Overview
The recent surge in global conflicts in recent times have contributed to heightened market uncertainty. The outbreak of the war in Ukraine of February 2022 shattered the long-standing post-WWII feeling of indefinite peace in Europe, with energy markets clearly reflecting such sentiment [1]. The most recent conflict affecting Israel and Gaza, as well as the increasing tensions between China and Taiwan, highlight the delicate geopolitical landscape. Any disruption of the current global order may cause an aggressive and uncontrolled escalation of unrest, accounting for the potential intervention of international defence alliances in response to perceived threats.
The intersection between this hypothetical scenario and the current challenges of the energy transition may have catastrophic consequences for both energy and carbon markets.
Methods
The following annual averages of monthly time-series data will be object of the econometric study, for the years 1973 to 2022:
• WPRO: World Oil Production Annual (Thousand Barrels Daily) (Source: BP statistics statistical review, 2022).
• SIP: Global Military Expenditure (Source: Stockholm International Peace Research Institute, 2022).
• DXY: ICE US Dollar Index - Index - C (DX-Y.NYB) (Source: Yahoo Finance, 2024).
• WTI: West Texas Intermediate (WTI) (Source: Federal Reserve Bank of St. Louis, 2022).
• WTO: World Total Global Merchandise Exports (Source: World Trade Organization, 2022).
• GOLD: Gold Price per Troy Ounce (USD) (Source: World Gold Council, 2023).
• FEDF: Federal Funds Effective Rate (Source: Federal Reserve Bank of St. Louis, 2022).
• WGDP: World GDP (Source: The World Bank, 2023).
The first differences of the logged series above will be assessed for stationarity through Augmented Dickey-Fuller tests. Multiple Covariate-Augmented Dickey-Fuller tests with results of statistical relevance will be reported, together with trends of relevance found within these time-series. Relevant information about each of these series from testing for seasonal ARIMA terms, harmonic regression terms and seasonal dummy variables will follow. A few linear dynamic models of relevance will be included, upon investigating seasonality, non-stationarity, structural breaks, Granger causality and dynamic relationships between multiple combinations of independent variables, being WTI the main dependent variable under investigation.
Results
The WTO time-series consistently showed a significant correlation with the WTI time-series across various model sets. This correlation remained statistically significant even after incorporating additional economic variables into the models.
A significant negative correlation was observed between trade and global military expenditure as a percentage of the global GDP.
Conclusions
The economic hypothesis that WTO plays as a proxy for the global economy activity will be proved to be statistically significant.
The SIP time series serves as an effective proxy indicator for disruptions in the long-term dynamic relationships between WTO, WPRO, WGDP, and GOLD, with WTI as the dependent variable. ARIMA models will be employed to forecast the impact of conflicts on the next few global economic cycles. That is proving that even regional conflicts can significantly affect global markets for years due to their liquidity and to the disruptions in the globalized supply chain.
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