Performance of a Novel Hybrid Model Through Simulation and Historical Financial Data

It is thoroughly acknowledged that the historical financial time series not linear, exhibits structural changes, and volatile. has been noticed in current literature because of existence breaks series, GARCH family models provide misleading results poor forecasts. Thus, it unavoidable to incorporate with nonlinearity conditional mean variance capture volatility dynamics more precisely than existing models. Therefore, inspiring this matter, study proposes a novel hybrid model exponential autoregressive (ExpAR) Markov-switching (MSGARCH) model. This also examines performances through simulation real-world data. Moreover, investigates downside risk management using 5% VaR (Value-at-Risk) back-testing. The empirical findings showed proposed outperforms benchmark for both captures meticulously