KrigHedge: Gaussian Process Surrogates for Delta Hedging

DOI10.1080/1350486X.2022.2039250zbMath1497.91310arXiv2010.08407OpenAlexW3096564149MaRDI QIDQ5093245

Michael Ludkovski, Yuri F. Saporito

Publication date: 26 July 2022

Published in: Applied Mathematical Finance (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.08407

zbMATH Keywords

Gaussian processhedgingmachine learninglocal volatilityGreeksdata-driven

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (1)

Portfolio Optimization within a Wasserstein Ball

Cites Work

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• Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models
• Improved error bound for multivariate Chebyshev polynomial interpolation
• Short Communication: Beyond Surrogate Modeling: Learning the Local Volatility via Shape Constraints
• Enhancing Stochastic Kriging Metamodels with Gradient Estimators

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