Finance & models
AuraScore 83/100

Treasury Liquidity Stress Testing and ALM Model Specification

Design a technical quantitative specification for banking asset-liability management and liquidity coverage stress modeling.

Use this template when structuring treasury risk models, interest rate risk in the banking book (IRRBB), or liquidity coverage ratio (LCR) engines for depository institutions. It produces a clear specification for risk engineers and ALM committees.

Template

Role: Head of Asset-Liability Management (ALM) and Treasury Quantitative Risk Modeler for regulated depository institutions.

Context

  • Balance sheet scope: {{institution_balance_sheet}}
  • Interest rate shock curves: {{rate_shock_scenarios}}
  • Liquidity buffer and LCR guidelines: {{liquidity_buffer_standards}}
  • Non-maturity deposit decay assumptions: {{deposit_beta_assumptions}}
  • Derivative hedging instruments: {{derivative_hedging_scope}}
  • Regulatory reporting cadence and audit framework: {{regulatory_reporting_cadence}}

Task

Formulate a rigorous quantitative and systems model specification for an institutional ALM Liquidity Stress Testing and Interest Rate Risk engine, providing precise behavioral modeling, cash outflow projections, and Net Interest Income (NII) sensitivity simulations.

Method

  1. Establish balance sheet aggregation rules, asset/liability bucketing conventions, and tenor maturities based on {{institution_balance_sheet}}.
  2. Formulate the interest rate risk modeling engine simulating Parallel, Steepener, Flattener, and Inverted rate shifts across {{rate_shock_scenarios}}.
  3. Specify the behavioral modeling algorithms for non-maturity deposits, calculating dynamic deposit betas and decay runoff curves via {{deposit_beta_assumptions}}.
  4. Design the 30-day survival horizon and Liquidity Coverage Ratio (LCR) engine incorporating outflow factors mandated by {{liquidity_buffer_standards}}.
  5. Model cash flows, mark-to-market valuations, and duration adjustments for hedge portfolios outlined in {{derivative_hedging_scope}}.
  6. Construct the Net Interest Income (NII) at Risk and Economic Value of Equity (EVE) analytical formulas across a 12-to-36 month forecast horizon.
  7. Detail the data ingestion pipelines, daily liquidity position reconciliation, and governance outputs matching {{regulatory_reporting_cadence}}.

Constraints

  • MUST adhere strictly to Basel III / Dodd-Frank Act Stress Testing (DFAST) quantitative principles.
  • MUST NOT treat non-maturity retail deposits as static contractual liabilities; behavioral decay curves must be dynamically linked to rate cycles.
  • Explicitly define the treatment of High-Quality Liquid Assets (HQLA) Level 1, 2A, and 2B hair-cuts and caps.
  • All cash flow projections must enforce time-bucket conservation of value without unallocated residual balancing.

Output format

Deliver the specification under these exact sections:

  1. Balance Sheet Classification & Behavioral Modeling Architecture
  2. Interest Rate Shock Engine (NII and EVE Formulations)
  3. 30-Day Liquidity Stress & HQLA Buffer Specification
  4. Hedging & Derivative Cash Flow Integration Module
  5. Stress Horizon Analytics & Governance Reporting Framework
  6. Data Schema, Granularity & Audit Trail Controls Total length should be between 1,200 and 1,800 words.

Self-review

  1. Ensure mathematical specifications for both EVE and NII are differentiated and explicitly defined.
  2. Confirm that deposit beta equations factor in competitive rate-lag dynamics under rapid upward rate shocks.
  3. Check that the HQLA calculation correctly enforces Level 2 asset caps and regulatory haircut schedules.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

business-strategy
business-finance
financial-services
treasury
alm
liquidity-risk