Insights into cracking model risk for prepayment models

October 2024
7 min read

This article examines different methods for quantifying and forecasting model risk in prepayment models, highlighting their respective strengths and weaknesses.


Within the field of financial risk management, professionals strive to develop models to tackle the complexities in the financial domain. However, due to the ever-changing nature of financial variables, models only capture reality to a certain extent. Therefore, model risk - the potential loss a business could suffer due to an inaccurate model or incorrect use of a model - is a pressing concern. This article explores model risk in prepayment models, analyzing various approaches to quantify and forecast this risk. 

There are numerous examples where model risk has not been properly accounted for, resulting in significant losses. For example, Long-Term Capital Management was a hedge fund that went bankrupt in the late 1990s because its model was never stress-tested for extreme market conditions. Similarly, in 2012, JP Morgan experienced a $6 billion loss and $920 million in fines due to flaws in its new value-at-risk model known as the ‘London Whale Trade’.  

Despite these prominent failures, and the requirements of CRD IV Article 85 for institutions to develop policies and processes for managing model risk,1 the quantification and forecasting of model risk has not been extensively covered in academic literature. This leaves a significant gap in the general understanding and ability to manage this risk. Adequate model risk management allows for optimized capital allocation, reduced risk-related losses, and a strengthened risk culture.  

This article delves into model risk in prepayment models, examining different methods to quantify and predict this risk. The objective is to compare different approaches, highlighting their strengths and weaknesses.  

Definition of Model Risk

Generally, model risk can be assessed using a bottom-up approach by analyzing individual model components, assumptions, and inputs for errors, or by using a top-down approach by evaluating the overall impact of model inaccuracies on broader financial outcomes. In the context of prepayments, this article adopts a bottom-up approach by using model error as a proxy for model risk, allowing for a quantifiable measure of this risk. Model error is the difference between the modelled prepayment rate and the actual prepayment rate. Model error occurs at an individual level when a prepayment model predicts a prepayment that does not happen, and vice versa. However, banks are more interested in model error at the portfolio level. A statistic often used by banks is the Single Monthly Mortality (SMM). The SMM is the monthly percentage of prepayments and can be calculated by dividing the amount of prepayments for a given month by the total amount of mortgages outstanding. 

Using the SMM, we can define and calculate the model error as the difference between the predicted SMM and the actual SMM: 

The European Banking Authority (EBA) requires financial institutions when calculating valuation model risk to set aside enough funds to be 90% confident that they can exit a position at the time of the assessment. Consequently, banks are concerned with the top 5% and lowest 5% of the model risk distribution (EBA, 2016, 2015). 2 Thus, banks are interested in the distribution of the model error as defined above, aiming to ensure they allocate the capital optimally for model risk in prepayment models.  

Approaches to Forecasting Model Risk 

By using model error as a proxy for model risk, we can leverage historical model errors to forecast future errors through time-series modelling. In this article, we explore three methods: the simple approach, the auto-regressive approach, and the machine learning challenger model approach.

Simple Approach

The first method proposed to forecast the expected value, and the variance of the model errors is the simple approach. It is the most straightforward way to quantify and predict model risk by analyzing the mean and standard deviation of the model errors. The model itself causes minimal uncertainty, as there are just two parameters which have to be estimated, namely the intercept and the standard deviation.

The disadvantage of the simple approach is that it is time-invariant. Consequently, even in extreme conditions, the expected value and the variance of model errors remain constant over time.

Auto-Regressive Approach

The second approach to forecast the model errors of a prepayment model is the auto-regressive approach. Specifically, this approach utilizes an AR(1) model, which forecasts the model errors by leveraging their lagged values. The advantage of the auto-regressive approach is that it takes into account the dynamics of the historical model errors when forecasting them, making it more advanced than the simple approach.

The disadvantage of the auto-regressive approach is that it always lags and that it does not take into account the current status of the economy. For example, an increase in the interest rate by 200 basis points is expected to lead to a higher model error, while the auto-regressive approach is likely to forecast this increase in model error one month later.

Machine Learning Challenger Model Approach                           

The third approach to forecast the model errors involves incorporating a Machine Learning (ML) challenger model. In this article, we use an Artificial Neural Network (ANN). This ML challenger model can be more sophisticated than the production model, as its primary focus is on predictive accuracy rather than interpretability. This approach uses risk measures to compare the production model with a more advanced challenger model. A new variable is defined as the difference between the production model and the challenger model.

Similar to the above approaches, the expected value of the model errors is forecasted by estimating the intercept, the parameter of the new variable, and the standard deviation. A forecast can be made and the difference between the production model and ML challenger model can be used as a proxy for future model risk.

The advantage of using the ML challenger model approach is that it is forward looking. This forward-looking method allows for reasonable estimates under both normal and extreme conditions, making it a reliable proxy for future model risk. In addition, when there are complex non-linear relationships between an independent variable and the prepayment rate, an ML challenger can be more accurate. Its complexity allows it to predict significant impacts better than a simpler, more interpretable production model. Consequently, employing an ML challenger model approach could effectively estimate model risk during substantial market changes.

A disadvantage of the machine learning approach is its complexity and lack of interpretability. Additionally, developing and maintaining these models often requires significant time, computational resources, and specialized expertise.

Conclusion 

The various methods to estimate model risk are compared in a simulation study. The ML challenger model approach stands out as the most effective method for predicting model errors, offering increased accuracy in both normal and extreme conditions. Both the simple and challenger model approach effectively predicts the variability of model errors, but the challenger model approach achieves a smaller standard deviation. In scenarios involving extreme interest rate changes, only the challenger model approach delivers reasonable estimates, highlighting its robustness. Therefore, the challenger model approach is the preferred choice for predicting model error under both normal and extreme conditions.

Ultimately, the optimal approach should align with the bank’s risk appetite, operational capabilities, and overall risk management framework. Zanders, with its extensive expertise in financial risk management, including multiple high-profile projects related to prepayments at G-SIBs as well as mid-size banks, can provide comprehensive support in navigating these challenges. See our expertise here.


Ready to take your IRRBB strategy to the next level?

Zanders is an expert on IRRBB-related topics. We enable banks to achieve both regulatory compliance and strategic risk goals by offering support from strategy to implementation. This includes risk identification, formulating a risk strategy, setting up an IRRBB governance and framework, and policy or risk appetite statements. Moreover, we have an extensive track record in IRRBB [EU1] and behavioral models such as prepayment models, hedging strategies, and calculating risk metrics, both from model development and model validation perspectives.

Contact our experts today to discover how Zanders can help you transform risk management into a competitive advantage. Reach out to: Jaap Karelse, Erik Vijlbrief, Petra van Meel, or Martijn Wycisk to start your journey toward financial resilience.

