Interaction between decentralized financial services and the traditional banking system: A comparative analysis

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This paper investigates the interaction between decentralized financial services and the traditional banking system by building VAR models, conducting Granger causality tests, building impulse response functions, and performing variance analysis. To implement the model, banking indicators of the USA, India, and Great Britain were selected: the volume of commercial and industrial loans, interest rate, consumer price index, total liabilities and capital of banks, aggregate deposits, federal funds rate (for the USA), and repo rate (for India). The study examined central bank data of the specified countries from July 2018 to January 2024 with the TVL indicator, which measures the sum of all assets locked in DeFi protocols. The results of the impulse response function (IRF) for countries demonstrate different interactions between TVL and bank indicators. The US response to TVL shocks demonstrates a stimulative monetary policy, with significant Fed rate reductions and increased commercial lending to boost economic activity. In contrast, India’s monetary stimulus, marked by declining repo rates and growth in banking sector liabilities and deposits, aims to enhance economic resilience. The UK, however, adopts a conservative monetary approach, with sharp bank rate increases and mixed lending and deposit responses, prioritizing financial stability. Analysis across these nations highlights different impacts of financial indicators on TVL. In the US, the evolving relationship between TVL and bank indicators reflects the financial system’s complexity. India’s sensitivity to monetary policy, credit conditions, and inflation significantly influences TVL. In the UK, central bank decisions, particularly the bank rate, play a crucial role in financial market dynamics.

Acknowledgment
The authors appreciate the assistance in the preparation of the article provided by the University of Debrecen Program for Scientific Publication and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

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    • Figure 1. Inverse roots of AR characteristic polynomial (USA)
    • Figure 2. Partial display of residual cross-correlations of model variables
    • Figure 3. Response to Cholesky one S.D. (d.f. adjusted) innovations ± 2 S.E. (USA)
    • Figure 4. Analysis of variance distribution for VAR model variables (USA)
    • Figure B1. Inverse roots of AR Characteristic polynomial (India)
    • Figure B2. Inverse roots of AR Characteristic polynomial (UK)
    • Figure C1. Partial display of residual cross-correlations of model variables for India
    • Figure C2. Partial display of residual cross-correlations of model variables for Great Britain
    • Figure F1. Response to Cholesky one S.D. (d.f. adjusted) innovations ± 2 S.E. (India)
    • Figure F2. Response to Cholesky one S.D. (d.f. adjusted) innovations ± 2 S.E. (UK)
    • Figure G1. Analysis of variance decomposition for VAR model variables (India)
    • Figure G2. Analysis of variance decomposition for VAR model variables (the UK)
    • Table 1. Indicators of the traditional banking system used in the study
    • Table 2. Descriptive statistics of incoming (raw) data for the US
    • Table 3. Residual serial correlation LM tests for the USA
    • Table 4. Results of the Granger causality test (dependent variable – TVL) for the USA
    • Table 5. Granger causality test results (variable TVL affects other model variables) for the USA
    • Table A1. Descriptive statistics of input (raw) data for India
    • Table A2. Descriptive statistics of input (raw) data for Great Britain
    • Table D1. Residual serial correlation LM tests (India)
    • Table D2. Residual serial correlation LM tests (UK)
    • Table E1. Granger causality test results (dependent variable – TVL) for India
    • Table E2. Granger causality test results (TVL variable affects other model variables) for India
    • Table E3. Granger causality test results (dependent variable – TVL) for the UK
    • Table E4. Granger causality test results (variable TVL affects other model variables) for the UK
    • Conceptualization
      Serhiy Frolov, Mariia Dykha, Iryna Shalyhina
    • Formal Analysis
      Serhiy Frolov, Vladyslav Hrabar
    • Funding acquisition
      Serhiy Frolov, Mariia Dykha, Vladyslav Hrabar
    • Resources
      Serhiy Frolov, Veronika Fenyves
    • Supervision
      Serhiy Frolov, Mariia Dykha, Iryna Shalyhina
    • Validation
      Serhiy Frolov, Iryna Shalyhina, Veronika Fenyves
    • Writing – review & editing
      Serhiy Frolov, Iryna Shalyhina
    • Data curation
      Maksym Ivasenko, Iryna Shalyhina
    • Investigation
      Maksym Ivasenko, Vladyslav Hrabar, Veronika Fenyves
    • Methodology
      Maksym Ivasenko, Veronika Fenyves
    • Software
      Maksym Ivasenko, Vladyslav Hrabar, Veronika Fenyves
    • Visualization
      Maksym Ivasenko, Vladyslav Hrabar
    • Writing – original draft
      Maksym Ivasenko, Mariia Dykha, Vladyslav Hrabar, Veronika Fenyves
    • Project administration
      Mariia Dykha, Iryna Shalyhina
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