Performance-Enhancing Market Risk Calculation Through Gaussian Process Regression and Multi-Fidelity Modeling

• Published in: Computation Vol. 13; no. 6; p. 134
• Main Authors: Lehdili, N., Oswald, P., Nguyen, H. D.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Accuracy; Algorithms; Approximation; Balance sheets; Banking; Basel III; Data mining; Efficiency; Engines; expected shortfall; Financial markets; FRTB; Gaussian process; Machine learning; Market risk; Methods; multi-asset option; Neural networks; option pricing; Portfolio management; Prediction models; Pricing; Regression models; Regulation of financial institutions; Risk; Risk factors; Risk management; Securities prices; value-at-risk; France
• ISSN: 2079-3197; 2079-3197
• DOI: 10.3390/computation13060134

The market risk measurement of a trading portfolio in banks, specifically the practical implementation of the value-at-risk (VaR) and expected shortfall (ES) models, involves intensive recalls of the pricing engine. Machine learning algorithms may offer a solution to this challenge. In this study, we investigate the application of the Gaussian process (GP) regression and multi-fidelity modeling technique as approximation for the pricing engine. More precisely, multi-fidelity modeling combines models of different fidelity levels, defined as the degree of detail and precision offered by a predictive model or simulation, to achieve rapid yet precise prediction. We use the regression models to predict the prices of mono- and multi-asset equity option portfolios. In our numerical experiments, conducted with data limitation, we observe that both the standard GP model and multi-fidelity GP model outperform both the traditional approaches used in banks and the well-known neural network model in term of pricing accuracy as well as risk calculation efficiency.