Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models

被引:99
作者
Zhang, Yue-Jun [1 ]
Wang, Jin-Li [1 ,2 ]
机构
[1] Hunan Univ, Sch Business, Changsha 410082, Hunan, Peoples R China
[2] Hunan Univ, Ctr Resource & Environm Management, Changsha 410082, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Stock market; Crude oil price forecast; MIDAS model; High frequency data; OUTPUT GROWTH; VOLATILITY SPILLOVERS; SUPPLY SHOCKS; RETURNS; RISK; REGRESSIONS; COMMODITY; MOVEMENT; DEMAND;
D O I
10.1016/j.eneco.2018.11.015
中图分类号
F [经济];
学科分类号
02 ;
摘要
Extensive studies have used stock market information to forecast crude oil prices, and stock market can more easily derive high-frequency data than crude oil market due to no revisions, which raises a question that whether high-frequency stock market data can improve the forecast performance of crude oil prices. Therefore, this paper employs the MIDAS model and the high-frequency data of four stock market indices to forecast WTI and Brent crude oil prices at lower frequency. The results indicate that the high-frequency stock market indices have certain advantage over the lower-frequency data in forecasting monthly crude oil prices, and the MIDAS model using high-frequency data proves superior to the ordinary model. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:192 / 201
页数:10
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