Low-Rank Tensor Approximation for Chebyshev Interpolation in Parametric Option Pricing-Reference-Cited by-同舟云学术

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

Published:2020-01 Issue:3 Volume:11 Page:897-927
ISSN:1945-497X
Container-title:SIAM Journal on Financial Mathematics
language:en
Short-container-title:SIAM J. Finan. Math.

Author:

Glau Kathrin,Kressner Daniel,Statti Francesco

Funder

Horizon 2020 Framework Programme

Seventh Framework Programme

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Finance,Numerical Analysis

Link

https://epubs.siam.org/doi/pdf/10.1137/19M1244172

Reference34 articles.

1. Kolmogorov widths and low-rank approximations of parametric elliptic PDEs

2. Algorithm 862

3. Hierarchical Tensor Approximation of Output Quantities of Parameter-Dependent PDEs

4. Smoothing the payoff for efficient computation of Basket option prices

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1. Riemannian preconditioned algorithms for tensor completion via tensor ring decomposition;Computational Optimization and Applications;2024-02-27

2. Black Box Approximation in the Tensor Train Format Initialized by ANOVA Decomposition;SIAM Journal on Scientific Computing;2023-08-11

3. Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats;SIAM Journal on Financial Mathematics;2023-04-25

4. Statistical Machine Learning for Quantitative Finance;Annual Review of Statistics and Its Application;2023-03-10

5. Accelerated American option pricing with deep neural networks;Quantitative Finance and Economics;2023

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