Optimal Inference for Spot Regressions
American Economic Review (Forthcoming)
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