Details

Autor(en) / Beteiligte

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

Titel

Bootstrapping High-Frequency Jump Tests

Ist Teil von

• Journal of the American Statistical Association, 2019-04, Vol.114 (526), p.793-803

Ort / Verlag

Alexandria: Taylor & Francis

Erscheinungsjahr

2019

Quelle

Taylor & Francis

Beschreibungen/Notizen

• 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 ^ i n } $\lbrace \hat{v}_{i}^{n}\rbrace $ ). We first discuss a set of high-level conditions on { v ^ i n } $\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 ^ i n } $\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.