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Accounting for the Accuracy of Beta Estimates in CAPM Tests on Assets with Time‐varying Risks

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  • Tom Berglund
  • Johan Knif

Abstract

This paper advocates two ways to make more efficient use of available information in reducing the bias of the risk premium estimate in two‐pass tests of the CAPM. First, explicit modelling of the time‐variability of betas can improve the accuracy of the beta forecasts. Second, the cross‐sectional information available can be exploited more efficiently using individual stocks instead of portfolios provided that noisy beta predictions are given a smaller weight than more accurate ones. This paper proposes an adjustment of the cross‐sectional regressions of excess returns against betas to give larger weights to more reliable beta forecasts. A significant positive relationship between returns and the beta forecast is obtained when the proposed approach is applied to data from the Helsinki Stock Exchange, while the traditional Fama–MacBeth approach as such finds no relationship at all.

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  • Tom Berglund & Johan Knif, 1999. "Accounting for the Accuracy of Beta Estimates in CAPM Tests on Assets with Time‐varying Risks," European Financial Management, European Financial Management Association, vol. 5(1), pages 29-42, March.
    • Handle: RePEc:bla:eufman:v:5:y:1999:i:1:p:29-42
    DOI: 10.1111/1468-036X.00078
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    Cited by:

    1. Rob Bauer & Mathijs Cosemans & Peter C. Schotman, 2010. "Conditional Asset Pricing and Stock Market Anomalies in Europe," European Financial Management, European Financial Management Association, vol. 16(2), pages 165-190, March.
    2. Amir Amel†Zadeh, 2011. "The Return of the Size Anomaly: Evidence from the German Stock Market," European Financial Management, European Financial Management Association, vol. 17(1), pages 145-182, January.
    3. Fernandez, Pablo, 2004. "Are calculated betas good for anything?," IESE Research Papers D/555, IESE Business School.
    4. Nader Virk & Hilal Butt, 2016. "Specification errors of asset-pricing models for a market characterized by few large capitalization firms," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(1), pages 68-84, January.
    5. Manuel Ammann & Michael Verhofen, 2008. "Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach," European Financial Management, European Financial Management Association, vol. 14(3), pages 391-418, June.
    6. Koutmos, Gregory & Knif, Johan, 2002. "Time variation and asymmetry in systematic risk: evidence from the Finnish stock exchange," Journal of Multinational Financial Management, Elsevier, vol. 12(3), pages 261-271, July.
    7. Inchauspe, Julian & Ripple, Ronald D. & Trück, Stefan, 2015. "The dynamics of returns on renewable energy companies: A state-space approach," Energy Economics, Elsevier, vol. 48(C), pages 325-335.
    8. Gareeva Yuliya & Dranev Yury & Kucherov Alexander, 2018. "The Impact of Innovation Capital on Firm Values," HSE Working papers WP BRP 79/STI/2018, National Research University Higher School of Economics.
    9. Johan Knif & James W. Kolari & Gregory Koutmos & Seppo Pynonen, 2023. "Modeling the Time Variation in Factor Exposures," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 12(2), pages 1-2.

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