  1. https://www.eba.europa.eu/regulation-and-policy/single-rulebook/interactive-single-rulebook/11665
    CRD IV Article 85: Competent authorities shall ensure that institutions implement policies and processes to evaluate and manage the exposures to operational risk, including model risk and risks resulting from outsourcing, and to cover low-frequency high-severity events. Institutions shall articulate what constitutes operational risk for the purposes of those policies and procedures. ↩︎
  2. https://extranet.eba.europa.eu/sites/default/documents/files/documents/10180/642449/1d93ef17-d7c5-47a6-bdbc-cfdb2cf1d072/EBA-RTS-2014-06%20RTS%20on%20Prudent%20Valuation.pdf?retry=1
    Where possible, institutions shall calculate the model risk AVA by determining a range of plausible valuations produced from alternative appropriate modelling and calibration approaches. In this case, institutions shall estimate a point within the resulting range of valuations where they are 90% confident they could exit the valuation exposure at that price or better. In this article, we generalize valuation model risk to model risk. ↩︎

Surviving Prepayments: A Comparative Look at Prepayment Modelling Techniques

June 2024
3 min read

We investigate different model options for prepayments, among which survival analysis


In brief

  • Prepayment modelling can help institutions successfully prepare for and navigate a rise in prepayments due to changes in the financial landscape.
  • Two important prepayment modelling types are highlighted and compared: logistic regression vs Cox Proportional Hazard.
  • Although the Cox Proportional Hazard model is theoretically preferred under specific conditions, the logical regression is preferred in practice under many scenarios.

The borrowers' option to prepay on their loan induces uncertainty for lenders. How can lenders protect themselves against this uncertainty? Various prepayment modelling approaches can be selected, with option risk and survival analyses being the main alternatives under discussion.

Prepayment options in financial products spell danger for institutions. They inject uncertainty into mortgage portfolios and threaten fixed-rate products with volatile cashflows. To safeguard against losses and stabilize income, institutions must master precise prepayment modelling.

This article delves into the nuances and options regarding the modelling of mortgage prepayments (a cornerstone of Asset Liability Management (ALM)) with a specific focus on survival models.

Understanding the influences on prepayment dynamics

Prepayments are triggered by a range of factors – everything from refinancing opportunities to life changes, such as selling a house due to divorce or moving. These motivations can be grouped into three overarching categories: refinancing factors, macroeconomic factors, and loan-specific factors.

  1. Refinancing factors
    This encompasses key financial drivers (such as interest rates, mortgage rates and penalties) and loan-specific information (including interest rate reset dates and the interest rate differential for the customer). Additionally, the historical momentum of rates and the steepness of the yield curve play crucial roles in shaping refinancing motivations.
  2. Macro-economic factors
    The overall state of the economy and the conditions of the housing market are pivotal forces on a borrower's inclination to exercise prepayment options. Furthermore, seasonality adds another layer of variability, with prepayments being notably higher in certain months. For example, in December, when clients have additional funds due to payment of year-end bonusses and holiday budgets.
  3. Loan-specific factors
    The age of the mortgage, type of mortgage, and the nature of the property all contribute to prepayment behavior. The seasoning effect, where the probability of prepayment increases with the age of the mortgage, stands out as a paramount factor.

These factors intricately weave together, shaping the landscape in which customers make decisions regarding prepayments.  Prepayment modelling plays a vital role in helping institutions to predict the impact of these factors on prepayment behavior. 

The evolution of prepayment modelling

Research on prepayment modelling originated in the 1980s and initially centered around option-theoretic models that assume rational customer behavior. Over time, empirical models that cater for customer irrationality have emerged and gained prominence. These models aim to capture the more nuanced behavior of customers by explaining the relationship between prepayment rates and various other factors. In this article, we highlight two important types of prepayment models: logistic regression and Cox Proportional Hazard (Survival Model).

Logistic regression

Logistic regression, specifically its logit or probit variant, is widely employed in prepayment analysis. This is largely because it caters for the binary nature of the dependent variable indicating the occurrence of prepayment events and it moreover flexible. That is, the model can incorporate mortgage-specific and overall economic factors as regressors and can handle time-varying factors and a mix of continuous and categorical variables.

Once the logistic regression model is fitted to historical data, its application involves inputting the characteristics of a new mortgage and relevant economic factors. The model’s output provides the probability of the mortgage undergoing prepayment. This approach is already prevalent in banking practices, and frequently employed in areas such as default modeling and credit scoring. Consequently, it’s favored by many practitioners for prepayment modeling.

Despite its widespread use, the model has drawbacks. While its familiarity in banking scenarios offers simplicity in implementation, it lacks the interpretability characteristic of the Proportional Hazard model discussed below. Furthermore, in terms of robustness, a minimal drawback is that any month-on-month change in results can be caused by numerous factors, which all affect each other.

Cox Proportional Hazard (Survival model)

The Cox Proportional Hazard (PH) model, developed by Sir David Cox in 1972, is one of the most popular models in survival analysis. It consists of two core parts:

  • Survival time. With the Cox PH model, the variable of interest is the time to event. As the model stems from medical sciences, this event is typically defined as death. The time variable is referred to as survival time because it’s the time a subject has survived over some follow-up period.
  • Hazard rate. This is the distribution of the survival time and is used to predict the probability of the event occurring in the next small-time interval, given that the event has not occurred beforehand. This hazard rate is modelled based on the baseline hazard (the time development of the hazard rate of an average patient) and a multiplier (the effect of patient-specific variables, such as age and gender). An important property of the model is that the baseline hazard is an unspecified function.

To explain how this works in the context of prepayment modelling for mortgages:

  • The event of interest is the prepayment of a mortgage.
  • The hazard rate is the probability of a prepayment occurring in the next month, given that the mortgage has not been prepaid beforehand. Since the model estimates hazard rates of individual mortgages, it’s modelled using loan-level data.  
  • The baseline hazard is the typical prepayment behavior of a mortgage over time and captures the seasoning effect of the mortgage.
  • The multiplier of the hazard rate is based on mortgage-specific variables, such as the interest rate differential and seasonality.

For full prepayments, where the mortgage is terminated after the event, the Cox PH model applies in its primary format. However, partial prepayments (where the event is recurring) require an extended version, known as the recurrent event PH model. As a result, when using the Cox PH model, , the modelling of partial and full prepayments should be conducted separately, using slightly different models.

The attractiveness of the Cox PH model is due to several features:

  • The interpretability of the model. The model makes it possible to quantify the influence of various factors on the likelihood of prepayment in an intuitive way.   
  • The flexibility of the model.  The model offers the flexibility to handle time-varying factors and a mix of continuous and categorical variables, as well as the ability to incorporate recurrent events.
  • The multiplier means the hazard rate can’t be negative. The exponential nature of mortgage-specific variables ensures non-negative estimated hazard rates.

Despite the advantages listed above presenting a compelling theoretical case for using the Cox PH model, it faces limited adoption in practical prepayment modelling by banks. This is primarily due to its perceived complexity and unfamiliarity. In addition, when loan-level data is unavailable, the Cox PH model is no longer an option for prepayment modeling.

Logistic regression vs Cox Proportional Hazard

In scenarios with individual survival time data and censored observations, the Cox PH model is theoretically preferred over logistic regression. This preference arises because the Cox PH model leverages this additional information, whereas logistic regression focuses solely on binary outcomes, disregarding survival time and censoring.

However, practical considerations also come into play. Research shows that in certain cases, the logistic regression model closely approximates the results of the Cox PH model, particularly when hazard rates are low. Given that prepayments in the Netherlands are around 3-10% and associated hazard rates tend to be low, the performance gap between logistic regression and the Cox PH model is minimal in practice for this application. Also, the necessity to create a different PH model for full and partial prepayment adds an additional burden on ALM teams.

In conclusion, when faced with the absence of loan-level data, the logistic regression model emerges as a pragmatic choice for prepayment modeling. Despite the theoretical preference for the Cox PH model under specific conditions, the real-world performance similarities, coupled with the familiarity and simplicity of logistic regression, provide a practical advantage in many scenarios.

