Hedging option books using neural-SDE market models

TL;DR

This paper demonstrates that neural-SDE market models can effectively hedge options, outperforming traditional methods like Black-Scholes and Heston models, especially during stressed market conditions.

Contribution

It introduces the use of arbitrage-free neural-SDE market models for option hedging and compares their performance to classical models using real-world data.

Findings

Neural-SDE models achieve lower hedging errors than Black-Scholes delta hedging.

They are less sensitive to the choice of hedging instrument tenors.

Neural-SDE models perform comparably to Heston models but are more robust in stressed markets.

Abstract

We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options. In particular, we derive sensitivity-based and minimum-variance-based hedging strategies using these models and examine their performance when applied to various option portfolios using real-world data. Through backtesting analysis over typical and stressed market periods, we show that neural-SDE market models achieve lower hedging errors than Black--Scholes delta and delta-vega hedging consistently over time, and are less sensitive to the tenor choice of hedging instruments. In addition, hedging using market models leads to similar performance to hedging using Heston models, while the former tends to be more robust during stressed market periods.