VaR (Value at Risk) is defined as the expected insolvent amount (defined as excess debt relative to collaterals for any account) in a given day for a protocol under extremely adverse market conditions. We use our models to pre-configure specific risky market scenarios and stress test protocols via simulations leveraging current user positions, asset prices, and varied liquidity conditions. VaR is an estimate of the expected insolvencies for a single day given a severe correlated market downturn.
VaR considers both correlated and uncorrelated asset price movements. The scenarios modeled are selected based on historical max drawdowns in a given market and weighted based on TVL to calculate the market level VaR metric.
In addition, our latest methodology update better accounts for unique risks posed by derivative assets (ie. staked assets) as well as stable coins. We model risks posed by these assets should they lose their price equivalence to the target asset.
LaR is calculated as the expected liquidation amount under extremely adverse market conditions. It represents our estimation on what would happen if the market crashes, etc..
Borrowing Power is a measure of capital efficiency, representing potential upside for the protocol. Borrowing power represents the total available borrows based on collateral supplied to the protocol, calculated as supplies multiplied by the collateral factors (liquidation threshold) of each collateral asset.
Borrowing power should always be considered in the context of borrow usage, which is the extent to which users take advantage of this given capital efficiency. Raising collateral factors for assets with high usage will generate revenues for the protocol and improve the customer experience. However, in cases where users are not leveraging their borrowing power, further increasing it will provide little benefit to the user nor the protocol.
Borrow Usage provides information about how aggressively depositors of collateral borrow against their supply. On a per-user level, Borrow Usage answers the question “of the amount a borrower is able to borrow given their collateral supplied, how much of that are they actually borrowing?” On a per-asset level, it is defined as:
We use Garmann Klass estimator to calculate 28 day volatility over the native asset. Find a detailed explanation of how we calculate and use volatility, here.
Gauntlet addresses “Market Risk.” Decentralized protocols face a number of risks that are more complex than those faced by their centralized counterparts. One of the main reasons for this is that the core function of liquidation, which aims to ensure that assets are always greater than liabilities, involves interactions external to the protocol. Centralized venues simply liquidate underwater collateral themselves without requiring counter-party risk, whereas decentralized protocols usually rely on liquidators competing to provide the service to the protocol. Smart contract audits cover endogenous risk (security risks within a contract) but do not assess market risks that concern exogenous interactions required for proper protocol function. Protocols that allow for supplying and borrowing of cryptoassets are particularly sensitive to price shocks. They require a number of different participants to be sufficiently incentivized to ensure liens are priced and liquidated correctly. Liquidators compete for defaulted collateral and are incentivized to participate by a combination of market forces and discounts provided by the protocol. The primary sources of market risk within decentralized lending protocols usually are:
Shocks to market prices of collateral that cause the contract to become insolvent due to under-collateralization
Loss of liquidity in an external market place, leading to a liquidators being disincentivized to liquidate defaulted collateral
Cascades of liquidations impacting external market prices which in turn lead to further liquidations (i.e. a deflationary spiral)
Other risks are prevalent in DeFi. Several are listed below.
Smart contract risk: logic: errors in the smart contract leading to undesirable outcomes
Governance risk: malicious actors acquiring / using governance power to enact protocol changes
Oracle risk: manipulation of oracle prices can lead to exploitative attacks, causing users and the protocol to lose funds
Custodial risk: although less prevalent in DeFi, centralized organizations may rely on custody solutions
There is always a tradeoff between risk and capital efficiency. Usually, allowing more capital efficiency in the protocol can result in increased market risk. “Too much risk” is dependent on the risk tolerance of the protocol"s stakeholders. Generally, it is useful to compare the risk to the reserves available to backstop potentially losses. Protocols that have large “reserves” to cover potential insolvencies should be able to withstand a greater aggregate amount of insolvencies. Of course, the reserve"s resilience and composition of assets are important factors in determining its ability to withstand market downturns.
After an asset is listed on a lending platform, we analyze borrower behavior and on-chain liquidity data. For our simulations, we usually need to see around 30 days of the asset being listed to form an understanding around volatility, liquidity, and trading data on DEXs over time. This helps prevent the problem of indexing on short-term events. In addition, the newly listed assets will have time to be battle-tested in the market. We always recommend a conservative onboarding approach to lending protocols to ensure that we aren"t exposed to outsized risk from both a mechanism perspective as well as market risk perspective. Once our simulations incorporate the asset, we are able to model how CF increases impact market risk and capital efficiency under a broad range of scenarios, and recommend parameter changes accordingly.
At Gauntlet, we leverage Agent-Based Simulation (ABS) to model tail market events and interactions between different users within DeFi protocols. At a high level, ABS allows a set of "agents" (pieces of code meant to mimic actual user behavior) to make rational actions against DeFi protocols according to some "what-if" market scenario. We run thousands of these scenarios to understand what happens in the case of a catastrophic market event (i.e. a major market crash in crypto).
Our models incorporate the relevant data and analysis in order to simulate our clients" protocols. This may include custom business logic, data pipelines, and analysis of user behavior to drive our agent models.
Since different accounts supply different mixes of collateral assets, each with different collateral factors, the max collateralization ratio will differ between them. Liquidations are triggered when an account"s health score goes below 1, which is partly a function of collateral factors, and partly a function of the total borrow vs collateral supplied.
Hf=Total Borrows in ETH∑CollateraliinETH×LiquidationThresholdi
All else equal, if an account has a lower collateralization ratio, it"s more likely to be liquidated. A similar calculation occurs for CDPs, except that liquidation threshold is calculated in the inverse, known as collateral factor as described by the following equation:
Hf=Total Borrows in ETH×Collateral Factori∑CollateraliinETH
Cascades of liquidations impact external market prices which in turn lead to further liquidations.
To give a concrete example, let"s say there"s collateral asset C, and borrow asset B.
User 1 supplies C and borrows B. Let"s say the price of C falls such that User 1"s account is now liquidatable. A liquidator sees this as an opportunity to profit (to earn the liquidation bonus). The liquidator purchases asset B, pays back the loan, and receives C in return (and may use a flash loan in order to do so). Liquidators usually would immediately sell the asset in order to lock in a profit. This selling pressure on asset C creates further downward pressure on asset C. Asset C falls in price, which causes more accounts to be flagged for liquidation. The process repeats itself.
Eventually, there may not be enough liquidity in the marketplace to absorb liquidations of asset C. Volatility conditions may have become extreme, and spreads on centralized exchanges have widened dramatically. This scenario may also occur in times of high gas fees which make liquidations unprofitable for liquidators. On decentralized exchanges, liquidity may have dried up as well. At this point, the liquidation mechanism (relying on third party liquidators) may fail, because their costs of liquidation (slippage from selling asset C as well as gas costs) may exceed the revenue from liquidation (the liquidation bonus/incentive). When this occurs, accounts can spiral straight to insolvency, which is bad debt for the protocol.
Gauntlet will not conduct simulations using fake data to assess the risk. Simulations do not lend well to this type of listing when there is no prior data. Gauntlet has in the past used borrower distributions from similar assets to assess risk. However, this has been a weak signal given how different each asset"s usage behavior is when actually incorporated into the lending platform. As such, we will not conduct simulation analysis to predict user behavior ahead of an asset"s listing.