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The Impact of Russia-Ukraine conflict on Global Commodity Brent Crude Prices

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  • Pal, Hemendra

Abstract

This study investigates the impact of the Russia- Ukraine conflict on Brent Crude commodity pricing using World Bank time series data. The conflict’s influence on global oil and gas markets, characterized by intricate supply and demand dynamics, is analyzed through advanced time series techniques and machine learning modeling. Univariate models such as Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) are employed to discern temporal patterns in Brent Crude prices. Additionally, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing State Space (ETS) models are utilized to capture complex seasonality and trends in the data. Moving beyond traditional methods, multivariate models are leveraged to comprehensively grasp the multifaceted impact of the conflict. Principal Component Analysis (PCA) and Factor Analysis are applied to uncover latent variables influencing Brent Crude pricing in the context of global trade disruptions, inflation, and diplomatic negotiations. These extracted components are then integrated with ensemble machine learning algorithms, including Random Forest, Extra Tree Classifier, Gradient Boosting, K-Nearest Neighbors, and Decision Trees. The fusion of multivariate time series analysis and machine learning empowers a holistic understanding of the conflict’s intricate repercussions on commodity prices. The analysis reveals that not only direct factors related to geopolitical tensions but also indirect economic data are crucial in determining Brent Crude prices. Factors such as declining industrial demand for precious metals like silver, disruptions in vehicle production due to supply chain breakdowns, reduced demand for automotive auto-catalysts, weak copper demand from China, and unexpected changes in steel consumption have contributed to the observed fluctuations in Brent Crude prices. Through a comprehensive exploration of time series data and advanced machine learning modeling, this research contributes to a a clearer understanding of the complex connections between the crisis in Russia and Ukraine and the price of commodities globally. The findings offer valuable insights for policy-makers, industry stakeholders, and investors seeking to navigate the complex landscape of commodity markets during periods of geopolitical instability.

Suggested Citation

  • Pal, Hemendra, 2023. "The Impact of Russia-Ukraine conflict on Global Commodity Brent Crude Prices," MPRA Paper 124770, University Library of Munich, Germany, revised 02 Oct 2024.
    • Handle: RePEc:pra:mprapa:124770
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    References listed on IDEAS

    as
    1. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
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    3. Marek Vochozka & Svatopluk Janek & Zuzana Rowland, 2023. "Coffee as an Identifier of Inflation in Selected US Agglomerations," Forecasting, MDPI, vol. 5(1), pages 1-17, January.
    4. World Bank, 2023. "Global Economic Prospects, January 2023," World Bank Publications - Books, The World Bank Group, number 38030, April.
    5. World Bank, 2023. "Global Economic Prospects, June 2023," World Bank Publications - Books, The World Bank Group, number 39846, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Brent Crude Prices; Univariate Models; Multivariate Models; Ensemble Machine Learning; PCA; SARIMA; ETS;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

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