Risk Assessment

Risk Assessment

Risk questions deserve numbers a risk committee would accept — not vibes, and not fabricated precision. This skill decomposes where the risk actually comes from (volatility, concentration, correlation, tail shape) and states plainly what the data cannot show.

Principles

1. Only the supplied history speaks. Every figure is computed from the portfolio's own return window. No assumed correlation matrices, no invented scenarios. If the sample never contained a crisis, say that the numbers understate crisis risk — do not simulate one silently.
2. Concentration is the risk most users cannot see. Ten highly-correlated names are one position in disguise. Effective N and average pairwise correlation are reported next to VaR, always.
3. The tail is not normal. Historical VaR/CVaR and a Cornish–Fisher adjustment are shown together; when skew/kurtosis diverge from normal, the report says which number to trust less.
4. Beta shocks are labeled as linear approximations. A −20% market shock estimate via beta is a floor, not a ceiling — real crashes raise correlations. The report says so verbatim, and omits the shock table entirely when no benchmark is supplied.

Workflow

1. Assemble inputs: per-asset return history (wide CSV), portfolio weights, optional benchmark. If weights don't sum to 1, the harness re-normalizes by gross exposure and discloses it — confirm with the user that gross exposure is what they meant.
2. Run the profile: python scripts/risk_profile.py --returns returns.csv --weights weights.csv [--benchmark bench.csv] --json report.json With no data, demonstrate with --demo.
3. Report in this order: risk level → main risk source → the flag list → core metrics → concentration/diversification → worst historical windows → beta shock estimate (if available). Lead with the diagnosis, not the table.
4. Translate flags for the user (see references/methodology.md for the thresholds and their rationale). "diversification_illusion" matters more to a retail holder than the CVaR decimal.
5. Any recommendation (reduce, hedge, diversify) must be framed as a research observation with its trigger flag attached — never as individualized investment advice. High-risk verdicts require explicit user confirmation before any downstream skill acts on them.
6. Always surface the disclosure lines from the report output. They are part of the deliverable, not boilerplate to trim.

Guardrails

• No fabricated stress scenarios, correlations, or forward-looking loss estimates beyond the labeled linear beta approximation.
• No "safe", "guaranteed", or "risk-free" language, at any risk level.
• Missing inputs degrade honestly (skipped + reason), never silently.
• A "low" risk level describes the sample window, not the future — say so.
• Position-reduction suggestions are observations tied to flags; execution decisions belong to the user.