A regression-based numerical scheme for backward stochastic differential equations

Deng Ding (), Xiaofei Li () and Yiqi Liu ()
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Deng Ding: University of Macau
Xiaofei Li: University of Macau
Yiqi Liu: University of Macau

Computational Statistics, 2017, vol. 32, issue 4, No 7, 1357-1373

Abstract: Abstract Based on Fourier cosine expansion, two approximations of conditional expectations are studied, and the local errors for these approximations are analyzed. Using these approximations and the theta-time discretization, a new and efficient numerical scheme, which is based on least-squares regression, for forward–backward stochastic differential equations is proposed. Numerical experiments are done to test the availability and stability of this new scheme for Black–Scholes call and calls combination under an empirical expression about volatility. Some conclusions are given.

Keywords: Characteristic functions; Least-squares regressions; Monte Carlo methods; European options (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00180-017-0763-x

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