Deep Learning-Based Least Square Forward-Backward Stochastic Differential Equation Solver for High-Dimensional Derivative Pricing

Autor:	Liang, Jian, Xu, Zhe, Li, Peter
Rok vydání:	2019
Předmět:	Quantitative Finance - Computational Finance Quantitative Finance - Mathematical Finance Quantitative Finance - Pricing of Securities
Druh dokumentu:	Working Paper
Popis:	We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the efficiency and accuracy of our least square backward deep neural network solver and its capability to provide accurate prices for complex early exercise derivatives such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an efficient means for pricing high-dimensional derivatives with early exercises features. Comment: 22 pages, 18 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.10578 Zobrazit plný text záznamu View this record from Arxiv