Paper: arXiv 2307.09844

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.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced reinforcement learning methods (TRVO) with complex optimization and risk measures, but also emphasizes practical realism by testing on real market data with transaction costs and discrete time.

Research Flowchart

  flowchart TD
  A["Research Goal: Find Optimal<br>Hedge for Credit Index Options"] --> B{"Methodology: Apply RL with<br>Practical Market Realism"}
  
  B --> C["Key Algorithm:<br>Trust Region Volatility Optimization TRVO"]
  B --> D["Core Constraints:<br>Discrete Time & Transaction Costs"]
  
  C --> E["Computational Process:<br>Reinforcement Learning Training"]
  D --> E
  
  E --> F["Validation: Backtest on<br>Real Market Data"]
  
  F --> G["Findings: RL Strategy<br>Outperforms Black-Scholes Delta Hedge"]