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Inference for nonparametric high-frequency estimators with an application to time variation in betas

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  • KALNINA, Ilze

Abstract

We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. We suggest a procedure for a data-driven choice of the bandwidth parameters. Our simulation study indicates that the subsampling method is much more robust than the plug-in method based on the asymptotic expression for the variance. Importantly, the subsampling method reliably estimates the variability of the Two Scale estimator even when its parameters are chosen to minimize the finite sample Mean Squared Error; in contrast, the plugin estimator substantially underestimates the sampling uncertainty. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semi-definite. We use the subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas within year 2006, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every five or twenty minutes. To capture this variation we estimate a simple dynamic model for betas. The variance estimation is also important for the correction of the errors-in-variables bias in such models. We find that the bias corrections are substantial, and that betas are more persistent than the naive estimators would lead one to believe.

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  • KALNINA, Ilze, 2015. "Inference for nonparametric high-frequency estimators with an application to time variation in betas," Cahiers de recherche 2015-08, Universite de Montreal, Departement de sciences economiques.
  • Handle: RePEc:mtl:montde:2015-08
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    File URL: http://hdl.handle.net/1866/12855
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    Cited by:

    1. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.

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    JEL classification:

    • F15 - International Economics - - Trade - - - Economic Integration
    • F34 - International Economics - - International Finance - - - International Lending and Debt Problems
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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