Modelling and Forecasting Monthly Crude Oil Price of Pakistan: A Comparative Study of ARIMA, GARCH and ARIMA Kalman Model

被引：19
|
作者：
Aamir, Muhammad [1 ]
Shabri, Ani [1 ]
机构：
[1] Univ Teknol Malaysia, Dept Math Sci, Fac Sci, Johor Baharu 81310, Malaysia
来源：
ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS | 2016年 / 1750卷
关键词：
ARIMA; Crude oil; Forecasting; GARCH; Kalman Filter;
D O I：
10.1063/1.4954620
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Crude oil is one of the most important commodity in the world and it is meaningful for every individual. This study aims at developing a more appropriate model for forecasting the monthly crude oil price of Pakistan. In this study, three-time series models are used namely Box-Jenkins ARIMA (Auto-regressive Integrated Moving Average), GARCH (Generalized Auto-regressive Conditional Hetero-scedasticity) and ARIMA Kalman for modelling and forecasting the monthly crude oil price of Pakistan. The capabilities of ARIMA, GARCH and ARIMA-Kalman in modelling and forecasting the monthly crude oil price are evaluated by MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error). It is concluded that the hybrid model of ARIMA Kalman perform well as compared to the Box-Jenkins ARIMA and GARCH models.
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页数：7
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