Weighting Temporary Change Outlier by Modified Huber Function with Monte Carlo Simulations

Abstract

Robust method is a popular approach to dealing the existence of outliers in the data. Many researchers have applied Huber weight function. The aim of this paper is to evaluate the performance of the Huber weight function and the modification of the Huber weight function on the temporary change (TC) outliers. The data used in this paper were generated as ARMA(1,0)-GARCH(1,2) model via the Monte Carlo simulation. There are three major situations in this simulations: without weight (WW), with Huber weight (WH) and with a modified Huber weight (WMH). Three different TC contamination (0%, 10% and 20%) and three different time series length (100, 500 and 1000) were tested. The performance of the three situations was compared on the basis of AIC, SIC, HQIC, MAE, MSE and RMSE. The results of the numerical simulations show that the performance in the WMH situation is better than the WH situation in the presence of TC outliers.

Building a Sustainable GARCH Model to Forecast Rubber Price: Modified Huber Weighting Function Approach

“…The outcome of the simulation by 30 suggested that median and median absolute deviation (MAD) or IQR could be reasonable alternatives to mean and variance. Nevertheless, this paper used the IQR/3 which was suggested by Ghani and Rahim 31 .…”