#TITLE_ALTERNATIVE# - DITA AGISTIA ( NIM: 10113043), MAULIA
Return shows the dynamic property that allow the behaviour change from one state to another state or called regime-switching phenomenon. Threshold stochastic model is developed to accommodate this phenomenon. Threshold is used as delimiter between regimes. In other words, for different regime, it is allowed to have another different time series model. In this final project, used variant of AR model and GARCH model with a threshold value, TAR(p) and TGARCH(1,1). In constructing both models, parameter estimation is required. The estimation methods are least square method for TAR(p) and maximum likelihood method for TGARCH(1,1). TAR separates the return into several regimes and used for return prediction. Different from TAR, regimes on TGARCH are separated based on the volatility. Volatility is an important aspect of the study of return, so that, besides having a good return prediction accuracy, a model also needs to have an ability to accommodate volatility aspect. In this final project, GARCH(1,1) and TGARCH(1,1) volatility models are used to predict the volatility. Using three stock indices, TGARCH(1,1) model gives more accurate volatility prediction than GARCH(1,1) model. It indicates that a threshold in TGARCH(1,1) model can improve the accuracy of the volatility prediction result.