Learning Parameter Dependence for Fourier-Based Option Pricing with Tensor Trains

Rihito Sakurai (), Haruto Takahashi and Koichi Miyamoto
Additional contact information
Rihito Sakurai: Department of Physics, The University of Tokyo, Tokyo 113-0033, Japan
Haruto Takahashi: Department of Physics, Saitama University, Saitama 338-8570, Japan
Koichi Miyamoto: Center for Quantum Information and Quantum Biology, The University of Osaka, Osaka 560-0043, Japan

Mathematics, 2025, vol. 13, issue 11, 1-16

Abstract: A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-based option pricing, utilizing the ability of tensor trains to compress high-dimensional tensors. In this study, we focus on another usage of the tensor train, which is to compress functions, including their parameter dependence. Here, we propose a pricing method, where, by a tensor train learning algorithm, we build tensor trains that approximate functions appearing in FT-based option pricing with their parameter dependence and efficiently calculate the option price for the varying input parameters. As a benchmark test, we run the proposed method to price a multi-asset option for the various values of volatilities or present asset prices. We show that, in the tested cases involving up to 11 assets, the proposed method outperforms Monte Carlo-based option pricing with 10 6 paths in terms of computational complexity while keeping better accuracy.

Keywords: tensor train; tensor train learning algorithm; Fourier-based option pricing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/11/1828/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/11/1828/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:11:p:1828-:d:1668322

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().