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Subsampling Inference for the Autocorrelations of GARCH Processes

Author

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  • Tucker McElroy
  • Agnieszka Jach

Abstract

We provide self-normalization for the sample autocorrelations of power GARCH(p, q) processes whose higher moments might be infinite. To validate the studentization, whose goal is to match the growth rate dependent on the index of regular variation of the process, we substantially extend existing weak-convergence results. Since asymptotic distributions are non-pivotal, we construct subsampling-based confidence intervals for the autocorrelations and cross-correlations, which are shown to have satisfactory empirical coverage rates in a simulation study. The methodology is further applied to daily returns of CAC40 and FTSA100 indices and their squares.

Suggested Citation

  • Tucker McElroy & Agnieszka Jach, 2019. "Subsampling Inference for the Autocorrelations of GARCH Processes," Journal of Financial Econometrics, Oxford University Press, vol. 17(3), pages 495-515.
    • Handle: RePEc:oup:jfinec:v:17:y:2019:i:3:p:495-515.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbx037
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    More about this item

    Keywords

    conditional heteroskedasticity; heavy tails; parameter-dependent convergence rates; self-normalization; studentization;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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