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

来源：

ENERGY ECONOMICS | 2019年 / 78卷

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

页码：192 / 201

页数：10

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