The ideal way moving forward was to have a single, and common C/R ratio of 150% for all the collaterals used to mint cAssets. Still, we concluded that there should be a dynamic C/R for every cAsset-collateral pair on further research.
We have defined a Risk parameter module where the collateral and the minted cAsset pairs are accessed on specific lengths to determine the C/R ratio accordingly.
The parameters are set for the collateral and the cAsset minted. The riskier the cAsset-Collateral ratio, the higher the C/R ratio for the pair.
The risk parameters for the cAssets currently are as follows(Values calculated for the last 90 days):
- Intraday worst drawdown for the last 90 days
- Volatility ( Standard deviation of the daily logarithmic value )
- Average 24 hr volume
- Minimum 7 day average 24 hr volume
Let’s break it down to understand it:
A token like $ATOM would score higher in the above parameters as it has more liquidity and good volumes as compared to something which is more volatile and has a bit less liquidity like $CMDX, the C/R ratio for keeping $ATOM as collateral would be less than that of keeping $CMDX as collateral as per the parameter scores. So, People would be able to borrow more capital efficiently for $ATOM compared to $CMDX as per the parameterized scoring mechanism.
The same would be applicable for the cAssets minted. Each one would be parameterized and given a score to how much C/R ratio would be needed to mint these assets.
This would enable a fluid C/R float as per the riskiness of an asset and protect user funds from unnecessary liquidation.
Open points to discuss upon:
- Should we adopt a dynamic C/R ratio model?
- What parameters other than those mentioned above should be considered while defining the C/R ratio?
- Any possible drawbacks while implementing the dynamic C/R ratio model?
- Time frames to be considered for backtesting the data?
I would like to hear your opinions & ideas and further discuss the topic.