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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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    Cited by:

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    2. Jiawei Wang & Zhen Chen, 2023. "Exploring Low-Risk Anomalies: A Dynamic CAPM Utilizing a Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    3. Alexander Koch & Toan Luu Duc Huynh & Mei Wang, 2024. "News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 5-34, January.
    4. Hollstein, Fabian, 2020. "Estimating beta: The international evidence," Journal of Banking & Finance, Elsevier, vol. 121(C).
    5. Pakorn Aschakulporn & Jin E. Zhang, 2022. "Bakshi, Kapadia, and Madan (2003) risk‐neutral moment estimators: An affine jump‐diffusion approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(3), pages 365-388, March.
    6. Marshall, Ben R. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2021. "Beta estimation in New Zealand," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    7. Doan, Bao & Lee, John B. & Liu, Qianqiu & Reeves, Jonathan J., 2022. "Beta measurement with high frequency returns," Finance Research Letters, Elsevier, vol. 47(PA).

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