Optimal risk-aware interest rates for decentralized lending protocols

TL;DR

This paper develops an agent-based model to determine optimal interest rates for decentralized lending protocols, using analytical and machine learning methods, and evaluates their performance through risk-adjusted profit analysis.

Contribution

It introduces a novel agent-based modeling framework and combines Riccati equations and deep learning to optimize interest rates in decentralized finance.

Findings

Optimal interest rate model derived from Riccati-type ODEs for linear agent responses.

Monte-Carlo and deep learning methods effectively approximate solutions for nonlinear behaviors.

Calibrated model demonstrates improved risk-adjusted profits over industry-standard models.

Abstract

Decentralized lending protocols within the decentralized finance ecosystem enable the lending and borrowing of crypto-assets without relying on traditional intermediaries. Interest rates in these protocols are set algorithmically and fluctuate according to the supply and demand for liquidity. In this study, we propose an agent-based model tailored to a decentralized lending protocol and determine the optimal interest rate model. When the responses of the agents are linear with respect to the interest rate, the optimal solution is derived from a system of Riccati-type ODEs. For nonlinear behaviors, we propose a Monte-Carlo estimator, coupled with deep learning techniques, to approximate the optimal solution. Finally, after calibrating the model using block-by-block data, we conduct a risk-adjusted profit and loss analysis of the liquidity pool under industry-standard interest rate models and benchmark them against the optimal interest rate model.