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A Performance Comparison of Various Bootstrap Methods for Diffusion Processes

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  • Jung S You
  • Minsoo Jeong

Abstract

In this paper, we compare the finite sample performances of various bootstrap methods for diffusion processes. Though diffusion processes are widely used to analyze stocks, bonds, and many other financial derivatives, they are known to heavily suffer from size distortions of hypothesis tests. While there are many bootstrap methods applicable to diffusion models to reduce such size distortions, their finite sample performances are yet to be investigated. We perform a Monte Carlo simulation comparing the finite sample properties, and our results show that the strong Taylor approximation method produces the best performance, followed by the Hermite expansion method.

Suggested Citation

  • Jung S You & Minsoo Jeong, 2021. "A Performance Comparison of Various Bootstrap Methods for Diffusion Processes," Journal of Economics and Behavioral Studies, AMH International, vol. 13(4), pages 1-7.
    • Handle: RePEc:rnd:arjebs:v:13:y:2021:i:4:p:1-7
    DOI: 10.22610/jebs.v13i4(J).3185
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    References listed on IDEAS

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    1. Donald W. K. Andrews, 2004. "the Block-Block Bootstrap: Improved Asymptotic Refinements," Econometrica, Econometric Society, vol. 72(3), pages 673-700, May.
    2. Joel L. Horowitz, 2003. "Bootstrap Methods for Markov Processes," Econometrica, Econometric Society, vol. 71(4), pages 1049-1082, July.
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