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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Published inJournal of econometrics Vol. 172; no. 1; pp. 49 - 65
Main Authors Dovonon, Prosper, Gonçalves, Sílvia, Meddahi, Nour
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.01.2013
Elsevier
Elsevier Sequoia S.A
Subjects
Online AccessGet full text
ISSN0304-4076
1872-6895
1872-6895
DOI10.1016/j.jeconom.2012.08.003

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Abstract 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.
AbstractList 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.
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. All rights reserved, Elsevier
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. [PUBLICATION ABSTRACT]
Author Gonçalves, Sílvia
Dovonon, Prosper
Meddahi, Nour
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Issue 1
Keywords Edgeworth expansions
C12
C15
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Bootstrap
Realized correlation
Realized regression
Realized beta
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Snippet We propose a bootstrap method for statistics that are a function of multivariate high frequency returns such as realized regression, covariance and correlation...
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SubjectTerms Asymptotic methods
Bootstrap
Bootstrap mechanism
Bootstrap method
Coefficient of correlation
correlation
covariance
econometrics
Edgeworth expansions
heteroskedasticity
Multivariate analysis
Realized beta
Realized correlation
Realized regression
Regression analysis
Stochastic processes
Studies
Volatility
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Title Bootstrapping realized multivariate volatility measures
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