Medium-term horizon volatility forecasting: A comparative study

In this paper, volatility is estimated and then forecast using unobserved components-realized volatility (UC-RV) models as well as constant volatility and GARCH models. With the objective of forecasting medium-term horizon volatility, various prediction methods are employed: multi-period prediction, variable sampling intervals and scaling, The optimality of these methods is compared in terms of their forecasting performance. To this end, several UC-RV models are presented and then calibrated using the Kalman filter, Validation is based on the standard errors on the parameter estimates and a comparison with other models employed in the literature such as constant volatility and GARCH models. Although we have volatility forecasting for the computation of Value-at-Risk in mind the methodology presented has wider applications. This investigation into practical volatility forecasting complements the substantial body of work on realized volatility-based modelling in business.