Reinforcement Learning for Credit Index Option Hedging
This work addresses hedging inefficiencies for financial practitioners in credit markets, but it is incremental as it applies an existing algorithm to a specific domain.
The paper tackled the problem of finding an optimal hedging strategy for credit index options by applying reinforcement learning with a focus on realism, including discrete time and transaction costs, and demonstrated that the derived strategy outperforms the practitioner's Black & Scholes delta hedge on real market data.
In this paper, we focus on finding the optimal hedging strategy of a credit index option using reinforcement learning. We take a practical approach, where the focus is on realism i.e. discrete time, transaction costs; even testing our policy on real market data. We apply a state of the art algorithm, the Trust Region Volatility Optimization (TRVO) algorithm and show that the derived hedging strategy outperforms the practitioner's Black & Scholes delta hedge.