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The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas

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  • Fabian Hollstein

    (School of Economics and Management, Leibniz University Hannover, 30167 Hannover, Germany)

  • Marcel Prokopczuk

    (School of Economics and Management, Leibniz University Hannover, 30167 Hannover, Germany; International Capital Market Association Centre, Henley Business School, University of Reading, Reading RG6 6BA, United Kingdom)

  • Chardin Wese Simen

    (International Capital Market Association Centre, Henley Business School, University of Reading, Reading RG6 6BA, United Kingdom)

Abstract

When using high-frequency data, the conditional capital asset pricing model (CAPM) can explain asset-pricing anomalies. Using conditional betas based on daily data, the model works reasonably well for a recent sample period. However, it fails to explain the size anomaly as well as three out of six of the anomaly component excess returns. Using high-frequency betas, the conditional CAPM is able to explain the size, value, and momentum anomalies. We further show that high-frequency betas provide more accurate predictions of future betas than those based on daily data. This result holds for both the time-series and the cross-sectional dimensions.

Suggested Citation

  • Fabian Hollstein & Marcel Prokopczuk & Chardin Wese Simen, 2020. "The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas," Management Science, INFORMS, vol. 66(6), pages 2474-2494, June.
    • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:6:p:2474-2494
    DOI: 10.1287/mnsc.2019.3317
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