How can Zanders support?

Zanders is a thought leader on IRRBB-related topics. We enable banks to achieve both regulatory compliance and strategic risk goals by offering support from strategy to implementation. This includes risk identification, formulating a risk strategy, setting up an IRRBB governance and framework, and policy or risk appetite statements. Moreover, we have an extensive track record in IRRBB and behavioral models such as prepayment models, hedging strategies, and calculating risk metrics, both from model development and model validation perspectives.

Are you interested in IRRBB-related topics such as prepayments? Contact Jaap Karelse, Erik Vijlbrief, Petra van Meel (Netherlands, Belgium and Nordic countries) or Martijn Wycisk (DACH region) for more information.

Why developing a non-maturing deposit model should be the top priority for banks in the Nordics 

September 2023
3 min read

We investigate different model options for prepayments, among which survival analysis


First and foremost, the long period of low and even negative swap rates was followed by strongly rising rates and a volatile market, which impacted the behavior of both customers and banks themselves. At the same time, regulatory developments, initiated by EBA’s new IRRBB guidelines, have shifted the banks’ focus to managing their earnings and earnings risk, rather than economic value risks.  

Non-maturing deposits (NMDs) are of particular interest in this respect, given the uncertainty regarding the future pricing strategy and volume developments involved in these products. Moreover, as NMDs are generally modeled with a rather short maturity, the portfolio plays a significant role in the stability of the NII, making this portfolio even more relevant to evaluate in light of the newly introduced Supervisory Outlier Test (SOT) limits on earnings risk (more specific NII), or the local equivalent. 

How does this affect IRRBB management at banks?

The exact impact of these developments is also heavily dependent on the bank’s local market, and corresponding laws and regulation, as well as the balance sheet composition of the bank. In Nordics countries, banks are affected more heavily, given that loans and mortgages generally have shorter maturities, as compared to other Western European countries like Germany and the Netherlands. This yields a smaller maturity mismatch with on-demand deposits at the liability side, such that a natural hedge exists to some extent within the balance sheet. Earlier on, this effect, combined with the rather stable markets, made active ALM, including IRRBB management, less urgent. The incentive to accurately model NMDs was therefore limited, so most banks simply replicate this funding overnight, while banks in other European countries tend to use a longer maturity, as illustrated by figure 1.

{Figure 1: Difference in average repricing maturity of NMDs between Nordic banks and other European banks, based on Pillar 3 IRRBBA and IRRBB1 disclosures from 2022 annual reports}

While the natural hedge already (partially) mitigates the risks from a value perspective to a large extent, investing the full NMDs portfolio overnight leads to relatively high NII volatility, thereby potentially violating regulatory limitations. The return on overnight investments will directly reflect any changes in the market rates, while deposit rates in reality are usually somewhat slower to include such developments. Although the resulting asymmetry between the investment return and deposit rate to be paid to customers yields a positive result under rising interest rates, it can reduce profits when interest rates start to fall.  

Historically, banks in the Nordics experienced less flexibility in the modeling of NMDs, due to regulatory guidelines being somewhat stricter than EBA guidelines. For instance, Sweden’s Finansinspektionen (FI) required banks to replicate these positions overnight. However, relatively recently, the FI updated its regulations (FI dnr 19-4434), allowing banks to somewhat extend the duration of the investment profile, for a limited portion of the portfolio, and up to a maturity of one year. This results in flexibility to update the investment profile to better reflect the expected repricing speed of deposit rates, which could lead to improved NII stability. Additionally, besides applying these revised NMD models for managing banking book risks, they can, when approved, also be used for effective and consistent capital charge calculations under Pillar 2. 

How can these developments be properly managed? 

Even though the recent market developments create additional challenges in IRRBB management for banks, they also provide opportunities. The margin on deposit products for banks is currently improving, since only part of the interest rate rises is passed through to customers. The increased interest rates also mean that more advanced NMD models, with longer maturity profiles, can have a positive impact on the P&L, while simultaneously improving the interest rate risk management. 

In such a rare win-win situation, it is more advantageous than ever to prioritize NMD modeling. In reassessing the interest rate risk management approach towards NMDs, banks should explicitly balance the tradeoff between value and earnings stability when making conceptual choices. These conceptual choices should align with the overall IRBBB strategy, as well as the intended use of the model, to ensure the risk in the portfolio is properly managed. 

In weighing these conceptual alternatives, it is essential to take portfolio-specific characteristics into account. This requires an analysis of historical behavior, and an interpretation of how representative this information is. If behavior is expected to change, a common approach is to supplement historical data with expert expectations of forward-looking scenarios to develop a model that reflects both. Periodically reassessing the conceptual choices ensures a proper model lifecycle of NMD portfolios. This is crucial for accurate measurement of interest rate risk as well as for staying competitive in the current market environment. 

Would you like to hear more? Contact Bas van Oers for questions on developing a non-maturing deposit model.

Grip on your EVE SOT

May 2023
3 min read

We investigate different model options for prepayments, among which survival analysis


Over the past decades, banks significantly increased their efforts to implement adequate frameworks for managing interest rate risk in the banking book (IRRBB). These efforts typically focus on defining an IRRBB strategy and a corresponding Risk Appetite Statement (RAS), translating this into policies and procedures, defining the how of the selected risk metrics, and designing the required (behavioral) models. Aspects like data quality, governance and risk reporting are (further) improved to facilitate effective management of IRRBB.

Main causes of volatility in SOT outcomes

The severely changed market circumstances evidence that, despite all efforts, the impact on the IRRBB framework could not be fully foreseen. The challenge of certain banks to comply with one of the key regulatory metrics defined in the context of IRRBB, the SOT on EVE, illustrates this. Indeed, even if regularities are assumed, there are still several key model choices that turn out to materialize in today’s interest rate environment:

  • Interest rate dependency in behavioral models: Behavioral models, in particular when these include interest rate-dependent relationships, typically exhibit a large amount of convexity. In some cases, convexity can be (significantly) overstated due to particular modeling choices, in turn contributing to a violation of the EVE SOT criterium. Some (small and mid-sized) banks, for example, apply the so-called ‘scenario multipliers’ and/or ‘scalar multipliers’ defined within the BCBS-standardized framework for incorporating interest rate-dependent relationships in their behavioral models. These multipliers assume a linear relationship between the modeled variable (e.g., prepayment rate) and the scenario, whereas in practice this relationship is not always linear. In other cases, the calibration approach of certain behavioral models is based on interest rates that have been decreasing for 10 to 15 years, and therefore may not be capable to handle a scenario in which a severe upward shock is added to a significantly increased base case yield curve.
  • Level and shape of the yield curve: Related to the previous point, some behavioral models are based on the steepness (defined as the difference between a ‘long tenor’ rate and a ‘short tenor’ rate) of the yield curve. As can be seen in Figure 1, the steepness changed significantly over the past two years, potentially leading to a high impact associated with the behavioral models that are based on it. Further, as illustrated in Figure 2, the yield curve has flattened over time and recently even turned into an inverse yield curve. When calculating the respective forward rates that define the steepness within a particular behavioral model, the downward trend of this variable that results due to the inverse yield curve potentially aggravates this effect.

Figure 1: Development of 3M EURIBOR rate and 10Y swap rate (vs. 3M EURIBOR) and the corresponding 'Steepness'

Figure 2: Development of the yield curve over the period December 2021 to March 2023.

