Gauntlet Compound Risk Dashboard Update

At Gauntlet, we aim to drive understanding and adoption of DeFi. We believe that robust risk management is necessary in order to bring DeFi to the mainstream.

In order to provide more clarity on market risk in Compound, we have made updates to our risk dashboard. Please see the below link for a detailed explanation.

Dashboard Update and VaR Deep Dive

To summarize, we have split our existing VaR into insolvencies (new definition of VaR) and liquidations (Liquidations at Risk or LaR). As a result of this change, VaR will now appear substantially lower and the bulk of capital at risk will appear in LaR.

We hope that the above post helps bring more clarity on risk in Compound. We plan on building more features into the dashboard as we improve our models and refine our recommendation methodology. In addition, we are always keen on receiving feedback from the community. If you have any feedback please feel free to reach out via the Send Feedback button on our dashboard, or directly to

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@pauljlei, The VaR Deep Dive post brings more clarity, and I appreciate you mentioning that you plan to add more explanation and features right on the Dashboard.

If I may, I would like to suggest adding some more color to the discussion on VaR, so users have a better appreciation of the risk that they are dealing with. (The Investopedia link within the Medium post has some explanation, but I would suggest highlighting the following right on the Dashboard.)

  • The assessment of potential loss represents the lowest amount of risk in a range of outcomes. For example, a VaR determination of 95% with 10% asset risk represents an expectation of losing at least 10% one of every 20 days (5% of the days) on average. In this calculation, a loss of 50% still validates the risk assessment.
  • By definition, VaR always has a time period associated with the potential loss. For example, a 1-day VaR is not the same as 1-week VaR. Due to the nature of fast liquidations that occur on Compound, I would assume that your model essentially reflects a 1-day VAR and LAR (and a 1-week VaR may not even make sense). However, this assumption (or alternative assumptions that you are using) need to be explained, because VaR definition is not complete without stating this time period.
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