risk-model-config

name: risk-model-config description: Invoke before analyze_portfolio_risk when the portfolio contains illiquid, private, or real estate holdings that lack yfinance market data. Queries finance-graph for asset metadata and valuation history, then assembles the illiquid_overrides parameter for portfolio-analytics risk analysis. user-invocable: false

risk-model-config — Illiquid Risk Overlay Configuration

This skill runs before portfolio-analytics.analyze_portfolio_risk when the portfolio contains illiquid or private holdings that lack yfinance market data.

Purpose

The portfolio-analytics risk engine drops symbols without yfinance data and renormalizes weights, causing risk to be computed on only the liquid slice. This skill bridges the gap by:

1. Querying finance-graph for illiquid asset metadata (PE, real estate, private holdings)
2. Estimating volatility and equity correlation for each illiquid position
3. Assembling the illiquid_overrides parameter for analyze_portfolio_risk

MCP Tool Map

• Asset metadata: finance-graph.list_assets
• Valuation history: finance-graph.list_valuation_observations
• Portfolio context (liquid weights): portfolio-analytics.get_condensed_portfolio_state
• Risk analysis target: portfolio-analytics.analyze_portfolio_risk

Execution Workflow

1. Identify Illiquid Positions

Query finance-graph for positions that will not have yfinance data:

finance-graph.list_assets(asset_class_code="securities")
finance-graph.list_assets(asset_class_code="real_estate")

Filter to positions with data_source="MANUAL" or no yfinance-compatible ticker.

2. Gather Valuation History

For each illiquid position, check for valuation time series:

finance-graph.list_valuation_observations(asset_id=<id>)

If a position has >= 8 quarterly observations, compute realized annual volatility from the observation series.

3. Check Valuation Staleness

For each illiquid position, after querying finance-graph.list_valuation_observations(asset_id=...):

• Compute days_since_valuation = (today - most_recent_observation.valuation_date).days
• Classify with asset-class-specific thresholds:

• Vol uplift: stale_mark → +5pp to annual_vol, very_stale_mark → +10pp
• Include in override: "valuation_age_days": N, "mark_staleness": "stale_mark" or "very_stale_mark"
• The risk engine passes these fields through to output and generates alerts for very_stale_mark

4. Apply Category Defaults

For positions without sufficient valuation history, use these defaults:

These are conservative estimates. Override with realized data when available.

5. Compute Position Weights

For each illiquid position, compute its weight relative to total portfolio value (including liquid positions). Use portfolio-analytics.get_condensed_portfolio_state or ghostfolio.portfolio(operation="summary") to obtain total portfolio value.

6. Assemble illiquid_overrides

Build the list of override dicts:

[
  {
    "symbol": "PVTCO",
    "weight": 0.10,
    "annual_vol": 0.30,
    "rho_equity": 0.65,
    "liquidity_discount": 0.15,
    "valuation_age_days": 200,
    "mark_staleness": "stale_mark"
  }
]

The valuation_age_days and mark_staleness fields are optional. When present, the risk engine passes them through to the overlay output and generates alerts for very_stale_mark positions.

7. Pass to Risk Analysis (First Pass)

Call portfolio-analytics.analyze_portfolio_risk with the assembled overrides, passing through any scope parameters from the calling skill:

portfolio-analytics.analyze_portfolio_risk(
  risk_model="auto",
  illiquid_overrides=<assembled list>,
  include_fx_risk=true
)

8. Regime-Conditional Adjustments (Second Pass)

After the first analyze_portfolio_risk call, check vol_regime.current_regime:

When regime is elevated or crisis:

1. Stress ρ: stressed_rho = min(base_rho × multiplier, 0.95)
2. For India PE positions, stress vol using market data:
   • market-intel-direct.get_symbol_history(symbol="USDINR=X", range="3mo") → compute inr_ratio = std(last_21d) / std(full_63d)
   • market-intel-direct.get_symbol_history(symbol="^NSEI", range="3mo") → compute nifty_ratio = std(last_21d) / std(full_63d)
   • stressed_vol = base_vol × max(1.0, max(inr_ratio, nifty_ratio))
3. Re-run analyze_portfolio_risk(illiquid_overrides=stressed_overrides) — second pass

When stressed, present both:

• Base ES: illiquid overlay with category defaults
• Stressed ES: illiquid overlay with regime-adjusted parameters

Output

This skill does not produce user-facing output. Its output is the illiquid_overrides parameter (base and optionally stressed) passed to analyze_portfolio_risk. The risk tool's illiquid_overlay section in its response contains the overlay results.

Constraints

• Never fabricate valuation data. If finance-graph has no data for a position, use category defaults and flag the assumption.
• The liquidity discount adjusts weight (valuation haircut), NOT variance.
• Cross-correlations between illiquid positions use a one-factor model (ρ_ij = ρ_i × ρ_j) unless the agent has explicit information to override.
• This skill is advisory — it configures the risk model but does not change portfolio positions.