Low-Rank Tensor Approximation for Chebyshev Interpolation in Parametric Option Pricing

DOI10.1137/19M1244172zbMath1452.91325arXiv1902.04367MaRDI QIDQ5131414

Kathrin Glau, Francesco Statti, Daniel Kressner

Publication date: 7 November 2020

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

Full work available at URL: https://arxiv.org/abs/1902.04367

zbMATH Keywords

Chebyshev interpolationtensor completiontensor train formathigh-dimensional problemlow-rank tensor approximationparametric option pricing

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Derivative securities (option pricing, hedging, etc.) (91G20) Numerical interpolation (65D05)

Related Items (5)

Improved error bound for multivariate Chebyshev polynomial interpolation ⋮ The deep parametric PDE method and applications to option pricing ⋮ Sparse grid method for highly efficient computation of exposures for xVA ⋮ Black Box Approximation in the Tensor Train Format Initialized by ANOVA Decomposition ⋮ Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats

Uses Software

• rbMIT
• TensorToolbox
• Chebfun
• Algorithm 862
• redbKIT

Cites Work

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