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A Decomposition of Conditional Risk Premia and Implications for Representative Agent Models

Author

Listed:
  • Fousseni Chabi-Yo

    (Isenberg School of Management, University of Massachusetts, Amherst, Massachusetts 01003)

  • Johnathan A. Loudis

    (Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556)

Abstract

We develop a methodology to decompose the conditional market risk premium and risk premia on higher-order moments of excess market returns into risk premia related to contingent claims on down, up, and moderate market returns. The decomposition exploits information about the risk-neutral market return distribution embedded in option prices, but does not depend on assumptions about the functional form of investor preferences or about the market return distribution. The total market risk premium is highly time-varying, as are the contributions from downside, upside, and central risk. Time-series variation in risk premia associated with each region is primarily driven by variation in risk prices associated with the probability of entering each region at short horizons, but it is primarily driven by variation in risk quantities at longer horizons. Analogous decompositions implied by prominent representative agent models generally fail to match the dynamic risk premium behavior implied by the data. Our results provide a set of new empirical facts regarding the drivers of conditional risk premia and identify new challenges for representative agent models.

Suggested Citation

  • Fousseni Chabi-Yo & Johnathan A. Loudis, 2024. "A Decomposition of Conditional Risk Premia and Implications for Representative Agent Models," Management Science, INFORMS, vol. 70(10), pages 6804-6834, October.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:10:p:6804-6834
    DOI: 10.1287/mnsc.2022.01663
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