Market Risk Model Validation Data Readiness Checklist

Role: Principal Quantitative Data Architect with 15+ years of experience in Basel regulatory compliance and market risk stress-testing architectures.

Context

• Regulatory Framework: {{regulatory_mandate}}
• Target Risk Methodology: {{risk_framework_type}}
• Scope of Financial Instruments: {{financial_asset_classes}}
• Ingestion Source Platforms: {{data_source_systems}}
• Historical Time Horizon: {{historical_lookback_period}}
• Cadence of Model Validation: {{validation_frequency}}

Task

Produce an exhaustive, pre-validation data readiness checklist designed to audit, verify, and document data pipelines feeding market risk models ahead of formal regulatory review, ensuring zero unmitigated pipeline failures or unverified data transformations.

Method

1. Inspect upstream ingestion feeds from {{data_source_systems}} for missing trade dates, price holidays, and stale timestamps across {{historical_lookback_period}}.
2. Detail reconciliation checkpoints comparing front-office trade capture pricing against middle-office risk database valuations for {{financial_asset_classes}}.
3. Formulate sanity checks on curve construction, volatility surface interpolation, and corporate action adjustments tailored to {{risk_framework_type}}.
4. Define schema enforcement and type validation rules for trade lifecycle state changes, including amendments, novations, and cancellations.
5. Design stress data coverage checks ensuring past extreme market shock windows are fully populated without interpolation bias.
6. Establish lineage documentation gates linking raw ticks to calculated factor sensitivities according to {{regulatory_mandate}}.
7. Structure sign-off criteria for anomaly resolution, out-of-bounds tolerance thresholds, and rerun procedures on a {{validation_frequency}} basis.

Constraints

• MUST format every deliverable item as a testable, actionable checklist item with an explicit acceptance condition.
• MUST NOT include subjective criteria such as "check if data looks reasonable"; specify exact statistical or boundary thresholds.
• All items must directly evaluate data feed integrity for {{financial_asset_classes}}.
• Prioritize audit defensibility required under {{regulatory_mandate}}.

Output format

Provide a structured checklist organized into the following four markdown sections:

1. Ingestion & Time-Series Completeness Checks (5-6 items)
2. Valuation & Curve Integrity Verification (5-6 items)
3. Lineage & Regulatory Traceability Gates (4-5 items)
4. Failover, Reconciliation & Sign-Off Criteria (4-5 items) Each item must include: [ ] Item Description | Verification Procedure | Pass/Fail Metric | Responsible Owner.

Self-review

• Did I incorporate all asset classes specified in {{financial_asset_classes}}?
• Are the validation rules aligned directly with {{regulatory_mandate}} expectations?
• Is every checklist row accompanied by a clear, numeric or binary Pass/Fail metric?