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Forecast intervals in ARCH models: bootstrap versus parametric methods

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  • Pilar Olave Robio

Abstract

This paper proposes an alternative bootstrap method for constructing prediction intervals for an ARMA process, when its innovations are represented by a linear GARCH model. The potential of this bootstrap method is assessed through a study which compares the proposed technique with a standard one.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:apeclt:v:6:y:1999:i:5:p:323-327
    DOI: 10.1080/135048599353320
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    Cited by:

    1. 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.
    2. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    3. Andrey Rafalson, 2012. "Bootstrap inference about integrated volatility (in Russian)," Quantile, Quantile, issue 10, pages 91-108, December.
    4. Pan, Li & Politis, Dimitris N, 2014. "Bootstrap prediction intervals for linear, nonlinear, and nonparametric autoregressions," University of California at San Diego, Economics Working Paper Series qt67h5s74t, Department of Economics, UC San Diego.
    5. Shimizu Kenichi, 2013. "The bootstrap does not alwayswork for heteroscedasticmodels," Statistics & Risk Modeling, De Gruyter, vol. 30(3), pages 189-204, August.

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