*PHD Student, Laboratory of Research in Entrepreneurship, Finance and Audit Laboratory (LAREFA), ENCG, Ibn Zohr University, Morocco
**PHD Student, Laboratory of Engineering Sciences and Energies, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco
***Professor, Laboratory of Research in Entrepreneurship, Finance and Audit Laboratory (LAREFA), ENCG, Ibn Zohr University, Morocco
****Professor, Laboratory of Engineering Sciences and Energies, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Morocco
Stock Market volatility has been extensively studied in finance literature. In this paper, we estimate Moroccan Stock Market return volatility by using Single-State GARCH models and Markov Regime Switching GARCH models. We proposed Back Propagation Neural Network algorithms to improve volatility forecasting of GARCH class models. The BPNN is combined with GARCH in such a way prediction of GARCH models is used as input of our Neural Network. Three volatility estimators are used for this purpose: Absolute return, Parkinson and Garman Klass. The forecasting accuracy of the models is examined using Mean Square Errors (MSE). The results indicate the efficiency of the neural network in enhancing the performance of GARCH models. The findings further clarify the superiority of the marriage of MRS-GARCH and EGARCH with neural network over considered models.
GARCH, MRSGARCH, Neural Network, Volatility
