Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices

Abstract

We address the question whether the evolution of implied volatility can be forecasted by studying a number of European and U.S. implied volatility indices. Both point and interval forecasts are formed by alternative model specifications. The statistical and economic significance of these forecasts is examined. The latter is assessed by trading strategies in the recently inaugurated CBOE volatility futures markets. Predictable patterns are detected from a statistical point of view. However, these are not economically significant since no abnormal profits can be attained. Hence, the hypothesis that the volatility futures markets are efficient cannot be rejected.JEL Classification: C53, G10, G12, G13, G14.

Sovereign rating actions and the implied volatility of stock index options

“…Consistent with prior literature (e.g. Konstantinidi et al, 2008), we find that ∆IV is stationary. We employ Augmented Dickey-Fuller, KSS (Kapetanios et al, 2003) and KPSS (Kwiatkowski, et al, 1992) ∆CCR denotes daily changes in the comprehensive credit rating from a given CRA (note that there are limited numbers of rating events during the 12-year sample period).…”

“…In other words, the option market already subsumes current financial, macro-economic fundamentals of the rated sovereigns, which is consistent with the view that the evolution of implied volatility cannot be forecasted (e.g. Konstantinidi et al, 2008;Jiang and Tian, 2012). Table 5 reports the results of Equation (1) Other results are similar to Table 4.…”

“…11 Prior literature shows that the option market is efficient and there is no significant evidence of forecastability of implied volatility. Konstantinidi et al (2008) use numerous economic indicators in several econometric models in an attempt to forecast the evolution of US and European option implied volatilities, but none of these variables is significant and the adjusted R 2 values of all the models are very near to zero. Moreover, out-of-sample forecasting evidence mostly favours the random walk model for evolution of the IVs.…”

On the Intraday Relation Between the VIX and its Futures

“…Prior research has used daily data to (partially) address this question (see e.g. Konstantinidi, Skiadopolous and Tzagkaraki, 2008;Konstantinidi and Skiadopolous, 2011;and Shu and Zhang, 2012). However, inferring informational efficiency and leadership is difficult using daily data as informational asymmetries between the markets could vanish in the data aggregation.…”

Are CDS spreads predictable? An analysis of linear and non-linear forecasting models

“…For clarity, it is worth mentioning that the MCP cannot be calculated for the random walk model. In this case, in order to be still able to compute the F-statistic, we follow Konstantinidi et al (2008) and assign a value of 50% for the MCP, based on the assumption that the possibility of having either a positive or negative forecast of CDS spread changes is equal to 50%. Table 5 and Table 6 report the out-of-sample performance of the forecasting models for the non- For both CDS indices, the tests clearly show that, based on the RMSE and MAE metrics, the random walk and the Markov switching structural model generate forecasts which are statistically different (at the 1% significance level) from the forecasts generated by our benchmark model, namely the AR(1) model.…”