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) (KAR id:61868)
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Official URL: http://dx.doi.org/10.1016/j.ejor.2017.05.048 |
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 |
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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: | Divisions > Kent Business School - Division > Department of Accounting and Finance |
Depositing User: | Radu Tunaru |
Date Deposited: | 26 May 2017 18:27 UTC |
Last Modified: | 08 Jan 2024 13:26 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/61868 (The current URI for this page, for reference purposes) |
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