Bootstrapping High-Frequency Jump Tests

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  • Prosper Dovonon, Concordia University
  • ,
  • Sílvia Gonçalves, McGill University
  • ,
  • Ulrich Hounyo
  • Nour Meddahi, Universite de Toulouse

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
Pages (from-to)793-803
Number of pages11
Publication statusPublished - 2019

    Research areas

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

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