In the evolving landscape of banking risk management, Interest Rate Risk in the Banking Book (IRRBB) remains a crucial concern for financial institutions. A key component of IRRBB is the management of Non-Maturing Deposits (NMDs), which play a fundamental role in interest rate risk modeling. Unlike fixed-term deposits, NMDs lack contractual maturity, making their modeling inherently uncertain. They serve as a vital funding source, allowing banks to hedge against interest rate fluctuations. However, their dynamic nature complicates predictive modeling, requiring financial institutions to balance risk sensitivity with stability. The challenge, therefore, is to develop robust models that can accurately capture the behavioral aspects of NMDs while complying with regulatory frameworks.
Regulatory Guidance on NMDs
Regulatory bodies such as the Basel Committee on Banking Supervision (BCBS) and the European Banking Authority (EBA) provide detailed guidance on IRRBB, emphasizing the necessity of accurately modeling NMDs to ensure financial stability. The Basel Committee on Banking Supervision (BCBS) has established a clear stance on the management of Non-Maturing Deposits (NMDs) within the framework of Interest Rate Risk in the Banking Book (IRRBB). NMDs are a critical component of bank funding, as they have no fixed maturity date, allowing depositors the flexibility to withdraw funds at any time. However, this inherent uncertainty creates significant challenges in interest rate risk management, necessitating robust frameworks to estimate their stability and pricing behavior accurately. The Basel Committee mandates that banks classify NMDs into retail and wholesale categories, further segmented into account characteristics (transactional and non-transactional deposits). A key distinction is made between core and non-core deposits, where core deposits represent the most stable portion, less sensitive to interest rate changes, while non-core deposits are considered more volatile and prone to shifts in depositor behavior.
To ensure prudent risk management, the Basel framework sets strict caps on the proportion of NMDs that can be classified as core deposits and places limitations on their assumed maturity. For example, up to 90% of retail transactional deposits can be considered core, with a maximum average maturity of five years, while retail non-transactional and wholesale deposits have lower limits. Banks must develop behavioral models to assess depositor stability, relying on historical data and market conditions to estimate how these deposits will behave under different interest rate scenarios. The Basel guidelines emphasize that banks must regularly validate and update these assumptions to align with changing depositor behavior and economic conditions. Furthermore, supervisors have the authority to mandate standardized methodologies where internal models are deemed inadequate.
Decay effects play a crucial role in the Basel Committee’s framework for managing Non-Maturing Deposits (NMDs). Decay rates refer to the speed at which deposits leave a bank over time, either due to withdrawals or customers shifting their funds in response to changes in interest rates. Since NMDs do not have contractual maturities, banks must estimate their expected retention period using historical data, depositor behavior, and market conditions. Higher decay rates indicate greater deposit volatility, requiring banks to assume shorter maturities and higher sensitivity to interest rate changes. Conversely, lower decay rates suggest a more stable deposit base, allowing banks to extend assumed maturities within the Basel-imposed caps. Regulators require banks to continuously validate their decay rate assumptions to ensure they reflect actual customer behavior and changing economic conditions.
The EBA IRRBB guidelines, discussed in more detail in a previous blog, further refine this by requiring banks to segment deposits into stable and volatile portions, ensuring that assumptions about deposit behavior align with empirical evidence.
Despite these regulations, global approaches to NMD modeling are not harmonized, and subject to varied supervisory interpretations. As banks interpret guidelines differently based on their regional market conditions, historical customer behavior, and liquidity risk management strategies. This divergence often results in inconsistencies in risk assessment across the industry, prompting the need for a harmonized approach to NMD modeling. However, any harmonization effort faces implementation hurdles and a trade off between precision and pragmatism.
Implementation Challenges in NMD Model Building
1. Behavioral Uncertainty and Customer Sensitivity
One of the primary difficulties in modeling NMDs is their unpredictable behavioral characteristics. Unlike term deposits, where interest rate changes directly impact renewal and withdrawal rates, NMD balances tend to remain stable despite fluctuations in interest rates. However, in extreme market conditions, customers may reallocate funds, leading to significant liquidity risks for banks. The Silicon Valley Bank ( SVB) episode clearly demonstrated the amplification effect of deposit volatility in the age of social media and digital banking. The challenge therefore lies in determining which portion of deposits can be considered ‘core’ and therefore relatively stable, versus the segment that is interest-rate sensitive and prone to outflows.
2. Static Balance Sheet Assumption and Non-Maturing Deposits in IRRBB
The Static Balance Sheet assumption is a fundamental concept in the Basel Committee’s approach to Interest Rate Risk in the Banking Book (IRRBB), particularly in the treatment of Non-Maturing Deposits (NMDs). Under this assumption, a bank’s balance sheet composition remains constant over time, meaning that maturing assets and liabilities are replaced with new instruments of identical characteristics in terms of amount, repricing structure, and maturity. This framework simplifies risk modeling by isolating the impact of interest rate changes on the bank’s existing balance sheet, without incorporating future strategic decisions such as growth, asset reallocation, or liability adjustments. By maintaining this steady-state scenario, banks can assess how interest rate fluctuations affect net interest income (NII) and the economic value of equity (EVE), key measures used in IRRBB calculations. The Basel framework mandates that IRRBB exposure be evaluated under a Static Balance Sheet assumption, ensuring comparability across banks and jurisdictions. However, while this provides a stable foundation for risk assessment, it may not fully capture the dynamic nature of banking operations, where deposit flows and customer behaviors evolve in response to changing interest rate environments.
