The Covid-19 pandemic triggered unprecedented market volatility, causing widespread failures in banks' internal risk models. These backtesting failures threatened to increase capital requirements and restrict the use of advanced models. To avoid a potentially dangerous feedback loop from the lower liquidity, regulators responded by granting temporary exemptions for certain pandemic-related model exceptions. To act faster to future crises and reduce unreasonable increases to banks’ capital requirements, more recent regulation directly comments on when and how similar exemptions may be imposed.

Although FRTB regulation briefly comments on such situations of market stress, where exemptions may be imposed for backtesting and profit and loss attribution (PLA), it provides very little explanation of how banks can prove to the regulators that such a scenario has occurred. On 28^th June, the EBA published its final draft technical standards on extraordinary circumstances for continuing the use of internal models for market risk. These standards discuss the EBA’s take on these exemptions and provide some guidelines on which indicators can be used to identify periods of extreme market stresses.

Background and the BCBS

In the Basel III standards, the Basel Committee on Banking Supervision (BCBS) briefly comment on rare occasions of cross-border financial market stress or regime shifts (hereby called extreme stresses) where, due to exceptional circumstances, banks may fail backtesting and the PLA test. In addition to backtesting overages, banks often see an increasing mismatch between Front Office and Risk P&L during periods of extreme stresses, causing trading desks to fail PLA.

The BCBS comment that one potential supervisory response could be to allow the failing desks to continue using the internal models approach (IMA), however only if the banks models are updated to adequately handle the extreme stresses. The BCBS make it clear that the regulators will only consider the most extraordinary and systemic circumstances. The regulation does not, however, give any indication of what analysis banks can provide as evidence for the extreme stresses which are causing the backtesting or PLA failures.

The EBA’s standards

The EBA’s conditions for extraordinary circumstances, based on the BCBS regulation, provide some more guidance. Similar to the BCBS, the EBA’s main conditions are that a significant cross-border financial market stress has been observed or a major regime shift has taken place. They also agree that such scenarios would lead to poor outcomes of backtesting or PLA that do not relate to deficiencies in the internal model itself.

To assess whether the above conditions have been met, the EBA will consider the following criteria:

1. Analysis of volatility indices (such as the VIX and the VSTOXX), and indicators of realised volatilities, which are deemed to be appropriate to capture the extreme stresses,
2. Review of the above volatility analysis to check whether they are comparable to, or more extreme than, those observed during COVID-19 or the global financial crisis,
3. Assessment of the speed at which the extreme stresses took place,
4. Analysis of correlations and correlation indicators, which adequately capture the extreme stresses, and whether a significant and sudden change of them occurred,
5. Analysis of how statistical characteristics during the period of extreme stresses differ to those during the reference period used for the calibration of the VaR model.

The granularity of the criteria

The EBA make it clear that the standards do not provide an exhaustive list of suitable indicators to automatically trigger the recognition of the extreme stresses.  This is because they believe that cases of extreme stresses are very unique and would not be able to be universally captured using a small set of prescribed indicators.

They mention that defining a very specific set of indicators would potentially lead to banks developing automated or quasi-automated triggering mechanisms for the extreme stresses. When applied to many market scenarios, this may lead to a large number of unnecessary triggers due the specificity of the prescribed indicators. As such, the EBA advise that the analysis should take a more general approach, taking into consideration the uniqueness of each extreme stress scenario.

Responses to questions

The publication also summarises responses to the original Consultation Paper EBA/CP/2023/19. The responses discuss several different indicators or factors, on top of the suggested volatility indices, that could be used to identify the extreme stresses:

• The responses highlight the importance of correlation indicators. This is because stress periods are characterised by dislocations in the market, which can show increased correlations and heightened systemic risk.
• They also mention the use of liquidity indicators. This could include jumps of the risk-free rates (RFRs) or index swap (OIS) indicators. These liquidity indicators could be used to identify regime shifts by benchmarking against situations of significant cross-border market stress (for example, a liquidity crisis).
• Unusual deviations in the markets may also be strong indicators of the extreme stresses. For example, there could be a rapid widening of spreads between emerging and developed markets triggered by regional debt crisis. Unusual deviations between cash and derivatives markets or large difference between futures/forward and spot prices could also indicate extreme stresses.
• They suggest that restrictions on trading or delivery of financial instruments/commodities may be indicative of extreme stresses. For example, the restrictions faced by the Russian ruble due to the Russia-Ukraine war.
• Finally, the responses highlighted that an unusual amount of backtesting overages, for example more than 2 in a month, could also be a useful indicator.

Zanders recommends

It’s important that banks are prepared for potential extreme stress scenarios in the future. To achieve this, we recommend the following:

• Develop a holistic set of indicators and metrics that capture signs of potential extreme stresses,
• Use early warning signals to preempt potential upcoming periods of stress,
• Benchmark the indicators and metrics against what was observed during the great financial crisis and Covid-19,
• Create suitable reporting frameworks to ensure the knowledge gathered from the above points is shared with relevant teams, supporting early remediation of issues.

Conclusion

During extreme stresses such as Covid-19 and the global financial crisis, banks’ internal models can fail, not because of modelling issues but due to systemic market issues.  Under FRTB, the BCBS show that they recognise this and, in these rare situations, may provide exemptions. The EBA’s recently published technical standards provide better guidance on which indicators can be used to identify these periods of extreme stresses. Although they do not lay out a prescriptive and definitive set of indicators, the technical standards provide a starting point for banks to develop suitable monitoring frameworks.

