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

Samuel N. Cohen, Christoph Reisinger and Sheng Wang

Journal of Computational Finance

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.

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Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ0:7956178

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