Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5096333 | Journal of Econometrics | 2013 | 17 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Prosper Dovonon, SÃlvia Gonçalves, Nour Meddahi,