Estimating risks of European option books using neural stochastic differential equation market models

Samuel N. Cohen, Christoph Reisinger and Sheng Wang

Abstract

In this paper we examine the capacity of arbitrage-free neural stochastic differential equation market models to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying. We subsequently demonstrate their 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 of option portfolios than standard filtered historical simulation approaches, with better coverage and less procyclicality.