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Systemic Risk in DeFi: A Network-Based Fragility Analysis of TVL Dynamics

Author

Listed:
  • Shiyu Zhang
  • Zining Wang
  • Jin Zheng
  • John Cartlidge

Abstract

Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing decentralized finance (DeFi) ecosystem, numerous studies have analyzed systemic risk through specific channels such as liquidity pressures, leverage mechanisms, smart contract risks, and historical risk events. However, these studies are mostly event-driven or focused on isolated risk channels, paying limited attention to the structural dimension of systemic risk. Overall, this study provides a unified quantitative framework for ecosystem-level analysis and continuous monitoring of systemic risk in DeFi. From a network-based perspective, this paper proposes the DeFi Correlation Fragility Indicator (CFI), constructed from time-varying correlation networks at the protocol category level. The CFI captures ecosystem-wide structural fragility associated with correlation concentration and increasing synchronicity. Furthermore, we define a Risk Contribution Score (RCS) to quantify the marginal contribution of different protocol types to overall systemic risk. By combining the CFI and RCS, the framework enables both the tracking of time-varying systemic risk and identification of structurally important functional modules in risk accumulation and amplification.

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  • Shiyu Zhang & Zining Wang & Jin Zheng & John Cartlidge, 2026. "Systemic Risk in DeFi: A Network-Based Fragility Analysis of TVL Dynamics," Papers 2601.08540, arXiv.org.
    • Handle: RePEc:arx:papers:2601.08540
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    References listed on IDEAS

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    1. Marco Bardoscia & Stefano Battiston & Fabio Caccioli & Guido Caldarelli, 2016. "Pathways towards instability in financial networks," Papers 1602.05883, arXiv.org, revised Feb 2017.
    2. Matteo Aquilina & Giulio Cornelli & Jon Frost & Leonardo Gambacorta, 2025. "Cryptocurrencies and decentralised finance: functions and financial stability implications," BIS Papers, Bank for International Settlements, number 156, May.
    3. Kremer, Manfred & Lo Duca, Marco & Holló, Dániel, 2012. "CISS - a composite indicator of systemic stress in the financial system," Working Paper Series 1426, European Central Bank.
    4. Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
    5. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    6. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    7. Raphael Auer & Stijn Claessens, 2018. "Regulating cryptocurrencies: assessing market reactions," BIS Quarterly Review, Bank for International Settlements, September.
    8. Olivier Ledoit & Michael Wolf, 2017. "Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4349-4388.
    9. repec:ecb:ecbwps:20111426 is not listed on IDEAS
    10. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    11. Natkamon Tovanich & Myriam Kassoul & Simon Weidenholzer & Julien Prat, 2023. "Contagion in Decentralized Lending Protocols: A Case Study of Compound," Post-Print hal-04221228, HAL.
    12. Andrew G. Haldane & Robert M. May, 2011. "Systemic risk in banking ecosystems," Nature, Nature, vol. 469(7330), pages 351-355, January.
    13. Sam M. Werner & Daniel Perez & Lewis Gudgeon & Ariah Klages-Mundt & Dominik Harz & William J. Knottenbelt, 2021. "SoK: Decentralized Finance (DeFi)," Papers 2101.08778, arXiv.org, revised Sep 2022.
    14. Marco Bardoscia & Stefano Battiston & Fabio Caccioli & Guido Caldarelli, 2017. "Pathways towards instability in financial networks," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
    15. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    16. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    17. Wenbin Wu & Kejiang Qian & Alexis Lui & Christopher Jack & Yue Wu & Peter McBurney & Fengxiang He & Bryan Zhang, 2025. "DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks," Papers 2511.22314, arXiv.org.
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    1. Aijie Shu & Wenbin Wu & Gbenga Ibikunle & Fengxiang He, 2026. "DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks," Papers 2602.03981, arXiv.org.

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