The comparison between ARIMA and ARFIMA model to forecast kijang emas (gold) prices in Malaysia using MAE, RMSE and MAPE / Atiqa Nur Azza Mahmad Azan, Nur Faizatul Auni Mohd Zulkifly Mototo and Pauline Jin Wee Mah

• Main Authors: Mahmad Azan, Atiqa Nur Azza, Mohd Zulkifly Mototo, Nur Faizatul Auni, Wee Mah, Pauline Jin
• Format: Article
• Language: English
• Published: UiTM Cawangan Perlis 2021
• Subjects: Precious metals. Bullion; Investment, capital formation, speculation; Time-series analysis
• Online Access: https://ir.uitm.edu.my/id/eprint/60329/1/60329.pdf; https://ir.uitm.edu.my/id/eprint/60329/; https://crinn.conferencehunter.com/

Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.