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

TL;DR

This paper introduces EX-DRL, a novel reinforcement learning approach that improves the estimation of extreme loss quantiles in derivatives hedging by modeling the tail with a Generalized Pareto Distribution, enhancing risk assessment accuracy.

Contribution

The paper proposes a new method combining distributional RL with GPD modeling to better estimate extreme quantiles in loss distributions, addressing limitations of traditional QR-based DRL.

Findings

EX-DRL provides more accurate extreme quantile estimates.

Improved risk metrics for derivatives hedging.

Enhanced reliability in financial risk management.

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.