Subsampling Inference for the Autocorrelations of GARCH Processes

Tucker McElroy () and Agnieszka Jach

Journal of Financial Econometrics, 2019, vol. 17, issue 3, 495-515

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.

Keywords: conditional heteroskedasticity; heavy tails; parameter-dependent convergence rates; self-normalization; studentization (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2019
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