  • Hidden vulnerability to ‘down’ scenarios: Previously, the interest rates were relatively close to, or even below, the EBA floor that is imposed on the SOT. Consequently, the ‘at-risk’ figures corresponding to scenarios in which (part of) the yield curve is shocked downward, were relatively small. Now that interest rates have moved away from the EBA floor, hidden vulnerability to ‘down’ scenarios become visible and likely the dominating scenario for the SOT on EVE.
  • Including ‘margin’ cashflows: Some banks determine their SOT on EVE including the margin cashflows (i.e., the spread added to the swap rate), while discounting at risk-free rates. While this approach is regulatory compliant, the inclusion of margin cashflows leads to higher (shocked) EVE values, and potentially leads to, or at least contributes to, a violation of the EVE threshold.

What can banks do?

Having identified the above issues, the question arises as to what measures banks should consider. Roughly speaking, two categories of actions can be distinguished. The first category encompasses actions that resolve an inadequate reflection of the actual risk. Examples of such actions include:

  • Identify and re-solve unintended effects in behavioral models: As mentioned above, behavioral models are key to determine appropriate EVE SOT figures. Next to revisiting the calibration approach, which typically is based on historical data, banks should assess to what extent there are unintended effects present in their behavioral models that adversely impact convexity and lead to unrepresentative sensitivities and unreliably shocked EVE values.
  • Adopt a pure IRR approach: An obvious candidate action for banks that still include margins in their cashflows used for the EVE SOT, is to adopt a pure interest rate risk view. In other words, align the cashflows with its discounting. This requires an adequate approach to remove the margin components from the interest cashflows.

The second category of actions addresses the actual, i.e., economic, risk position of bank. One could think of the following aspects that contribute to steering the EVE SOT within regulatory thresholds:

  • Evaluate target mismatch: As we wrote in our article ‘What can banks do to address the challenges posed by rising interest rates’, a bank’s EVE is most likely negatively affected by the rise in rates. The impact is dependent on the duration of equity taken by the bank: the higher the equity duration, the larger the decline in EVE when rates rise (and hence a higher EVE risk). In light of the challenges described above, a bank should consider re-evaluating the target mismatch (i.e. the duration of equity).
  • Consider swaptions as an additional hedge instrument: Convexity, in essence, cannot be hedged with plain vanilla swaps. Therefore, several banks have entered into ‘far out of the money’ swaptions to manage negative convexity in the SOT on EVE. From a business perspective, these swaptions result in additional, but accepted costs and P&L volatility. In case of an upward-sloping yield curve, the costs can be partly offset since the bank can increase its linear risk position (increase duration), without exceeding the EVE SOT threshold. This being said, swaptions can be considered a complex instrument that presents certain challenges. First, it requires valuation models – and expertise on these models – to be embedded within the organization. Second, setting up a heuristic that adequately matches the sensitivities of the swaptions to those of the commercial products (e.g., mortgages) is not a straightforward task.

How can Zanders support?

Zanders is thought leader in supporting banks with IRRBB-related topics. We enable banks to achieve both regulatory compliance and strategic risk goals, by offering support from strategy to implementation. This includes risk identification, formulating a risk strategy, setting up an IRRBB governance and framework, policy or risk appetite statements. Moreover, we have an extensive track record in IRRBB and behavioral models, hedging strategies, and calculating risk metrics, both from a model development as well as a model validation perspective.

Are you interested in IRRBB related topics? Contact Jaap KarelseErik Vijlbrief (Netherlands, Belgium and Nordic countries) or Martijn Wycisk (DACH region) for more information.

The EBA expects banks to expand their CSRBB framework

February 2022
3 min read

On 2 December 2021, the European Banking Authority (EBA) published three consultation papers related to its ‘Guidelines on the management of interest rate risk arising from non-trading book activities’ (in short, the IRRBB Guidelines). In this article, we focus on one of these consultation papers, concerning the update of the IRRBB Guidelines.


The current version of the IRRBB Guidelines, published in 2018, came into force on 30 June 2019. At that time, the IRRBB Guidelines were aligned with the Standards on interest rate risk in the banking book, published by the Basel Committee on Banking Supervision (in short, the BCBS Standards) in April 2016.

This new update is triggered by the revised Capital Requirements Regulation (CRR2) and Capital Requirements Directive (CRD5). Both documents were adopted by the Council of the EU and the European Parliament in 2019 as part of the Risk Reduction Measures package. The CRR2 and CRD5 included numerous mandates for the EBA to come up with new or adjusted technical standards and guidelines. These are now covered in three separate consultation papers

  1. The first consultation paper1 describes the update of the IRRBB Guidelines themselves.
  2. The second paper2 concerns the introduction of a standardized approach (SA) which should be applied when a competent authority deems a bank’s internal model for IRRBB management non-satisfactory. It also introduces a simplified SA for smaller and non-complex institutions.
  3. The third consultation paper3 offers updates to the supervisory outlier test (SOT) for the Economic Value of Equity (EVE) and the introduction of an SOT for Net Interest Income (NII). Read our analysis on this consultation paper here »

In this article we focus on the first consultation paper. The update of the IRRBB Guidelines can be split up in three topics and each will be discussed in further detail:

  • Additional criteria for the assessment and monitoring of the credit spread risk arising from non-trading book activities (CSRBB)
  • The criteria for non-satisfactory IRRBB internal systems
  • A general update of the existing IRBBB Guidelines

CSRBB

The 2018 IRRBB Guidelines introduced the obligation for banks to monitor CSRBB. However, the publication did not describe how to do this. In the updated consultation paper the EBA defines the measurement of CSRBB as a separate risk class in more detail. The general governance related aspects such as (management) responsibilities, IT systems and internal reporting framework are separately defined for CSRBB, but are similar to those for IRRBB.

Also similar to IRRBB is that banks must express their risk appetite for CSRBB both from an NII as well as an economic value perspective.

The EBA defines CSRBB as:

“The risk driven by changes of the market price for credit risk, for liquidity and for potentially other characteristics of credit-risky instruments, which is not captured by IRRBB or by expected credit/(jump-to-) default risk. CSRBB captures the risk of an instrument’s changing spread while assuming the same level of creditworthiness, i.e. how the credit spread is moving within a certain rating/PD range.”

EBA

quote

Compared to the previous definition, rating/PD migrations are explicitly excluded from CSRBB. Including idiosyncratic spreads could lead to double counting since these are generally covered by a credit risk framework. However, the guidelines give some flexibility to include idiosyncratic spreads, as long as the results are more conservative than when idiosyncratic spreads are excluded. This is because, based on the Quantitative Impact Study of December 2020 (QIS 2020), banks indicated to find it difficult to separate the idiosyncratic spreads from the credit spread.

Also, the scope of CSRBB has changed from the current IRRBB Guidelines. All assets, liabilities and off-balance-sheet items in the banking book that are sensitive to credit spread changes are within the scope of CSRBB whereas the 2018 IRRBB Guidelines focused only on the asset side. Based on the results of the QIS 2020, the EBA concluded that most of the exposures to CSRBB are debt securities which are accounted for at fair value (via Profit and Loss or Other Comprehensive Income). However, this does not rule out that other assets or liabilities could be exposed to CSRBB. It is stated that banks cannot ex-ante exclude positions from the scope of CSRBB. Any potential exclusion of instruments from the scope of CSRBB must be based on the absence of sensitivity to credit spread risk and appropriately documented. At a minimum, banks must include assets accounted at fair value in their scope.

