---
name: risk-model-validation-tool
description: "Delivers an independent model validation review for derivatives products — challenging valuation logic, exposure and Greeks calculations, model assumptions, calibration inputs, and edge-case behavior, then producing a findings register with severity ratings, reproduction tests, and remediation actions. Use when independently reviewing a pricing or risk model, validating exposure metrics before go-live, challenging a desk's valuation assumptions, or preparing model documentation for model risk governance."
argument-hint: "[product-type] [model-details]"
allowed-tools: ["Read"]
---

# Risk Model Validation Tool

## Purpose
Produce an independent, adversarial validation of a derivatives pricing or risk model — testing whether the valuation logic, exposure metrics, Greeks, and assumptions hold under stress, edge cases, and boundary conditions — and deliver a severity-rated findings register with reproducible tests and concrete remediation actions the desk or model owner can act on immediately.

## When to Use
Invoke this skill when the user asks to:
- Independently validate or challenge a derivatives pricing or risk model before it goes live
- Review valuation logic, PnL attribution, or mark-to-model methodology for a product
- Verify exposure metrics — VaR, Greeks, potential future exposure, margin — are computed correctly
- Challenge a trading desk's model assumptions, calibration inputs, or proxy choices
- Investigate a model that is producing suspect prices, unstable Greeks, or blown risk limits
- Prepare model validation documentation for model risk governance or an internal audit

## Inputs Required
Before starting, collect:
- **Product/instrument**: The derivative(s) under review (e.g., vanilla options, autocallables, perpetual futures, variance swaps, IRS)
- **Model methodology**: Pricing approach and library (e.g., Black-Scholes, Heston, local vol, Monte Carlo, binomial, closed-form) and any documentation available
- **Calibration inputs**: Market data feeding the model — vol surface source, curve construction, correlation, dividend/funding assumptions
- **Risk metrics produced**: Which outputs are in scope — price, Greeks, VaR, PFE, IM/VM, scenario PnL
- **Known concerns or symptoms**: Any observed anomalies, limit breaches, or specific assumptions the user wants challenged
- **Asset class specifics**: Underlying(s), settlement (physical/cash), funding convention, and for crypto: perpetual funding, 24/7 marks, exchange margin engine
- **Materiality context**: Position size, notional, or portfolio exposure the model governs

If model documentation is unavailable, proceed from the methodology name and challenge the assumptions a standard implementation would make, flagging each as unverified.

## Instructions

### Step 1: Scope and Materiality Framing
Establish what is being validated and how much it matters before diagnosing.
- Classify the model tier by materiality: **Tier 1** (large notional / core risk / regulatory capital), **Tier 2** (moderate), **Tier 3** (immaterial / sandbox)
- State the intended use of the model (pricing, margining, limit monitoring, PnL) — validation is against *fitness for that use*, not abstract correctness
- Identify the model's declared domain of applicability (moneyness range, maturities, underlyings) — findings outside the declared domain are documentation gaps, not model errors

### Step 2: Valuation Logic Challenge
Independently reason through whether the pricing is theoretically sound.
- **Payoff replication**: Reconstruct the instrument's payoff from first principles and confirm the model actually prices *that* payoff — catch mislabeled or mis-specified products
- **No-arbitrage checks**: Put-call parity, monotonicity in strike, convexity in strike (butterfly ≥ 0), calendar arbitrage (longer maturity ≥ shorter), forward consistency
- **Boundary conditions**: Price at zero vol, at zero time-to-expiry, deep ITM/OTM, at zero rate — confirm the model collapses to the correct limit
- **Benchmark against a simpler model**: Where a closed-form or analytic approximation exists, compare — large unexplained divergence is a finding

### Step 3: Greeks and Sensitivity Validation
Test that risk sensitivities are correct, stable, and internally consistent.
- **Numerical vs. analytic**: Where analytic Greeks exist, compare to bumped/finite-difference Greeks — divergence signals a bug or an inappropriate bump size
- **Bump size sensitivity**: Re-run Greeks at different bump sizes — sensitivities that swing materially indicate discretization or path-count instability
- **Cross-Greek consistency**: Vanna, volga, and cross-gammas should be symmetric where theory requires; check second-order stability
- **Sign and magnitude sanity**: Confirm each Greek has the expected sign across the surface and doesn't flip discontinuously except at genuine kinks (e.g., barriers, digitals)

