Last updated
Last updated
The first crucial step in our risk methodology is determining the risk class of a given asset. This initial step helps us define certain risk parameters associated with the token, as well as assess its suitability for use as collateral. Tokens with lower volatility are typically better suited for collateral purposes, whereas highly volatile tokens are less ideal. Our analysis focuses on quantifying the following aspects associated with a token's risk:
Smart Contract Risks: This category assesses the technical robustness and security of the token's underlying smart contract. Factors considered include the thoroughness and results of audits performed by reputable firms, the number of days the contract has been live (which can indicate stability and reliability over time), and the total number of transactions executed on the contract (which provides insight into its operational history and potential exposure to risk).
Counterparty Risks: This involves evaluating the potential risk associated with the parties holding and transacting with the token. Key factors include the number of holders (which affects decentralization and the likelihood of concentrated risk), the maximum supply of the token (which indicates the total potential dilution), the circulating supply (which shows how much of the token is can be potentially traded), and permissions within the token's smart contract (which could allow certain parties to perform privileged actions that could introduce risk).
Market Risks: This assesses the financial characteristics of the token that influence its market behavior and potential price stability. We consider the token's volatility, which directly impacts its suitability as collateral (lower volatility means less risk of rapid devaluation); market capitalization, which reflects the overall size and perceived stability of the token; and average trading volume, which indicates liquidity and the ease with which positions can be entered or exited without significant price impact.
These three key-areas are reflected through the following Risk-Factors:
Audit (Smart Contract Risks): Independent audits verify the security of the token and its smart contracts. Lack of audits or negative audit results increase vulnerability to hacks and bugs, raising the risk.
Days (Smart Contract Risks): The time since the smart contract was created. The longer a token has been active, the more likely any vulnerabilities have been identified and resolved, reducing risk. We can easily extract the smart contract deployment date from a Hedera Blockscouter, which indicates when a given smart contract is deployed.
Transactions (Smart Contract Risks): The cumulative number of transactions involving the token since its smart contract deployment. A high volume of transactions indicates a well-tested network, reducing the risk of bugs or vulnerabilities in the contract. A wide range of analytics like this, is gathered from TierBotAI and its Defiants Collection exclusive community. While working with their team to provide real-time APIs that would allow us to automatically update the dashboard, we manually extract these stats from their dApp.
Number of Holders (Counterparty Risks): A large number of token holders suggests broader and more decentralized ownership, lowering the risk of price manipulation or collusion between a few parties. A wide range of analytics like this, is gathered from TierBotAI and its Defiants Collection exclusive community. While working with their team to provide real-time APIs that would allow us to automatically update the dashboard, we manually extract these stats from their dashboard.
Circulating Supply (Counterparty Risks): The number of tokens in circulation. If a significant portion of the supply is held by a few individuals, this increases the risk of market manipulation. Moreover, higher circulating supply held by more users, indicate a lower risk of infinity mint hacks, if the token has a SupplyCap set. A wide range of analytics like this, is gathered from TierBotAI and its Defiants Collection exclusive community. While working with their team to provide real-time APIs that would allow us to automatically update the dashboard, we manually extract these stats from their dApp.
Top-3 Wallets Holdings (Counterparty Risks): The percentage of tokens held by the top three wallets. High concentration in a few wallets increases the risk of collusion or sudden mass selling, impacting the market, and a higher risk of market manipulation actions. A wide range of analytics like this, is gathered from TierBotAI and its Defiants Collection exclusive community. Checking the Holders list from the Defiants dashboard, all the holders are listed, sorted by the amount of tokens they hold.
Permissions Supply, Freeze, Admin Keys (Counterparty Risks): The presence of admin keys or special permissions that allow modifying supply or freezing funds increases the risk of attacks from bad actors. This parameter is also correlated to the Circulating Supply one on the infinite minting attack aspect. We can extract this number from the Hedera Blockscouter page of the Token. This parameter is a multiplier of other Counterparty Risks, and if keys are managed by users, then itβs the worst case. If they are connected to a smart contract or Multi-Sig, then we consider the half. If there are no keys, we consider the maximum amount of Counterparty Risk-Factor sum.
Market Cap (Market Risks): The total market value of the token. A low market cap can indicate higher volatility and vulnerability to market shocks, while a high market cap generally reduces these risks.
Daily Volume (Market Risks): The daily trading volume of the token. Low volume can indicate low liquidity, increasing the risk of slippage and difficulty in converting tokens without significantly impacting the price. The volume is calculated across two different time frames: 1 Month and 3 Months. We then calculate the average of those two values to mitigate risks of sudden volume manipulations.
Liquidity (Market Risks): Liquidity is a critical factor in evaluating the risk profile of a token. High liquidity indicates a robust market where large volumes of the token can be traded without significantly affecting its price. This reduces the risk of slippage and provides stability for traders and investors. Conversely, low liquidity presents several risks, including heightened price volatility, difficulty in executing large trades without impacting the market price, and a higher chance of market manipulation. In lending protocols, low liquidity can lead to delays in liquidations, insufficient collateralization, and greater exposure to systemic risks in the event of rapid market movements. In this case, weβll just consider liquidity available on DEXes, where liquidations may happen more likely.
Volatility (Market Risks): The fluctuation in the tokenβs price over a certain period. High volatility implies a greater risk of significant losses or gains in a short time, raising uncertainty for investors. This is the most relevant factor in our list, and its weight will have a crucial role in the definition of the Confidence Factor, that will influence the Loan-To-Value of the token. Volatility is calculated using the standard deviation of the logarithmic returns. This metric is in line with industry standards used by or.