The application of the Static Balance Sheet assumption to Non-Maturing Deposits (NMDs) presents unique challenges. Under the Static Balance Sheet assumption, core NMDs are slotted into specific maturity buckets based on estimated stability, while non-core deposits are treated as overnight liabilities, subject to immediate repricing. This approach helps banks assess their true exposure to interest rate risk, as sudden changes in interest rates can trigger shifts in depositor behavior that may not align with static assumptions. Additionally, the framework requires banks to regularly validate and stress-test their NMD assumptions, ensuring that their modeling reflects actual deposit retention patterns and pass-through effects. While the Static Balance Sheet assumption provides a structured methodology for measuring IRRBB, banks must remain aware of its limitations in capturing real-world deposit dynamics, particularly in volatile interest rate environments.
Traditional deposit modeling approaches assume static betas, which measure the sensitivity of deposit rates to changes in market interest rates. However, empirical data suggests that deposit betas exhibit dynamic behavior, influenced by macroeconomic conditions, market competition, and customer expectations. Using static assumptions may misprice interest rate risk, leading to inadequate hedging strategies. To deal this, it is often suggested that Banks that have significant IRRBB exposures should transition towards dynamic models that incorporate machine learning techniques, error correction models, and Monte Carlo simulations to better forecast deposit behaviors under various interest rate environments.
3. Decay Rate Estimation
A critical component of NMD modeling is estimating deposit decay rates, which determine how quickly deposit balances decline over time. The challenge arises from the need to segment deposit bases into stable (core) and non-stable portions, which requires sophisticated time-series analysis and sufficient data to conduct historical behavioral studies. Moreover, the decay rates of deposits are not constant, they tend to demonstrate positive correlation with rising interest rates, making it difficult to establish a uniform model that works across different economic cycles.
4. Impact of Market Competition and Regulatory Constraints
Another challenge in modeling NMDs is the influence of market competition. In a highly competitive banking environment, institutions may offer higher rates on deposits to attract or retain customers, thereby altering the expected decay rates and pass-through effects. The market dynamics has also altered with the entry of challenger banks and rapid adoption of digital banking channels. Regulatory constraints further complicate the picture, as prudential guidelines limit the duration assumptions that banks can apply to NMDs, affecting how banks align their deposit modeling strategies with compliance requirements.
5. Equity as a Non-Maturing Liability in IRRBB
In the Basel Committee’s Interest Rate Risk in the Banking Book (IRRBB) framework, equity is treated as a non-maturing liability due to its indefinite maturity and lack of contractual repricing. Unlike deposits or borrowings, equity capital does not have a fixed repayment schedule, making it a stable funding source that remains on the bank’s balance sheet for an extended period. This characteristic aligns equity with Non-Maturing Deposits (NMDs), but with a key distinction—equity is not subject to withdrawal risk from depositors and does not reprice based on market rates. As a result, banks typically allocate equity to the longest repricing bucket in IRRBB models, reflecting its stability and its role in absorbing long-term interest rate shocks.
However, despite its apparent stability, equity carries implicit interest rate risk that must be factored into IRRBB management. Since equity acts as a buffer for asset fluctuations, changes in interest rates can impact the economic value of equity (EVE), particularly for banks with large fixed-rate asset portfolios. Basel guidelines require banks to assess how equity supports interest rate risk-taking activities, ensuring that it is prudently allocated in risk models. The Basel framework states, “In the same way as with NMDs, a bank’s own equity capital liability represents an important source of structural risk and endowment return. Given that equity may be written down as a result of losses, regulators will normally focus on the EVE risk associated with any earnings profile ascribed to equity that may materialise as losses under stress events”. While equity provides a strong funding base, its role as a non-maturing liability requires careful calibration to avoid underestimating potential interest rate risks in the banking book.
Conclusion: Balancing Precision and Pragmatism
To address these challenges, banks must adopt a pragmatic approach that balances precision with adaptability. A hybrid modeling strategy that integrates historical analysis, behavioral segmentation, and advanced econometric techniques can enhance the accuracy of NMD modeling. Additionally, incorporating stress testing and scenario analysis can help banks better anticipate the impact of interest rate fluctuations on deposit behaviors. A jurisdiction specific, proportionate approach to NMD treatment is the middle ground that can pave the way for quick adoption of IRRBB in Bank’s strategic decision making. Ultimately, a robust NMD model should provide banks with the flexibility to adapt to changing economic conditions while maintaining regulatory compliance. The future of NMD modeling lies in leveraging data analytics, behavioral insights, and risk-sensitive methodologies to ensure resilience in the face of market uncertainties.
References
• Basel Committee on Banking Supervision (BCBS). Interest Rate Risk in the Banking Book (IRRBB). Available at: https://www.bis.org/basel_framework/
• European Banking Authority (EBA). Guidelines on IRRBB. Available at: https://www.eba.europa.eu
• Newson, P. Interest Rate Risk in the Banking Book (2nd Edition). Risk.net, 2021. Available at: https://www.risk.net
• CRISIL. Modeling Short-Term Bank Deposits in a Post-SVB World, March 2023. https://www.crisil.com/content/dam/crisil/our-analysis/reports/global-research-and-risk-solutions/2023/03/modelling-short-term-bank-deposits.pdf
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