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How do alphas and betas move? Uncertainty, learning and time variation in risk loadings

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  • Carmine Trecroci

Abstract

I employ a parsimonious model with learning but without conditioning information to extract time-varying measures of market-risk sensitivities, pricing errors and pricing uncertainty. Parameters estimated for U.S. equity portfolios show significant fluctuations, along patterns that change across size and book-to-market categories of stocks. Time-varying betas display superior predictive accuracy for portfolio returns against constant and rolling-window OLS estimates. I also study the relationship of betas with business-cycle variables, finding that those of high BE/ME stocks move pro-cyclically, unlike those of low BE/ME stocks. Investment growth, rather than consumption, predicts the betas of high BE/ME and small-firm portfolios.

Suggested Citation

  • Carmine Trecroci, 2010. "How do alphas and betas move? Uncertainty, learning and time variation in risk loadings," Working Papers 1012, University of Brescia, Department of Economics.
    • Handle: RePEc:ubs:wpaper:1012
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    Cited by:

    1. Giovanna Bua & Carmine Trecroci, 2019. "International equity markets interdependence: bigger shocks or contagion in the 21st century?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 155(1), pages 43-69, February.
    2. Carmine Trecroci, 2012. "Uncertainty and the Dynamics of Multifactor Loadings and Pricing Errors," Economics Bulletin, AccessEcon, vol. 32(3), pages 2453-2463.
    3. Carmine Trecroci, 2014. "How Do Alphas and Betas Move? Uncertainty, Learning and Time Variation in Risk Loadings," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 257-278, April.
    4. Ciner, Cetin, 2015. "Time variation in systematic risk, returns and trading volume: Evidence from precious metals mining stocks," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 277-283.
    5. Jiang, Minqi & Liu, Jiapeng & Zhang, Lu, 2021. "An extended regularized Kalman filter based on Genetic Algorithm: Application to dynamic asset pricing models," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 28-44.

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