Energy price crisis and crash early warning system

Over the past fifty years, numerous energy price crises and energy price crashes have occurred worldwide, defined as cases exceeding two standard deviations (large increases or decreases) from the mean of the Energy Market Pressure Index, which is constructed from energy prices and U.S. inflation. These crises and crashes have caused numerous economic, political, military, social, and environmental issues in countries, depending on their energy import-export structures. The objective of this study is to develop an Early Warning System model that provides sufficient time for preventive measures before energy crises and crashes occur. The 14-variable model, created using Multinomial Logistic Regression analysis and based on monthly data from January 1973 to December 2023, provides the ability to predict the probabilistic changes of crisis or crash cases on a monthly basis from month 1 to month 6, using lagged variable values, and thus enables forecasting of potential crises or crashes in the upcoming 6th month. The empirical results of the model show that these cases are determined by indicators related to energy supply-demand imbalances, economic and financial disruptions, energy investments (drilling activities), and geopolitical risks and expectations. Furthermore, the model is able to predict energy trends (non-crisis/crash months) with 99.5 % accuracy, crises with 75 %, crashes with 60 %, and all cases overall with 98.3 % accuracy. In conclusion, this model, which can anticipate energy crises and crashes in advance, offers a practical and effective tool for governments, energy market actors, and analysts to use in policy development, investment planning, and risk management.