On 12 Feb 2025, the HKMA issued Annex 2 providing good practices for behavioural models in IRRBB measurement, based on a review of AIs' approaches post-2023 Banking Turmoil. The guidance emphasises robust model governance, granular NMD segmentation, forward-looking elements, regular performance monitoring, and jurisdiction-specific calibration of third-party models. AIs are advised to adopt these practices to enhance the reliability and accuracy of IRRBB measurements.
This article was generated using SAMS, an AI technology by Timothy Loh LLP.
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Introduction
On 12 Feb 2025, the Hong Kong Monetary Authority (HKMA) issued Annex 2 to its Supervisory Policy Manual module IR-1, providing good practices for authorized institutions (AIs) on using behavioural models to measure interest rate risk in the banking book (IRRBB), following a review of model approaches after the 2023 Banking Turmoil in the US and Europe.
Model Governance and Senior Management Oversight
The HKMA observed that most AIs had established model governance frameworks covering policies, documentation, validation, monitoring, and reporting, but with varying depth. Good practices include well-defined policies and procedures with clear roles across the three lines of defence, regular senior management oversight involving critical challenge of model assumptions and outputs, comprehensive documentation detailing methodologies and limitations, and an effective internal audit function strengthened through targeted recruitment and training to conduct in-depth reviews of behavioural models.
Modelling Approaches
Most AIs met the minimum requirements in SPM IR-1, with some exceeding them. Good practices involve granular segmentation of non-maturity deposits (NMDs) using criteria such as depositor demographics, account features, and relationship stability to capture heterogeneity in depositor behaviour. AIs also incorporated forward-looking elements (e.g., market trends, digitalisation, business strategies) to address limitations of historical data, and implemented scenario-specific modelling of interest rate effects on NMDs under SPM IR-1 shock scenarios to reflect shifting deposit preferences.
Model Performance Monitoring and Validation
While most AIs conducted annual model validation, some with longer intervals were deemed inadequate. Good practices include regular performance monitoring through backtesting and sensitivity analyses against model performance indicators and tolerance thresholds, enabling timely remedial actions. Independent validation was conducted not only at model development but also ongoing, and ad hoc reviews were triggered by specific conditions (e.g., interest rate spikes, competitive shifts) or expert judgment to address dynamic market changes.
Use of Model Outputs and Third-Party Models
Good practices for model output use include comprehensive reporting to senior management incorporating detailed analyses for strategic decision-making, beyond regulatory reporting, to understand risk-opportunity trade-offs. For third-party or group-level models, AIs demonstrated good practices by calibrating models with their own data and adapting them for jurisdiction-specific factors (e.g., regulatory requirements, market conditions), and establishing in-house independent validation teams to rigorously challenge assumptions and integrate these models into the AI's model risk governance framework with regular reviews and feedback protocols.
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