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Abstract <p>This paper addresses the problem of delta-hedging in illiquid markets, where transaction costs, limited depth of the limit order book, and market impact of large trades play a significant role. Classical approaches based on the Black–Scholes model assume continuous trading and infinite liquidity, which leads to significant distortions in practice. To overcome these limitations, we propose a reinforcement learning approach with a risk-averse Bellman operator. As a training environment, we employ an agent-based exchange simulator with support for trading the underlying asset and options, which reproduces the market microstructure and limit order book dynamics. A DeepLOB convolutional encoder is used to extract order book features and capture hidden liquidity characteristics. Numerical experiments show that the proposed method produces a realized PnL distribution centered around zero with lighter tails compared to the classical Black–Scholes delta-hedger. Furthermore, the risk aversion parameter <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\lambda \)</EquationSource> <!--DANMath2570044Dineev-m1--> </InlineEquation> enables control over the trade-off between mean profitability and tail risk management. The results demonstrate the efficiency of the approach and its applicability for constructing robust hedging strategies in illiquid markets.</p>

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Reinforcement Learning for Delta-Hedging in Illiquid Markets

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • I. R. Dineev,
  • P. P. Lukianchenko

摘要

Abstract

This paper addresses the problem of delta-hedging in illiquid markets, where transaction costs, limited depth of the limit order book, and market impact of large trades play a significant role. Classical approaches based on the Black–Scholes model assume continuous trading and infinite liquidity, which leads to significant distortions in practice. To overcome these limitations, we propose a reinforcement learning approach with a risk-averse Bellman operator. As a training environment, we employ an agent-based exchange simulator with support for trading the underlying asset and options, which reproduces the market microstructure and limit order book dynamics. A DeepLOB convolutional encoder is used to extract order book features and capture hidden liquidity characteristics. Numerical experiments show that the proposed method produces a realized PnL distribution centered around zero with lighter tails compared to the classical Black–Scholes delta-hedger. Furthermore, the risk aversion parameter \(\lambda \) enables control over the trade-off between mean profitability and tail risk management. The results demonstrate the efficiency of the approach and its applicability for constructing robust hedging strategies in illiquid markets.