Aggregate risk register copula

What it's for

Turns a flat risk register into a dependence-aware financial loss distribution, showing executives which risks dominate the severe portfolio tail.

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.

{
  "correlation_matrix": [
    [
      1,
      0.5,
      0.3
    ],
    [
      0.5,
      1,
      0.4
    ],
    [
      0.3,
      0.4,
      1
    ]
  ],
  "risks": [
    {
      "id": "outage",
      "occurrence_probability": 0.2,
      "severity_coefficient_of_variation": 0.5,
      "severity_mean": 100
    },
    {
      "id": "security",
      "occurrence_probability": 0.1,
      "severity_coefficient_of_variation": 1,
      "severity_mean": 300
    },
    {
      "id": "vendor",
      "occurrence_probability": 0.15,
      "severity_coefficient_of_variation": 0.7,
      "severity_mean": 150
    }
  ],
  "seed": 42,
  "simulation_draws": 5000
}

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": [
    "Occurrence probabilities, severity means/CVs, and latent dependence are calibrated on one stable horizon and loss perimeter with consistent zeros, censoring, inflation, and currency.",
    "The Gaussian copula captures only represented latent dependence and has no asymptotic tail dependence; severe common-cause scenarios require explicit stress additions.",
    "Monte Carlo precision, marginal-family choice, parameter uncertainty, nonstationarity, and omitted risks remain outside the point estimates and must be sensitivity-tested."
  ],
  "decision": "aggregate_risk_register_tail_estimated",
  "method": "gaussian_copula_frequency_severity_risk_register_v1",
  "risk_diagnostics": [
    {
      "occurrence_probability": 0.1,
      "portfolio_cvar_tail_share": 0.5889,
      "risk_id": "security",
      "severity_coefficient_of_variation": 1,
      "severity_mean": 300,
      "simulated_expected_loss": 26.2048,
      "standalone_expected_loss": 30
    },
    {
      "occurrence_probability": 0.15,
      "portfolio_cvar_tail_share": 0.2917,
      "risk_id": "vendor",
      "severity_coefficient_of_variation": 0.7,
      "severity_mean": 150,
      "simulated_expected_loss": 23.2596,
      "standalone_expected_loss": 22.5
    },
    {
      "occurrence_probability": 0.2,
      "portfolio_cvar_tail_share": 0.1195,
      "risk_id": "outage",
      "severity_coefficient_of_variation": 0.5,
      "severity_mean": 100,
      "simulated_expected_loss": 18.8873,
      "standalone_expected_loss": 20
    }
  ],
  "simulation_diagnostics": {
    "confidence_level": 0.95,
    "minimum_correlation_eigenvalue": 0.4819732209,
    "seed": 42,
    "simulation_draws": 5000
  },
  "summary": {

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Freeze one loss horizon/perimeter, calibrated occurrence and severity marginals, a symmetric positive-semidefinite latent dependence matrix, simulation precision, and random seed.
2. 2 Simulate correlated occurrence and independent correlated severity latent draws, apply each marginal, aggregate portfolio loss, and calculate expected loss, VaR, CVaR, dependence amplification, and component tail shares.
3. 3 Stress marginal family, parameter uncertainty, nonstationarity, and explicit common-cause catastrophes because a Gaussian copula has no asymptotic tail dependence.

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.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 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. 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. 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. 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. 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": "aggregate riskregister occurrence and lognormal severity" }
  → finds "aggregate_risk_register_copula"

gitrevio_capability_describe
  { "capability_id": "aggregate_risk_register_copula" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "aggregate_risk_register_copula", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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