Agent-based modelling of lending market risks. A case study: 0VIX

We assess the market risk of the 0VIX lending protocol using a multi-asset agent- based model to simulate ensembles of users subject to price-driven liquidation risk. Our multi-asset methodology shows that the protocol’s systemic risk is small under stress and that enough collateral is always present to underwrite active loans. Our simulations use a wide variety of historical data to model the market volatility and run the agent-based simulation to show that even if all the assets like ETH, BTC and MATIC increase their hourly volatility by more than 10x times, the protocol has less than 1% chance of default.