Abstract
In financial data there is asymmetric volatility, which denotes the different movements on conditional volatility of increase and decrease financial asset returns. The exponential GARCH and threshold GARCH models can be used to capture asymmetric volatility, called leverage effect. The aim of this research is to determine the best model between exponential GARCH and threshold GARCH models, and to know the results of forecasting volatility the LQ-45 stock index using the best model. The research showed that the best model to predicting volatility is EGARCH(2,1), because it has the smallest AIC value compared to other models. Then forecasting volatility of the LQ-45 stock index using EGARCH(2,1) showed that volatility increase from the first period until fourteenth period, this means that it has high volatility.
Highlights
which denotes the different movements on conditional volatility of increase
The aim of this research is to determine the best model between exponential generalized autoregressive conditional heteroscedasticity (GARCH)
The research showed that the best model to predicting volatility is
Summary
Data deret waktu (time series) merupakan sekumpulan data berupa angka yang didapat dalam suatu periode waktu tertentu. Data deret waktu berfluktuasi secara cepat dari waktu ke waktu sehingga memiliki varians yang tidak konstan atau heterogen. Karena data finansial memiliki volatilitas yang sangat tinggi sehingga model ARCH memerlukan orde yang tinggi dalam memodelkan variansnya. Pada tahun 1986 Bollerslev menyempurnakan model ARCH menjadi model generalized autoregressive conditional heteroscedasticity (GARCH). Dalam beberapa kasus terdapat respons volatilitas yang bersifat asimetris (leverage effect), sehingga model GARCH dikembangkan dengan mengakomodasi adanya respons volatilitas yang bersifat asimetris, yaitu model exponential GARCH (EGARCH) oleh Nelson tahun 1991 dan model threshold GARCH (TGARCH) oleh Zakoian tahun 1994 (Tsay, 2013). Model EGARCH dan TGARCH banyak diterapkan dalam pasar modal yaitu pada saham. Berdasarkan hal tersebut, adapun tujuan dari penelitian ini yaitu mengetahui model terbaik di antara model EGARCH dan TGARCH, serta. Mengetahui hasil peramalan volatilitas indeks saham LQ-45 untuk periode 10 Juni 2019 hingga 27 Juni 2019
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