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Good and Bad Variance Premia and Expected Returns ∗ Mete
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kilic, ∗. Mete Shaliastovich, Ivan |
| Copyright Year | 2015 |
| Abstract | We measure “good” and “bad” variance premia that capture risk compensations for the realized variation in positive and negative market returns, respectively. The two variance premium components jointly predict excess returns over the next 1 and 2 years with statistically significant negative (positive) coefficients on the good (bad) component. The R2s reach about 10% for aggregate equity and portfolio returns and 20% for corporate bond returns. To explain the new empirical evidence, we develop an economic model which underscores the difference in investors’ risk attitudes towards upside and downside uncertainty risks. ∗First draft: February 2015. Mete Kilic (mkilic@wharton.upenn.edu) and Ivan Shaliastovich (corresponding author, ishal@wharton.upenn.edu) are at Wharton School, University of Pennsylvania, 3620 Locust Walk, Philadelphia, PA 19104, Phone: (215) 746-0005. We are grateful to Torben Andersen, Geert Bekaert, Bjorn Eraker, Nicola Fusari, Juan M. Londono, Jessica Wachter, Amir Yaron, and Hao Zhou as well as seminar participants at Emory University, Temple University, University of Maryland, University of North Carolina at Chapel Hill, University of Wisconsin-Madison, the Wharton School, the 2015 Midwest Finance Association meetings, and the 2016 American Finance Association meetings for helpful comments. We thank The Rodney L. White Center for Financial Research for financial support. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://jacobslevycenter.wharton.upenn.edu/wp-content/uploads/2016/07/Good-and-Bad-Variance-Premia-and-Expected-Returns.pdf |
| Alternate Webpage(s) | https://bus.wisc.edu/~/media/bus/knowledge-expertise/academic-departments/finance/ks_gb.pdf?la=en |
| Alternate Webpage(s) | https://www.aeaweb.org/conference/2016/retrieve.php?pdfid=472 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
