Option Hedging

Option hedging aims to minimize the risk associated with holding options by dynamically adjusting a portfolio's composition. Recent research heavily utilizes deep reinforcement learning (DRL), particularly deep deterministic policy gradient (DDPG) and other advanced algorithms like Trust Region Policy Optimization, to develop sophisticated hedging strategies that outperform traditional methods like Black-Scholes delta hedging, especially when considering transaction costs and market impacts. These studies often incorporate realistic market features such as stochastic volatility and liquidity constraints, leading to more robust and practical hedging approaches. The resulting improvements in hedging performance have significant implications for financial institutions and investors seeking to manage risk effectively.