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Asset pricing models and economic risk premia: A decomposition

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  • Balduzzi, Pierluigi
  • Robotti, Cesare

Abstract

The risk premia of linear factor models on economic (non-traded) risk factors can be decomposed into: i) the premium on maximum-correlation portfolios mimicking the factors; ii) (minus) the covariance between the non-traded components of the pricing kernel and the factors; and iii) (minus) the mispricing of the maximum-correlation portfolios. For a given set of assets available for investment, the first component is the same across models and is typically estimated with little bias and high precision. We conclude that the premia on maximum-correlation portfolios are appealing alternatives to the risk premia of linear factor models, with the dividend yield being the only economic factor significantly priced.

Suggested Citation

  • Balduzzi, Pierluigi & Robotti, Cesare, 2010. "Asset pricing models and economic risk premia: A decomposition," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 54-80, January.
  • Handle: RePEc:eee:empfin:v:17:y:2010:i:1:p:54-80
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    Cited by:

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    2. Hanno Lustig & Stijn Van Nieuwerburgh & Adrien Verdelhan, 2013. "The Wealth-Consumption Ratio," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 3(1), pages 38-94.
    3. Gospodinov, Nikolay & Robotti, Cesare, 2021. "Common pricing across asset classes: Empirical evidence revisited," Journal of Financial Economics, Elsevier, vol. 140(1), pages 292-324.
    4. Kan, Raymond & Robotti, Cesare, 2008. "Specification tests of asset pricing models using excess returns," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 816-838, December.
    5. Patrick Gagliardini & Diego Ronchetti, 2020. "Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 333-394.
    6. Raymond Kan & Cesare Robotti, 0. "Comment on: Pseudo-True SDFs in Conditional Asset Pricing Models," Journal of Financial Econometrics, Oxford University Press, vol. 18(4), pages 729-735.

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