The current model works by implementing two hard thresholds - Pmin and Pmax.
Whenever the price reaches one of these values, the system automatically intervenes.
The current model has two major problems:
As these values are fixed (Pmin and Pmax) and pre-determined, this can be seen as a trader who placed to infinitely large buy/sell walls at Pmin and Pmax respectively. A smart day-trader would act accordingly and place large buy/sell walls at Pmin+epsilin and Pmax-epsilon respectively. While the price is still stable, this leads to the price never hitting Pmax.
The ROI model relies on the system hitting Pmax. Consider the scenario in which the price is fixed at 1.09, this means there is real usage and people appreciate the coin at a higher value than the promised reserves (0.9) yet during this whole period the ROI is 0 as Pmax isn’t hit and thus there is no redistribution of inflationary coins.
The proposed solution:
We define the base value of XRD to be Pmin.
The system acts as follows
When a buy order is placed at a value v which is higher than Pmin, the system will match this buy order with probability P(v). This probability it’s monotonous, i.e., it grows higher with v. The difference between the buy order and Pmin is given to the bag-holders/node runners as incentive/distribution of inflationary value.
When a sell order is placed at under Pmin, the system will match this order with probability Q(v). Similarly as above, Q(v) should grow as the order is nearer to v=0 and should be equal to 100% when someone attempts to sell at 0.
This proposal can be seen as a generalization of the current stable mechanisms.
When you set P and Q to be 100% at $1 you have a pegged token to $1 similar to Tether.
When you set P to be a step function at 1.1 (e.g., the system only matches buy orders above 1.1 - but matches them all) and Q to be a step function at Pmin (e.g., the system matches all sell orders below Pmin) then you have the current model.
Challenges of the model
The major challenge of this proposal is to select ‘good’ functions for P and Q. As P and Q can be seen as the amount of force the market is required to place in order to move the value of the token - on the one hand you want to allow the price to float freely around 1 without applying too much negative force but on the other hand you want the value to float “far” only if a large force is applied for a long period of time (similar to how a spring works, the more you push on it the denser it becomes and the more contradictive force it returns).
Pmin can be set as a fixed value (0.9) if you still want to ensure a base price.
This system can be seen as an elastic pricing model. It will put pressure on the price to move towards Pmin however if everyone wants to BUY and no one is SELLING then the price can continue to grow (similarly to Dan’s original vision).
As the thresholds are SOFT and not HARD this system can’t be gamed and there is ROI as long as people use the system (e.g., the value is above Pmin).
This model is different from the previously proposed model (a few years back) as it doesn’t provide stability by creating and burning currency, it does it by matching buy and sell orders similarly to how any player in the market would do, similarly to how a country which wants their currency to be worth Pmin will intervene more strongly as their currency moves further away from the goal value (Pmin).