近日，贸经系青年教师吴优博士的合作论文Changing determinant driver and oil volatility forecasting: A comprehensive analysis在Energy Economics 2024年第129卷正式发表。Energy Economics为ABS 3期刊，北京工商大学认定的经济与商科ESI A2期刊。

01 内容摘要

Academic research relies on exogenous drivers to enhance the accuracy of forecasting oil volatility. Following the relevant literature, this study collects 62 exogenous drivers that reflect the movements of oil demand, oil supply, oil inventory, macroeconomic fundamentals, financial indicators, and measures of uncertainty. Our empirical results indicate that dimension reduction regressions, especially principal component analysis regression (PCA), successfully predict both WTI and Brent oil volatility at the one-month ahead forecast horizon. Shrinkage methods, on the other hand, outperform their counterparts for medium- and long-term forecast horizons. Furthermore, the unsupervised learning method (PCA) achieves superior forecasting performance during periods of oil price decrease, whereas supervised learning methods (i.e., shrinkage methods) significantly improve volatility accuracy. Additionally, the empirical results reveal that movements in the Kilian index, World industrial production index, global economic conditions index, U.S. steel production, Chicago Fed national activity index, capacity utilization for manufacturing, U.S. default yield spread, and MSCI emerging market index have a significant impact on oil volatility.

学术研究依靠外生驱动因素来提高石油波动率预测的准确性。根据相关文献，本文收集62个外生驱动因素，涵盖石油需求、石油供应、石油库存、宏观经济基本面、财务指标以及不确定性指标的变动等。实证结果表明，降维回归分析方法，尤其是主成分分析回归（PCA），可以成功预测未来一个月的WTI和布伦特原油的波动率。另一方面，收缩法在中长期范围内的预测表现优于同类方法。此外，无监督学习方法（PCA）在油价下跌期间具有优异的预测性能，而监督学习方法（即收缩方法）显著提高波动率预测的准确性。此外，本文结果还显示，Kilian指数、世界工业生产指数、全球经济状况指数、美国钢铁产量、芝加哥联储全国活动指数、制造业产能利用率、美国违约收益率息差以及MSCI国际新兴市场指数的变动对石油波动率存在显著影响。

02 作者简介

吴优，bat365在线平台贸经系讲师，硕士生导师，期货从业人员资格考试命题组成员。博士毕业于北京航空航天大学，长期致力于金融市场研究。在Energy Economics,International Review of Economics & Finance,Finance Research Letters,《金融研究》《管理科学》等期刊发表20余篇论文。