Forecasting stock market volatility using (non-linear) Garch models
Article Abstract:
The out-of-sample forecasting ability of the generalized autoregression conditionally heteroscedasticity model (GARCH) and its two variants, the GJR and the quadratic GARCH, were evaluated with respect to stock market volatility. Weekly indices from the German, Dutch, Spanish, Italian and Swedish stock markets were used as the data set. The results showed that the QGARCH provided the best fit among the GARCH and a random walk models.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1996
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Forecasting time series with long memory and level shift
Article Abstract:
Economic time series can be well explained using models, which allow for either long memory or for occasional level shifts. An analysis of the advantages of these models is presented using a new model. The new model captures two essential features of these models, representation and estimation.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 2005
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Do seasonal unit roots matter for forecasting monthly industrial production?
Article Abstract:
A model selection approach is proposed to identify the number of seasonal unit roots for forecasting monthly industrial production series. AIC and BIC are the most popular criteria in practice to determine the number of seasonal unit roots.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 2004
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