On the risk return relationship

Jianxin Wang and Minxian Yang

Journal of Empirical Finance, 2013, vol. 21, issue C, 132-141

Abstract: While the risk return trade-off theory suggests a positive relationship between the expected return and the conditional volatility, the volatility feedback theory implies a channel that allows the conditional volatility to negatively affect the expected return. We examine the effects of the risk return trade-off and the volatility feedback in a model where both the return and its volatility are influenced by news arrivals. Our empirical analysis shows that the two effects have approximately the same size with opposite signs for the daily excess returns of seven major developed markets. For the same data set, we also find that a linear relationship between the expected return and the conditional standard deviation is preferable to polynomial-type nonlinear specifications. Our results have a potential to explain some of the mixed findings documented by previous studies.

Keywords: Risk premium; Volatility feedback; GARCH-in-mean; Maximum likelihood; Mixture distributions; Time series (search for similar items in EconPapers)
JEL-codes: C22 G10 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927539813000029
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:21:y:2013:i:c:p:132-141

DOI: 10.1016/j.jempfin.2013.01.001

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().