Stress testing
Don't just ask what a given shock does to you. Solve it backwards: what would it take to breach a limit? Find that scenario in a modeled, controlled environment - before the market finds it for you.
How it works: prescribed, historical & idiosyncratic scenarios · reverse stress · concurrent multi-factor.
Stress testing
Beyond canned, single-factor scenarios: reverse stress, correlated multi-factor shocks where economic variables drive market moves and market moves feed back into the economy, and a broad library of regulatory and historical scenarios.
Every figure shown is illustrative and represents a hypothetical bank - not any actual institution.
Solve for the scenario that produces a defined adverse outcome - surfacing vulnerabilities that scenario-driven stress can miss.
Multiple correlated shocks at once - economic variables driving market moves, and market moves feeding back into the economy - with institution-defined correlations and dependencies.
Crypto price, stablecoin-depeg, collateral-haircut, and funding-cascade shocks as a first-class factor category, with rate-equivalent calibration.
A dollar liquidity move - deposit run, contingent draws, and wholesale-funding rollover - sized in dollars and translated to LCR, NSFR, and survival horizon.
Physical and transition climate scenarios (NGFS, Fed climate analysis) plus operational shocks - cyber, vendor failure - in the same enterprise stress space.
Eight historical replays (1994 rate shock, 1998 LTCM, 2008 GFC, 2020 COVID, 2022 hikes, and more) plus CCAR? Severely Adverse, Adverse, and Baseline.
Scenario generation
Deterministic, macro-linked, stochastic, and user-defined scenarios from one generator, with two-way feedback between markets and the economy. Every scenario flows to NII?, EVE, liquidity, capital, and FTP? in the same run.
12 standard rate shocks - parallel, steepener, flattener, and short-rate moves spanning −300 to +300 bps - generated every run. Deterministic digital-asset shocks (crypto price, stablecoin depeg, collateral haircut) are on deck.
GDP, unemployment, HPI, and CRE paths drive the market factors - and market dislocations feed back into the macro path.
Distributional simulation across thousands of paths - percentile bands and tail metrics, not just point estimates.
Board and ad-hoc scenarios on demand - a generator slot is always reserved, with no code change required.
Calibrated from real episodes - 1994, 1998 LTCM, 2008 GFC, 2020 COVID, 2022 hikes - applied to today’s balance sheet.
Solve for the scenario that produces a defined adverse outcome, then inspect the exact path that gets there.
Stress & sensitivity
Start from the company's base position, then apply a parallel rate shock or a named stress scenario (digital-asset price shocks are on deck) - and watch capital, liquidity, and performance move together, each change shown explicitly.
Scenario
Base case
Parallel rate shock?
0 bp
Digital-asset price shock?On deck
0%
≈ 0 bp rate-equivalent (comparable magnitude)
Shock applied
| Metric | Base | Δ rates | Δ crypto | Shocked |
|---|---|---|---|---|
| Capital | ||||
| CET1 ratio? | 12.4% | - | - | 12.4% |
| Leverage ratio | 9.2% | - | - | 9.2% |
| AOCI → capital (net OCI) | $0M | - | - | $0M |
| Liquidity | ||||
| LCR? | 118% | - | - | 118% |
| NSFR? | 121% | - | - | 121% |
| HQLA · market value | $7.40B | - | - | $7.40B |
| Survival horizon · stressed TTF | 41 days | - | - | 41 days |
| Metric | Base | Δ rates | Δ crypto | Shocked |
|---|---|---|---|---|
| Performance | ||||
| Net interest income · 12-mo | $570M | - | - | $570M |
| EVE? · Δ vs base | 0.0% | - | - | 0.0% |
| Digital-asset stress On deck | ||||
| Crypto-collateralized loan loss | $0M | - | $0M | $0M |
| Digital-asset deposit flow | $0.0B | - | $0.0B | $0.0B |
| Digital-asset RWA add | $0M | - | $0M | $0M |
| Digital-asset holdings · mark to market | $0M | - | $0M | $0M |
| Combined earnings | ||||
| Total earnings · 12-mo | $720M | - | - | $720M |
At base, every metric equals its position above. NSFR is structural (carrying-value / factor based) and rate-insensitive; LCR moves because HQLA? is marked to market.
Worked example · reverse stress testing
Reverse stress testing runs the platform backward: you define the failure, and Bulls-Eye solves for the mildest scenario that gets you there - across rate and market factors at once, with digital-asset factors on deck.
You define the failure
CET1 falls below 7.0%
Regulatory minimum + conservation buffer
Any platform metric - capital, liquidity, earnings - can define the adverse outcome to solve backward from.
Bulls-Eye solves the scenario
Where it breaks
CET1 · +300
At the threshold - 0 bp of headroom
Binding driver
AOCI marks on AFS securities + funding migration
Recovery & resolution
Stress feeds the recovery & resolution framework directly - the same platform that surfaces a breach quantifies the options to recover, and the playbook to resolve if recovery fails.
Capital, liquidity, profitability, and asset-quality indicators with green / amber / red trigger bands - a breach escalates automatically, tagged with the stressed metric that tripped it.
Capital raises, asset sales, deleveraging, funding actions, and dividend cuts - each sized for its capital and liquidity benefit, time to execute, and feasibility under the prevailing stress.
Resolution-plan inputs - critical operations, separability, and a least-cost resolution estimate - produced from the same shared dataset, so the recovery and resolution narrative ties to the numbers.
Ready to see it live?
A guided demonstration using public filings and disclosed assumptions. Institution-specific risk estimates depend on data availability; production analysis requires more detailed inputs.
Walk through scenarios, assumption changes and attribution in the platform.
Architecture review for risk, technology, and model-risk leadership - model-risk governance and integration design.
Capital, liquidity, and reporting capability review for chief risk officers and regulatory-affairs teams.
About us
Bulls-Eye Solutions builds the enterprise platform for financial institutions - one platform that unifies risk, capital, liquidity, funds transfer pricing and attribution on one shared dataset, with optimization on deck. Founded by veterans of top-tier bank treasury and risk management, we pair the platform with experienced treasury and risk practitioners, delivered as Risk-as-a-Service.