We believe that the obligation to report CSRBB for all assets accounted for at fair value will be challenging for exposures that do not have quoted market prices. Without a deep liquid market, it will be difficult to establish the credits spread risk (even when idiosyncratic risk is included).

Another possible candidate to be included in the scope of CSRBB is the issued funding on the liability side of the banking book, especially in a NII context. When market spreads increase, this could become harmful when the wholesale funding needs to be rolled over against higher credit spread without being able to increase client interest on the asset side. Similar to IRRBB, the exposure to this risk depends on the repricing gap of the assets and liabilities. In this case, however, swaps cannot be used as hedge.

Other products such as consumer loans, mortgages, and consumer deposits, which are typically accounted for at amortized cost, are less likely to be included. This is also stated by the BCBS standards. The BCBS states that the margin (administrative rate) is under absolute control of the bank and hence not impacted by credit spreads. However, it is unclear whether this is sufficient to rule these products out of scope.

Non-satisfactory IRRBB internal systems

The EBA is mandated to specify the criteria for determining an IRRBB internal system as non-satisfactory. The EBA has identified specific items for this that should be considered. At a minimum, banks should have implemented their internal system in compliance with the IRRBB Guidelines, taking into account the principle of proportionality. More specifically:

  • Such a system must cover all material interest rate risk components (gap risk, basis risk, option risk).
  • The system should capture all material risks for significant assets, liabilities and off-balance sheet type instruments (e.g. non-maturing deposits, loans, and options).
  • All estimated parameters must be sufficiently back tested and reviewed, considering the nature, scale and complexity of the bank.
  • The internal system must comply with the model governance and the minimum required validation, review and control of IRRBB exposures as detailed by the IRRBB guidelines.
  • Competent authorities may require banks to use the standardized approach3 if the internal systems are deemed non-satisfactory.

General update of existing IRRBB Guidelines

Major parts of the guidelines for managing IRRBB have not changed. In the section on IRRBB stress testing, however, a new article (#103) for products with significant repricing restrictions (e.g. an explicit floor on non-maturing deposits – NMDs) is introduced. As part of their stress testing, banks should consider the impact when these products are replaced with contracts with similar characteristics, even under a run-off assumption. The exact intention of this article is unclear. For NII-purposes it is common practice to roll over products with similar characteristics (or use another balance sheet development assumption). Our interpretation of this article is that banks are expected to measure the risk of continued repricing restrictions in an economic value perspective when the maturity of those funding sources is smaller than the maturity of the asset portfolio. This may for example materialize when banks roll over NMDs that are subject to a legally imposed floor.

Another update is the restriction on the maximum weighted average repricing maturity of five years for NMDs. This cap was prescribed for the EVE SOT and is now included for the internal measurement of IRRBB. We believe that the impact of this will be limited since only a few banks will have separate NMD models for internal measurement and the SOT.

Finally, some minor additions have been included in the guidelines. For example, the guidelines emphasize multiple times that when diversification assumptions are used for the measurement of IRRBB, these must be appropriately stressed and validated.

Conclusion

It is expected that the final guidelines will not deviate significantly from the consultation paper. Banks can therefore start preparing for these new expectations. For the measurement of IRRBB, limited changes are introduced in the consultation. Although the exact intention of the EBA is unclear to us, it is interesting to notice that the updated IRRBB Guidelines include the expectation that banks pay special attention in their stress tests to products with significant repricing restrictions. Furthermore, banks must invest in their CSRBB measurement. For their entire banking book, banks need to assess whether market wide credit spread changes will have an impact on an NII and/or economic value perspective. The scope of CSRBB measurement may need to be extended to include the funding issued by the bank. And to conclude, the obligation to measure CSRBB for fair value assets that do not have quoted market prices will be a challenge for banks.

References
  1. CP Draft GL on IRRBB and CSRBB.pdf
  2. CP Draft RTS on SA.pdf
  3. CP Draft RTS on SOTs.pdf

The EBA faces banks with a new supervisory outlier test on net interest income

February 2022
3 min read

On 2 December 2021, the European Banking Authority (EBA) published three consultation papers related to its ‘Guidelines on the management of interest rate risk arising from non-trading book activities’ (in short, the IRRBB Guidelines). In this article, we focus on one of these consultation papers, concerning the update of the IRRBB Guidelines.


In this article, we focus on one of these consultation papers, which concerns updates to the supervisory outlier test (SOT) for the Economic Value of Equity (EVE) and the introduction of an SOT for Net Interest Income (NII).

The current version of the IRRBB Guidelines, published in 2018, came into force on 30 June 2019. At that time, the IRRBB Guidelines were aligned with the Standards on interest rate risk in the banking book, published by the Basel Committee on Banking Supervision (in short, the BCBS Standards) in April 2016.

The new updates are triggered by the revised Capital Requirements Regulation (CRR2) and Capital Requirements Directive (CRD5). Both documents were adopted by the Council of the EU and the European Parliament in 2019 as part of the Risk Reduction Measures package. The CRR2 and CRD5 included numerous mandates for the EBA to come up with new or adjusted technical standards and guidelines. These are now covered in three separate consultation papers:

  1. The first consultation paper1 describes an update of the IRRBB Guidelines themselves. The main changes are the specification of criteria to identify “non-satisfactory internal models for IRRBB management” and the specification of criteria to assess and monitor Credit Spread Risk in the Banking Book (CSRBB). Read our analysis on this consultation paper here »
  2. The second paper2 concerns the introduction of a standardized approach (SA) which should be used when a competent authority deems a bank’s internal model for IRRBB management non-satisfactory. It also introduces a Simplified SA for smaller and non-complex institutions.
  3. The third consultation paper3 offers updates to the supervisory outlier test (SOT) for the Economic Value of Equity (EVE) and the introduction of an SOT for Net Interest Income (NII).

Please note that we recently also published an article about the new disclosure requirements for IRRBB which is closely related to this topic.

Changes to the supervisory outlier test

Banks have been subject to an SOT already since the 2006 IRRBB Guidelines. The SOT is an important tool for supervisors to perform peer reviews and to compare IRRBB exposures between banks. The SOT measures how the EVE responds to an instantaneous parallel (up and down) yield curve shift of 200 basis points. Changes in EVE that exceed 20% of the institution’s own funds will trigger supervisory discussions and may lead to additional capital requirements.

Some changes to the SOT were included in the 2018 update of the IRRBB Guidelines. Next to further guidance on its calculation, the existing SOT was complemented with an additional SOT. The additional SOT was based on the same metric and guidelines, but the scenarios applied were the six standard interest rate scenarios introduced in the BCBS Standards. Also, a threshold of 15% compared to Tier 1 capital was applied. In the 2018 IRRBB Guidelines, the additional SOT was considered an ‘early warning signal’ only.

The new update of the IRRBB Guidelines includes two important SOT-related changes, which are incorporated through amendments to Article 98 (5) of the CRD: the replacement of the 20% SOT for EVE and the introduction of the SOT for NII. Both changes are discussed in more detail below.

Changes to the supervisory outlier test

Banks have been subject to an SOT already since the 2006 IRRBB Guidelines. The SOT is an important tool for supervisors to perform peer reviews and to compare IRRBB exposures between banks. The SOT measures how the EVE responds to an instantaneous parallel (up and down) yield curve shift of 200 basis points. Changes in EVE that exceed 20% of the institution’s own funds will trigger supervisory discussions and may lead to additional capital requirements.

