# Hedging option books using neural-SDE market models

arXiv: https://arxiv.org/abs/2205.15991 · HTML: https://mlsanity.com/paper/2205.15991/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 57 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Neural networks for option pricing and hedging: a literature review](https://mlsanity.com/paper/1911.05620/) (2019)
- [Arbitrage-free neural-SDE market models](https://mlsanity.com/paper/2105.11053/) (2021)
- [Estimating risks of option books using neural-SDE market models](https://mlsanity.com/paper/2202.07148/) (2022)

## Built on by (most cited first, 2 of 2)

- [Universal approximation property of neural stochastic differential equations](https://mlsanity.com/paper/2503.16696/) (2025)
- [Neural stochastic Volterra equations: learning path-dependent dynamics](https://mlsanity.com/paper/2407.19557/) (2024)

## Abstract (arXiv)

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.