Short Communication: Beyond Surrogate Modeling: Learning the Local Volatility via Shape Constraints

DOI10.1137/20M1381538zbMath1476.91178OpenAlexW3194974717MaRDI QIDQ5162840

Stéphane Crépey, Areski Cousin, Djibril Gueye, Marc Chataigner, Matthew F. Dixon

Publication date: 5 November 2021

Published in: SIAM Journal on Financial Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/20m1381538

zbMATH Keywords

neural networksGaussian processesoption pricinglocal volatilityno-arbitrage

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (2)

KrigHedge: Gaussian Process Surrogates for Delta Hedging ⋮ Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoders

Uses Software

• PMTK

Cites Work

• Kriging of financial term-structures
• Maximum likelihood estimation for Gaussian processes under inequality constraints
• Gaussian process emulators for computer experiments with inequality constraints
• Arbitrage-free SVI volatility surfaces
• Unnamed Item

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