Portmanteau test for the asymmetric power GARCH model when the power is unknown

Stock market dynamics in a regime-switching asymmetric power GARCH model

International Review of Financial Analysis ◽

10.1016/j.irfa.2005.08.002 ◽

2006 ◽

Vol 15 (2) ◽

pp. 109-129 ◽

Cited By ~ 16

Author(s):

Thierry Ané ◽

Loredana Ureche-Rangau

Keyword(s):

Stock Market ◽

Regime Switching ◽

Garch Model ◽

Market Dynamics ◽

Asymmetric Power

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Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

The Scientific World JOURNAL ◽

10.1155/2014/497941 ◽

2014 ◽

Vol 2014 ◽

pp. 1-21 ◽

Cited By ~ 9

Author(s):

Melike Bildirici ◽

Özgür Ersin

Keyword(s):

Neural Networks ◽

Stock Returns ◽

Emerging Market ◽

Markov Switching ◽

Forecast Accuracy ◽

Garch Model ◽

Stock Index ◽

Garch Models ◽

Approximation Properties ◽

Asymmetric Power

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.

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