Three-Layer Artificial Neural Network for Pricing Multi-Asset European Option

被引：1

作者：

Zhou, Zhiqiang ^{[1
]}

Wu, Hongying ^{[2
]}

Li, Yuezhang ^{[1
]}

Kang, Caijuan ^{[1
]}

Wu, You ^{[1
]}

机构：

[1] Xiangnan Univ, Sch Econ & Management, Chenzhou 423000, Peoples R China

[2] Xiangnan Univ, Sch Math & Informat Sci, Chenzhou 423000, Peoples R China

来源：

MATHEMATICS | 2024年 / 12卷 / 17期

基金：

中国国家自然科学基金;

关键词：

multi-asset option; European option; high-dimensional PDE; artificial neural network; three layers; RADIAL BASIS FUNCTION; LAPLACE TRANSFORM METHODS; BASIS FUNCTION PARTITION; CONTOUR INTEGRAL METHOD; MODEL; PDES;

D O I：

10.3390/math12172770

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper studies an artificial neural network (ANN) for multi-asset European options. Firstly, a simple three-layer ANN-3 is established with undetermined weights and bias. Secondly, the time-space discrete PDE of the multi-asset option is given and the corresponding discrete data are fed into the ANN-3. Then, using least squares error as the objective function, the weights and bias of ANN-3 are trained well. Numerical examples are carried out to confirm the stability, accuracy and efficiency. Experiments show the ANN's relative error is about 0.8%. This method can be extended into multi-layer ANN-q(q>3) and extended into American options.

引用

页数：22

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