去中心化借贷协议的最优风险感知利率
Optimal risk-aware interest rates for decentralized lending protocols
AI总结：
本研究提出最优风险感知利率模型，结合流动性风险惩罚项最大化贷方预期财富，线性强度时由Riccati型ODE推导、非线性时用蒙特卡洛估计与深度学习近似，经逐区块数据验证其优于行业标准模型。
摘要：
去中心化借贷协议中的利率由算法设定，并根据流动性的供需进行调整。本研究提出一种最优利率模型，该模型在纳入流动性风险和利率稳定的惩罚项的同时，最大化贷方的预期财富。这一目标使市场双方均受益：它提高了贷方的收益并降低了流动性风险，同时通过更高的利用率间接鼓励借方活动，并通过稳定的借贷成本直接鼓励借方活动。利用率的动态变化使用点过程建模，其强度取决于利率。当强度为线性时，最优利率模型由一组Riccati型常微分方程推导得出；在非线性情况下，我们将其与深度学习技术结合，使用蒙特卡洛估计器对其进行近似。最后，我们使用逐区块数据进行风险调整后的损益分析，以比较行业标准利率模型与基于深度学习的利率模型。
英文摘要：
Interest rates in decentralized lending protocols are set algorithmically and adjust to supply and demand for liquidity. In this study, we propose an optimal interest rate model that maximizes the expected lender wealth while incorporating penalties for liquidity risk and interest rate stabilization. This objective benefits both sides of the market: it improves yield and reduces liquidity risk for lenders, while encouraging borrower activity indirectly through higher utilization and directly through stabilized borrowing costs. The dynamics of the utilization rate are modeled using point processes whose intensities depend on the interest rate. When intensities are linear, the optimal interest rate model is derived from a system of Riccati-type ODEs. In the nonlinear case, we approximate it using a Monte-Carlo estimator coupled with deep learning techniques. Finally, using block-by-block data, we conduct a risk-adjusted profit and loss analysis to compare industry-standard interest rate models to the deep learning-based one.
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