Aftermath of 2008 Financial Crisis on Oil Prices

被引:0
|
作者
Sehgal, Neha [1 ]
Pandey, Krishan K. [2 ]
机构
[1] OP Jindal Global Univ, Jindal Global Business Sch, Sonipat 131001, Haryana, India
[2] Univ Petr & Energy Studies, Coll Management & Econ Studies, Dehra Dun 248007, India
关键词
Feature Selection; Mutual Information; Interaction Information; Neural Networks; Oil Price Forecasting;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Geopolitical and economic events had strong impact on crude oil markets for over 40 years. Oil prices steadily rose for several years and in July 2008 stood at a record high of $145 per barrel. Further, it plunged to $43 per barrel by end of 2008. There is need to identify appropriate features (factors) explaining the characteristics of oil markets during booming and downturn period. Feature selection can help in identifying the most informative and influential input variables before and after financial crisis. The study used an extended version of MI3 algorithm i.e. (IMI2)-M-2 algorithm together with general regression neural network as forecasting engine to examine the explanatory power of selected features and their contribution in driving oil prices. The study used features selected from proposed methodology for one-month ahead and twelve-month ahead forecast horizon. The forecast from the proposed methodology outperformed in comparison to EIA's STEO estimates. Results shows that reserves and speculations were main players before the crisis and the overall mechanism was broken due to 2008 global financial crisis. The contribution of emerging economy (China) emerged as important variable in explaining the directions of oil prices. EPPI and CPI remain the building blocks before and after crisis while influence of Non-OECD consumption rises after the crisis.
引用
收藏
页码:235 / 240
页数:6
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