Publication Type
Journal Article
Version
submittedVersion
Publication Date
3-2024
Abstract
Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The "local Gaussian" property of the generic continuous-time benchmark model enables optimal "finite-sample" inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures.
Keywords
Beta, high-frequency data, optimal estimation, leveraged ETFs, event study
Discipline
Econometrics
Research Areas
Econometrics
Publication
American Economic Review
Volume
114
Issue
3
First Page
678
Last Page
708
ISSN
0002-8282
Identifier
10.1257/aer.20221338
Publisher
American Economic Association
Citation
BOLLERSLEV, Tim; LI, Jia; and REN, Yuexuan.
Optimal inference for spot regressions. (2024). American Economic Review. 114, (3), 678-708.
Available at: https://ink.library.smu.edu.sg/soe_research/2645
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1257/aer.20221338