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

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  • Samuel N. Cohen
  • Christoph Reisinger
  • 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.

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Handle: RePEc:rsk:journ0:7956178
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File URL: https://www.risk.net/system/files/digital_asset/2023-03/jcf_wang_web_final.pdf
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