Tokens

Credora methodologies are designed to provide a comprehensive risk assessment for a wide range of tokens or assets, capturing unique characteristics and risks inherent to their structures.

The Token Rating Framework is a layered and integrated set of methodologies.

Outputs generated by anchoring methodologies serve as inputs for subsequent methodologies. This approach ensures foundational risk evaluations are incorporated into broader assessments. The structure enables a comprehensive and consistent process.

Token Categories

Tokens rated under the Token Rating Framework are divided into two primary categories, Derivative Tokens and Stablecoins.

Derivative Tokens include Wrapped Tokens, Liquid Staking Tokens, and Liquid Restaking Tokens.

• Wrapped Tokens (WT) are digital assets pegged to the value of another cryptocurrency, often issued on a different blockchain to enable cross-chain interoperability. WTs are backed by collateral held in custody—either via smart contracts or centralized entities. The key risks arise from the security and integrity of this custodial structure.
• Liquid Staking Tokens (LST) represent ownership claims on staked digital assets that support network operations, such as transaction validation, in exchange for rewards. Beyond smart contract custody risks, LSTs expose holders to validator performance and slashing risks.
  Slashing—a protocol-enforced penalty for validator misconduct or downtime—can directly reduce the collateral backing the token.
• Liquid Restaking Tokens (LRT) represent ownership claims on Liquid Staking Tokens (LSTs) that have been re-staked across additional protocols to generate incremental yield. They are backed by staked assets held in smart contract custody. LRTs inherit validator performance and smart contract risks from underlying LSTs while introducing additional layers of complexity. By extending staking across multiple protocols, LRTs compound both reward potential and slashing exposure. Their reliance on interconnected systems heightens sensitivity to validator performance, coordination failures, and governance vulnerabilities.

Stablecoins are tokens designed to maintain a stable value, typically pegged to a fiat currency. Their risk profile depends on the mechanism used to preserve price stability—such as fiat-backed reserves, tokenized cash equivalents, or alternative collateral structures. Key risks include reserve adequacy, peg maintenance, and resilience under stress conditions. Differences in custody models require different analysis, reflecting variations in transparency and counterparty dependence.

• Fiat Backed Stablecoins are fully or predominantly backed by reserves held in cash and cash-equivalent instruments denominated in the reference fiat currency (e.g. Circle’s USDC). Their stability depends on the quality, liquidity, and transparency of these reserves, as well as the operational integrity of the issuing entity.
• Alternative Asset Stablecoins are primarily collateralized by non-fiat assets, typically crypto assets held within collateralized debt positions (CDPs) or comparable structures (e.g. Sky’s USDS). Their stability relies on loan parameters, liquidation mechanisms, and underlying smart contract infrastructure.
• Active Strategy Stablecoins seek to maintain price stability while generating yield through actively managed strategies, often delta-neutral positions, across decentralized or centralized venues (e.g., Ethena’s USDe). Risk exposures arise from strategy execution and the operational soundness of associated trading and custody arrangements.

By grouping tokens into these categories, Credora tailors risk assessment methodologies to the distinct features and challenges associated with each type.

Framework Architecture

The architecture of this framework is specifically designed to accommodate the evaluation of a broad spectrum of tokens, recognizing that risk exposures differ significantly based on the unique structural components and foundational risks associated with each token.

Anchor Methodologies determine the starting point for any token assessment, in Probability of Default (PD) terms. The anchoring methodologies are Asset Quality and Custody Risk.

• Asset Quality assesses the creditworthiness and stability of the reserves, underlying assets, or strategies supporting a token’s value. The following methods are used to quantify Asset Quality, with certain tokens requiring multiple approaches:
  • Proxy PD: Quantifies default probability based on the historical performance of comparable assets and current market- or ratings agency-implied probabilities of default.
  • Monte Carlo Simulation: Applied primarily to Alternative Asset Stablecoins to model the probability distribution of realized losses across loan portfolios.
  • Merton Model: Evaluates the return dynamics of a specific investment strategy and quantifies the probability of a loss event sufficient to impair reserve assets.
• Custody Risk evaluates the reliability of the structures safeguarding underlying assets, encompassing both the financial and operational soundness of centralized custodians or the exploit probability of smart contract custody mechanisms.

Subsequently, the Anchor PD output is passed through a series of modifiers depending on the token type. The entire list of modifiers is summarized as follows:

• Reserve Management evaluates the operational competence and track record of the institution or individuals managing productive assets. Applicable only where reserves are actively managed or yield-generating.
• Regulatory Cover assesses the scope and relevance of licenses held by the issuing entity, and evaluates bankruptcy remoteness as it pertains to both the issuer and custodian structures.
• User Rights examines the clarity and enforceability of user redemption terms, asset segregation provisions, and the recognition of beneficial ownership within the project’s documentation.
• Audit Quality analyzes the frequency and scope of completed smart contract audits, the credibility of the auditors, and the size and structure of any bug bounty programs. Contract complexity impacts the weight of the modifier.
• Contract Maturity assesses the duration and performance history of deployed smart contracts as an indicator of exploit risk and protocol stability.
• Collateralization measures total reserve coverage, including any dedicated insurance vehicles, relative to outstanding token supply. For Alternative Asset and Active Strategy Stablecoins, collateralization is assessed within the Asset Quality framework.
• Reserves Transparency evaluates the mechanisms by which reserve adequacy is demonstrated, including on-chain verification, audit attestations, and third-party assurance processes.
• Peg Track Record evaluates the token’s historical performance in maintaining its intended peg, incorporating quantitative measures of volatility, deviation frequency, and drawdown severity relative to the reference asset.
• Market Adoption analyzes total value locked (TVL) or market capitalization relative to leading peers in the same category, serving as a proxy for market confidence and the incentive to address material deficiencies.
• Governance reviews structures and controls governing protocol upgrades, including documentation of authority, quorum requirements, time locks, and associated security mechanisms.

The framework integrates quantitative modeling with qualitative assessment to produce a Probability of Default (PD) for each token. This output reflects a comprehensive evaluation of the token’s intrinsic and structural risks, incorporating primary loss drivers such as asset quality, custody robustness, operational integrity, and governance effectiveness. The resulting PD provides a standardized measure of credit risk across diverse token types, supporting consistent, data-driven comparisons and informed risk management decisions.