||Liberalization and economic integration become topics of discussion and research in|
recent years. Indonesia is one of the countries that actively participates in the achievement
of liberalization and economic integration, especially in the ASEAN region. Indonesia stock
market has a high degree of volatility which can be used to produce high investment returns,
which is one of the reasons to attract foreign investors to enter Indonesia stockmarket.
Volatility plays an important role for market participants to control and reduce their market
risk of financial assets
In this study we establish the volatility models for the stocks listed in the Indonesia stock
market index LQ45. The models we considered include the Autoregressive Conditional Heteroskedasticity
(ARCH) proposed by Engle (1982), Generalized Autoregrassive Conditional
Heteroskedasticity (GARCH) by Bollerslev (1986), the Stochastic Volatility Model (SVM) by
Jacquier, Polson and Rossi (1994), and Autoregressive Moving Average (ARMA) by Box, Jankins, and Reinsel (1994). We use the daily log returns to establish the models and select the best
one via Akaike information criterion (AIC).Moreover, we use it to predict the future volatility.
In the end, we also apply machine learning application such as the K-means method to figure
out how itsmovement of the clusters volatility in Indonesia stocks.