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EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning

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Listed:
  • Parvin Malekzadeh
  • Zissis Poulos
  • Jacky Chen
  • Zeyu Wang
  • Konstantinos N. Plataniotis

Abstract

Recent advancements in Distributional Reinforcement Learning (DRL) for modeling loss distributions have shown promise in developing hedging strategies in derivatives markets. A common approach in DRL involves learning the quantiles of loss distributions at specified levels using Quantile Regression (QR). This method is particularly effective in option hedging due to its direct quantile-based risk assessment, such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). However, these risk measures depend on the accurate estimation of extreme quantiles in the loss distribution's tail, which can be imprecise in QR-based DRL due to the rarity and extremity of tail data, as highlighted in the literature. To address this issue, we propose EXtreme DRL (EX-DRL), which enhances extreme quantile prediction by modeling the tail of the loss distribution with a Generalized Pareto Distribution (GPD). This method introduces supplementary data to mitigate the scarcity of extreme quantile observations, thereby improving estimation accuracy through QR. Comprehensive experiments on gamma hedging options demonstrate that EX-DRL improves existing QR-based models by providing more precise estimates of extreme quantiles, thereby improving the computation and reliability of risk metrics for complex financial risk management.

Suggested Citation

  • Parvin Malekzadeh & Zissis Poulos & Jacky Chen & Zeyu Wang & Konstantinos N. Plataniotis, 2024. "EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning," Papers 2408.12446, arXiv.org, revised Aug 2024.
  • Handle: RePEc:arx:papers:2408.12446
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    References listed on IDEAS

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    1. Yi He & Liang Peng & Dabao Zhang & Zifeng Zhao, 2022. "Risk Analysis via Generalized Pareto Distributions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 852-867, April.
    2. Saeed Marzban & Erick Delage & Jonathan Yu-Meng Li, 2022. "Equal risk pricing and hedging of financial derivatives with convex risk measures," Quantitative Finance, Taylor & Francis Journals, vol. 22(1), pages 47-73, January.
    3. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    4. Qiyun Pan & Eunshin Byon & Young Myoung Ko & Henry Lam, 2020. "Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(7), pages 524-547, October.
    5. Jay Cao & Jacky Chen & Soroush Farghadani & John Hull & Zissis Poulos & Zeyu Wang & Jun Yuan, 2022. "Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning," Papers 2205.05614, arXiv.org, revised Jan 2023.
    6. Shige Peng & Shuzhen Yang & Jianfeng Yao, 2023. "Improving Value-at-Risk Prediction Under Model Uncertainty," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 228-259.
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