Markov Switching Asymmetric GARCH Model and Artificial Neural Networks: applied on volatility forecasting for MSM Index

Volatility is a real measure of the scattering of profits for a given security or market Index. The stock exchange is a standout amongst the most critical hotspots for organizations to raise cash. This facilitates organizations to be traded on an open market, or raise extra money related capital for development by offering offers of responsibility for organization in an open market. The liquidity that a trade manages the speculators gives them the capacity to rapidly and effortlessly offer securities. Generally, financial time series exhibit different stylized facts, namely, asymmetry and nonlinearity, which require a particular specification to capture market volatility behavior. This project suggests backpropagation neural networks (BPNN) to improve the MSM30 (Muscat securities market index) returns volatility forecast. The estimated volatility based on the Markov-Switching asymmetric GJR-GARCH (MS GJR-GARCH) model and the VIX index (i.e., Volatility index) series are used respectively as input and output of our artificial neural networks model. The empirical results reveal that the proposed combination has succeeded in enhancing the forecast ability by mastering the exhibited volatility clustering (GARCH), asymmetry (GJR-GARCH), and nonlinearity (BPNN) effects. In addition, the use of the MS GJR-GARCH model only may lead to the worst results, especially in crisis periods.