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An Improved Least Squares Monte Carlo Valuation Method Based on Heteroscedasticity

Fabozzi, Frank J., Paletta, Tommaso, 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. (doi:10.1016/j.ejor.2017.05.048)

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Abstract

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 payoff's 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
DOI/Identification number: 10.1016/j.ejor.2017.05.048
Uncontrolled keywords: Finance, American options, Heteroscedasticity, Weighted least squares, Least squares Monte Carlo pricing method
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
H Social Sciences > HG Finance
Divisions: Faculties > Social Sciences > Kent Business School > Accounting and Finance
Depositing User: Radu Tunaru
Date Deposited: 26 May 2017 18:27 UTC
Last Modified: 31 May 2019 23:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/61868 (The current URI for this page, for reference purposes)
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