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Agents' Behavior and Interest Rate Model Optimization in DeFi Lending

Author

Listed:
  • Charles Bertucci

    (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Louis Bertucci
  • Mathis Gontier Delaunay
  • Olivier Guéant

    (UPCité - Université Paris Cité, LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Matthieu Lesbre

Abstract

ABSTRACT Contrasting sharply with traditional money, bond, and bond futures markets, where interest rates emerge organically from participant interactions, DeFi lending platforms employ rule‐based interest rates that are algorithmically set. Thus, the selection of an effective interest rate model (IRM) is paramount for the success of a lending protocol. This paper investigates the modeling of agents' behaviors on lending platforms and proposes a theoretical framework for formulating optimal IRMs. We show that, under perfect information, an optimal control model with a state constraint generates an optimal interest rate policy that has a shape similar to that of popular markets. Furthermore, we formally analyze interest rate policies based on PID controllers, which work efficiently based on fewer assumptions. Using public data of popular markets on the Ethereum blockchain, we analyze agents' behavior, build a realistic simulation environment, and highlight the main tradeoffs in the design of interest rates for decentralized lending platforms.

Suggested Citation

  • Charles Bertucci & Louis Bertucci & Mathis Gontier Delaunay & Olivier Guéant & Matthieu Lesbre, 2025. "Agents' Behavior and Interest Rate Model Optimization in DeFi Lending," Post-Print hal-05420749, HAL.
    • Handle: RePEc:hal:journl:hal-05420749
    DOI: 10.1111/mafi.70002
    as

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