American Economic Review
ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)
Optimal Inference for Spot Regressions
American Economic Review
vol. 114,
no. 3, March 2024
(pp. 678–708)
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.Citation
Bollerslev, Tim, Jia Li, and Yuexuan Ren. 2024. "Optimal Inference for Spot Regressions." American Economic Review, 114 (3): 678–708. DOI: 10.1257/aer.20221338Additional Materials
JEL Classification
- C22 Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C58 Financial Econometrics
- G12 Asset Pricing; Trading Volume; Bond Interest Rates
- G23 Pension Funds; Non-bank Financial Institutions; Financial Instruments; Institutional Investors