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Convergence of the stochastic mesh estimator for pricing Bermudan options

Avramidis, A.N. and Matzinger, H. (2004) Convergence of the stochastic mesh estimator for pricing Bermudan options Journal of Computational Finance, 7, (4), pp. 73-91.

Record type: Article

Abstract

Broadie and Glasserman (2004) proposed a Monte Carlo algorithm they named “stochastic mesh” for pricing high-dimensional Bermudan options. Based on simulated states of the assets underlying the option at each exercise opportunity, the method produces an estimator of the option value at each sampled state. We derive an asymptotic upper bound on the probability of error of the mesh estimator under the mild assumption of the finiteness of certain moments. Both the error size and the probability bound are functions that vanish with increasing sample size. Moreover, we report the mesh method’s empirical performance on test problems taken from the recent literature. We find that the mesh estimator has large positive bias that decays slowly with the sample size.

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Published date: 2004
Organisations: Operational Research

Identifiers

Local EPrints ID: 48878
URI: http://eprints.soton.ac.uk/id/eprint/48878
ISSN: 1460-1559
PURE UUID: fa2918ad-f7d7-4dd4-9e00-3a224541d4c2

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Date deposited: 17 Oct 2007
Last modified: 17 Jul 2017 14:57

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Contributors

Author: A.N. Avramidis
Author: H. Matzinger

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