Optimal Inference for Spot Regressions
Tim Bollerslev, Jia Li, Yuexuan Ren
American Economic Review · 2024 · Volume 114 · Issue 3
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
Single Equation Models, Single Variables: Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes, Financial Econometrics, Asset Pricing, Trading Volume, Bond Interest Rates, Pension Funds, Non-bank Financial Institutions, Financial Instruments, Institutional Investors
Enable JavaScript for citation tools and additional paper interactions.