Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4959397 | European Journal of Operational Research | 2017 | 9 Pages |
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 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Frank J. Fabozzi, Tommaso Paletta, Radu Tunaru,