Ratio Risk Ratings
In this article, Ratio Finance introduces the concept of “Ratio Risk Ratings” (RRR), which describes our approach towards risk management in our collateralized debt position (CDP) platform. Importantly, the RRR is a financial primitive that will be governed through the Ratio Finance DAO, and one that can be incorporated into other projects to adjust to changes in the marketplace. This article will focus on the conclusions of our risk analysis, to protect the integrity of our product as we launch. A future article will introduce the methodologies behind our analysis.
For all financial instruments risk can take on many forms, but broadly speaking crypto assets are subject to two types of risk: Systematic and Unsystematic risk.
● Systematic risk refers to the risk inherent to the entire market or ecosystem. This type of risk is normally seen as a combination of two or more underlying risks ie. market risk, volatility, credit risk and liquidity risk.
● Unsystematic risk is unique to an asset, asset class, protocol or platform. The prototypical unsystematic risk in the crypto space is the rug-pull, where founders or developers abandon a project and take off with their investors’ money.
It is important to note that systematic risks are in most cases quantifiable whereas unsystematic risks are specific to a sector and are more qualitative than quantitative although variables like idiosyncratic risk are often used to quantify unknown forces. A known weakness of DeFi, that has been identified by Ratio, is the lack of focus on the integration of different risk types. This is important to keep stability in the market, due to the fact that investors tend to leave the ecosystem during crypto winters because of how difficult it is to quantify and manage risk in crypto.
Qualitative and quantitative risks should not be treated in isolation, and drawing a direct parallelism with TradFi, Ratio Protocol has created a rating system incorporating both types of risks where crypto securities are assigned a rating from AAA to C, with AAA being the highest quality asset and C the lowest. There are similar approaches to risk rating in DeFi (see Aave Ark), however Ratio’s approach is not restricted to the currencies used on a specific protocol; instead by creating the Ratio Risk Rating, the goal is to understand and assess the risks on DeFi as a whole, and to bring together both quantitative and qualitative principles to De-risk DeFi.
For the first iteration of Ratio’s Protocol we are going to accept LP tokens formed by two or more stablecoins. By the time of writing, there are around 30+ LPs on Solana’s AMMs where the assets making up these pools are USD-pegged stablecoins; thus, for the security of Ratio’s Protocol, it is necessary to understand the risks of the underlying assets within these derivative assets. In the following we will focus our attention on Stablecoin Risk and the creation of the Stablecoin Risk Rating system.
Stablecoin Risk Rating
Stablecoins are crypto assets designed to be pegged to another asset like fiat money, or any type of exchange-traded commodities, such as synthetic assets. The principal reason for having stablecoins in any ecosystem is to protect investors in times of volatility. However, over the last couple of years there has been a rise in stablecoin usage for remittances and other types of payments.
The goal of any stablecoin is to provide to all market players some certainty of the stability of their investment against volatility and other market forces. Based on this, stablecoins should contain these three pillars: Price Stability, Capital Efficiency and Decentralization. Even though most of the stablecoins in the market aim to incorporate all these features into their model; typically they do not achieve this, and in fact they favor one feature over the other.
Stablecoins can be classified into three categories depending on the mechanics used to reach a 1:1 peg to the US dollar. The categories are:
1. Fiat Backed: Assets are backed by the U.S. dollar and managed by centralized companies, such as USDC (backed by Circle) and USDT (backed by Tether Limited). These assets are supported by the Proof of Reserves mechanism where the total number of tokens in circulation are always fully backed by an equal amount of fiat currency held in reserves.
2. Crypto Backed: The peg to the U.S. dollar is achieved by using crypto assets packed into a series of smart contracts. The golden standard of these assets is DAI (by MakerDao) which keeps its peg through an automated system of smart contracts on the Ethereum blockchain.
3. Algorithmic Backed: These are not backed by fiat or by other crypto currencies, instead smart contracts and efficient algorithms are used to control the price of the token pegged to the dollar. A good example of these is UST from The Terra Protocol, where stability is achieved by controlling the supply of their native token LUNA.
The main objective of this post is to show how Ratio has created a rating system integrating the precision and consistency of a rigorous mathematical approach, and a diligent qualitative analysis of each of the top stablecoins by market capitalization. In the following we show the results of our quantitative and qualitative assessments of the top 20 stablecoins by market capitalization.
These numbers were pulled from coingecko’s API the 12th of April, 2022 at 11pm EST. By the time of writing this piece, the market capitalization of USD stablecoin assets is around $185bn USD with a 24h trading volume of $71bn USD.
