A regression-based Monte Carlo method to solve two-dimensional forward backward stochastic differential equations

被引：0
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作者：
Xiaofei Li
Yi Wu
Quanxin Zhu
Songbo Hu
Chuan Qin
机构：
[1] Zhuhai Da Hengqin Science and Technology Development Co Ltd,Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics
[2] Hubei University,School of Information and Mathematics
[3] Hengqin Finance Research Institute of Jilin University,MOE LCSM, School of Mathematics and Statistics
[4] Yangtze University,Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health
[5] Hunan Normal University,College of Engineering and Technology
[6] Nanchang University,undefined
[7] Yangtze University,undefined
来源：
Advances in Difference Equations | / 2021卷
关键词：
Forward backward stochastic differential equations; Fourier cos-cos transform; Characteristic functions; Least-squares regressions; Monte Carlo; 60H35; 65C20; 60H10;
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学科分类号：
摘要：
The purpose of this paper is to investigate the numerical solutions to two-dimensional forward backward stochastic differential equations(FBSDEs). Based on the Fourier cos-cos transform, the approximations of conditional expectations and their errors are studied with conditional characteristic functions. A new numerical scheme is proposed by using the least-squares regression-based Monte Carlo method to solve the initial value of FBSDEs. Finally, a numerical experiment in European option pricing is implemented to test the efficiency and stability of this scheme.
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