Optimal inference for spot regressions

• المؤلفون الرئيسيون: BOLLERSLEV, Tim, LI, Jia, REN, Yuexuan
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2024
• الموضوعات: Beta; high-frequency data; optimal estimation; leveraged ETFs; event study; Econometrics
• الوصول للمادة أونلاين: https://ink.library.smu.edu.sg/soe_research/2645; https://ink.library.smu.edu.sg/context/soe_research/article/3644/viewcontent/OptimalInferenceSpotRegressions_sv.pdf
• المؤسسة: Singapore Management University

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.