Refining the Least Squares Monte Carlo Method by Imposing Structure

32 Pages Posted: 8 May 2012 Last revised: 28 Feb 2013

See all articles by Pascal Letourneau

Pascal Letourneau

University of Wisconsin - Whitewater

Lars Stentoft

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

Date Written: February 1, 2013

Abstract

The least squares Monte Carlo method of Longstaff and Schwartz (2001) has become a standard numerical method for option pricing with many potential risk factors. An important choice in the method is the number of regressors to use and using too few or too many regressors leads to biased results. This is so particularly when considering multiple risk factors or when simulation is computationally expensive and hence relatively few paths can be used. In this paper we show that by imposing structure in the regression problem we can improve the method by reducing the bias. This holds across different maturities, for different categories of moneyness and for different types of option payoffs and often leads to significantly increased efficiency.

Keywords: American options, bias reduction, constrained regression, simulation

JEL Classification: C15, G12, G13

Suggested Citation

Letourneau, Pascal and Stentoft, Lars, Refining the Least Squares Monte Carlo Method by Imposing Structure (February 1, 2013). Available at SSRN: https://ssrn.com/abstract=2053755 or http://dx.doi.org/10.2139/ssrn.2053755

Pascal Letourneau (Contact Author)

University of Wisconsin - Whitewater ( email )

Whitewater, WI 53190
United States

Lars Stentoft

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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