Open AccessGold Price Forecasting using Box-Jenkins Method
Author(s) -
Nur Atikah Khalid,
Nurfadhlina Abdul Halim
Publication year - 2019
Publication title -
universiti malaysia terengganu journal of undergraduate research
Language(s) - English
Resource type - Journals
ISSN - 2637-1138
DOI - 10.46754/umtjur.v1i3.75
Subject(s) - autoregressive integrated moving average , mean absolute percentage error , commodity , econometrics , hedge , economics , inflation (cosmology) , gold as an investment , gold standard (test) , box–jenkins , value (mathematics) , standard deviation , time series , financial economics , statistics , finance , mathematics , mean squared error , ecology , physics , theoretical physics , biology
In general, the nature of gold that acts as a hedge against inflation and its stable price over the course of the financial crisis has made it a unique commodity. Price forecasts are a must for gold producers, investors and central bank to know the current trends in gold prices. Forecasting the future value of a variable is often done with time series analysis method. This study was conducted to determine the best model for forecasting gold commodity prices as well as forecasting world gold commodity prices in 2018 using Box-Jenkins approach. The data used in this study was obtained from Investing.com from 2015 until 2017. This study shows that ARIMA (1,1,1) is the best model to predict gold commodity prices based on Mean Absolute Percentage Error (MAPE). MAPE value for ARIMA (1,1,1) is 0.02%, where this value proves that forecasting using ARIMA (1,1,1) is the best forecasting because MAPE value is less than 10%.