Some changes to the SOT were included in the 2018 update of the IRRBB Guidelines. Next to further guidance on its calculation, the existing SOT was complemented with an additional SOT. The additional SOT was based on the same metric and guidelines, but the scenarios applied were the six standard interest rate scenarios introduced in the BCBS Standards. Also, a threshold of 15% compared to Tier 1 capital was applied. In the 2018 IRRBB Guidelines, the additional SOT was considered an ‘early warning signal’ only.

The new update of the IRRBB Guidelines includes two important SOT-related changes, which are incorporated through amendments to Article 98 (5) of the CRD: the replacement of the 20% SOT for EVE and the introduction of the SOT for NII. Both changes are discussed in more detail below.

Replacement of the 20% SOT for EVE

The first part of the amended Article 98 (5) concerns the replacement of the original 20% SOT by the 15% SOT. While many banks are probably already targeting levels below 15%, we expect that this change will limit the maneuvering capabilities of banks as they will likely choose to implement a management buffer. Note that not only the threshold is lower (15% instead of 20%), but also the denominator (Tier 1 capital instead of own funds). Furthermore, the worst outcome of all six supervisory scenarios should be used, as opposed to worst outcome of just the two parallel ones. Combined, this leads to a significant reduction in the EVE risk to which a bank may be exposed.

Some other noteworthy updates to the SOT for EVE that are not directly related to the CRD amendment are listed below:

  • The post-shock interest floor decreases from -100 to -150 basis points and it increases to 0% over a 50-year instead of a 20-year period.
  • In the calibration of the interest rate shocks for currencies for which the shocks have not been prescribed, the most recent 16-year period should be used (instead of the 2000-2015 period which is still underlying the shocks for the other currencies).
  • When aggregating the results over currencies, some additional offsetting (80% as opposed to 50%) is granted in case of Exchange Rate Mechanism (ERM) II currencies with a formally agreed fluctuation band narrower than the standard band of +/-15%. Currently, only positions in the Denmark Krona (DKK) qualify for this treatment.

Introduction of the SOT for NII

The second part of the amended Article 98 (5) concerns the introduction of an entirely new SOT. It is aimed at measuring the potential decline in NII for two standard interest rate shock scenarios. Compared to the SOT for EVE, the SOT for NII requires many more modeling assumptions, in particular to determine the expected balance sheet development. The consultation paper provides clarity on the approach the EBA wants to take but two decisions are explicitly consulted.

The SOT for NII compares the NII for a baseline scenario with the NII in a shocked scenario over a one-year horizon. The two shocks that need to be applied are the two instantaneous parallel shocks that are also used in the SOT for EVE. Furthermore, the same requirements that are specified for the SOT for EVE apply, for example the use of the floor and the aggregation approach. The two exceptions are the requirements to use a constant balance sheet assumption (as opposed to a run-off balance sheet) and to include commercial margins and other spread components in the calculations. The commercial margins of new instruments should equal the prevailing levels (as opposed to historical ones).

The two decisions for which the EBA is seeking input are:

The scope/definition of NII
In its narrowest definition, the SOT will focus on the difference between interest income and interest expenses. The EBA, however, also considers using a broader definition where the effect of market value changes of instruments accounted for at Fair Value (∆FV) is added, and possibly also interest rate sensitive fees and commissions.

The definition of the SOT’s threshold
Article 98 (5) requires the EBA to specify what is considered a ‘large decline’ in NII, in which case the competent authorities are entitled to exercise their supervisory powers. This first requires a metric. The EBA is consulting two:

  • The first metric is calculating the change in NII (the difference between the shocked and baseline NII) relative to the Tier 1 Capital:
  • The second metric is calculating the change in NII relative to the baseline scenario, after correcting for administrative expenses that can be allocated to NII:
  • where α is the historical share of NII relative to the operating income as reported based on FINREP input.

The pros of the narrow definition of NII are improved comparability and ease of computation, where the main pro of the broader definition is that it achieves a more comprehensive picture, which is also more in line with the EBA IRRBB Guidelines. With respect to the metrics, the first (capital-based) metric is the simplest and it is comparable to the approach taken for the SOT for EVE. The second metric is close to a P&L-based metric and the EBA argues that its main advantage is that “it takes into account both the business model and cost structure of a bank in the assessment of the continuity of the business operations”. It does involve, however, the application of some assumptions on determining the α parameter.

Thresholds for the four possible combinations

For each of the four possible combinations (definition of NII and specification of the metric), the EBA has determined, using data from the December 2020 Quantitative Impact Study (QIS), what the corresponding thresholds should be. Their starting point has been to make the SOT for NII as stringent as the SOT for EVE. Effectively, they reverse engineered the threshold to achieve a similar number of outliers under both measures. We expect that the proposed threshold for any of the four possible combinations will not be constraining for the majority of banks. The resulting proposed thresholds are included in the table below:

Table 1 – Comparison of the proposed thresholds for each combination of metric and scope

The impact of including Fair Value changes seems arbitrary as it increases the threshold for the capital-based metric and decreases the threshold for the P&L-based metric. Also, from a comparability and computational perspective, the narrow definition of NII may be preferred. Furthermore, the capital-based metric is less intuitive for NII than it is for EVE, and consequently, the P&L-based one may be preferred. It is also noted in the consultation paper that if the shocked NII after the correction for administrative expenses (the numerator) is negative, it will also be considered an outlier.

Conclusion

In the past years, many banks have invested heavily in their IRRBB framework following the 2018 update of the IRRBB Guidelines. Once again, an investment is required. Even though there are not many surprises in the proposed updates related to the SOTs, small and large banks alike will need to carefully assess how the changes to the existing SOT and the introduction of the new SOT will impact their interest rate risk management. Banks still have the opportunity to respond to all three consultation papers until 4 April 2022.

References
  1. CP Draft GL on IRRBB and CSRBB.pdf
  2. CP Draft RTS on SA.pdf
  3. CP Draft RTS on SOTs.pdf

Updated IRRBB guidelines pose new challenges for banks

March 2018
3 min read

On 2 December 2021, the European Banking Authority (EBA) published three consultation papers related to its ‘Guidelines on the management of interest rate risk arising from non-trading book activities’ (in short, the IRRBB Guidelines). In this article, we focus on one of these consultation papers, concerning the update of the IRRBB Guidelines.


This long-awaited update for the management of Interest Rate Risk in the Banking Book (IRRBB) builds on the original guidelines published in May 2015. It also effectively is the translation to European law of the IRRBB Standards published by the Basel Committee on Banking Supervision (BCBS) in April 2016. Market participants had until 31 January 2018 to put forward their feedback on the updated guidelines. After completion, the guidelines will apply from 31 December 2018. Certain aspects of the BCBS standards from April 2016 are not addressed in the updated EBA guidelines. The EBA is still working on a number of technical standards as part of the ongoing CRD and CRR revision in which they for example will prescribe disclosure requirements and the standardised approach for IRRBB.These technical standards will be published separately at a later stage.

Compared to the 2015 version, the guidelines have increased in size significantly. As published in our infographic, the guidelines contain over 40% new articles, which originate partly from the BCBS standards, but also contain some new guidelines. This article discusses the three main changes introduced in the consultation. First of all, the major overhaul of the supervisory outlier test. Next, the strong increase in the guidance on governance and model risk management. And finally, the shift EBA requires from the more traditional Net Interest Income (NII) metrics to a true earnings-based approach. The article concludes with an overview of the main comments provided by market participants in response to the consultation.

