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Bootstrapping prediction intervals on stochastic volatility models

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  • Yun-Huan Lee
  • Tsai-Hung Fan

Abstract

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.

Suggested Citation

  • Yun-Huan Lee & Tsai-Hung Fan, 2006. "Bootstrapping prediction intervals on stochastic volatility models," Applied Economics Letters, Taylor & Francis Journals, vol. 13(1), pages 41-45.
    • Handle: RePEc:taf:apeclt:v:13:y:2006:i:1:p:41-45
    DOI: 10.1080/13504850500377967
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    References listed on IDEAS

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    1. Pilar Olave Robio, 1999. "Forecast intervals in ARCH models: bootstrap versus parametric methods," Applied Economics Letters, Taylor & Francis Journals, vol. 6(5), pages 323-327.
    2. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    3. Ruiz, Esther, 1994. "Quasi-maximum likelihood estimation of stochastic volatility models," Journal of Econometrics, Elsevier, vol. 63(1), pages 289-306, July.
    4. Kent D. Wall & David S. Stoffer, 2002. "A State space approach to bootstrapping conditional forecasts in arma models," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(6), pages 733-751, November.
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    Cited by:

    1. Matthew Klepacz, 2021. "Price Setting and Volatility: Evidence from Oil Price Volatility Shocks," International Finance Discussion Papers 1316, Board of Governors of the Federal Reserve System (U.S.).

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