Modeling volatility of SMR20: GARCH and Markov regime switching GARCH.

The study analyzes and assess the traditional generalized autoregressive conditional heteroscedasticity (GARCH) model with the Markov regime-switching GARCH model (MRS GARCH). Evaluation based on the accuracy of forecasting volatility and risk of Malaysia natural rubber grade Standard Malaysia Rubber 20 (SMR20). We fitted these models under six distributions which are normal, Student-t, generalized error distribution (GED), and their skewed version. Based on log-likelihood (LL), Akaike information criterion (AIC), and Bayesian information criterion (BIC), we found that the MRS GARCH models outperform the traditional GARCH models. We also found that the SMR20 returns are fat tails and skewedly distributed. Further, we forecasted one-day Value-at-Risk (VaR) for each model and compared their adequacy using the conditional coverage (CC) test and Dynamic Quantile (DQ) test. We discovered that the best accurate VaR prediction for SMR20 risk management comes from the MRS GARCH model of skewed Student-t distribution. [ABSTRACT FROM AUTHOR]