Deep Hedging with Market Impact
CoRR(2024)
摘要
Dynamic hedging is the practice of periodically transacting financial
instruments to offset the risk caused by an investment or a liability. Dynamic
hedging optimization can be framed as a sequential decision problem; thus,
Reinforcement Learning (RL) models were recently proposed to tackle this task.
However, existing RL works for hedging do not consider market impact caused by
the finite liquidity of traded instruments. Integrating such feature can be
crucial to achieve optimal performance when hedging options on stocks with
limited liquidity. In this paper, we propose a novel general market impact
dynamic hedging model based on Deep Reinforcement Learning (DRL) that considers
several realistic features such as convex market impacts, and impact
persistence through time. The optimal policy obtained from the DRL model is
analysed using several option hedging simulations and compared to commonly used
procedures such as delta hedging. Results show our DRL model behaves better in
contexts of low liquidity by, among others: 1) learning the extent to which
portfolio rebalancing actions should be dampened or delayed to avoid high
costs, 2) factoring in the impact of features not considered by conventional
approaches, such as previous hedging errors through the portfolio value, and
the underlying asset's drift (i.e. the magnitude of its expected return).
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