IDEAS home Printed from https://ideas.repec.org/p/zbw/esprep/341616.html

Navier-Stokes-Inspired Global Liquidity-Flow and Systemic-Stress Modelling: A Nondimensional Macro-Financial Stress-Testing Framework

Author

Listed:
  • Gondauri, Davit

Abstract

This study develops a global Navier-Stokes-inspired macro-financial stress-testing framework for measuring systemic stress as a nondimensional flow-pressure-friction-shock-cycle-closure process rather than as a collection of isolated macro-financial indicators. The framework does not claim that financial liquidity is a physical fluid, does not solve the mathematical Navier-Stokes problem and does not establish a universal crisis-prediction law. Instead, it translates the structural logic of velocity, acceleration, pressure gradients, diffusion/friction, external forcing and closure into an empirically auditable economic analogue. The empirical architecture is organized around a 100-country annual panel for 2010-2024, yielding 1,500 country-year observations, supplemented by a World aggregate anchor, macro-regional aggregation, rolling validation windows, robustness layers, external-target validation, Monte Carlo uncertainty analysis and policy counterfactuals. Systemic stress is constructed from normalized stress-direction components and interpreted jointly with liquidity resilience. The central balance decomposes stress into liquidity velocity weakness, liquidity acceleration, macro-financial pressure gradients, liquidity friction/diffusion, stochastic shocks, Fourier GDP-cycle forcing and a restricted hidden-adjustment term epsilon(t). The closure equation is modelled through observable or proxy-observable state variables, including lagged closure, shadow-economy pressure, external transfers, geopolitical shock, fiscal buffers, foreign aid/support and economic inertia, preventing epsilon(t) from operating as an unrestricted residual plug. The results show stable weighting robustness, coherent closure signs, strong rolling one-year-ahead validation performance over 2017-2024, favorable benchmark comparison against ARIMA, VAR, GARCH and composite-index baselines, statistically coherent fixed-effects associations, plausible external-target validation, residual stability, robustness under perturbation and interpretable Monte Carlo and policy-counterfactual effects. The contribution is methodological and empirical: a transparent, reproducible and scientifically bounded stress-testing architecture for global liquidity-flow diagnostics and systemic-risk interpretation.

Suggested Citation

  • Gondauri, Davit, 2026. "Navier-Stokes-Inspired Global Liquidity-Flow and Systemic-Stress Modelling: A Nondimensional Macro-Financial Stress-Testing Framework," EconStor Preprints 341616, ZBW - Leibniz Information Centre for Economics.
  • Handle: RePEc:zbw:esprep:341616
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/341616/1/Navier-Stokes-Gondauri.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Song, Wei-Ling & Uzmanoglu, Cihan, 2016. "TARP announcement, bank health, and borrowers’ credit risk," Journal of Financial Stability, Elsevier, vol. 22(C), pages 22-32.
    2. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    3. Chen, Sichong, 2013. "How do leverage ratios affect bank share performance during financial crises: The Japanese experience of the late 1990s," Journal of the Japanese and International Economies, Elsevier, vol. 30(C), pages 1-18.
    4. Chang, Carolyn W. & Li, Xiaodan & Lin, Edward M.H. & Yu, Min-Teh, 2018. "Systemic risk, interconnectedness, and non-core activities in Taiwan insurance industry," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 273-284.
    5. Xin Huang & Hao Zhou & Haibin Zhu, 2012. "Systemic Risk Contributions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 55-83, October.
    6. Rishika Khetan & Varda Sardana & Shubham Singhania & Jagvinder Singh, 2025. "Financial stability through a global perspective: an in-depth integrative review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(8), pages 2912-2929, August.
    7. Hamdi Jbir & Cornel Oros & Alexandra Popescu, 2024. "Macroprudential policy and financial system stability: an aggregate study," Empirical Economics, Springer, vol. 66(5), pages 1941-1973, May.
    8. Schaeck, K. & Silva Buston, C.F. & Wagner, W.B., 2013. "The Two Faces of Interbank Correlation," Discussion Paper 2013-077, Tilburg University, Center for Economic Research.
    9. Yang, Xite & Zhang, Qin & Liu, Haiyue & Liu, Zihan & Tao, Qiufan & Lai, Yongzeng & Huang, Linya, 2024. "Economic policy uncertainty, macroeconomic shocks, and systemic risk: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    10. Haibei Chen & Xianglian Zhao, 2023. "Use intention of green financial security intelligence service based on UTAUT," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10709-10742, October.
    11. Roland Füss & Daniel Ruf, 2018. "Office Market Interconnectedness and Systemic Risk Exposure," Working Papers on Finance 1830, University of St. Gallen, School of Finance.
    12. van de Leur, Michiel C.W. & Lucas, André & Seeger, Norman J., 2017. "Network, market, and book-based systemic risk rankings," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 84-90.
    13. Armstrong, Christopher & Nicoletti, Allison & Zhou, Frank S., 2022. "Executive stock options and systemic risk," Journal of Financial Economics, Elsevier, vol. 146(1), pages 256-276.
    14. Greenwood, Robin & Landier, Augustin & Thesmar, David, 2015. "Vulnerable banks," Journal of Financial Economics, Elsevier, vol. 115(3), pages 471-485.
    15. Arnold, M., 2017. "The impact of central clearing on banks’ lending discipline," Journal of Financial Markets, Elsevier, vol. 36(C), pages 91-114.
    16. Chatziantoniou, Ioannis & Colak, Gonul & Filippidis, Michail & Filis, George & Tzouvanas, Panagiotis, 2025. "Systemic risk and oil price volatility shocks," Journal of Financial Stability, Elsevier, vol. 79(C).
    17. Wan-Shin Mo & Shun-Chuan Chuang, 2026. "Time-Varying Stock Return Spillovers and Their Determinants: Evidence from Developed Economies," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 54(1), pages 3-17, March.
    18. Ayadi, Rym & Bongini, Paola & Casu, Barbara & Cucinelli, Doriana, 2025. "The origin of financial instability and systemic risk: Do bank business models matter?," Journal of Financial Stability, Elsevier, vol. 78(C).
    19. Sangwon Suh & Inwon Jang & Misun Ahn, 2013. "A Simple Method For Measuring Systemic Risk Using Credit Default Swap Market Data," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 38(4), pages 75-100, December.
    20. Xisong Jin, 2018. "How much does book value data tell us about systemic risk and its interactions with the macroeconomy? A Luxembourg empirical evaluation," BCL working papers 118, Central Bank of Luxembourg.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • 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
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • F30 - International Economics - - International Finance - - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:esprep:341616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.