Estimating risks of option books using neural-SDE market models

• URL: http://arxiv.org/abs/2202.07148v1
• Date: Tue, 15 Feb 2022 02:39:42 GMT
• Title: Estimating risks of option books using neural-SDE market models
• Authors: Samuel N. Cohen and Christoph Reisinger and Sheng Wang
• Abstract summary: We use an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying. 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.
• Score: 6.319314191226118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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