Forecasting crude oil prices with the Google Index

被引：23
|
作者：
Yao, Ting [1 ,2 ]
Zhang, Yue-Jun [1 ,2 ]
机构：
[1] Hunan Univ, Business Sch, Changsha 410082, Hunan, Peoples R China
[2] Hunan Univ, Ctr Resource & Environm Management, Changsha 410082, Hunan, Peoples R China
来源：
8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016) | 2017年 / 105卷
基金：
中国国家自然科学基金;
关键词：
WTI crude oil; Forecast; Google Index; ARIMA; ARMA-GARCH; TIME-SERIES; SEARCH DATA; UNIT-ROOT; CONSUMPTION;
D O I：
10.1016/j.egypro.2017.03.880
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
As crude oil price volatility is susceptible to oil-related events and the Internet search data can effectively reflect the psychological behaviours of investors in crude oil market. In this sense, we explore the effect and predictive power of the Google Index on crude oil prices by incorporating the Google Index as an exogenous variable into the ARIMA (Auto-regressive Integrated Moving Average) and ARMAGARCH (Auto-regressive Moving Average-Generalized Auto-Regressive Conditional Heteroscedasticity) models. The empirical results indicate that there exists a negative effect of the Google Index on crude oil prices, but the Google Index cannot help to forecast crude oil prices. (C) 2016 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：3772 / 3776
页数：5
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