Abstract
Analysis with high frequency returns has become a core part of modern financial econometrics. Particularly in the measurement and forecasting of variance, covariance, correlation and Capital Asset Pricing Model (CAPM) beta. This paper studies CAPM beta measurement and forecasting with high frequency returns and evaluates trade-offs between bias and variability from different approaches. Our main finding is that the increasing of the return sampling frequency to a suitably high level with the inclusion of a lead and lag in the beta estimation, can result in substantial improvements in the bias and variability trade-off, relative to standard realized beta estimators with returns over a range of sampling frequencies.
Published Version
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