Good and Bad Variance Premia and Expected Returns

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 R 2 s 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 AbstractWe 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 R 2 s reach about 10% for aggregate equity and portfolio returns and about 20% for corporate bond returns. We show that an asset pricing model that features distinct time variation in positive and negative shocks to fundamentals can explain the good and bad variance premium evidence in the data.

Asymmetric Connectedness of Fears in the U.S. Financial Sector

“…This finding is in line with recent literature on the decomposed variance risk premium (e.g. Kilic and Shaliastovich, 2018) assessing how the mixture of information coming from the decomposed premia is more helpful to predict, in their case, future asset returns. We have conducted several robustness checks on the predictive exercise as well; however, our results appear to be robust 13 .…”

“…Then, we assemble a new asymmetric fear connectedness index that is used as a forward-looking systemic risk monitoring tool. a growing strand of literature studying the asymmetric characteristics of volatility (see Barndorff-Nielsen et al, 2010;Patton and Sheppard, 2015;Segal et al, 2015;Feunou et al, 2017;Kilic and Shaliastovich, 2018).…”

“…However, other events that will increase "good" volatility and transmit across the system can create positive connectedness (see Segal et al, 2015;Baruník et al, 2016;Feunou et al, 2017). For instance, Kilic and Shaliastovich (2018) found a "synergy" between the good and bad components of the variance risk premium when they are included in the same model, increasing return predicability compared to the aggregate measure of the variance premium. In addition, they found that the variance premium decomposition uncovers long-horizon return predictability, where the aggregate variance premium fails instead.…”

Variance Risk Premium Components and International Stock Return Predictability

“…These different compensations reveal another form of asymmetry or non-linearity that can help explain the (joint) dynamics of higher-order asset moments. Kilic and Shaliastovich (2018), Held, Kapraun, Omachel, and Thimme (2018), and Feunou, Jahan-Parvar, and Okou (2017) propose various ways to decompose the VP into its downside (negative returns or bad states) and upside (positive returns or good states) components. We contribute to this literature by examining a wide range of models to obtain the physical expectation of downside and upside variances and, thus, to calculate the VP under different market environments.…”

“…DVP and UVP capture asymmetric investors' attitudes toward variance risks emanating from the left and right tail of the return distribution, respectively. Yet, there is scanty research on the estimation, dynamics, and economic interpretation of the VP components (with a few notable exceptions like Kilic and Shaliastovich (2018) and Feunou, Jahan-Parvar, and Okou (2017)).…”