Subsampling Inference for the Autocorrelations of GARCH Processes. (19th December 2017)

• Record Type: Journal Article
• Authors: McElroy, Tucker; Jach, Agnieszka
• Is Part Of: Journal of financial econometrics. Volume 17:Number 3(2019:Summer)
• Page Start: 495
• Page End: 515
• Publication Date: 2017-12-19
• Subjects: conditional heteroskedasticity -- heavy tails -- parameter-dependent convergence rates -- self-normalization -- studentization; C13 -- C14; Capital market -- Law and legislation -- Periodicals; Financial institutions -- Law and legislation -- Periodicals; 338.544205
• DOI: 10.1093/jjfinec/nbx037
• Languages: English
• ISSNs: 1479-8409

Abstract

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.