Garch Models Comparison with Symmetric and Asymmetric Process for Univariate Econometric Series

Abstract:

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model was modeled using both symmetric and asymmetric processes. Secondary data from January 2005 to December 2021 on the Consumer Price Index, Exchange Rate, Crude Oil Price, and Inflation Rate were used for this study. The research was conducted using the statistical software packages Minitab and E-view. The aforementioned four macroeconomic variables show a tendency for volatility to cluster across time. In both symmetric and asymmetric processes, the volatility condition and leverage impact coefficients were present. By contrasting the symmetric models (ARCH, GARCH, and GARCH-M) and the asymmetric models, the best model was chosen using Akaike Information Criteria (E-GARCH, T-GARCH and APARCH). For the investigated univariate economic variables, the results indicated that the found asymmetric model GARCH models outperformed the symmetric model GARCH models. Therefore, these models can be applied to the forecasting of these series of economic indicators. Models include the Asymmetric E-GARCH (1, 1) Model for Consumer Price Index, Crude Oil Price, and Inflation Rate Series and the Asymmetric T-GARCH (1, 1) Model for Exchange Rate Series.