[GIP-15] Engagement with Risk DAO

Summary

RiskDAO is a service DAO spearheaded by B.Protocol and 1kx that focuses on providing a public risk assessment framework and associated audits to DeFi lending and borrowing protocols.

It is proposed that the Gearbox DAO engage with the RiskDAO to:

• Develop mathematical simulation models for stress tests of correlated assets depegging (in particular for sUSD and FRAX stable coins).
• Maintain a public risk monitor dashboard to feature stress test results for all of Gearbox markets and key risk indicators.

EDIT: Item (2) includes stress tests and LTV recommendation also for existing Gearbox markets. And also for future ones.

Motivation

Setting the right risk-related parameters, most notably loan-to-value (LTV, aka collateral factor), is crucial for lending markets solvency and adoption. Aggressive parameters would encourage borrowers adoption at the expense of higher insolvency risk, and conservative parameters would do the opposite.

Gearbox has an in-house Risk Committee, however it is becoming best practice to engage with external risk assessment firms (e.g., see Aave) similarly to how external code auditors are engaged.

Further, the RiskDAO offers additional monitoring tools and simulation models w.r.t the tools the risk committee is offering.

The Risk DAO

At Risk DAO we’ve developed a novel simulation model. We take real world liquidation data of popular assets from centralised exchanges, along with the price trajectory of the assets. We then extrapolate the liquidation sizes and price trajectory to the asset we wish to analyse, and simulate the outcome based on the asset’s available DeFi liquidity. Our approach eliminates most assumptions over user behaviour during market crashes, and makes it more feasible to analyse the risk of a platform prior to its launch, and for multichain lending platforms, where the data for user behaviour is even more sparse.

The simulation model is used to reason about the safety of collateral factor and debt ceiling values (and in the full report we also use it to reason about additional risk parameters).

With the model we run hundreds of thousands of simulations to estimate the expected system insolvency under each scenario. The figure below shows a single simulation run, with price trajectory (in green), market liquidity (red), liquidation volume (yellow) and stability pool size (orange) change over time.

The online dashboard shows the result of daily simulations w.r.t current market conditions, and (potentially) recommends new values for collateral factors.

In addition, the dashboard also tracks key indicators such as dex liquidity, oracle integrity, and alerts when whales open big positions.

Furthermore, it also lets the dev team understand the root cause of collateral factor recommendations, and let them simulate different scenarios.

Below are some snapshots of the system:

Expected liquidations

Market composition - shows the % of each asset out of the collateral and debt

Collateral factor recommendation

Engagement model

Risk DAO is a revenue driven service DAO. The pricing model is 33.3k USDC payment and 1,111,111 GEARs locked for 3 months and then linearly vested over 9 months (similar to other contributors).