Bootstrapping High-Frequency Jump Tests

Prosper Dovonon*, Sílvia Gonçalves, Ulrich Hounyo, Nour Meddahi

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

2 Citations (Scopus)
39 Downloads (Pure)

Abstract

The main contribution of this article is to propose a bootstrap test for jumps based on functions of realized volatility and bipower variation. Bootstrap intraday returns are randomly generated from a mean zero Gaussian distribution with a variance given by a local measure of integrated volatility (which we denote by {vˆni}). We first discuss a set of high-level conditions on {vˆni} such that any bootstrap test of this form has the correct asymptotic size and is alternative-consistent. We then provide a set of primitive conditions that justify the choice of a thresholding-based estimator for {vˆni}. Our cumulant expansions show that the bootstrap is unable to mimic the higher-order bias of the test statistic. We propose a modification of the original bootstrap test which contains an appropriate bias correction term and for which second-order asymptotic refinements are obtained.

Original languageEnglish
JournalJournal of the American Statistical Association
Volume114
Issue526
Pages (from-to)793-803
Number of pages11
ISSN0162-1459
DOIs
Publication statusPublished - 2019

Keywords

  • Asymptotic refinements
  • Bias correction
  • Jump tests
  • Thresholding volatility bootstrap
  • PRICES
  • MODELS
  • STOCHASTIC VOLATILITY

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