Methodology
We allocated a qualitative risk rating for each of the stablecoins based on these 4 features. We then proceeded to analyze assets and protocols and score them accordingly. These scores go from 1 to 5:
The qualitative factors used to analyze stablecoins will be disseminated shortly after Ratio’s mainnet release.
Quantitative Risk Assessment
Methodology
For the listed assets above we used coingecko’s API to download all available opening, high, low, and closing prices over time, as well as their market capitalizations and trading volumes. Afterwards all data was processed, standardized and analyzed using our proprietary python module. Since most stablecoins started trading at different times over the last 3 years, our quantitative analysis concentrates on the last 180 trading days to make all data comparable.
Market Capitalization and Trading Volume
The market capitalization of a crypto asset is the total value of all the coins that have been minted. It is calculated by multiplying the total number of assets in circulation by the market price of a single asset. For most crypto assets, due to the volatility of their prices, market cap can swing dramatically at any given moment. However, since the goal of stablecoins is to have a 1:1 peg to the US dollar, market cap can be viewed as an indication of growth and positioning of the token in the ecosystem.
In the following figure we observe that over the last 6 months, the distribution of market capitalization for USD pegged stablecoins has changed significantly for large market cap assets (USDC was an exception). This is mainly due to the emergence of UST, FRAX and MIM, which are algorithmic based assets with supply dynamics such that, in highly volatile market conditions, the price impact is reduced greatly.
In order to determine the market cap ratings of the 20 stablecoins listed above, we created a system that incorporates two basic elements: temporal stability and trend. Temporal stability refers to the positioning of the stablecoin in the market throughout time, i.e., the temporal variation of market cap since the day the stablecoin was introduced to the market and throughout different market cycles. Trend, on the other hand, refers to the growth or decline of market cap in a shorter time span and is measured in the same way trends are determined for stocks and other stochastic processes.
Another way to confirm the existence, or continuation of a trend in the market is by looking at trading volume. That is the total number of assets that are being traded during a period of time, in our case 24hrs. It is also a good indicator of the sentiment of the market and, since the seminal work of Karpoff in the 80’s, it has been observed to be correlated to fluctuations in price and in turn to liquidity. We rank trading volume in a simple way, by observing the trend of the market and analyzing the change in time.
As with market cap, on the above figure a clear trend has emerged in the market over the last couple of months, the adoption of algorithmic based assets, UST in the plot.
Price Stability and Descriptive Statistics
It is well known that financial returns are at the core of financial analytics, and in the past there has been a lot of controversy in the way they should be calculated (see Attilio Meucci or this blog). Since the focus of this analysis is the temporal behavior of stablecoins, and given the fact that daily yield is a nonstationary random variable, it is reasonable to use simple descriptive statistics for log-returns as a way to compare assets. However, recent academic research indicates that another way to analyze these assets is using realized volatility since this indicator exhibits very high kurtosis which implies a high recurrence of outliers.
As mentioned before, our full analysis encompasses the top 20 stablecoins shown above and in our docs this information is readily available. For simplicity and in order to make this post more readable we focus on USDT, USDC, BUSD, UST, DAI and MIM, and their time series from 2021–12–12 to 2022–04–12. In the following image the log-returns of these assets is shown.
In reality a visual inspection of returns is only helpful to understand what the upper and lower boundaries of the time series are. In order to better understand the behavior of these seemingly random variables, we need to pay attention to the integral across all intervals, that is, the probability distribution function. For UST, DAI and USDC, the probability distribution function is shown in the following plot:
As we can see from the above image, the returns are fairly distributed around 0, which implies that these stablecoins have a successful pegging system. However we observe that all these distributions have a “problem” with fat tails, which means that extreme events away from the center of the distribution play a very large role.
Final Quantitative Risk Rating
To account for all market cap, volume and descriptive statistics results, we use a weighted average system to get a final rank of the stablecoins. This breakdown will be made publicly available soon.
Conclusions, criticism and further work
On this first iteration of the Stablecoin Risk Rating system we have reached a comprehensive rating that takes into account the qualitative and quantitative nuances of the stablecoin ecosystem. We believe that this is by no means a finished work but there are many things that will evolve and improve together with Ratio’s Protocol and Governance. With this work however, we are laying the foundations for interesting developments within the crypto ecosystem that will allow us to achieve our goal of helping investors to De-risk DeFi.
Stay connected via the following links:
Telegram | Telegram Announcements | Twitter | Discord | Medium | Website
//
Ratio Finance’s mission is to enhance liquidity and De-Risk DeFi, to allow both retail and institutional investors to participate in these novel markets.
Our long-term vision is to be the Risk Rating Agency for all of Decentralized Finance.
