在COVID-19时代预测原油价格:机器可以更好地学习吗?
Guangning Tian1, Yuchao Peng2,3, Yuhao Meng2
1School of Economics and Management, North China Electric Power University, China.
Energy economics
|June 26, 2023
概括
由于COVID-19的流行,原油价格的可预测性下降. 组合方法在疫情期间的预测中表现优于收缩方法,更好地适应不断变化的市场动态.
科学领域:
- 能源经济学 能源经济学
- 计量经济学 计量经济学
- 机器学习 机器学习
背景情况:
- COVID-19大流行对全球能源市场产生了重大影响,导致价格波动增加.
- 原油现货价格的可预测性一直是一个持续的挑战,由疫情造成的经济破坏加剧了这一挑战.
研究的目的:
- 评估基于机器学习的收缩和组合方法对预测原油现货价格的有效性.
- 为了比较这些方法在COVID-19大流行之前和期间的预测性能.
主要方法:
- 利用了两种机器学习技术:收缩和组合方法.
- 将这些方法应用于历史原油现货价格数据,分析COVID-19大流行前和期间的时期.
主要成果:
- 随着COVID-19大流行,经济不确定性增加,许多预测模型的预测准确性下降.
- 虽然收缩方法通常提供强大的样本外性能,但在COVID-19期间,组合方法的准确性更高.
- 收缩方法未能适应大流行引起的预测因素-价格相关性变化,导致信息丢失.
结论:
- 组合方法展示了在COVID-19大流行前所未有的经济转变中对原油价格的卓越适应性和预测能力.
- 这些发现凸显了当金融市场突然发生结构性崩时,传统收缩方法的局限性.
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