Malaysia is one of the largest producers of natural rubber in the world. Among the various types of natural rubber which contribute to the country’s agricultural sector is the Standard Malaysian Rubber Grade 20 (S.M.R 20). Since 2008, the rubber price has received attention of investors and Malaysia Rubber Board due to price fluctuation. The price of rubber is characterized by the existence of heavy tails and volatility clustering. These properties play a significant impact on parameter estimation and forecasting performance resulting from S.M.R 20 rubber price data. The approach used in modeling S.M.R 20 rubber price data, is Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. The aims of this paper are to find the best ARMA-GARCH model by using different specifications structures and to forecast the daily price for 20 days ahead. There are 20 models produced from different specifications in ARMA(R,M) dan GARCH(p,q) models. In this study, 1953 daily price data of S.M.R 20 are taken into consideration. The validity comparison of diagnostic checking and forecasting performance are based on AIC, AICC, SBC, HQC, MSE, RMSE and MAPE. The results reveals that ARMA(1,0)-GARCH(1,2) model is the best volatility modeling in S.M.R 20 rubber price. Based on the implications of the results, the scope of the future research directions has been widen.
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.
The unstable and uncertain nature of natural rubber prices makes them highly volatile and prone to outliers, which can have a significant impact on both modeling and forecasting. To tackle this issue, the author recommends a hybrid model that combines the autoregressive (AR) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. The model utilizes the Huber weighting function to ensure the forecast value of rubber prices remains sustainable even in the presence of outliers. The study aims to develop a sustainable model and forecast daily prices for a 12-day period by analyzing 2683 daily price data from Standard Malaysian Rubber Grade 20 (SMR 20) in Malaysia. The analysis incorporates two dispersion measurements (IQR/3 and Sn) and three levels of IO contamination 0%, 10%, and 20%. The results indicate that using the Huber weighting function with the IQR/3 measurement to build the AR(1)-GARCH(2,1) model leads to better sustainability. These findings have the potential to enhance the GARCH model by modifying the weighting function of the M-estimator
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