Research Article
Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns
Table 4
Markov switching GARCH neural network models: training results.
| ā | MSE | RMSE |
| Model Group 1: MS-ARMA-GARCH-neural network models | MS-ARMA-GARCH-RNN | 0.035103533 | 0.187359368 (4th) | MS-ARMA-GARCH-RBF | 0.013009577 | 0.114059534 (1st) | MS-ARMA-GARCH-ELMAN RNN | 0.051024351 | 0.225885704 (5th) | MS-ARMA-GARCH-HYBRID MLP | 0.034996508 | 0.187073536 (3rd) | MS-ARMA-GARCH-MLP | 0.034665716 | 0.186187315 (2nd) |
| Model Group 2: MS-ARMA-APGARCH-neural network models | MS-ARMA-APGARCH-RNN | 0.031530707 | 0.177568881 (4th) | MS-ARMA-APGARCH-RBF | 0.056680761 | 0.238077218 (5th) | MS-ARMA-APGARCH-ELMAN RNN | 0.027644691 | 0.166266929 (3rd) | MS-ARMA-APGARCH-HYBRID MLP | 0.026504969 | 0.162803470 (1st) | MS-ARMA-APGARCH-MLP | 0.026591934 | 0.163070336 (2nd) |
| Model Group 3: MS-ARMA-FIAPGARCH-neural network models | MS-ARMA-FIAPGARCH-RNN | 0.029951509 | 0.173065042 (1st) | MS-ARMA-ARMA-FIAPGARCH-RBF | 0.045969206 | 0.214404305 (5th) | MS-ARMA-FIAPGARCH-ELMAN RNN | 0.033859273 | 0.184008896 (4th) | MS-ARMA-FIAPGARCH-HYBRID MLP | 0.031056323 | 0.176228044 (2nd) | MS-ARMA-FIAPGARCH-MLP | 0.031221803 | 0.176696926 (3rd) |
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