Financial security of Ukraine under martial law: Impact of macroeconomic determinants

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Russia’s open aggression against Ukraine has resulted in significant changes across all sectors of the Ukrainian economy and its financial sphere, including financial security. The paper aims to identify the impact of the primary macroeconomic determinants, i.e., military defense spending, non-performing bank loans, exchange rate, foreign debt, and state (total) reserves, on the financial security of Ukraine under martial law. The canonical correlation analysis is employed to assess the strength of the relationship between the above macroeconomic indicators and the level of the state’s financial security. It was found that the reduction of the state’s financial security level in 2022 was 63.9%, explained exactly by the changes in the above macroeconomic determinants after the start of a full-scale invasion. The study determined the degree of influence of each indicator on Ukraine’s financial security level. An increase in the level of military defense spending, non-performing bank loans, hryvnia’s devaluation, and external debt growth had a direct negative impact on Ukraine’s financial security. At the same time, an upsurge in total reserves had an indirect negative impact (through the external debt growth). The research findings confirm the necessity for effective monitoring and management of the macroeconomic indicators to maintain both Ukraine’s financial security and macro-financial stability in order to ensure its’ sustainable economic development during the postwar recovery period.

Acknowledgment
This research is financially supported by the NATO SPS Program “Security of territorial communities: evidence from the Eastern European countries”.

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    • Figure 1. Indicators for determining the effect of impact on the financial security of the state
    • Figure 2. Eigenvalues of the canonical roots
    • Figure 3. Сanonical analysis
    • Figure 4. Multiple regression analysis
    • Figure 5. Normal probability graph of multiple regression analysis
    • Table 1. Input data for the canonical analysis
    • Conceptualization
      Fedir Zhuravka, Yuriy Petrushenko, Tetiana Kubakh
    • Investigation
      Fedir Zhuravka, Tetiana Kubakh, John Soss
    • Project administration
      Fedir Zhuravka
    • Supervision
      Fedir Zhuravka
    • Writing – review & editing
      Fedir Zhuravka, Stanislaw Alwasiak, John Soss
    • Data curation
      Svitlana Chorna, Yuriy Petrushenko, Tetiana Kubakh
    • Formal Analysis
      Svitlana Chorna, Yuriy Petrushenko, Tetiana Kubakh, Yevgeniya Mordan, John Soss
    • Methodology
      Svitlana Chorna, Stanislaw Alwasiak, Yevgeniya Mordan
    • Software
      Svitlana Chorna, Stanislaw Alwasiak, Tetiana Kubakh, Yevgeniya Mordan
    • Writing – original draft
      Svitlana Chorna, Yevgeniya Mordan
    • Funding acquisition
      Yuriy Petrushenko, Stanislaw Alwasiak
    • Resources
      Yuriy Petrushenko, Tetiana Kubakh, Yevgeniya Mordan, John Soss
    • Validation
      Yuriy Petrushenko, Stanislaw Alwasiak, John Soss
    • Visualization
      Stanislaw Alwasiak, Yevgeniya Mordan, John Soss