Supervisory outlier test

The existing supervisory outlier test (SOT) measures how the Economic Value of Equity (EVE) responds to an instantaneous +/- 200 basis points parallel yield curve shift. The SOT is an important tool for supervisors to perform peer reviews and to compare IRRBB exposures between banks. Changes in EVE that exceed 20% of the institution’s own funds will trigger supervisory discussions and may lead to additional capital requirements.

In the BCBS standards, a different SOT definition was proposed, introducing a 15% trigger compared to Tier 1 capital in combination with six interest rate scenarios that also include non-parallel shocks. The EBA has decided to implement both SOTs. The combination of more scenarios and an additional trigger level will restrict the maneuvering capabilities of banks, even though the new SOT is considered an ‘early warning signal’ only.

In an attempt to further improve the comparability of results between banks, the EBA has strengthened the guidance for the calculation of the SOTs. This covers both scoping requirements (e.g. non-performing loans and pension obligations and pension plan assets now need to be included) and measurement requirements (e.g. lowering the zero interest rate floor, now ranging from -150 basis points for overnight positions to zero basis points for 30 years and more).

One of new measurement requirements for the calculation of the SOT, is particularly noteworthy. The EBA guidelines require the use of risk-free discounting for the calculation of the SOT. With respect to the cash flows, it is up to banks to decide whether or not they want to include the commercial margin and other spread components. This level of flexibility should primarily be interpreted as an escape route in case banks are not able to strip the commercial margins from their cash flows. From an interest rate risk management perspective it is clear that alignment between discounting and cash flows is preferable. The interesting development in the guidelines is that a bank can only choose to use stripped cash flows in the SOT calculation if this is consistent with the way the bank manages and hedges IRRBB. In this way, aiming for alignment between discounting and cash flows for the SOT may have large consequences, depending on the choices a bank has made for the internal management of IRRBB.

Governance and model risk management

The section in the updated guidelines on governance has significantly increased in size compared to the original version. It includes new guidelines on the risk management framework, risk appetite and model governance. These may seem new, but on close inspection, the majority of these added guidelines are direct copies of the BCBS standards, as can also be seen in Figure 1. This figure provides a graphical overview of the main areas in the EBA consultation and how the original EBA guidelines and BCBS standards have been incorporated in the consultation. While the guidelines on the risk management framework and risk appetite can be considered a more detailed explanation of the original guidelines, the main addition is on model risk management. This requires institutions to set up a model governance, not only for any behavioural models, but for all IRRBB measurement methods that traditionally have not always been in scope of a model governance.

Where the majority of the original EBA guidelines have been transferred to the consultation, sometimes with more detail, some of the BCBS standards have not been included at all. This is especially true for ‘Principle 5: Behavioral optionalities’ and ‘Principle 8: IRRBB Disclosure’. Guidelines on Behavioral optionalities were already far more detailed in the original EBA guidelines and therefore the BCBS standards were not incorporated in the EBA consultation. The guidelines on IRRBB Disclosure have not been included as these will be addressed in the separate reporting technical standards mentioned earlier.

From NII to earnings

One of the main additions to the guidelines, which didn’t originate from the original guidelines nor from the BCBS standards, is the requirement to also include market value changes in earnings metrics. This change will require banks to start modeling a true IFRS Profit & Loss at Risk and take into account the increase or reduction in total earnings and capital. Traditionally, earnings metrics just focus on NII and ignore any interest rate sensitivity in other areas of the Profit and Loss (P&L) account. It will have a significant impact on the modeling of earnings measures, as the accounting treatment of instruments will start to determine how the measure will be impacted. Although this seems a logical extension of an earnings metric, it presents significant challenges especially in the area of derivatives used for hedge accounting and instruments in an Available-for-Sale portfolio, for which only coupon payments were included up till now. Adding market value movements of these instruments introduces the risk of double counting and therefore requires a clear definition of how the interest rate sensitivity impacts the P&L. Integrating these effects in a forward looking calculation will pose challenges to the implementation in systems.

Consultation responses

In total, 19 organizations responded to the consultation. Some of them responded to the 16 questions in the consultation, while others chose to add a more general response to the consultation.

One of the main critiques is around the inclusion of CSRBB in the scope of IRRBB. Especially the lack of a proper definition (currently defined as “any kind of spread risk that is not IRRBB or credit risk”) and the inclusion in an IRRBB context is commented on by the respondents. The general response is to remove CSRBB from the scope of the IRRBB guidelines and to create separate guidelines on CSRBB instead. Another area of concern is the guidance on capital calculation. Although primarily copied from the original guidelines, this particular part of the guidelines raises a considerable number of questions. In particular, whether capital should be calculated for variability risk or loss risk and how capital for earnings risk and value risk should be integrated in a consistent framework, without duplications, remains unclear. Finally, the date of implementation is also considered challenging by a number of respondents, as a December 2018 implementation date effectively requires banks to implement all changes in six to nine months.

Conclusion

For many banks the implementation of the 2015 EBA guidelines is still a work in progress. The recent update of the guidelines poses new challenges for banks. Given the substantial number of changes compared to the previous version, the December 2018 implementation deadline will prove to be challenging. And we haven’t seen the end of it, because a number of technical standards as part of the ongoing CRD and CRR revision are still in the pipeline. This includes for example requirements to standardize the disclosure of IRRBB, which currently shows a lot of variety between various jurisdictions and will likely require a significant effort for all banks as well. As a result, IRRBB will remain on the agenda of the regulator and the management board of many banks in the years to come.

IRRBB Quick Scan

Should you want to assess your bank’s IRRBB framework, Zanders offers an IRRBB Quick Scan. Based on a review of available model documentation, risk reports and interviews with your bank’s risk specialists, the scan provides an independent and objective assessment of your bank’s IRRBB implementation relative to the new IRRBB principles and best-market practices. More information on the IRRBB Quick Scan can be found here.

Standardizing Financial Risk Management – ING’s Accelerating Think Forward Strategy and IRRBB Framework Transformation

In 2014, with its Think Forward strategy, ING set the goal to further standardize and streamline its organization. At the time, changes in international regulations were also in full swing. But what did all this mean for risk management at the bank? We asked ING’s Constant Thoolen and Gilbert van Iersel.


According to Constant Thoolen, global head of financial risk at ING, the Accelerating Think Forward strategy, an updated version of the Think Forward strategy that they just call ATF, comprises several different elements.

"Standardization is a very important one. And from standardization comes scalability and comparability. To facilitate this standardization within the financial risk management team, and thus achieve the required level of efficiency, as a bank we first had to make substantial investments so we could reap greater cost savings further down the road."

And how exactly did ING translate this into financial risk management?

Thoolen: "Obviously, there are different facets to that risk, which permeates through all business lines. The interest rate risk in the banking book, or IRRBB, is a very important part of this. Alongside the interest rate risk in trading activities, the IRRBB represents an important risk for all business lines. Given the importance of this type of risk, and the changing regulatory complexion, we decided to start up an internal IRRBB program."

So the challenge facing the bank was how to develop a consistent framework in benchmarking and reporting the interest rate risk?

