Modeling and predicting the CBOE market volatility index

This paper performs a thorough statistical examination of the time-series properties of the daily market volatility index (VIX) from the Chicago Board Options Exchange (CBOE). The motivation lies on the widespread consensus that the VIX is a barometer to the overall market sentiment as to what concerns investors’ risk appetite. Our preliminary analysis suggests that the VIX index displays long-range dependence. This is well line with the strong empirical evidence in the literature supporting long memory in both options-implied and realized variances. We thus resort to both parametric and semiparametric heterogeneous autoregressive (HAR) processes for modeling and forecasting purposes. Our main findings are as follows. First, we confirm the evidence in the literature that there is a strong negative relationship between the VIX index and the S&P 500 index return as well as a positive contemporaneous link with the volume of the S&P 500 index. Second, we find that the VIX index tends to decline as the long-run oil price increases. This is not entirely surprising given the high demand from oil in the last years as well as the recent trend of shorting energy prices in the hedge fund industry. Third, the term spread has no long-run impact in the VIX index despite of the positive contemporaneous link. Fourth, there is some weak evidence that increases in the value of the US dollar tend to move down options-implied market volatility. Finally, we cannot reject the linearity of the above relationships, neither in sample nor out of sample. As for the latter, we actually show that it is pretty hard to beat the pure HAR process because of the very persistent nature of the VIX index. It is not impossible, though. We set out a semiparametric HAR-type model that performs very well across different forecasting horizons by using the above explanatory variables in a quite efficient manner. JEL classification numbers: G12, C22, C53, E44