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Optimal Inference for Spot Regressions

Author

Listed:
  • Tim Bollerslev
  • Jia Li
  • Yuexuan Ren

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.

Suggested Citation

  • Tim Bollerslev & Jia Li & Yuexuan Ren, 2024. "Optimal Inference for Spot Regressions," American Economic Review, American Economic Association, vol. 114(3), pages 678-708, March.
    • Handle: RePEc:aea:aecrev:v:114:y:2024:i:3:p:678-708
    DOI: 10.1257/aer.20221338
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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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