Stellar testnet build · four engines certification pending, one live Explorer ↗
Parallar
How it works The rule The proof Architecture Risk engines Partnerships On testnet
Documentation Open the app
The risk-engine estate
Machines that price risk, built to show their work
Every number checkable· Every model versioned· Every assumption disclosed
Before Parallar sells protection on a tokenised asset, three questions need answers: how exposed is this asset, what is fair protection worth, and what does the market think? Each answer is computed by a certified engine, and every number comes with a cryptographic receipt showing exactly how it was made.
How exposed is this asset?
An engine reads the obligated party's own numbers the way a seasoned underwriter would: leverage, earnings, cash flow, payment history. From these it scores the chance of a covered event. A built-in safety rule stops it from ever calling anyone "too safe to fail."
Fundamentals engine
What is fair protection worth?
A second engine turns that risk score into a fair annual price for protection, priced the same way the professional credit-insurance market does it. Safer asset, cheaper protection; riskier asset, dearer.
CDS pricing engine
What does the market think?
Where a real market quote exists for the same name, a third engine reads it as a second opinion and blends it in. The recipe for the blend is published, never improvised.
Market benchmark + blend engines
The five engines
EngineWhat it doesStatusAccuracy check
Fundamentals
v2 · cohort-anchored
 Reads the obligated party's own numbers and scores the risk, anchored to decades of real-world default history by credit grade. A good story can tilt the score but never rewrite the base rate. built · cert pending exact
CDS pricer
route A
 Turns the risk score into the fair price of protection, using the same two-sided arithmetic the global credit-default-swap market runs on. The asset's terms are inputs; the method never bends per deal. built · cert pending exact
Market benchmark
credit triangle
 Reads a real, timestamped market quote and converts it into the market's own implied view of the risk. Kept clearly separate from the model's view: a second opinion, honestly labeled as one. built · cert pending 1 in 1bn
Blend
§2(d) · 70 / 30 pinned
 Combines the model's view and the market's view into one number, with the mixing recipe (70% model / 30% market) published and fixed. An asset with no market quote keeps its model score. Nothing is invented. built · cert pending exact
Portfolio
live engine
 The engine already running behind the live testnet: it turns the whole covered book, across coverage types, into a premium, an expected loss, and a worst-case reserve. These are the numbers a settlement actually pays on. live on testnet matched twin
The safety floor, visibly
Chance of a covered event in a year, before and after the anchor
A model looking only at financial statements can talk itself into absurd confidence: odds of 1-in-a-million for a spotless balance sheet. The anchor forbids that: nothing scores better than the safest credit grade's real historical base rate. Ordinary and troubled assets are barely moved.
Archetypev1 · raw logisticv2 · cohort-anchored
investment grade0.012 bp2.02 bp
strong0.13 bp5.14 bp
mid1.370%1.359%
stressed99.99%99.98%
near-default99.998%99.996%
The same engine, four assets
What protection costs
Protection on a safe asset costs almost nothing per year. On a troubled one, the annual price explodes, and the expected years of premium collapse, because an asset close to failing won't be paying premiums for long. That is exactly how the professional market behaves, which is the point: the engine reproduces it, checkably.
AssetFair spreadRPV01
investment grade / strong< 1 bp4.63 y
mid75 bp4.47 y
stressed40,832 bp0.15 y
near-default43,856 bp0.14 y
One proof. Two chains.
Every settlement boils down to a small sealed receipt, 208 bytes plus a proof, that any chain can check for itself. Stellar checks it and pays out. An Ethereum-style chain checks the same receipt and publishes the verified facts, so lending markets there can rely on them without trusting anyone's word, including ours.
No bridge. No wrapped assets. No oracle committee. Read-only verification mirroring: the mirror is a fact oracle whose facts are proofs.
journal.v1 · 208 B frozen layout · sha256-keyed on both chains · 5-input Groth16 layout shared verbatim
What the receipts do and don't prove
A receipt proves the math was done correctly, by the exact published version of the engine, on the exact data provided. It does not prove the data itself is true (that is a separate, signed attestation), and it does not prove the model is wise.
The example calibrations on this page are realistic illustrations, not fitted to live data. Every prediction is recorded so its accuracy can be checked against reality later. The engines shown as "certification pending" are finished, tested code awaiting their final cryptographic registration.
For practitioners: PD/LGD scorecard with a grade × sector through-the-cycle cohort anchor · ISDA-lineage two-leg CDS pricing on a flat hazard · credit-triangle market-implied PD (risk-neutral, labeled) · pinned log-odds blend with a disclosed Q→P haircut · bit-exact fixed-point↔float parity gates · full specifications in the documentation.
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See a price with its receipt attached
For underwriters Open the app