Improved lower and upper bound algorithms for pricing American options by simulationCoauthor(s): Menghui Cao.
This paper introduces new variance reduction techniques and computational improvements to Monte Carlo methods for pricing American-style options. For simulation algorithms that compute lower bounds of American option values, we apply martingale control variates and introduce the local policy enhancement, which adopts a local simulation to improve the exercise policy. For duality-based upper bound methods, specifically the primal-dual simulation algorithm, we have developed two improvements. One is sub-optimality checking, which saves unnecessary computation when it is sub-optimal to exercise the option along the sample path; the second is boundary distance grouping, which reduces computational time by skipping computation on selected sample paths based on the distance to the exercise boundary. Numerical results are given for single asset Bermudan options, moving window Asian options and Bermudan max options. In some examples the computational time is reduced by a factor of several hundred, while the confidence interval of the true option value is considerably tighter than before the improvements.
This is a preprint of an article whose final and definitive form has been published in Quantitative Finance, 2008, copyright Taylor & Francis; Quantitative Finance is available online; this article has the open URL of < http://dx.doi.org/10.1080/14697680701763086 >.
Source: Quantitative Finance
Broadie, Mark, and Menghui Cao. "Improved lower and upper bound algorithms for pricing American options by simulation." Quantitative Finance 8, no. 8 (December 2008): 845-861.
Date: 12 2008