ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.

The objective of this study was to determine the best model that describe the pattern of cayenne pepper productivity in Magelang Regency. This study uses primary data which was obtained from the results of a survey of cayenne pepper production by the General Director of Horticulture on several sample plots in Magelang District, Central Java Province in 2018. The process of data analysis was divided into two parts: grouping the sample plots based on the similarity in productivity pattern and then fitting models in each group. The models used to fit data were Logistic Growth Model, Monomolecular Growth Model, Exponential Growth Model, Polynomial Model and Linear B-Spline Model. The best model was determined based on R2 and MAPE. The results showed that the pattern of cayenne pepper productivity in Magelang District had eight different characteristics. Characteristics of each groups were illustrated by the similarity of their productivity pattern. The best model in each group was B-Spline Linear Model.

The objective of this study was to determine the best model that describe the pattern of cayenne pepper productivity in Magelang Regency. This study uses primary data which was obtained from the results of a survey of cayenne pepper production by the General Director of Horticulture on several sample plots in Magelang District, Central Java Province in 2018. The process of data analysis was divided into two parts: grouping the sample plots based on the similarity in productivity pattern and then fitting models in each group. The models used to fit data were Logistic Growth Model, Monomolecular Growth Model, Exponential Growth Model, Polynomial Model and Linear B-Spline Model. The best model was determined based on R2 and MAPE. The results showed that the pattern of cayenne pepper productivity in Magelang District had eight different characteristics. Characteristics of each groups were illustrated by the similarity of their productivity pattern. The best model in each group was B-Spline Linear Model.