Optimal Parameters for Pricing of the American Put Options with Least Square Monte Carlo Simulation

被引：0

作者：

侯乃聪 ^{[1
]}

张贯立 ^{[2
]}

机构：

[1] Anderson Graduate School of Management,University of California Los Angeles

[2] Australian School of Business,University of New South Wales Anzac Pde & High St,Kensington

来源：

Journal of Beijing Institute of Technology | 2010年 / 19卷 / 04期

关键词：

least square Monte Carlo(LSMC); basis function; simulation;

D O I：

10.15918/j.jbit1004-0579.2010.04.016

中图分类号：

F830.91 [证券市场]; F224 [经济数学方法];

学科分类号：

020204 ; 0701 ; 070104 ; 1201 ;

摘要：

Pricing the American put options requires solving an optimal stopping problem and therefore is a challenge for the setting up of simulation parameters.This paper uses least square Monte Carlo(LSMC) simulation to price the American put options and output the optimal simulation steps and number of Hermite basis functions.The results suggest:with different time cost and error tolerance,investors can choose the optimal simulation steps and number of basis function individually to price American put options numerically.Generally,with the pre-limitation in the section "least square Monte Carlo simulation",a number of basis equals 4,15 000 simulation steps for Hermite basis function appear to be sufficient for the method.

引用

页码：499 / 502

页数：4