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Approach to forecasting the Russian oil industry trade flows under international sanctions using graph neural networks model

Author

Listed:
  • Pavlov, P.

    (Russian Presidential Academy of National Economy and Public Administration (RANEPA), Moscow, Russia)

  • Kosarev, V.

    (Analytical Center for the Government of the Russian Federation, Moscow, Russia)

Abstract

The article considers an approach to forecasting trade flows of the Russian oil industry under international sanctions using a graph neural networks (GNN) model. Among other things, classical factors from the gravity model of international trade, including GDP and multilateral resistance, are considered as factors determining the volume of trade flows. The architecture of the proposed GNN model includes two convolutional layers and a multilayer perceptron. Comtrade data on global trade flows of oil and oil products, including "mirror" customs statistics for the Russian Federation, aggregated to semi-annual intervals for the period 2015-2023, are used to calibrate the model. Estimates of the predictive properties of the proposed GNN model are constructed: it is determined that for the Russian oil products market, the accuracy of forecasts is higher than for the crude oil market, and more accurate forecasts based on the GNN model using gravity trade factors were obtained for countries against which sanctions were not applied. The implications of a hypothetical strengthening of the secondary sanctions regime for the global oil industry for the period up to the end of 2025 were examined.

Suggested Citation

  • Pavlov, P. & Kosarev, V., 2025. "Approach to forecasting the Russian oil industry trade flows under international sanctions using graph neural networks model," Journal of the New Economic Association, New Economic Association, vol. 69(4), pages 36-56.
    • Handle: RePEc:nea:journl:y:2025:i:69:p:36-56
    DOI: 10.31737/22212264_2025_4_36-56
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    References listed on IDEAS

    as
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    Keywords

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    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • F51 - International Economics - - International Relations, National Security, and International Political Economy - - - International Conflicts; Negotiations; Sanctions

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