Markov Switching Artificial Neural Networks and Volatility Modeling with an Application to a Turkish Stock Index

The study analyzes the family of regime switching GARCH neural network models, which allow the generalization of MS type RS-GARCH models to MS-GARCH-NN models by incorparating with neural network architectures with different dynamics and forecasting capabilities both in addition to the family of GARCH models. In addition to the Gray (1996) RS-GARCH model which allows for within regime heteroskedasticity with markov switching of Hamilton (1989), the models analyzed in the study allow regime switching modeled with GARCH-NN specifications developed by Donaldson and Kamstra (1996) and further investigated by Bildirici and Ersin (2009). In addition to regime swiching type nonlinearity, proposed models incorporate different neural network architectures based on Multi Layer Perceptron (MLP), and Hybrid MLP models. Obtained models are MS-GARCH-MLP and MS-GARCH-Hybrid MLP. Above mentioned models are further extended to account for fractional integration (FI) in GARCH specification to obtain MS-FIGARCH-MLP and MS-FIGARCH-Hybrid MLP. By allowing asymmetric power transformation as modeled in APGARCH model, models are augmented to obtain MS-APGARCH-RBF and MS-FIGARCH-Hybrid MLP. Models are evaluated with MAE, MSE and RMSE criteria and equal forecast accuracy is tested with modified Diebold-Mariano tests. Among the models analyzed, though models which allow fractional integration and asymmetric power transformation perform better in modeling the daily returns in IMKB100 stock index, hybrid MLP and time lag recurrent architectures such as MS-FIAPGARCH-HybridMLP provide significant forecast and modeling performance. Overall, results suggest models with markov switching and neural network methodologies in modeling volatility in forecasting future returns in an emerging market stock index