Mathematics-in-Industry Case Studies, Vol 2 (2010)

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Optimized Least-squares Monte Carlo for Measuring Counterparty Credit Exposure of American-style Options

Kin Hung (Felix) Kan, Greg Frank, Victor Mozgin, Mark Reesor

Abstract


Building on the least-squares Monte Carlo (LSM) method that was originally proposed by Longstaff and Schwartz (2001) to price American options, we develop a new version of the LSM method, which we term "optimized least-squares Monte Carlo" (OLSM), to measure the counterparty credit exposure of American options. In order to enhance its performance, OLSM is integrated with the following three techniques: variance reduction, initial state dispersion and multiple bucketing (piecewise linear regression). Numerical results demonstrate the power of the OLSM method.

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Mathematics-in-Industry Case Studies. ISSN: 1913-4967