Optimal Inference for Spot Regressions

Tim Bollerslev, Jia Li and Yuexuan Ren

American Economic Review, 2024, vol. 114, issue 3, 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.

JEL-codes: C22 C58 G12 G23 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1257/aer.20221338

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