Estimate depeg probability, expected peg deviation, repeg probability, liquidity fragmentation, route risk, balance migration, and collateral stress across stablecoins, chains, venues, and protocols.
Built for institutions that need stablecoin risk monitoring without advisory, operational, or transaction instruction.
Stablecoins are used across exchanges, wallets, payment flows, DeFi protocols, treasuries, and settlement systems. Descriptive data can show current balances, flows, pool composition, and peg deviation. Amonhen adds a forward-looking layer that estimates how peg risk, route risk, liquidity fragmentation, and collateral stress may evolve.
A stablecoin can appear close to peg while liquidity, bridge flows, or collateral conditions are beginning to shift.
The same stablecoin may have different liquidity and route-risk characteristics across Ethereum, Solana, Base, Arbitrum, Tron, and other networks.
Risk teams need forecast confidence, data freshness, coverage status, scenario estimates, and historical model accountability.
Seven typed risk surfaces built on peg, liquidity, route, bridge, balance, and collateral data — each delivered with a confidence score and neutral, informational output.
Estimate the probability that a stablecoin moves outside a defined peg monitoring band over selected horizons.
Estimate the likely range of peg deviation under normal and stressed conditions.
Estimate the probability that a stablecoin returns to its peg range after a deviation event.
Estimate whether liquidity may become fragmented across chains, venues, pools, or routes.
Estimate route-level risk for stablecoin movement across selected chains, venues, or transaction profiles.
Estimate whether stablecoin balances may shift abnormally across exchanges, DEX pools, bridges, issuer-related addresses, lending protocols, or holder categories.
Estimate whether a stablecoin or collateral asset may experience elevated stress in lending, borrowing, or collateralized systems.
Every output describes probability and risk. Amonhen never states which stablecoin to hold, redeem, route, accept, or collateralize.
Forecast coverage for highly liquid stablecoins such as USDC, USDT, PYUSD, FDUSD, and related assets where data quality supports model output.
Risk estimates for assets such as DAI and crvUSD using peg, liquidity, collateral, protocol, and balance-migration data.
Forecasts for more complex structures such as USDe where route, collateral, liquidity, and market-regime context may affect risk estimates.
Forward-looking route-risk and liquidity-fragmentation estimates across chains such as Ethereum, Solana, Base, Arbitrum, Optimism, Tron, and BNB Chain where data coverage supports model output.
When data coverage is insufficient, Amonhen downgrades confidence or suppresses forecast output rather than overstating precision.
Every output is a typed object with a defined horizon, an example estimate, and explicit confidence support.
| Forecast object | Description | Example horizon | Example output | Confidence |
|---|---|---|---|---|
| Depeg probability | Probability of peg deviation outside defined monitoring band | 1h · 24h · 7d · 30d | 1.8% depeg probability | Yes |
| Expected peg deviation | Expected peg deviation range | 1h · 24h · 7d | −0.12% to +0.08% | Yes |
| Repeg probability | Probability of returning to peg range after deviation | 1h · 24h · 7d | Repeg probability: high | Yes |
| Liquidity fragmentation forecast | Probability liquidity becomes fragmented across venues, pools, or chains | 1h · 24h · 7d | Risk status: watch | Yes |
| Route-risk estimate | Forward-looking route-level risk estimate | 1h · 24h | Low-to-watch | Yes |
| Balance migration forecast | Probability of abnormal balance movement across monitored categories | 24h · 7d · 30d | Bridge outflow pressure: moderate | Yes |
| Collateral stress probability | Probability of elevated collateral stress in monitored systems | 24h · 7d · 30d | Stress probability: moderate | Yes |
| Forecast attribution | Key drivers behind forecast output | All horizons | Peg spread · pool imbalance · bridge flows · lending stress · liquidity depth | Yes |
Amonhen presents stablecoin risk as probabilistic monitoring data, not as an instruction to transact, redeem, or route.
Neutral, model-generated scenarios — each with a probability, an expected peg deviation range, an expected duration, a confidence level, and the main drivers behind it.
Monitor stablecoin peg risk, liquidity fragmentation, and confidence changes across supported assets and chains.
Surface stablecoin route-risk estimates and peg-risk context in user-facing experiences without providing operational advice.
Use route-level risk estimates, liquidity-fragmentation forecasts, and stablecoin monitoring outputs as risk-awareness inputs.
Monitor depeg probability, repeg probability, balance migration, and stablecoin exposure risk across supported assets.
Use collateral stress probability, liquidity-fragmentation estimates, and peg-risk forecasts to support risk-monitoring workflows.
Track model-generated risk estimates across peg, route, liquidity, balance migration, and coverage conditions.
Amonhen tracks stablecoin forecasts against realized peg behavior, route conditions, liquidity fragmentation, balance migration, and collateral stress outcomes. The system supports historical forecast archives, calibration metrics, confidence scoring, data freshness awareness, and coverage gating.
Predicted probabilities are measured against realized peg behavior.
A durable record of every risk forecast ever issued.
Every forecast carries a calibrated confidence level.
Estimates are withheld where data coverage is insufficient.
Forecasts expose the recency of their input data.
Each output is traceable to the drivers behind it.
{ "stablecoin": "USDC", "module": "stablecoin_risk_forecasts", "chain": "ethereum", "horizon": "24h", "depeg_probability": 0.018, "expected_peg_deviation": { "lower": -0.0012, "upper": 0.0008 }, "repeg_probability": "high", "liquidity_fragmentation_risk": "low", "route_risk_estimate": "low", "collateral_stress_probability": "low", "confidence": "high", "status": "normal", "disclaimer": "model_generated_informational_risk_estimate" }
Amonhen transforms peg, liquidity, route, bridge, balance migration, and collateral data into probabilistic risk forecasts and confidence-scored monitoring outputs.