Insight into risk on Compound

As we mentioned in our recent post, we’re going to be making more proposals on Compound to improve risk and capital efficiency. Our platform runs daily stress tests on the protocol that allow us to quantify potential risks and continuously monitor them. Today, we’re releasing the beta version of a risk dashboard for Compound, as we’re hoping to get feedback from the community as we work towards a v1 release.

Gauntlet Dashboard

The dashboard provides statistics on the overall health of the protocol:

The safety grade here is the same one we provide to DeFi Pulse and you can find out how we calculate it here, along with more info on what factors contribute to risk. We also provide statistics for each collateral type which align with those factors:

  1. Collateral Safety - This is a result of our daily simulation runs on the protocol. We currently bucket each collateral type (High / Medium / Low) based on the expected contribution to protocol insolvency risk.
  2. Volatility - Simply, this is 7-day average volatility, annualized.
  3. Liquidity Ratio - This measures the amount of liquidity for each market. This compares the real daily trading volume to the maximum amount of collateral that could be liquidated. A liquidity ratio of 349% means that the daily volume of ZRX is 3.49 times the maximum amount of ZRX collateral liquidators would have to sell given a precipitous drop in ZRX price.
  4. Collateralization Ratio - This is the ratio of the amount of collateral posted to the amount of assets borrowed against it. A CR of 372% means that there are $3.72 of ZRX for every $1 of assets borrowed against it.

Using an analytical methodology to manage risk drastically increases both the capital efficiency and safety of DeFi protocols. We’ll continue to suggest parameters on Compound based on our models, but we’re trying to do this with all of you in the community. Hopefully, by giving the community insight into the risks and opportunities we see, we can move forward together to improve the protocol.

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That would be a great data contribution!

I appreciate the dashboard, very informative. The biggest difference I see between ETH and WBTC which are currently set to the same market parameters on compound is the liquidity ratio. You’re scoring ETH with >500% liquidity and WBTC currently listed with 31% liquidity. I guess this makes sense. The problem I see is that decreasing collateral factor on WBTC now that it’s been raised will likely cause some borrowers to get liquidated if they are unable to reduce their borrow ratio in time.

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That’s certanly very interesting. Maybe you can describe a bit more how your platform accounts for on-chain liquidity via AMM, when calculating Liquidity Ratio?

Since the most concerning simulation is about WBTC, so let’s take it for example. The biggest on-chain liquidity pool for WBTC is currently WBTC-ETH pair on sushiswap, which holds over 1 billion liquidity. Certanly, the daily trading volume on that pair recently is about 50 m daily, but since it’s AMM, obviously there is no issue in increasing trading volume 5 times of that. It’s just there’s no daily demand for that volume, but liquidity certanly could accomodate it with low slippage.

Does your simulations account for that? WBTC-DAI or WBTC-USDC pools at AMM might be small, but there’s little trouble in executing WBTC-ETH-DAI or WBTC-ETH-USDC swaps.

I can understand usage of trading volume for centralized exchanges, as it’s hard to evaluate potential liquidity. But for on-chain liquidity daily trading volume might be not the only indicator, as onchain liquidity is proven and available onchain, even if daily volume doesn’t fully utilize it.

So, is approach same for onchain AMM and centralized exchanges when simulation estimates Liquidity ratio for the token?

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