Vault as a credit instrument

TL;DR

This paper develops a formal framework to quantify and analyze credit risk in DeFi lending vaults, incorporating unique on-chain risk channels and providing practical estimation methods.

Contribution

It introduces a three-level decomposition of vault risk, identifying novel on-chain loss channels and creating an implementable credit risk measurement architecture.

Findings

Identifies six unique on-chain risk channels affecting vaults.

Develops a measurable vault credit score based on risk components.

Provides estimation strategies and stress testing for vault risk assessment.

Abstract

We derive five tractable credit risk metrics for DeFi lending vault depositors, grounded in a formal three level decomposition of vault risk into mechanical loss channels (Level 1), governance quality (Level 2) and smart contract code integrity (Level 3). For Level 1, we show that six structural features of onchain execution (oracle execution divergence, endogenous recovery, full information run dynamics, timelock constrained governance, oracle manipulation and congestion driven liquidation failure) break canonical TradFi analogies and generate depositor loss channels absent from standard credit frameworks. Vault credit risk metrics translate these channels into measurable risk components which are aggregated into a vault credit score. The empirical contribution is an implementable estimation architecture for credit risk metrics, including required onchain data, identification strategies for core parameters, partial identification bounds and a coherent stress scenario methodology. The results have direct implications for vault risk management and for minimum transparency standards necessary for depositor risk assessment.