How bilateral trade data becomes
dollar-denominated exposure.
Written for risk professionals evaluating fit before a conversation. Covers the five-stage data pipeline, the three structured outputs, validation approach, and stated limitations, at the level of detail required to assess the model's credibility without disclosing proprietary routing logic.
A bilateral dependency model. Purpose-built for commercial risk analysis.
The Narrows model quantifies the financial exposure of importing countries to disruptions in specific trade corridors. It answers a different question from AIS-based monitoring tools, which measure what is happening to throughput. Narrows measures what a disruption costs: in dollars, by country, by commodity, under a scenario you define.
The core output of the model is a dependency score at the (importer, corridor, commodity) level: the share of that importer's total seaborne supply of a given commodity that transits a given corridor, derived from UN Comtrade bilateral trade data. Applied against annual trade values and a user-defined disruption scenario, these scores produce three structured outputs: value at risk, rerouting penalty, and fleet at risk. Every parameter is substitutable with your own assumptions.
Data quality is explicit. The model distinguishes Tier 1 flows (direct bilateral data where both reporter and partner filed with UN Comtrade) from Tier 3 flows constructed from IEA and BP energy statistics as bilateral proxies. The large majority of value in the current model is Tier 1. Quality tier is disclosed per flow in the full dependency matrix.
Five stages. Each produces a discrete, auditable output.
The model runs in five sequential stages. The input is bilateral trade flow data: reported volumes between specific country pairs across 17 commodity classes, drawn from UN Comtrade 2023 bilateral submissions. The transformation at each stage is where the analytical value sits: not in the underlying data, which is publicly available, but in how flows are matched to maritime corridors, scored for dependency, and converted into scenario-driven outputs.
Three outputs. Each answers a specific professional question.
Every output connects to a downstream use case. The model is designed to produce numbers that slot into existing analytical workflows: accumulation models, trading desk pricing, and credit stress tests. Not numbers that require translation before they can be used.
Dollar-denominated trade exposure by country and commodity under a user-defined disruption. Severity (0–100%) and duration are variable. Capacity sensitivity is adjustable per corridor.
Per-voyage cost overrun by vessel class: detour days, bunker uplift, charter rate differential, and inventory carrying cost. Tagged by audience: underwriter, lender, cargo owner. All rate assumptions substitutable.
Estimated vessel count in-window at event onset, cargo value on water, and portfolio-level rerouting loss across the fleet. The accumulation number for P&I and cargo underwriters.
Strait of Malacca: 100% closure, 14 days.
The following is a live model output, not a hypothetical illustration. Parameters shown are the defaults for this scenario. All can be substituted in a demo session.
Country-level exposure: crude petroleum
Dependency percentage is a fixed output of the dependency matrix. Duration and severity are the variables you control in the simulator.
Values are 14-day simulation outputs from 2023 base data. Dependency percentages are model-derived from bilateral trade flow analysis.
Corridor assignments are cross-checked against three independent aggregated sources.
The model's corridor assignment logic is validated by checking whether predicted chokepoint shares are directionally consistent with observed vessel traffic distributions from independent sources. Validation is described as directional rather than exact: trade data and vessel traffic data do not map to the same time period or granularity.
Weekly vessel call counts at major ports and chokepoint transit volumes derived from aggregated AIS data. Used to validate total traffic distribution across corridors.
Weekly and monthly aggregated tanker flow data from official energy statistics, covering Hormuz, Bab el-Mandeb, Suez, and Malacca. Primary validation for energy commodity assignments.
Annual statistics on container flows and bulk cargo through major corridors from international shipping bodies. Used for annual cross-check of overall corridor share estimates.
What the model is, and is not.
These limitations are stated because the model is intended to support serious risk analysis. A professional who understands where the model is uncertain is better positioned to use it appropriately, and to substitute their own assumptions where they disagree with ours.
Full documentation is available
under NDA for serious counterparties.
This page describes the model at a level sufficient to evaluate its credibility and fit. Full documentation, including corridor definitions, the complete dependency matrix, simulation API specification, and integration notes, is available on request to qualified counterparties under a standard NDA. Request a demo and we will send the documentation pack before the call.