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Asymptotic Distribution of the EMS Option Price Estimator

Author

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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)

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.

Suggested Citation

  • Jin-Chuan Duan & Geneviève Gauthier & Jean-Guy Simonato, 2001. "Asymptotic Distribution of the EMS Option Price Estimator," Management Science, INFORMS, vol. 47(8), pages 1122-1132, August.
    • Handle: RePEc:inm:ormnsc:v:47:y:2001:i:8:p:1122-1132
    DOI: 10.1287/mnsc.47.8.1122.10234
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    References listed on IDEAS

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    1. Boyle, Phelim & Broadie, Mark & Glasserman, Paul, 1997. "Monte Carlo methods for security pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1267-1321, June.
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    Cited by:

    1. Huang, Shih-Feng & Tu, Ya-Ting, 2014. "Asymptotic distribution of the EPMS estimator for financial derivatives pricing," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 129-145.
    2. Mark Broadie & Jerome B. Detemple, 2004. "ANNIVERSARY ARTICLE: Option Pricing: Valuation Models and Applications," Management Science, INFORMS, vol. 50(9), pages 1145-1177, September.
    3. Alexandru Badescu & Robert J. Elliott & Juan-Pablo Ortega, 2012. "Quadratic hedging schemes for non-Gaussian GARCH models," Papers 1209.5976, arXiv.org, revised Dec 2013.
    4. Zhushun Yuan & Gemai Chen, 2009. "Asymptotic Normality for EMS Option Price Estimator with Continuous or Discontinuous Payoff Functions," Management Science, INFORMS, vol. 55(8), pages 1438-1450, August.
    5. Joan del Castillo & Juan-Pablo Ortega, 2011. "Hedging of time discrete auto-regressive stochastic volatility options," Papers 1110.6322, arXiv.org.
    6. Christian Menn & Svetlozar Rachev, 2009. "Smoothly truncated stable distributions, GARCH-models, and option pricing," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 411-438, July.
    7. Christian Wolff & Thorsten Lehnert & Cokki Versluis, 2009. "A Cumulative Prospect Theory Approach to Option Pricing," LSF Research Working Paper Series 09-03, Luxembourg School of Finance, University of Luxembourg.

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