Measuring skewness premia |
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| Affiliation: | 1. University of Colorado, United States;2. University of Michigan, United States;3. Federal Reserve Bank of Chicago, United States;4. NBER, United States;1. Fox School of Business, Temple University, Philadelphia, PA, United States;2. Department of Economics Marco Biagi, University of Modena and Reggio Emilia, Italy;3. CEFIN, University of Modena and Reggio Emilia, Italy |
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| Abstract: | We provide a new methodology to empirically investigate the respective roles of systematic and idiosyncratic skewness in explaining expected stock returns. Using a large number of predictors, we forecast the cross-sectional ranks of systematic and idiosyncratic skewness, which are easier to predict than their actual values. Compared to other measures of ex ante systematic skewness, our forecasts create a significant spread in ex post systematic skewness. A predicted systematic skewness risk factor carries a significant and robust risk premium that ranges from 6% to 12% per year. In contrast, the role of idiosyncratic skewness in pricing stocks is less robust. |
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