written: January 29, 2024

Abstract

Research in quantitative finance has demonstrated that reinforcement learning (RL) methods have delivered promising outcomes in the context of hedging financial portfolios. For example, hedging a portfolio of European options (call/put) using RL achieves better PnL distribution than the trading hedging strategies like Delta neutral and Delta-Gamma neutral

[Cao et al., 2020]. There is great attention given to the hedging of vanilla options, however, very little is mentioned on hedging a portfolio of structured products such as Autocallable notes. Hedging structured products is much more complex and the traditional RL approaches tend to fail in this context due to the underlying complexity of these products. These are more complicated due to presence of several barriers and coupon payments, and having a longer maturity date (from 7 years to a decade), etc. In this direction, we propose a distributional RL based method
to hedge a portfolio containing an Autocallable structured note. We will demonstrate our RL hedging strategy using American and Digital options as hedging instruments. Through several empirical analysis, we will show that distributional RL provides better PnL distribution than traditional approaches and learns a better policy depicting lower value-at-risk (VaR) and conditional value-at-risk (CVaR), showcasing the potential for enhanced risk management.

JEL Classification: C61, C58

Suggested Citation

Noh, Jaesun, Hedging Beyond the Mean: A Distributional Reinforcement Learning Perspective for Hedging Portfolios with Structured Products (January 29, 2024). Available at SSRN: https://ssrn.com/abstract=4709441 or http://dx.doi.org/10.2139/ssrn.4709441

Jaesun Noh (Contact Author)

Korea Advanced Institute of Science and Technology (KAIST) - Graduate School of Finance ( email )

100 Adelaide Street West
PO Box 1
Toronto, Ontario M5H 0B3
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4164731865 (Phone)

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