Each Risk Factor is divided into five risk categories in ascending order (from most to least risky), and each category is assigned an increasing score (the most risky category receives the lowest score, while the least risky receives the highest score). This table extensively shows parameters, categories and scores:
When analyzing the risk profile of a token, we follow this process:
We obtain the data related to each risk factors;
We check which category it falls into;
We assign a score to the category;
We sum the score of each individual category.
Confidence Level Factor. This factor plays a crucial role in defining the Loan-To-Value ratio. Higher CLF values are attributed to less risky tokens, and vice versa.
Caps Risk Profiles. Following the methodology proposed by Gauntlet in Aave's DAO, the definition of Supply Cap and Borrow Cap can take either a conservative or aggressive approach. We reserve the former for riskier tokens and the latter for less risky ones.
Reserves. The reserve factor allocates a share of the protocolβs interests to a collector contract as reserve for the protocol. Moreover, they can be used to cover insolvencies in case of loans not liquidated. Risker tokens have high reserves, less risky ones need less reserves since they are less likely to not be liquidated by someone.
This table shows how we correlate Risk Factor to CLV and Caps Risk Approach Methodology:
What we do, in a nutshell, is:
Considering the Minimum and Maximum Value of Safety Score and Confidence Level Factor Scales
Considering the Min-Max Scaler formula, Inputting the Safety Score of the token, and getting the CLF as output
In the next paragraph, we will explain the approach used to calculate the Caps first and the LTV afterward. We prioritized the Caps because they are then used in the LTV formula.
In DeFi ecosystems populated by tokens with low levels of decentralization, low on-chain liquidity, and high volatility, the Supply Cap is a parameter that plays an extremely important role. It limits the maximum amount of tokens that can be deposited into the dApp. The implementation of a Supply Cap helps mitigate various types of risks, including:
Infinite minting exploits. If a bad actor, on the side of any token listed, succeeded to mint any amount of tokens, deposit it on the Protocol, and borrow against other tokens to generate as profit, Supply Cap wouldnβt allow it.
Market Manipulations (Long-Attacks). Large holder of a token listed, wouldnβt be allowed to deposit his holdings into Sirio and use his high Collateral and Borrowable Amount to manipulate other tokens' price, limiting the maximum amount that he can ever supply and eventually use as collateral to make borrows profitables by manipulating their prices.
We will propose two methods for the Supply Cap calculation: a conservative approach, useful for riskier tokens, and an aggressive approach, that can be used for less risky tokens. The Risk of a token is determined by the Risk-Per-Asset Methodology explained above.
Our conservative recommendation framework is summarized below.
Take the minimum value of :
The token amount required to move DEX pricing by 25% i.e. we consider the slippage in the top-3 pools. We wonβt consider others for an additional safety measure.
30% of the circulating token supply on-chain
Our aggressive recommendation framework is summarized below
Take the minimum value of
10 * 4% aggregate liquidity across all centralized and decentralized sources
70% * average daily volume across all centralized and decentralized sources
50% of the circulating token supply on-chain
For stablecoins, we will just consider an aggressive recommendation approach, by taking 60% of circulating supply.
Borrow caps define the maximum amount of an asset which can be borrowed. Itβs an additional layer of security to the already mentioned Supply Cap, which indirectly already sets up a maximum cap of Borrowable Amount by allowing users to deposit a certain amount of tokens into the Protocol. Itβs a great way to decrease risk exposure of the protocol to different types of attack vectors:
Market Manipulations (Short-Attacks). Users that can borrow, i.e. go short on a given asset, with a lot of collateral available, may easily manipulate the price of a borrowable asset on Sirio. For example, users that supplies enough collateral to borrow 20/30/40% of circulating supply of a given token, may trigger significant price drops, liquidation spirals
Liquidation Spirals & Insolvency Risks. Allowing users to borrow 100m$ of a token with a 200m$ FDV makes all the positions impossible to be liquidated. Moreover, a significant price drop in tokens, may result in the impossibility of liquidating all those positions, bringing the Protocol to insolvency.
Our conservative recommendations take the minimum value of:
Borrow cap cannot exceed the supply cap for the asset
Total token amount of the top 3 wallets on a specific chain
Our aggressive recommendation framework takes the minimum value of:
Total token amount of the top 5 wallets on a specific chain
Borrow cap cannot exceed the supply cap for the asset
Notes:
We do not think borrow caps on stablecoins will significantly impact the risk profile of the protocol. In addition, there is a significant capital efficiency consideration with allowing for high utilization of stablecoins for suppliers and a cap is already applied with the Supply Cap.
The Loan to Value (βLTVβ) ratio defines the maximum amount of assets that can be borrowed with a specific collateral. It is expressed as a percentage (e.g., at LTV=75%, for every 1 HBAR worth of collateral, borrowers will be able to borrow 0.75 HBAR worth of the corresponding currency). Itβs a risk parameter extremely used to reduce volatility risk exposure on tokens deposited as collaterals, moreover itβs a great way to incentivize certain tokens to be collateralized (high LTVs). Our initial approach to the LTV parameter is inspired from the RiskDAO recommendation, that for the first time, considers the Confidence Level Factor as a relevant parameter to define the perfect LTV. The CLV allows us to take in count all Risk-Factors that qualify the Risk-Value of a Token, aside from the solely consideration of the typical parameters such as Liquidity and Volatility. This is the formula used:
Where:
Ξ² is the liquidation bonus
β and ο½ are the available liquidity and the borrow cap of the asset respectively
c is the confidence level factor.
It is important to note that for higher values of c the chances of insolvency decrease, and vice versa.
The total score of a token is called the Safety Score. Calculated as the sum of the Risk-Factors scores: The Risk Value helps indicate three important values:
π is the annualized price volatility calculated with the Parkinson Formula:
