Stablecoin: Moody’s methodology reveals risks, figures, and mechanisms behind the rating

How Stablecoins Really Work: Structure, Reserves, and Redemption Promise

Stablecoins are often perceived as the simplest and safest tool in the crypto world. In reality, behind the promise of maintaining a stable value relative to a fiat currency lies a complex structure that combines elements of traditional finance, blockchain technology, and risk management.

According to the methodology published by Moody’s on March 17, 2026, a stablecoin can only be rated if its assets are effectively segregated from the rest of the issuer’s balance sheet. This means that the reserves must be exclusively available to meet the holders’ demands, even in the event of the company’s bankruptcy.

The stablecoins analyzed are those that are fully collateralized, meaning they are backed by real assets and convertible into fiat currency “on demand.” Algorithmic stablecoins, which use supply and demand mechanisms without direct collateralization, are not included in this scope.

From an operational standpoint, the functioning is relatively straightforward:

users deposit fiat currency

the issuer “mints” new tokens

the reserves are invested according to a defined policy

in the event of a refund, the tokens are “burned” and the user receives fiat

The settlement generally occurs within 1-2 business days, although there may be variations related to KYC and AML.

However, what seems simple conceals a network of players: depository banks, collateral managers, custodians, and digital platforms. All these elements introduce levels of risk that the rating must capture.

The Core of Evaluation: Credit Quality and Market Risk of Reserves

The first pillar of Moody’s methodology is the analysis of the reserve pool credit quality.

Here, a fundamental concept comes into play: the WAEL (Weighted Average Expected Loss), which is the weighted average expected loss of the portfolio. This indicator is calculated by combining:

weight of each asset in the portfolio

loss rate associated with the asset rating

The analysis is not limited to the average: Moody’s also considers the so-called “weakest link”, which is the asset with the lowest rating. If the difference between the average quality and the worst asset exceeds a certain threshold, the rating can be penalized.

Among the most common assets, we find:

Government Bonds (T-Bill): the risk is tied to the sovereign rating

Bank deposits: exposed to the risk of the custodian bank

Repo (repurchase agreements): treated as cash-like instruments if they meet stringent criteria

In the case of bank deposits, Moody’s also introduces an interesting mechanism: the possible rating uplift of up to 5 notches if there are bank substitution clauses below a certain threshold (for example, A2).

The second pillar is the analysis of market risk, which assesses how much the value of reserves can fluctuate over time.

This is where advance rates come into play, which are haircut percentages applied to assets. Some examples (with active liquidation trigger):

US 1-month T-Bill: up to 99.6%

US 1-year T-Bill: approximately 97.4%

EU 1-year T-Bill: approximately 98.0%

Without liquidation triggers, haircuts become more severe:

US 1-year T-Bill drops to approximately 92.4%

This reflects a key concept: the ability to quickly liquidate assets is crucial for the stability of the stablecoin.

The Quantitative Model: Black-Scholes, Volatility, and Liquidity Stress

One of the most intriguing aspects of the methodology is the use of an advanced quantitative approach to estimate risk.

Moody’s uses a framework based on Black-Scholes, typically employed for financial options. In this context:

the value of the reserves follows a geometric Brownian motion

the loss is modeled as a put option

The simplified formula for loss is:

Loss = max(0, D − MV)

or in percentage: max(0, 1 − MV/D)

Where:

MV = market value of reserves

D = debt (stablecoin in circulation)

This is further complemented by the Liquidity Haircut (LHC), calculated as:

maximum observed bid-ask spread

plus the worst daily drop

This parameter incorporates the effects of a stressed market, including “fire sale” phenomena.

The final Expected Loss becomes:

EL = N(-d₂) − (1 − LHC)/AR × N(-d₁)

The model uses historical data, rolling volatility, and extreme percentiles (typically the 99th percentile) to simulate crisis scenarios.

This approach makes the rating of stablecoins much more akin to that of structured finance products than to a simple crypto evaluation.

Liquidity, Operations, and Technology: The Less Visible but Decisive Risks

In addition to quantitative models, Moody’s pays great attention to operational and structural risks.

Liquidity

Reserves are classified into 5 categories:

Category A: cash held at banks

Category B: short-term government securities

Category C: securities up to 3 months

Category D: available lines of credit

Category E: overnight repo

The most robust stablecoins have portfolios concentrated in categories A and B.

Operational Risk

Operational risk includes:

payment errors

delays in reimbursements

counterparty failure

Moody’s highlights that operational issues can be exacerbated in stress situations, such as during redemption runs (bank run scenario).

Technological Risk

Stablecoins rely on blockchain and smart contracts. The main risks include:

51% attacks

bugs in contracts

network fork

Even though the issuer does not control the blockchain, it is responsible for the choice of infrastructure and risk management.

Regulation, data, and ratings: the future of stablecoins hinges on transparency

The final part of the methodology addresses increasingly central themes for the industry.

Data Quality

Moody’s requires:

complete and updated data

independent audits

transparency on counterparties

The quality of data can directly influence the rating.

Sovereign Risk and Regulation

Since many reserves are invested in government bonds, the rating of stablecoins is often linked to the stability of the reference country.

Additionally, local regulations can:

impose limits on assets

impact liquidity

modify the operational structure

Issuer Support

In some cases, direct support from the issuer can enhance the rating, especially if there is an explicit guarantee or strong financial capacity.

Conclusion: Towards Standards Increasingly Similar to Traditional Finance

Moody’s methodology demonstrates that stablecoins are not merely simple digital tools, but rather complex financial structures.

The rating is not based solely on the promise of stability, but on a combination of:

quality of reserves

liquidation capacity

operational robustness

technological robustness

With the increasing entry of institutional investors, these criteria could become a standard for the entire sector.

In other words, the future of stablecoins could become increasingly less “crypto-native” and more aligned with the stringent models of traditional finance.