Open AccessA Performance Comparison of Various Bootstrap Methods for Diffusion Processes
Author(s) -
Jung S. You,
M. S. Jeong
Publication year - 2021
Publication title -
journal of economics and behavioral studies
Language(s) - English
Resource type - Journals
ISSN - 2220-6140
DOI - 10.22610/jebs.v13i4(j).3185
Subject(s) - monte carlo method , sample size determination , diffusion , sample (material) , computer science , hermite polynomials , statistical physics , econometrics , mathematics , mathematical optimization , algorithm , statistics , physics , mathematical analysis , thermodynamics
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.