Comparison of BEKK GARCH and MEWMA Methods on IDX Composite and Exchange Rate Volatility

At the beginning of 2020, the world was busy with a new virus namely COVID-19. In Indonesia, COVID-19 virus was first identified on March 2nd, 2020. This global pandemic made several impacts. One of the impact is on Country's Economy that can be seen in the decline of IDX Composite and the weakening of US Dollar exchange rate to Rupiah. The movement of IDX Composite and US Dollar exchange rate to Rupiah often increases and decreases every day. This condition can be caused by volatility due to fluctuation. There are several methods to cover the volatility of multivariate data, one of them can be approached using Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) model. In addition to the GARCH model, there is another approach that can also be used to cover volatility data, that is Multivariate Exponential Weighted Moving Average (MEWMA) model. Based on the analysis results of the three training data, it was found that the RMSE of the BEKK GARCH method was greater than the RMSE of the MEWMA method and VAR(2)-MEWMA that be used on the three training data had the consistently volatility predict of IDX Composite return and US Dollar exchange rate to Rupiah return. MEWMA method can be said to have a better predictive ability, so VAR(2)-MEWMA is used to model IDX Composite return data and US Dollar exchange rate to Rupiah return data from November 2019 to August 2021 and is used to predict the volatility of the next month on September 2021. MEWMA model’s ability is quite good in predicting volatility of IDX Composite return data and US Dollar exchange rate to Rupiah return data.