Bootstrapping realized multivariate volatility measures

We propose a bootstrap method for statistics that are a function of multivariate high frequency returns such as realized regression, covariance and correlation coefficients. We show that the finite sample performance of the bootstrap is superior to the existing first-order asymptotic theory. Neverth...

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Veröffentlicht in:Journal of econometrics Jg. 172; H. 1; S. 49 - 65
Hauptverfasser: Dovonon, Prosper, Gonçalves, Sílvia, Meddahi, Nour
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.01.2013
Elsevier
Elsevier Sequoia S.A
Schlagworte:
Analysis of covariance
Asymptotic methods
Bootstrap
Bootstrap mechanism
Bootstrap method
Bootstrapping
Coefficient of correlation
correlation
covariance
econometrics
Edgeworth expansions
heteroskedasticity
Multivariate analysis
Realized beta
Realized correlation
Realized regression
Regression analysis
Statistics
Stochastic models
Stochastic processes
Studies
Volatility
Edgeworth expansions
C12
C15
C14
Bootstrap
Realized correlation
Realized regression
Realized beta
ISSN:0304-4076, 1872-6895
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Beschreibung
Zusammenfassung:We propose a bootstrap method for statistics that are a function of multivariate high frequency returns such as realized regression, covariance and correlation coefficients. We show that the finite sample performance of the bootstrap is superior to the existing first-order asymptotic theory. Nevertheless, and contrary to the existing results in the bootstrap literature for regression models subject to error heteroskedasticity, the Edgeworth expansion for the pairs bootstrap that we develop here shows that this method is not second-order accurate. We argue that this is due to the fact that the conditional mean parameters of realized regression models are heterogeneous under stochastic volatility.
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2012.08.003