Monte Carlo Valuation of American Options through Computation of the Optimal Exercise Frontier
http://www.erasmusenergy.com/articles/107/1/Monte-Carlo-Valuation-of-American-Options-through-Computation-of-the-Optimal-Exercise-Frontier/Page1.html
Alfredo Ibanez
By Alfredo Ibanez
Published on 12/10/2007
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Published in:
Publication year: 2002
Co-author 1: Fernando Zapatero
This paper introduces a Monte Carlo simulation method for pricing multidimensional American options based on the computation of the optimal exercise frontier. We consider Bermudan options that can be exercised at a finite number of times and compute the optimal exercise frontier recursively. We show that for every date of possible exercise, any single point of the optimal exercise frontier is a fixed point of a simple algorithm. Once the frontier is computed, we use plain vanilla Monte Carlo to price the option and get a low-biased estimator. We illustrate the method with applications to several types of options.
Published in:
Publication year: 2002
Co-author 1: Fernando Zapatero
This paper introduces a Monte Carlo simulation method for pricing multidimensional American options based on the computation of the optimal exercise frontier. We consider Bermudan options that can be exercised at a finite number of times and compute the optimal exercise frontier recursively. We show that for every date of possible exercise, any single point of the optimal exercise frontier is a fixed point of a simple algorithm. Once the frontier is computed, we use plain vanilla Monte Carlo to price the option and get a low-biased estimator. We illustrate the method with applications to several types of options.
Monte Carlo Valuation of American Options through Computation of the Optimal Exercise Frontier
This paper introduces a Monte Carlo simulation method for pricing multidimensional American options based on the computation of the optimal exercise frontier. We consider Bermudan options that can be exercised at a finite number of times and compute the optimal exercise frontier recursively. We show that for every date of possible exercise, any single point of the optimal exercise frontier is a fixed point of a simple algorithm. Once the frontier is computed, we use plain vanilla Monte Carlo to price the option and get a low-biased estimator. We illustrate the method with applications to several types of options.