Measuring actual daily volatility from high frequency intraday returns of the S&P futures and index observations

In this study 10 min frequency realized variance series are used to forecast the volatility of S&P 500 index (SPX) daily returns. The logarithm-transformed realized variances are modeled directly in the AR(FI)MA model specification in which the structure of the model is optimized using the AICc criterion. As reported in previous literature, the approximately normal structure of distribution of the logarithm-transformed realized variance series can be modeled directly in structure of the AR(FI)MA process. However, in this study, it is recognized the statistically significant non-normal property of the logarithm-transformed realized variances. Hence, to forecast volatility the non-normality is exploited to improve efficiency of volatility forecasts. It is also observed that in the context of the AR(FI)MA model specification the futures and index based deseasonalized returns for the realized variance estimates improve the forecast performance. Considering the seasonality effect and the distributional properties of the estimated realized variance series, it is evident that the information content of the futures (ES) high frequency observations produces the most accurate forecasts. (C) 2015 Elsevier Ltd. All rights reserved.