Forecasting Volatility in European Stock Markets with Non-Linear GARCH Models

Authors: Matteo Manera, Gianfranco Forte

Year: 2002

Series: FEEM Working Paper  n.2002.098

ISSN: 2037-1209

Keywords: Volatility, GARCH, Forecast evaluation

JEL n.: A10, C10, C50, G10

 

Abstract

This paper investigates the forecasting performance of three popular variants of the non-linear GARCH models, namely VS-GARCH, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of classical criteria, as well as forecast combination techniques with constant and non-constant weights. With respect to the standard GARCH specification, the non-linear models generally lead to better forecasts in terms of both smaller forecast errors and lower biases. In-sample forecast combination regressions are better than those from single Mincer-Zarnowitz regressions. The out-of-sample performance of combining forecasts is less satisfactory, irrespective of the type of weights adopted. 

Forecasting Volatility in European Stock Markets with Non-Linear GARCH Models