The VIX, the variance premium and stock market volatility

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Information on all of the papers published in the ECB Working Paper Series can be found on the ECB's website, http://www.ecb.europa.eu/pub/scientific/wps/date/html/index.en.html Terms of use: Documents in EconStor may AcknowledgementsWe thank the Editor, Alok Bhargava, and three anonymous reviewers for useful comments and suggestions that improved the paper. Carlos Garcia de Andoain provided excellent research assistance. Geert Bekaert acknowledges financial support from Netspar. The views expressed do not necessarily reflect those of the European Central Bank or the Eurosystem. Geert Bekaert Columbia UniversityMarie Hoerova (corresponding author) European Central Bank; e-mail: marie.hoerova@ecb.europa.eu 1 AbstractWe decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium. JEL Classification: C22, C52, G12, E32Keywords: option implied volatility; realized volatility; VIX; variance risk premium; risk aversion; stock return predictability; risk-return trade-off; economic uncertainty; financial instability. How to measure the variance premium is not without controversy however, because it relies on an estimate of the conditional variance of stock returns. In this article, we tackle several measurement issues for the variance premium, assessing a plethora of state-of-the-art volatility models and making full use of overlapping daily data, rather than sparse end-of-month data, which is standard.The conditional variance measure is of interest in its own right. uses the VIX index to measure uncertainty, so that his results may actually be driven by the variance premium rather than uncertainty per se.Using more plausible estimates of the variance premium and stock market volatility, we then assess whether they predict stock returns, economic activity, as well as financial instability, an economic outcome that has garnered considerable policy interest in the aftermath of the recent fin...

Variance Premium and Implied Volatility in a Low-Liquidity Option Market

“…In particular, we confirm Bekaert & Hoerova (2014) finding that the predicting properties of the variance premium can be significant as early as a month in advance rather than only at the quarterly frequency found by Bollerslev et al (2009). However, we go beyond Bekaert & Hoerova (2014) and directly relate the risk aversion series obtained from the variance premium with future returns. We show the risk-aversion series is a strong predictor of future returns with a slightly superior fit than the variance premium.…”

“…Our paper relates to Bekaert & Hoerova (2014) that shows the variance premium calculated with an econometric model for expected variance has a higher predicting power over future returns than the variance premium calculated under the assumption that the expected variance follows a random walk process, as in Bollerslev et al (2009). In particular, we confirm Bekaert & Hoerova (2014) finding that the predicting properties of the variance premium can be significant as early as a month in advance rather than only at the quarterly frequency found by Bollerslev et al (2009).…”

“…Following the recent literature on variance forecasting (Chen & Ghysels, 2011, Corsi, 2009, Corsi & Renò, 2012, and Bekaert & Hoerova, 2014, we construct several explanatory variables (predictors) from a 5-minute returns data set. 13 First, we include in the set of explanatory variables lags of realized variance at heterogeneous frequencies to account for the clustering feature of stock returns variance.…”

“…Finally, we follow Bekaert & Hoerova (2014) and include the lagged implied variance as explanatory variable. Importantly, as will be shown, this variable contains information about future realized variance that is not contained in lagged realized variance and other measures based on observed stock returns.…”

“…We estimate component (i) by searching for the best forecasting model for future variance, based on Bekaert & Hoerova (2014). Following the recent literature on variance forecasting (Chen & Ghysels, 2011;Corsi, 2009;and Corsi & Renò, 2012), we use high-frequency data for this task.…”