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.
Keywords
- Bootstrap Search this journal for Bootstrap (opens in a new tab)
- Diffusion Process Search this journal for Diffusion Process (opens in a new tab)
- Strong Taylor Approximation Search this journal for Strong Taylor Approximation (opens in a new tab)
- Stationarity Search this journal for Stationarity (opens in a new tab)
- Monte Carlo Simulation Search this journal for Monte Carlo Simulation (opens in a new tab)
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Keywords
- Bootstrap Search this journal for Bootstrap (opens in a new tab)
- Diffusion Process Search this journal for Diffusion Process (opens in a new tab)
- Strong Taylor Approximation Search this journal for Strong Taylor Approximation (opens in a new tab)
- Stationarity Search this journal for Stationarity (opens in a new tab)
- Monte Carlo Simulation Search this journal for Monte Carlo Simulation (opens in a new tab)
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