Reinforcement Learning for Credit Index Option Hedging

TL;DR

This paper develops a reinforcement learning-based hedging strategy for credit index options, incorporating real-world trading conditions, and demonstrates its superiority over traditional methods using market data.

Contribution

It introduces a novel application of reinforcement learning with TRVO algorithm for credit index option hedging, considering practical trading constraints.

Findings

RL-based hedge outperforms Black & Scholes delta hedge

Incorporates transaction costs and discrete trading

Validated on real market data

Abstract

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.