Modeling Indonesian Crude Price Using Markov Switching Generalized Autoregressive Conditional Heteroscedasticity - Ria Retna Herliyasari, Nur Iriawan, Heri Kuswanto (International Journal of Advanced Science and Technology, 2020)
Indonesian Crude Price (ICP) is one of the important factors in determining the state budget. If ICP prediction is far from its realization value, it will give a risk to state revenue. The risks given by ICP predictions can be minimized by learning the characteristics and patterns of ICP data. Like other financial time series data, the crude oil price has a nonlinear pattern and fairly high volatility so that it has a nonconstant variance (heteroscedasticity). Moreover, ICP data also have a structural change. Previous studies showed that Generalized Autoregressive Conditional Heteroscedasticity (GARCH) can handle the heteroscedasticity property in the data. However, GARCH can not capture the structural change. Markov Switching is an alternative of time series data modeling having structural change. In Markov Switching, structural change is considered as a random event. In this study, the combination of GARCH and Markov Switching called Markov Switching GARCH (MS-GARCH) was applied to model Indonesian Crude Oil Price and compare it to the single regime GARCH model. Both of volatility models are obtained under normal and Student’s t distribution. The best model is chosen based on the smallest Akaike Information Criterion (AIC). According to AIC values, Markov Switching GARCH gives better performance than single regime GARCH and Student’s t distribution beat the normal distribution.