"The ATF strategy has set requirements for the consistency and standardization of tooling," explains Gilbert van Iersel, head of financial risk analysis. "On the one hand, our in-house QRM program ties in with this. We are currently rolling out a central system for our ALM activities, such as analyses and risk measurements—not only from a risk perspective but from a finance one too. Within the context of the IRRBB program, we also started to apply this level of standardization and consistency throughout the risk-management framework and the policy around it. We’re doing so by tackling standardization in terms of definitions, such as: what do we understand by interest rate risk, and what do benchmarks like earnings-at-risk or NII-at-risk actually mean? It’s all about how we measure and what assumptions we should make."

What role did international regulations play in all this?

Van Iersel: "An important one. The whole thing was strengthened by new IRRBB guidelines published by the EBA in 2015. It reconciled the ATF strategy with external guidelines, which prompted us to start up the IRRBB program."

So regulations served as a catalyst?

Thoolen: "Yes indeed. But in addition to serving as a foothold, the regulations, along with many changes and additional requirements in this area, also posed a challenge. Above all, it remains in a state of flux, thanks to Basel, the EBA, and supervision by the ECB. On the one hand, it’s true that we had expected the changes, because IRRBB discussions had been going on for some time. On the other hand, developments in the regulatory landscape surrounding IRRBB followed one another quite quickly. This is also different from the implementation of Basel II or III, which typically require a preparation and phasing-in period of a few years. That doesn’t apply here because we have to quickly comply with the new guidelines."

Did the European regulations help deliver the standardization that ING sought as an international bank?

Thoolen: "The shift from local to European supervision probably increased our need for standardization and consistency. We had national supervisors in the relevant countries, each supervising in their own way, with their own requirements and methodologies. The ECB checked out all these methodologies and created best practices on what they found. Now we have to deal with regulations that take in all Eurozone countries, which are also countries in which ING is active. Consequently, we are perfectly capable of making comparisons between the implementation of the ALM policy in the different countries. Above all, the associated risks are high on the agenda of policymakers and supervisors."

Van Iersel: "We have also used these standards in setting up a central treasury organization, for example, which is also complementary to the consistency and standardization process."

Thoolen: "But we’d already set the further integration of the various business units in motion, before the new regulations came into force. What’s more, we still have to deal with local legislation in the countries in which we operate outside Europe, such as Australia, Singapore, and the US. Our ideal world would be one in which we have one standard for our calculations everywhere."

What changed in the bank’s risk appetite as a result of this changing environment and the new strategy?

Van Iersel: "Based on newly defined benchmarks, we’ve redefined and shaped our risk appetite as a component part of the strategic program. In the risk appetite process we’ve clarified the difference between how ING wants to manage the IRRBB internally and how the regulator views the type of risk. As a bank, you have to comply with the so-called standard outlier test when it comes to the IRRBB. The benchmark commonly employed for this is the economic value of equity, which is value-based. Within the IRRBB, you can look at the interest rate risk from a value or an income perspective. Both are important, but they occasionally work against one another too. As a bank, we’ve made a choice between them. For us, a constant stream of income was the most important benchmark in defining our interest rate risk strategy, because that’s what is translated to the bottom line of the results that we post. Alongside our internal decision to focus more closely on income and stabilize it, the regulator opted to take a mainly value-based approach. We have explicitly incorporated this distinction in our risk appetite statements. It’s all based on our new strategy; in other words, what we are striving for as a bank and what will be the repercussions for our interest rate risk management. It’s from there that we define the different risk benchmarks."

Which other types of risk does the bank look at and how do they relate to the interest rate risk?

Van Iersel: “From the financial risk perspective, you also have to take into account aspects like credit spreads, changes in the creditworthiness of counterparties, as well as market-related risks in share prices and foreign exchange rates. Given that all these collectively influence our profitability and solvency position, they are also reflected in the Core Tier I ratio. There is a clear link to be seen there between the risk appetite for IRRBB and the overall risk appetite that we as a bank have defined. IRRBB is a component part of the whole, so there’s a certain amount of interaction between them to be considered; in other words, how does the interest rate risk measure up to the credit risk? On top of that, you have to decide where to deploy your valuable capacity. All this has been made clearer in this program.”

Does this mean that every change in the market can be accommodated by adjusting the risk appetite?

Thoolen: “Changing behavior can indeed influence risks and change the risk appetite, although not necessarily. But it can certainly lead to a different use of risk. Moreover, IFRS 9 has changed the accounting standards. Because the Core Tier 1 ratio is based on the accounting standard, these IFRS 9 changes determine the available capital too. If IFRS 9 changes the playing field, it also exerts an influence on certain risk benchmarks.”

In addition to setting up a consistent framework, the standardization of the models used by the different parts of ING was also important. How does ING approach the selection and development of these models?

Thoolen: “With this in mind, we’ve set up a structure with the various business units that we collaborate with from a financial risk perspective. We pay close attention to whether a model is applicable in the environment in which it’s used. In other words, is it a good fit with what’s happening in the market, does it cover all the risks as you see them, and does it have the necessary harmony with the ALM system? In this way, we want to establish optimum modeling for savings or the repayment risk of mortgages, for example.”

But does that also work for an international bank with substantial portfolios in very different countries?

Thoolen: “While there is model standardization, there is no market standardization. Different countries have their own product combinations and, outside the context of IRRBB, have to comply with regulations that differ from other countries. A savings product in the Netherlands will differ from a savings product in Belgium, for example. It’s difficult to define a one-size-fits-all model because the working of one market can be much more specific than another—particularly when it comes to regulations governing retail and wholesale. This sometimes makes standardization more difficult to apply. The challenge lies in the fact that every country and every market is specific, and the differences have to be reconciled in the model.”

Van Iersel: “The model was designed to measure risks as well as possible and to support the business to make good decisions. Having a consistent risk appetite framework can also make certain differences between countries or activities more visible. In Australia, for example, many more floating-rate mortgages are sold than here in the Netherlands, and this alters the sensitivity of the bank’s net interest income when the interest rate changes. Risk appetite statements must facilitate such differences.”

To what extent does the use of machine learning models lead to validation issues?

“Seventy to eighty percent of what we model and validate within the bank is bound by regulation – you can't apply machine learning to that. The kind of machine learning that is emerging now is much more on the business side – how do you find better customers, how do you get cross-selling? You need a framework for that; if you have a new machine learning model, what risks do you see in it and what can you do about it? How do you make sure your model follows the rules? For example, there is a rule that you can't refuse mortgages based on someone's zip code, and in the traditional models that’s well in sight. However, with machine learning, you don't really see what's going on ‘under the hood’. That's a new risk type that we need to include in our frameworks. Another application is that we use our own machine learning models as challenger models for those we get delivered from modeling. This way we can see whether it results in the same or other drivers, or we get more information from the data than the modelers can extract.”

Thoolen: “But opting for a single ALM system imposes this model standardization on you and ensures that, once it’s integrated, it will immediately comply with many conditions. The process is still ongoing, but it’s a good fit with the standardization and consistency that we’re aiming for.”


In conjunction with the changing regulatory environment, the Accelerating Think Forward Strategy formed the backdrop for a major collaboration with Zanders: the IRRBB project. In the context of this project, Zanders researched the extent to which the bank’s interest rate risk framework complied with the changing regulations. The framework also assessed ING’s new interest rate risk benchmarks and best practices. Based on the choices made by the bank, Zanders helped improve and implement the new framework and standardized models in a central risk management system.

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