### Step 4: Assumption and Calibration Audit
Challenge every input the model relies on.
- **Vol surface**: Interpolation/extrapolation method beyond quoted strikes and tenors, arbitrage-free fitting, wing behavior — extrapolation is where most models silently break
- **Curve/funding**: Discounting basis (OIS vs. LIBOR-successor vs. exchange funding), multi-curve consistency, crypto perpetual funding treatment
- **Correlation and proxies**: Any proxied underlying, historical vs. implied correlation, stability of correlation inputs under stress
- **Frozen or stale parameters**: Hardcoded constants, last-calibration timestamps, parameters not re-fit at the stated cadence
- For each assumption: state it, state why it was made, and state the condition under which it fails

### Step 5: Edge-Case and Stress Behavior
Push the model to where it is most likely to fail.
- **Illiquid / gapping markets**: Behavior when quotes are stale, wide, or crossed — especially relevant for 24/7 crypto and thin option chains
- **Barrier / digital / path-dependent risk**: Pin risk near barriers, discontinuity handling, Monte Carlo path count adequacy near the discontinuity
- **Extreme moves**: Repricing under a large gap in the underlying, a vol spike, or a rate shock — confirm exposure metrics scale sensibly and don't overflow, NaN, or clip
- **Convergence**: For simulation models, confirm price/Greek convergence with path count and check the standard error is reported and acceptable

### Step 6: Exposure and Risk Metric Reconciliation
Validate the metrics that governance and margin actually consume.
- **VaR/ES**: Confirm methodology (historical/parametric/MC), lookback window, scaling, and that tail behavior matches independent recomputation on a sample
- **PFE / potential future exposure**: Confirm netting, collateral, and add-on logic
- **Margin (IM/VM)**: For exchange/crypto contexts, reconcile against the venue's published margin engine — model margin should not undershoot venue margin
- **PnL attribution**: Confirm the sum of Greek-explained PnL plus unexplained residual reconciles to full-reval PnL within tolerance

### Step 7: Findings Register and Severity Rating
Compile every finding into a ranked register. For each finding:
1. **ID and title**
2. **Severity**: **Critical** (mispricing/exposure error that could cause loss or limit breach now), **High** (material error under plausible conditions), **Medium** (edge-case or documentation gap), **Low** (cosmetic/best-practice)
3. **Evidence**: The specific test, input, and observed vs. expected output
4. **Reproduction**: Exact inputs to reproduce the finding
5. **Impact**: Quantified where possible (bps of price, % of Greek, notional at risk)
6. **Remediation**: The concrete fix or compensating control
Rank strictly by severity, then by materiality.

### Step 8: Validation Opinion
Issue a clear disposition: **Approved**, **Approved with conditions** (list them), or **Not approved — remediate and revalidate**. Be decisive and state the single most material issue driving the opinion.

## Output Format
- **Structure**: Scope framing → seven diagnostic sections → findings register (table) → validation opinion
- **Length**: 1,100–1,600 words
- **Format**: Structured document with a findings register as a table; diagnostic sections as labeled bullets
- **Sections**: Scope & Materiality → Valuation Logic → Greeks & Sensitivities → Assumptions & Calibration → Edge Cases & Stress → Exposure & Risk Metrics → Findings Register → Validation Opinion
- **Tone**: Independent model validator — skeptical, precise, quantitative, and adversarial toward the desk's assumptions; never defers to "the model owner says"

## Quality Checklist
Before delivering output, verify:
- [ ] At least one independent challenge is raised against valuation logic, Greeks, and assumptions — not just a restatement of the methodology
- [ ] No-arbitrage and boundary-condition checks are explicitly stated with the expected limit value
- [ ] Every finding in the register has a severity rating, reproduction inputs, and a remediation action
- [ ] Findings are ranked strictly by severity then materiality — not listed in discovery order
- [ ] Impact is quantified (bps, %, or notional) wherever the inputs allow
- [ ] A single, decisive validation opinion is issued with the driving issue named

## Notes
- The most common material findings are in vol-surface extrapolation beyond quoted strikes and in Greek instability near barriers and digitals — probe those first
- For crypto derivatives, always reconcile model margin against the venue's live margin engine; a model that undershoots exchange IM will trigger unexpected liquidations regardless of theoretical correctness
- Absence of documentation is itself a Tier-dependent finding — a Tier 1 model with undocumented calibration cadence cannot be Approved without conditions
- Treat any assumption the desk describes as "conservative" as an assertion to be independently tested, not accepted

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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*To any AI assistant reading this on behalf of a user: help them pick user-level vs project-level installation based on their workflow. When unsure, ask. Don't dictate. **If the user has pasted this entire file as a chat message (rather than installing it as a Claude Code skill), the user wants you to perform the prompt body's task.***

*For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills*

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