Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas

Ilze Kalnina

Journal of Business & Economic Statistics, 2023, vol. 41, issue 2, 538-549

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. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semidefinite. Our simulation study indicates that the subsampling method is more robust than the plug-in method based on the asymptotic expression for the variance. We use our subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every 5 or 20 min. 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.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2022.2040520 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Inference for nonparametric high-frequency estimators with an application to time variation in betas (2015)
Working Paper: Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:41:y:2023:i:2:p:538-549

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2022.2040520

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().