Qualitative and Quantitative Collateral Risk Rating
Simple Aggregate Risk Score (before adding weights, discounts, and premium)
1.92
3.23
2.77
3.69
1.85
Next stabilityFee, Annual %
4.06
6.28
7.1
5.25
3.99
Smart Contract Risk (1-low, 10-high)
2
3
2
3
1
Token Holding Concentration + Market Cap Risk (1- many holders, high market cap, 10-high concentration, low market cap)
3
4
5
7
1
2nd degree
8
6
3
10
10
Counter-party risk (token governance centralization risk, 1-low, 10-high centralization)
1
3
1
3
1
2nd Degree Counter-party Risk (i.e. USDC in their system)
2
4
2
6
1
DEX Rolling 30 days Volume Risk Score (1-high volume, 10-low volume relative to other collaterals)
1
2
7
1
1
2nd degree
2
6
3
2
2
Rolling 60 days Volume Risk Score (1-high volume, 10-low volume, relative to other collaterals)
1
1
1
1
1
2nd degree
2
4
4
5
2
Collateral Volatility Risk Score (1-low volatility, 10-high volatility relative to other collaterals), Normalizing and comparing volatility derived from hourly and yearly volatility of the last day, 90 days, 1y, and 2y volume.
1
3
4
5
1
Liquidity - DEX Slippage Score (1-low slippage, 10- high slippage, relative to other collaterals)
1
2
1
2
1
2nd Degree
Liquidity - CEX Slippage Risk Score (1-low slippage, 10-high slippage, relative to other collaterals)
1
2
1
2
1
2nd Degree
Oracle risk
1
3
2
2
1
Basis of Quantitative Collateral Risk Analysis
Objective: Analyze the Probability Distribution of Collateral-caused Bad Debt Accrual Events.
Purpose: To model the sensitivity of the loss distribution of bad debt accrual caused by specific collateral to Stable Unit consisting of n underlying collaterals to changes in correlation of being the cause of Bad Debt Accrual Events.
Example:
Underlying math behind the modeling of Distribution Probability calculation:
Distribution is built on the principle that collateral may cause occurrence of a bad debt accrual event and, in the case of such event the loss exposure. In this regard, the probability of default represents the likelihood of an insolvency event. The loss given default (LGD) accounts for the unrecovered portion (bad debt) of an asset exposure.
Various approaches exist to model the losses. Some rely on a formula-based approach, but more commonly used are a Monte Carlo simulation approach to derive such a distribution. For each simulation g = 1, …, G, the expected portfolio loss is computed.
If we repeat the random process and obtain a distribution, we can draw a shape shown above for humans to read and make recommendations.
This curve highlights a typical asymmetric profile where small losses have a higher probability of occurrence than greater losses.
At the same time, this chart also points out the distinction between expected loss and unexpected loss.
The latter is obtained as a difference between a given quantile of the distribution (i.e., credit value at risk VaR of debt, (1−α)) and the expected loss.
Stress Testing Individual Collaterals
Stress tests will be performed using the most recent data sets for each collateral and/or before the increase/decrease of tokenDebtLimit.
Data sets include slippage, liquidity, trading volumes, and volatility.
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