Estimate systemic portfolio contagion
Estimate nonlinear financial distress propagation across a directed portfolio network under coherent joint shocks, separating direct from contagion loss and reporting CVaR, convergence, spectral instability, tail attribution, and finite-round loss influence.
What it's for
Shows executives and investors where one platform, product or portfolio-company shock can become a system-wide capital loss—and which risk is direct versus transmitted through the network.
What you give it
Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| convergence_tolerance | number ≥ 1e-12, ≤ 0.1 | Your calibration | Optional |
| edges | array of objects (4 fields) ≥ 1 item | Evidence | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_cvar_loss | number ≥ 0 | Your calibration | Optional |
| maximum_expected_contagion_loss | number ≥ 0 | Your calibration | Optional |
| maximum_propagation_rounds | integer ≥ 1, ≤ 500 | Your calibration | Optional |
| maximum_spectral_radius | number ≥ 0 | Your calibration | Optional |
| nodes | array of objects (2 fields) ≥ 2 items | Evidence | Yes |
| scenario_shocks | array of objects (4 fields) ≥ 1 item | Evidence | Yes |
| scenarios | array of objects (2 fields) ≥ 2 items | Evidence | Yes |
| tail_probability | number ≥ 0.001, ≤ 0.5 | Your calibration | Optional |
Each edges
record
| Field | Type | Required |
|---|---|---|
| id | string (non-empty) | Yes |
| source_node_id | string (non-empty) | Yes |
| target_node_id | string (non-empty) | Yes |
| transmission_fraction | number (≥ 0, ≤ 1) | Yes |
{
"edges": [
{
"id": "platform-product",
"source_node_id": "platform",
"target_node_id": "product",
"transmission_fraction": 0.5
},
{
"id": "product-company",
"source_node_id": "product",
"target_node_id": "company",
"transmission_fraction": 0.5
}
],
"maximum_cvar_loss": 350,
"maximum_expected_contagion_loss": 100,
"maximum_spectral_radius": 0.9,
"nodes": [
{
"id": "platform",
"value_at_risk": 100
},
{
"id": "product",
"value_at_risk": 200
},
{
"id": "company",
"value_at_risk": 300
}
],
"scenario_shocks": [
{
"id": "platform-direct",
"node_id": "platform",
"scenario_id": "platform-shock",
"shock_fraction": 1
},
{
"id": "company-direct",
"node_id": "company",
"scenario_id": "company-shock",
"shock_fraction": 1Truncated for display — the full payload is 58 lines.
What you get back
This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.
{
"assumptions": [
"Nodes are aggregate projects, products, platforms or portfolio companies with finance-owned value at risk; directed weights measure the additional target distress fraction caused by a unit of newly propagated source distress on one horizon.",
"Initial shocks are coherent joint scenarios and propagation is nonlinear, additive by newly transmitted distress, capped at full node severity and iterated through feedback loops. Omitted edges, recovery, timing, capacity rationing, liquidity and common causes can materially change contagion.",
"The transmission spectral radius is a local linear feedback diagnostic; a value above a governed limit signals amplification potential but is not a probability, causal proof, forecast date or guarantee of self-sustaining failure. Finite-round influence is likewise model-conditional.",
"Tail contributions reconcile modeled portfolio loss and identify structural exposure, not causal blame or company/employee quality. The result supports risk review and stress design, never automatic divestment, funding withdrawal, project cancellation, investigation or employment action."
],
"configuration": {
"convergence_tolerance": 1e-8,
"edge_orientation": "source distress transmits a fraction into target distress",
"maximum_cvar_loss": 350,
"maximum_expected_contagion_loss": 100,
"maximum_propagation_rounds": 50,
"maximum_spectral_radius": 0.9,
"severity_cap": 1,
"tail_probability": 0.5
},
"decision": "systemic_portfolio_contagion_clears_governed_risk_gates",
"method": "weighted_nonlinear_portfolio_distress_contagion_v1",
"node_diagnostics": [
{
"expected_direct_loss": 150,
"expected_final_loss": 187.5,
"expected_incoming_contagion_loss": 37.5,
"finite_round_linear_loss_amplification_if_shocked": 1,
"node_id": "company",
"tail_conditional_final_loss": 300,
"tail_loss_share": 1,
"value_at_risk": 300
},
{
"expected_direct_loss": 50,
"expected_final_loss": 50,
"expected_incoming_contagion_loss": 0,
"finite_round_linear_loss_amplification_if_shocked": 2.75,
"node_id": "platform",
"tail_conditional_final_loss": 0,
"tail_loss_share": 0,
"value_at_risk": 100
},
{
"expected_direct_loss": 0,
"expected_final_loss": 50,
"expected_incoming_contagion_loss": 50,Truncated for display — the full payload is 96 lines.
How it works
Network & dependency analysis — Trace how load, failure and knowledge propagate through a graph of teams, services or components.
- 1 Represent projects, products, platforms or portfolio companies as value-at-risk nodes; estimate directed transmission fractions from a distressed source to each target; and freeze coherent scenario-specific initial shocks.
- 2 Propagate only newly transmitted severity through the weighted network, cap cumulative node distress at one, iterate to convergence, and retain direct and contagion loss separately rather than double counting initial shocks.
- 3 Compute expected and tail loss, exact tail-node reconciliation, transmission spectral radius and finite-round marginal loss influence; abstain on non-convergence and apply independently governed CVaR, contagion-loss and structural-instability gates.
Before you trust it
Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.
Assumptions & guardrails
- Nodes, edges, direction, time window, missing-link policy, and aggregation boundary represent the coordination or dependency mechanism of interest.
- Nodes are aggregate economic units, value-at-risk is comparable, edge direction and fractions describe financial distress transmission rather than ordinary dependency, joint scenarios contain common shocks, and the bounded propagation mechanism is adequate for the review horizon.
- Structural centrality, fragility, or clustering describes a modeled graph and must not be interpreted as intent, guilt, or personal value.
- Network loss and influence are conditional on submitted topology and shock mechanics; they are not default probabilities, causal proof, security attribution, valuation advice, individual rankings or authority to divest, cancel or reorganize.
Minimum evidence
- nodes: at least 2 rows/items
- edges: at least 1 rows/items
- scenarios: at least 2 rows/items
- scenario_shocks: at least 1 rows/items
How to validate it
Validate on held-out periods or aggregate units, perturb edge definitions and missing links, and report sensitivity to graph construction before using structural rankings.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- tenant-calibrated nonlinear distress-propagation graph preserving common causes, feedback, missing links, node recovery or saturation, and one comparable financial-loss horizon across projects, products, platforms or portfolio companies
- network and valuation perimeter, edge mechanism and evidence standard, scenario and shock design, tail probability, CVaR/contagion/spectral gates, propagation horizon, convergence tolerance, missing-edge stress policy, and finance/risk/investment ownership
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
- 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
- 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.
Call it from your AI
You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.
gitrevio_capabilities_search
{ "q": "estimate nonlinear financial distress propagation across" }
→ finds "estimate_systemic_portfolio_contagion"
gitrevio_capability_describe
{ "capability_id": "estimate_systemic_portfolio_contagion" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "estimate_systemic_portfolio_contagion", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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