A Decomposition of Conditional Risk Premia and Implications for Representative Agent Models
Fousseni Chabi-Yo and Johnathan A. Loudis (2023). A Decomposition of Conditional Risk Premia and Implications for Representative Agent Models. Management Science. Published online on November 22, 2023.
209 Pages Posted: 1 Dec 2020 Last revised: 9 Feb 2024
Date Written: June 22, 2023
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.
Keywords: Market risk premium; Variance risk premium; Crash risk; Risk-neutral moments; Preferences; Stochastic Discount Factor
JEL Classification: E44; G1; G12; G13
Suggested Citation: Suggested Citation