Optimal Inference for Spot Regressionst

• 成果类型: Article
• 署名作者: Bollerslev, Tim; Li, Jia; Ren, Yuexuan
• 署名单位: Duke University; National Bureau of Economic Research; Singapore Management University
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20221338
• 发表日期: 2024
• 页码: 678-708
• 关键词: instrumental variables regression; microstructure noise; volatility; RISK; asymptotics; covariance; variance; tests

摘要

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 C22, C58, G12, G23)