 Bootstrapping High-Frequency Jump TestsProsper Dovonon, Silvia Goncalves (), Ulrich Hounyo and Nour Meddahi Journal of the American Statistical Association, 2019, vol. 114, issue 526, 793-803 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 {v^in}$\lbrace \hat{v}_{i}^{n}\rbrace $). We first discuss a set of high-level conditions on {v^in}$\lbrace \hat{v}_{i}^{n}\rbrace $ 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 {v^in}$\lbrace \hat{v}_{i}^{n}\rbrace $. 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. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:114:y:2019:i:526:p:793-803 Ordering information: This journal article can be ordered from DOI: 10.1080/01621459.2018.1447485 Access Statistics for this article Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson More articles in Journal of the American Statistical Association from Taylor & Francis Journals |
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