A Review on Regression-based Monte Carlo Methods for Pricing American Options

Michael Kohler ()
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Michael Kohler: Technische Universität Darmstadt, Department of Mathematics

A chapter in Recent Developments in Applied Probability and Statistics, 2010, pp 37-58 from Springer

Abstract: Abstract In this article we give a review of regression-based Monte Carlo methods for pricing American options. The methods require in a first step that the generally in continuous time formulated pricing problem is approximated by a problem in discrete time, i.e., the number of exercising times of the considered option is assumed to be finite. Then the problem can be formulated as an optimal stopping problem in discrete time, where the optimal stopping time can be expressed by the aid of so-called continuation values. These continuation values represent the price of the option given that the option is exercised after time t conditioned on the value of the price process at time t. The continuation values can be expressed as regression functions, and regression-based Monte Carlo methods apply regression estimates to data generated by the aid of artificial generated paths of the price process in order to approximate these conditional expectations. In this article we describe various methods and corresponding results for estimation of these regression functions.

Keywords: Option Price; Payoff Function; Conditional Expectation; Nonparametric Regression; Price Process (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2598-5_2

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DOI: 10.1007/978-3-7908-2598-5_2

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