The Efficiency of Volatility Financial Model with Additive Outlier: A Monte Carlo Simulation

Observation that lies outside the overall pattern of its distribution is called outlier. The presence of outliers in time series data will effects on the modelling and also forecasting. Among the various types of outliers that effects the behavioral of finance series is additive outliers. This situation occurred because of recording errors, measurement errors or external factor. Therefore, the intention of this research is to investigate the effectiveness of volatility financial model with the presence of additive outliers. The appropriate approach in this paper is Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) model. In this paper, data was simulated using ARMA (1, 0)-GARCH (1, 2) model via Monte Carlo method. There are three different sample size used in simulation study which are 500, 1000 and 1400. The comparison of effectiveness ARMA-GARCH model are based on MAE, MSE, RMSE, AIC, SIC and HQIC. The results of the numerical simulation indicate that when sample size increase, the effectiveness of ARMA-GARCH model diminished in the presence of additive outliers.