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 statebudget. If ICP prediction is far from its realization value, it will give a risk to staterevenue. The risks given by ICP predictions can be minimized by learning thecharacteristics and patterns of ICP data. Like other financial time series data, the crudeoil price has a nonlinear pattern and fairly high volatility so that it has a nonconstantvariance (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 cannot capture the structural change. Markov Switching is an alternative of time series datamodeling having structural change. In Markov Switching, structural change is consideredas a random event. In this study, the combination of GARCH and Markov Switchingcalled Markov Switching GARCH (MS-GARCH) was applied to model Indonesian CrudeOil Price and compare it to the single regime GARCH model. Both of volatility modelsare obtained under normal and Student’s t distribution. The best model is chosen basedon the smallest Akaike Information Criterion (AIC). According to AIC values, MarkovSwitching GARCH gives better performance than single regime GARCH and Student’s tdistribution beat the normal distribution.