Bootstrapping prediction intervals on stochastic volatility models

Yun Huan Lee, Tsai Hung Fan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The parametric bootstrap method is applied to derive the prediction intervals for stochastic volatility models. The study adopts the parameters estimation developed by So et al. (1997) and proves the validity of the proposed bootstrap procedure for this process. The basic stochastic volatility model specifies the mean equation with standard normal error. It is found, via simulation study, that the same algorithm can be employed to the model with heavy-tailed innovations, which demonstrates the potential of the bootstrap techniques. This methodology is also applied to a real data example to predict the daily observations on the S&P 500 index and the results confirm that our interval predictions are satisfactory.

Original languageEnglish
Pages (from-to)41-45
Number of pages5
JournalApplied Economics Letters
Issue number1
StatePublished - 15 Jan 2006


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