Compound Risk Dashboard by Gauntlet Launched

Hi Folks!

Over the last couple of months, the Gauntlet team has been building the Compound Risk Dashboard. We are happy to announce that the Compound Risk Dashboard is now live at!! We are very excited to release this dashboard to the community and start to gather feedback so we can continue to iterate on it and maximize its impact.

A few high level points/reminders:

  • There are two goals for the initial dashboard launch: a) help the community understand our methodology and recommendations and b) help the community understand how Gauntlet’s recommendations help reduce risk and increase capital efficiency for the protocol.
  • Value at Risk (VaR) and Borrow Usage (BU) are topline measures of Risk and Capital Efficiency respectively. VaR conveys capital at risk due to insolvencies and liquidations when markets are under duress (i.e. Black Thursday). We currently compute VaR (based on a measure of protocol insolvency) at the 95th percentile of our simulation runs assuming higher than normal volatility. Borrow Usage is defined in detail in our Dynamic Risk Parameters post .
  • You may notice VaR being $0 for quite a few assets. This is due to the fact that these assets do not represent a significant portion of Compound’s borrow positions and that liquidator agents prefer large positions due to higher guarantees on making a profit. This leads to no liquidations or insolvencies happening in our sims for the smaller assets and positions in Compound.
  • To get an even more in depth understanding of our methodology for running Simulations take a look at our Compound report, in particular, Appendix Section 11. While we have updated the actual data underlying the sims, the methodology remains relevant.

With the Dashboard, we are excited about making the Compound protocol safer and more capital efficient. With the most recent round of parameter changes being proposed in CP066 , we are able to reduce Value at Risk by $8.81M, while increasing Borrow Usage by 1.4 percentage points! Going forward we plan on keeping the dashboard up-to-date with the current state of the Compound protocol and our recommended parameter changes.

This is just the start of our Risk Dashboard product for Compound and we plan on refining it with the community. Feel free to give feedback in the comments here, or directly to us using the Send Feedback button on the dashboard!

Quick Links:
Compound Report:


@shaan and Gauntlet team, congrats on launching this!

Here’s some initial feedback (some of which I shared during the User Study). I recognize that this is your first iteration, and that you will keep making improvements, so these are just high-level observations. Personally, I would consider the Risk Dashboard a success if a routine user, not well-versed in risk-management, can intuitively develop a “feel” for these metrics. So, my feedback is generally geared towards that.

  1. Dashboard “Gauntlet Top Recommendations” section: Your recommendation is to increase the Collateral Factor for every asset therein listed. However, in the next section, Value At Risk (which I believe is at the overall System level) is shown to decrease. This is completely counterintuitive. I think there needs to be a good write-up (or links to documents) that explain this fully and intuitively. Also, if your recommendations aren’t causing the VAR to decrease, then what factors are causing the decrease?

  2. Clicking through to asset-level page: Taking DAI as an example, Gauntlet isn’t recommending any changes to the Collateral Factor, Reserve Factor and Borrow Cap. Yet, there’s a change shown to occur to topline measures VAR and BU due to Gauntlet recommendations. This needs to be explained too.

  3. Terminology cleanup: There’s usage of Aave terminology, including the words “LTV” and “Aave” (in help popup). Also, the word “throughput” isn’t explained anywhere including in the “Market Risk Assessment” document.


Thanks for the feedback @RogerS! As you mentioned we are looking to improve the Dashboard to make it more accessible for more users, and this feedback can help us improve!

For 1. Thanks for pointing this out, there was a bug in our sims which underreported the VaR post recommendation, this is fixed now and is reflected in the updated dashboard (we are now showing an increase in VaR for increasing CFs per CP69). We have heard from the community that more explanation is needed on the specifics of our VaR calculation, we will be publishing more detail in a follow up post that we will link on the dashboard.

For 2. This is due to the nature of Compound in general. Since borrowers can borrow any collateral against any other collateral, changes to params for other assets e.g. USDC can affect others e.g. DAI. This is particularly relevant for stable coins which are very heavily borrowed against a bunch of different collaterals.

For 3. Thanks for catching this, we updated the copy on the dashboard to capture the tradeoffs between risk and capital efficiency (as well as the other typos!)


Thanks for the charts!

Most of the charts appear to be efficiency or borrower focused. I’m coming at using Compound from a little different angle - the protocol I work with lends stables to Compound, and doesn’t do much else here.

So my only dashboard need is to know what are the odds of a cascading liquidation spiral that results in one or more stablecoin pools going insolvent, under different market conditions. It seems like exactly what you are testing from the simulation. Is this being shown in a chart anywhere?

Similarly, I don’t see USDT in the list anywhere? I know it can’t be used as collateral, but lending to it still has a risk profile, that I would love to see.


Thanks @dvf for your feedback. It is great to hear how users are using the dashboard. We don’t have a chart for liquidation cascades, however, you can click on each individual collateral and see the net insolvent % in the Sim Statistics section.

Currently, we only look at risk for assets that can be used as collateral as we are concerned with insolvencies and liquidations for Compound. Nonetheless, this is great feedback as we evolve our simulations to incorporate a deeper understanding of risk.