Estimating risks of option books using neural-SDE market models
Computational Finance
2022-02-16 v1 Probability
Risk Management
Statistical Finance
Machine Learning
Abstract
In this paper, we examine the capacity of an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying. We subsequently demonstrate its use as a risk simulation engine for option portfolios. Through backtesting analysis, we show that our models are more computationally efficient and accurate for evaluating the Value-at-Risk (VaR) of option portfolios, with better coverage performance and less procyclicality than standard filtered historical simulation approaches.
Keywords
Cite
@article{arxiv.2202.07148,
title = {Estimating risks of option books using neural-SDE market models},
author = {Samuel N. Cohen and Christoph Reisinger and Sheng Wang},
journal= {arXiv preprint arXiv:2202.07148},
year = {2022}
}