Optimal Inference for Spot Regressions
提出一种新的计量框架，利用高频数据非参数估计时变贝塔，实现有限样本最优推断，比传统大样本方法更可靠，并应用于杠杆ETF跟踪表现和日内事件研究。
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.