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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

type="main" xml:lang="en"> 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. The evolution of these quantities has interesting implications for macroeconomic dynamics. Parameters estimated for US equity portfolios display significant low-frequency fluctuations, along patterns that change across size and book-to-market stocks. Time-varying betas display superior predictive accuracy for returns against constant and rolling-window OLS estimates. As to the relationship of betas with business-cycle variables, value stocks’ betas move pro-cyclically, unlike those of growth stocks. Investment growth, rather than consumption, predicts the betas of value and small-firm portfolios.

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  • 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.
    • Handle: RePEc:bla:obuest:v:76:y:2014:i:2:p:257-278
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    Cited by:

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    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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