Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

Mark Jensen and John Maheu

Working Paper series from Rimini Centre for Economic Analysis

Abstract: The relationship between risk and return is one of the most studied topics in finance. The majority of the literature is based on a linear, parametric relationship between expected returns and conditional volatility. This paper models the contemporaneous relationship between market excess returns and contemporaneous log-realized variances nonparametrically with an infinite mixture representation of their joint distribution. The conditional distribution of excess returns given log-realized variance will also have a infinite mixture representation but with probabilities and arguments depending on the value of realized variance. Our nonparametric approach allows for deviation from Gaussianity by allowing for higher order non-zero moments and a smooth nonlinear relationship between the conditional mean of excess returns and contemporaneous log-realized variance. We find strong robust evidence of volatility feedback in monthly data. Once volatility feedback is accounted for, there is an unambiguous positive relationship between expected excess returns and expected log-realized variance. This relationship is nonlinear. Volatility feedback impacts the whole distribution and not just the conditional mean.

Date: 2014-11
New Economics Papers: this item is included in nep-rmg
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis (2018)
Working Paper: Risk, Return, and Volatility Feedback: A Bayesian Nonparametric Analysis (2014)
Working Paper: Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:31_14

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