An improved least squares Monte Carlo valuation method based on heteroscedasticity

Fabozzi, Frank J, Paletta, Tommaso and Tunaru, Radu (2017) An improved least squares Monte Carlo valuation method based on heteroscedasticity. European Journal of Operational Research, 263 (2). pp. 698-706. ISSN 0377-2217

[img] PDF - Accepted Version
Available under License Creative Commons Attribution-NonCommercial No Derivatives.

Download (672kB)


Longstaff–Schwartz’s least squares Monte Carlo method is one of the most applied numerical methods for pricing American-style derivatives. We examine the algorithms regression step, demonstrating that the OLS regression is not the best linear unbiased estimator because of heteroscedasticity. We prove the existence of heteroscedasticity for single-asset and multi-asset payoffs numerically and theoretically, and propose weighted-least squares MC valuation method to correct for it. An extensive numerical study shows that the proposed method produces significantly smaller pricing bias than the Longstaff–Schwartz method under several well-known price dynamics. An empirical pricing exercise using market data confirms the advantages of the improved method.

Item Type: Article
Keywords: Finance; American options; Heteroscedasticity; Weighted least squares; Least squares Monte Carlo pricing method;
Schools and Departments: University of Sussex Business School > Accounting and Finance
Research Centres and Groups: Quantitative International Finance Network
Subjects: H Social Sciences > HG Finance > HG0101 Theory. Method. Relation to other subjects > HG0106 Mathematical models
Depositing User: Radu Tunaru
Date Deposited: 23 Jan 2020 10:40
Last Modified: 02 Feb 2021 11:19

View download statistics for this item

📧 Request an update