Volatility Forecasting - A comparative study of different forecasting models.

Abstract

This study evaluates the out-of-sample forecasting performance of different volatility mod- els. When applied to XACT OMXS30, we use GARCH(1,1), EGARCH(1,1), and t- GAS(1,1) to forecast squared daily returns while Realized GARCH(1,1) and HAR-RV are used to forecast Realized Variance. We forecast both measures with open-close as well as close-close data. One-day-ahead forecasts are computed using a five year mov- ing window. The performance is measured with two different loss functions, MSE and QLIKE. The Diebold-Mariano test is then used to test significance. Our findings indicate that EGARCH(1,1) is superior when forecasting squared daily returns and that HAR-RV is superior when forecasting Realized Variance. Comparing EGARCH and HAR-RV, we find that the latter is more accurate for a symmetrical loss function while EGARCH is superior using the QLIKE loss function. We find no evidence indicating that Student’s t-distribution for the conditional volatility improves forecasting accuracy. Finally, we con- clude that open-close data generates smaller forecast errors than close-close data.