American Option Pricing Using Simulation and Regression: Numerical Convergence Results

43 Pages Posted: 22 Nov 2011

See all articles by Lars Stentoft

Lars Stentoft

Department of Economics, University of Western Ontario; Center for Interuniversity Research and Analysis on Organization (CIRANO); Aarhus University - CREATES

Date Written: November 22, 2011

Abstract

Recently, simulation methods combined with regression techniques have gained importance when it comes to American option pricing. In this paper we consider such methods and we examine numerically their convergence properties. We first consider the Least Squares Monte-Carlo (LSM) method of Longstaff and Schwartz (2001) and report convergence rates for the cross-sectional regressions as well as for the estimated price. The results show that the method converges fast, and this holds even with multiple early exercises and with multiple stochastic factors as long as the payoff function is smooth. We also compare the convergence rates to those obtained when using the related methods proposed by Carriere (1996) and Tsitsiklis and Van Roy (2001). The results show that the price estimates from the latter methods converge significantly slower in the multi-period situation.

Keywords: American options, convergence, Monte Carlo simulation, regression

JEL Classification: C15, G12, G13

Suggested Citation

Stentoft, Lars, American Option Pricing Using Simulation and Regression: Numerical Convergence Results (November 22, 2011). Available at SSRN: https://ssrn.com/abstract=1963057 or http://dx.doi.org/10.2139/ssrn.1963057

Lars Stentoft (Contact Author)

Department of Economics, University of Western Ontario ( email )

London, Ontario N6A 5B8
Canada

Center for Interuniversity Research and Analysis on Organization (CIRANO)

2020 rue University, 25th floor
Montreal H3C 3J7, Quebec
Canada

Aarhus University - CREATES

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

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