The recently passed Approve New DXD Token Model created a DXD Monetary Policy Committee to meet monthly and go over the execution of the DXD token model.
The first meeting will beThursday 2023-01-26T16:00:00Z and the call will take place here: Jitsi Meet. You can add to your calendar here.
- DXD Redemptor stats
- DXD volume & liquidity
- Inverse bond(ing curve)
- Existing smart contracts and other similar DeFi systems
Any and all DXD holders are encouraged to attend. The call will be recorded and slides posted here afterwards.
Hi all. Great first meeting of the DXD Monetary Policy Committee. See video above and click here for all the slides and download trade volume data here.
The call focused on:
- Review of past month of data, from DXD liquidity to buybacks and DXD member redemption balancer
- Any immediate changes to current policy levers
- Discussion on long-term model, implementation of inverse bonds and how to get DXD trading above 70% NAV
Member balancer redemptions
Proposals in grey have not yet gone through. With those counted, DXD supply would contract by 8,616 to 26,455, in return for $6m of treasury assets. This translates to an increase of $68 per DXD share of 70% of Treasury NAV.
On-chain DXD volume
As part of the new token model, DXdao is now providing its own liquidity on Swapr ($300k), which has helped on-chain volume. The bigger increase, however has come from “on-chain extra” which is mostly 1inch limit orders
You can download the full data here.
The only major policy discussion was on the Liquidity Weight Ratio for some treasury assets. As the buybacks and member balancer redemptions occur in stablecoins and ETH, the treasury is gradually increasing its share of “non-core” assets (ENS, SWPR, GNO) increased from 2.53% of treasury assets to 3.17%. At the moment, all of these contribute 25% of their current value to Treasury NAV.
- Lower SWPR liquidity ratio to 10%
- Raise ENS liquidity ratio to 50%
Be on the lookout for a vote on these.
Discussion on long-term model
The second half of the meeting was focused on implementing the long-term model through inverse bonds. Full deck here, some highlights below