标题:Influential factors in crude oil price forecasting
作者:Miao, Hong; Ramchander, Sanjay; Wang, Tianyang; Yang, Dongxiao
作者机构:[Miao, Hong; Ramchander, Sanjay; Wang, Tianyang] Colorado State Univ, Dept Finance & Real Estate, Ft Collins, CO 80523 USA.; [Yang, Dongxiao] Agr Ba 更多
通讯作者:Miao, Hong
通讯作者地址:[Miao, H]Colorado State Univ, Dept Finance & Real Estate, Ft Collins, CO 80523 USA.
来源:ENERGY ECONOMICS
出版年:2017
卷:68
页码:77-88
DOI:10.1016/j.eneco.2017.09.010
关键词:Oil prices; Forecasting; Least Absolute Shrinkage and Selection Operator; (LASSO); Mean Squared Prediction Error (MSPE); Success ratio
摘要:This paper identifies factors that are influential in forecasting crude oil prices. We consider six categories of factors (supply, demand, financial market, commodities market, speculative, and geopolitical) and test their significance in the context of estimating various forecasting models. We find that the Least Absolute Shrinkage and Selection Operator (LASSO) regression method provides significant improvements in the forecasting accuracy of prices compared to alternative benchmarks. Relative to the no-change and futures-based models, LASSO forecasts at the 8-step ahead horizon yield significant reductions in Mean Squared Prediction Error (MSPE), with MSPE ratios of 0.873 and 0.898, respectively. We also document substantial improvements in forecasting performance of the factor-based model that employs only a subset of variables chosen by LASSO. Finally, the time-varying nature of the relationship between factors and oil prices is used to explain recent movements in crude oil prices. (C) 2017 Elsevier B.V. All rights reserved.
收录类别:EI;SCOPUS;SSCI
WOS核心被引频次:3
Scopus被引频次:3
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033438712&doi=10.1016%2fj.eneco.2017.09.010&partnerID=40&md5=e98a48bf174bb73a3ca165ed7f04e3b4
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