Forecasting Volatility in European Stock Markets with Non-Linear GARCH Models
University of Milan-Bicocca, Italy - Department of Economics, Quantitative Methods and Business Strategies (DEMS); Fondazione Eni Enrico Mattei (FEE), Milan, Italy
Università degli Studi di Milano-Bicocca - Department of Management and Business Administration
FEEM Working Paper No. 98.2002
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.
Number of Pages in PDF File: 40
Keywords: Volatility, GARCH, Forecast Evaluation
JEL Classification: A10, C10, C50, G10working papers series
Date posted: November 22, 2002
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