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.

Research, Models, Econometrics, Stock price forecasting, Prediction theory

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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.

Analysis, Usage, Economic forecasting, Time-series analysis, Time series analysis, Econometric models

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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.

United States, Forecasts, trends, outlooks, Production management, Management dynamics, Management, Forecasts and trends, Measurement, Market trend/market analysis, Company business management, Organization for Economic Cooperation and Development, Industrial productivity, Seasonal industries, Productivity

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