2015-09-29-奥纬咨询-Stress_Testing_102_6页_151kb

STRESS TESTING 102: Summary of PPNR Modelling

Core Content

This document explores the rise of PPNR (Pre-Provision Net Revenue) modelling in the context of financial stress testing, highlighting its significance and the challenges in its implementation. It outlines a strategic framework for banks to adopt PPNR modelling as a critical component of their financial planning and risk management processes.

Main Points

What is PPNR Modelling?

PPNR modelling is an advanced method for forecasting Profit and Loss (P&L) and Balance Sheet items. It differs from traditional forecasting in the following ways:

• Driver Analysis: PPNR items are broken down into underlying drivers using a "driver tree," which enhances the robustness of forecasts and the quality of analysis.
• Data Use: Relationships between drivers and economic/market conditions are derived from historical data using statistical techniques, though this is not a perfect analogy to traditional 'Risk' models.
• Model Coverage: It extends the scope of macro-economic factors and driver models, going beyond traditional ALM models used for NII (Net Interest Income) forecasting.
• Expert Judgment: While PPNR is influenced by systemic drivers, it also incorporates specific factors such as management decisions and regulatory changes, which are essential for accurate forecasting.

PPNR modelling provides a scientific foundation for financial forecasts, which can be challenged and refined through management input.

Why is PPNR Modelling Important?

1. Risk Management: PPNR is a major driver of financial risk, and accurate forecasting is essential for understanding long-term risks.
2. Regulatory Compliance: Regulatory bodies such as the Federal Reserve, Bank of England, and ECB/EBA are increasing their focus on PPNR in stress testing, especially in the US and Europe.
3. Strategic Planning: PPNR modelling offers a more objective and efficient approach to financial planning, enabling better scenario analysis and management decision-making.

Challenges in Implementing PPNR Modelling

1. Culture: PPNR modelling challenges existing planning and management practices, requiring a shift in mindset.
2. Application: Balancing statistical forecasting with management judgment is complex but necessary.
3. Data Availability: Granular historical data is often scarce or contaminated by specific strategies or conditions.
4. Segmentation: Models must be tailored to available data and planning needs, with some areas (like fee-based investment banking) being more challenging to model.
5. Validation and Back-Testing: Developing robust validation techniques is essential, though statistical robustness may be misleading due to data limitations.
6. Process Rationalisation: PPNR modelling requires integration with other planning processes, which can be complex and resource-intensive.
7. Cost: The development of PPNR capabilities involves significant investment in resources and expertise.

How Should Banks Proceed?

Banks should follow a structured and phased approach to developing PPNR capabilities:

• Define Applications: Clarify the purpose of the models (e.g., regulatory stress testing vs. internal planning).
• Set Ambition Level: Start with core P&L and Balance Sheet components, using simpler models for less critical areas.
• Implement with a Revised Operating Model: Ensure efficient integration with existing systems and processes, not just new analytics.

Key Information

• PPNR modelling is a critical evolution in financial planning and stress testing.
• It is not a new concept, but its regulatory focus and statistical rigor have increased significantly.
• While challenges exist, they are manageable and similar to those faced in traditional 'Risk' model development.
• Successful implementation requires cross-functional collaboration, careful planning, and a culture shift.

Conclusion

PPNR modelling is becoming an essential tool for banks to improve risk management, regulatory compliance, and financial planning. Although the implementation is complex, the benefits are substantial, and the trend is irreversible. Banks that adopt PPNR modelling early are likely to gain a competitive advantage in navigating uncertain economic environments.

Footnotes:

1. Pre-Provision Net Revenue refers to the revenue generated from core banking activities before accounting for credit losses and other provisions.
2. PPNR is a major driver of financial risk, especially in long-term planning and non-crisis periods.