The global food prices have surged to historical highs, and there is no consensus on the reasons behind this round of price increases in academia. Based on theoretical analysis, this study uses monthly data from January 2000 to May 2022 and machine learning models to examine the root causes of that period’s global food price surge and global food security situation. The results show that: Firstly, the increase in the supply of US dollars and the rise in oil prices during pandemic are the two most important variables affecting food prices. The unlimited quantitative easing monetary policy of the US dollar is the primary factor driving the global food price surge, and the alternating impact of oil prices and excessive US dollar liquidity are key features of the surge. Secondly, in the context of the global food shortage, the impact of food production reduction and demand growth expectations on food prices will further increase. Thirdly, attention should be paid to potential agricultural import supply chain risks arising from international uncertainty factors such as the ongoing Russia-Ukraine conflict. The Russian-Ukrainian conflict has profoundly impacted the global agricultural supply chain, and crude oil and fertilizers have gradually become the main driving force behind the rise in food prices.

About the Project KAPSARC is analyzing the shifting dynamics of the global gas markets, which have turned upside down during the past five years. North America has emerged as a large potential future LNG exporter while gas demand growth has been slowing down as natural gas gets squeezed between coal and renewables. While the coming years will witness the fastest LNG export capacity expansion ever seen, many questions are raised on the next generation of LNG supply, the impact of low oil and gas prices on supply and demand patterns and how pricing and contractual structure may be affected by both the arrival of U.S. LNG on global gas markets and the desire of Asian buyers for cheaper gasKey PointsAround 150 mtpa of LNG export capacity will come to global gas markets over 2015-20. While Asia seems unlikely now to be able to absorb it all, Europe emerges as a residual market for flexible volumes. The question is, therefore, which outcome(s) in the global LNG market could set the stage for a battle for market share in the European gas market between LNG suppliers and the incumbent pipeline suppliers, most importantly Russia, and how that country could respond to the potential challenge of large quantities of LNG supplies flooding European gas markets? Russia’s gas export strategy in Europe so far has been based on value maximization rather than on protecting its market share. But if increasing LNG supply to Europe becomes an extended threat to Russia’s market share, it may change its position from reactive to proactive and attempt to defend it. Whether a confrontation between Russian gas and LNG takes place and how Russia could respond depends crucially on the build-up of total LNG trade and the appetite of China for LNG. Russia has the advantage of being a low cost producer with ample spare productive capacity and underutilized pipeline capacity to Europe. A low price environment (up to $40/bbl) would actually benefit Russia more than a higher price environment, from a market share perspective, as it can reduce its prices below the variable costs of U.S. LNG and can push U.S. volumes out of the European market. In a higher price environment, U.S. LNG would continue to flow. The competition between Russian gas and U.S. LNG in Europe is also about pricing models, driven on one hand by oil market fundamentals, with some influence from Europe spot markets, and on the other hand driven by the fundamentals of the U.S. gas market and the LNG trade. The geopolitical aspect is also important. While relations between Russia and Europe have become frosty, cheap and abundant Russian gas could potentially help mend commercial ties. However, the tensions between the U.S. and Russia have been increased by the Ukraine situation, the war in Syria and sanctions. The competition between U.S. LNG and Russian pipeline gas in Europe is about more than the pure commercial aspects and will be influenced by the geopolitical standoff of the two powers.

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Ukraine and Russia are major exporters of agricultural commodities, including wheat, corn, sunflower oil, and fertilizer. Together, they account for about 30% of the world's wheat exports, 60% of the world's sunflower oil exports, and 20% of the world's corn exports. The war in Ukraine has disrupted global food supplies, as Ukrainian ports have been blocked and Russian exports have been sanctioned. This has led to rising food prices and concerns about food shortages in some countries. The United Nations has warned that the war could have a "devastating impact" on global food security. Here are some specific examples of how the war in Ukraine has affected global food supplies:

Wheat prices have risen by more than 50% since the start of the war. Sunflower oil prices have doubled. The price of corn has risen by about 30%. The price of fertilizer has risen by more than 100%.

Data Dictionary

Risk managers and traders must understand the mechanisms of volatility spillovers from one market to another due to the implications on profits and stock prices. The use of econometric models such as the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and heterogeneous autoregressive (HAR) to predict volatility spillover effects is limited by their requirement for linearity and their inability to detect rapid changes in volatility within the market. Combining econometric models with machine learning algorithms to generate hybrid models is an effective strategy to improve their performance in detecting volatility spillovers between markets. This study aimed to compare how the HAR model performed relative to the hybrid LSTM-HAR model when predicting volatility spillover effects from the WTI crude oil market to the agricultural stock market in the US. Data was obtained from the FRED database in the period 2015-2025, where monthly global prices were sourced for WTI crude oil, wheat, cotton, corn, and soybeans. The generated HAR model showed the volatility spillover existence from the oil to the soybeans market, which was explained to arise from the disruptions from the global COVID-19 pandemic and the Russia-Ukraine war. However, the HAR model outperformed the LSTM-HAR model for all commodities and was explained to arise from the small dataset available, which was dominated by linear data.