Asymptotic Distribution of the EMS Option Price Estimator

Jin-Chuan Duan (), Geneviève Gauthier () and Jean-Guy Simonato ()
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Jin-Chuan Duan: Joseph L. Rotman School of Management, University of Toronto, Toronto, Ontario, Canada M5S 3E6, and Department of Finance, Hong Kong University of Science ... Technology, Clear Water Bay, Kowloon, Hong Kong
Geneviève Gauthier: École des Hautes Études Commerciales, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada H3T 2A7
Jean-Guy Simonato: École des Hautes Études Commerciales, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada H3T 2A7

Management Science, 2001, vol. 47, issue 8, 1122-1132

Abstract: Monte Carlo simulation is commonly used for computing prices of derivative securities when an analytical solution does not exist. Recently, a new simulation technique known as empirical martingale simulation (EMS) has been proposed by Duan and Simonato (1998) as a way of improving simulation accuracy. EMS has one drawback however. Because of the dependency among sample paths created by the EMS adjustment, the standard error of the price estimate is not readily available from using one simulation sample. In this paper, we develop a scheme to estimate the EMS accuracy. The EMS price estimator is first shown to have an asymptotically normal distribution. Through a simulation study, we then find that the asymptotic normal distribution serves as a good approximation for samples consisting of as few as 500 simulation paths.

Keywords: Monte Carlo; Empirical Martingale Simulation; Linear Control Variate; Options (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:47:y:2001:i:8:p:1122-1132

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