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Refining the least squares Monte Carlo method by imposing structure |
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| Authors: | Pascal Létourneau Lars Stentoft |
| Affiliation: | 1. Department of Finance at HEC Montréal , 3000 C?te-Sainte-Catherine , H3T 2A7 Montréal (QC) , Canada pascal.letourneau@hec.ca;3. Department of Finance at HEC Montréal , 3000 C?te-Sainte-Catherine , H3T 2A7 Montréal (QC) , Canada;4. Centre interuniversitaire de recherche en analyse des organisations , 2020 rue University , H3A 2A5 Montréal (QC) , Canada;5. Centre interuniversitaire sur le risque, les politiques économiques et l’emploi ESG UQAM , Université du Québec à Montréal , H3C 3P8 Montréal (QC) , Canada;6. Department of Economics and Business, School of Business and Social Sciences , Aarhus University , DK-8210 Aarhus V , Denmark |
| Abstract: | The least squares Monte Carlo method of Longstaff and Schwartz 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 Simulations |