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Shock Propagation in Decentralized Lending Networks: Evidence from the Compound Protocol

Author

Listed:
  • Natkamon Tovanich

    (Institute of Visual Computing & Human-Centered Technology - TU Wien - Vienna University of Technology = Technischen Universität Wien)

  • Stefania Marcassa

    (THEMA - Théorie économique, modélisation et applications - CNRS - Centre National de la Recherche Scientifique - CY - CY Cergy Paris Université)

  • Stefan Kitzler

    (CSHV - Complexity Science Hub Vienna, AIT - Austrian Institute of Technology [Vienna])

  • Christos A. Makridis

    (ASU - Arizona State University [Tempe])

  • Julien Prat

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

Abstract

We study the propagation of financial shocks in decentralized finance (DeFi) using a novel dataset from Compound, a prominent DeFi lending application on Ethereum. Unlike traditional interbank networks, Compound exhibits a bipartite structure in which users lend and borrow via lending pools implemented as smart contracts. We construct daily balance sheets for users and pools from January 2020 to June 2024, model the liability network, and apply the DebtRank algorithm to simulate distress cascades following tokenspecific price shocks. Our findings show that the network topology is the most robust predictor of contagion, outperforming standard financial indicators. We further show that the structure of systemic risk varies over time and across asset types, with stablecoin pools exhibiting more concentrated and persistent vulnerabilities than crypto-asset pools. These results underscore the need for topology-aware risk monitoring in algorithmic credit systems.

Suggested Citation

  • Natkamon Tovanich & Stefania Marcassa & Stefan Kitzler & Christos A. Makridis & Julien Prat, 2025. "Shock Propagation in Decentralized Lending Networks: Evidence from the Compound Protocol," Working Papers hal-05542210, HAL.
    • Handle: RePEc:hal:wpaper:hal-05542210
    Note: View the original document on HAL open archive server: https://hal.science/hal-05542210v1
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