This document shows different evaluations performed to analyze the behavior of the MOC (Money On Chain) model during the March 13th Bitcoin crash.
We run simulations against actual data with different parameters, which showed the robustness of the model under extremely unfavorable conditions.
In addition to these simulations on BTC values, we present the performance of the model with real data from the main RSK blockchain.
This simulation uses a series of real values of the BTC price, covering from 2014–02–01 to 2020–04–02.
In some periods, the value of the moving average exceeds the value of BTC, while in other periods it is the other way around. We always use the minimum of both prices to calculate the coverage needed to sustain the model.
The following figure shows the BTC value along with its moving average.
In a bull run, with real market conditions, it is expected that new collateral will be added to the system, since people will seek the free leverage of the BPro, therefore increasing the coverage.
However, in our simulations, we assume that no new collateral is added to increase the stress of the simulation and the chance of liquidation.
These are the parameters used in the simulation:
We observe that even under very negative conditions, the coverage did not drop below two in the March 13th Bitcoin crash, not requiring selling BPro with a discount (a mechanism designed to provide an incentive for adding collateral to the system, which reduces the risk of BPro liquidation).
This simulation is focused on the interval from 2020/03/02 to 2020/04/02 to have closer data visualization.
It was made with the same parameters as the previous one.
In the following analysis, the actual data series taken from the main blockchain were used.
The date range used is from 2020/02/13 6:40:50 (RSK block 2111334) to 2020/03/17 10:47:02 (RSK block 2197734) when the BTC price dropped about 50%.
Graphs are shown with the BTC block value and the moving average, with the overall coverage and objective of the system.
Under a worst-case scenario, the model showed robustness and sustained the Bitcoin crash by maintaining coverage out of risk at all times.
Mechanisms designed to handle extreme conditions were not triggered in the BTC crash.