Subsampling inference for the autocorrelations of GARCH processes

Tucker McElroy, Agnieszka Jach

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Non-refereed scientific journalJournal of Financial Econometrics
ISSN1479-8409
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article - refereed

Keywords

  • 112 Statistics and probability

Fingerprint Dive into the research topics of 'Subsampling inference for the autocorrelations of GARCH processes'. Together they form a unique fingerprint.

Cite this