For more information on this topic, contact Dilbagh Kalsi (Partner) or Hardial Kalsi (Manager).

Challenges with backtesting Expected Shortfall

Recent regulations are increasingly moving toward the use of Expected Shortfall (ES) as a measure to capture risk. Although ES fixes many issues with VaR, there are challenges when it comes to backtesting.

Although VaR has been widely-used for decades, its shortcomings have prompted the switch to ES. Firstly, as a percentile measure, VaR does not adequately capture tail risk. Unlike VaR, which gives the maximum expected portfolio loss in a given time period and at a specific confidence level, ES gives the average of all potential losses greater than VaR (see figure 1). Consequently, unlike Var, ES can capture a range of tail scenarios. Secondly, VaR is not sub-additive. ES, however, is sub-additive, which makes it better at accounting for diversification and performing attribution. As such, more recent regulation, such as FRTB, is replacing the use of VaR with ES as a risk measure.

Figure 1: Comparison of VaR and ES

Elicitability is a necessary mathematical condition for backtestability. As ES is non-elicitable, unlike VaR, ES backtesting methods have been a topic of debate for over a decade. Backtesting and validating ES estimates is problematic – how can a daily ES estimate, which is a function of a probability distribution, be compared with a realised loss, which is a single loss from within that distribution? Many existing attempts at backtesting have relied on approximations of ES, which inevitably introduces error into the calculations.

The three main issues with ES backtesting can be summarised as follows:

1. Transparency
   • Without reliable techniques for backtesting ES, banks struggle to have transparency on the performance of their models. This is particularly problematic for regulatory compliance, such as FRTB.
2. Sensitivity
   • Existing VaR and ES backtesting techniques are not sensitive to the magnitude of the overages. Instead, these techniques, such as the Traffic Light Test (TLT), only consider the frequency of overages that occur.
3. Stability
   • As ES is conditional on VaR, any errors in VaR calculation lead to errors in ES. Many existing ES backtesting methodologies are highly sensitive to errors in the underlying VaR calculations.

Ridge Backtesting: A solution to ES backtesting

One often-cited solution to the ES backtesting problem is the ridge backtesting approach. This method allows non-elicitable functions, such as ES, to be backtested in a manner that is stable with regards to errors in the underlying VaR estimations. Unlike traditional VaR backtesting methods, it is also sensitive to the magnitude of the overages and not just their frequency.

The ridge backtesting test statistic is defined as:

where 𝑣 is the VaR estimation, 𝑒 is the expected shortfall prediction, 𝑥 is the portfolio loss and 𝛼 is the confidence level for the VaR estimation.

The value of the ridge backtesting test statistic provides information on whether the model is over or underpredicting the ES. The technique also allows for two types of backtesting; absolute and relative. Absolute backtesting is denominated in monetary terms and describes the absolute error between predicted and realised ES. Relative backtesting is dimensionless and describes the relative error between predicted and realised ES. This can be particularly useful when comparing the ES of multiple portfolios. The ridge backtesting result can be mapped to the existing Basel TLT RAG zones, enabling efficient integration into existing risk frameworks.

Figure 2: The ridge backtesting methodology

Sensitivity to Overage Magnitude

Unlike VaR backtesting, which does not distinguish between overages of different magnitudes, a major advantage of ES ridge backtesting is that it is sensitive to the size of each overage. This allows for better risk management as it identifies periods with large overages and also periods with high frequency of overages.

Below, in figure 3, we demonstrate the effectiveness of the ridge backtest by comparing it against a traditional VaR backtest. A scenario was constructed with P&Ls sampled from a Normal distribution, from which a 1-year 99% VaR and ES were computed. The sensitivity of ridge backtesting to overage magnitude is demonstrated by applying a range of scaling factors, increasing the size of overages by factors of 1, 2 and 3. The results show that unlike the traditional TLT, which is sensitive only to overage frequency, the ridge backtesting technique is effective at identifying both the frequency and magnitude of tail events. This enables risk managers to react more quickly to volatile markets, regime changes and mismodelling of their risk models.

Figure 3: Demonstration of ridge backtesting’s sensitivity to overage magnitude.

The Benefits of Ridge Backtesting

Rapidly changing regulation and market regimes require banks enhance their risk management capabilities to be more reactive and robust. In addition to being a robust method for backtesting ES, ridge backtesting provides several other benefits over alternative backtesting techniques, providing banks with metrics that are sensitive and stable.

Despite the introduction of ES as a regulatory requirement for banks choosing the internal models approach (IMA), regulators currently do not require banks to backtest their ES models. This leaves a gap in banks’ risk management frameworks, highlighting the necessity for a reliable ES backtesting technique. Despite this, banks are being driven to implement ES backtesting methodologies to be compliant with future regulation and to strengthen their risk management frameworks to develop a comprehensive understanding of their risk.

Ridge backtesting gives banks transparency to the performance of their ES models and a greater reactivity to extreme events. It provides increased sensitivity over existing backtesting methodologies, providing information on both overage frequency and magnitude. The method also exhibits stability to any underlying VaR mismodelling.

In figure 4 below, we summarise the three major benefits of ridge backtesting.

Figure 4: The three major benefits of ridge backtesting.

Conclusion

The lack of regulatory control and guidance on backtesting ES is an obvious concern for both regulators and banks. Failure to backtest their ES models means that banks are not able to accurately monitor the reliability of their ES estimates. Although the complexities of backtesting ES has been a topic of ongoing debate, we have shown in this article that ridge backtesting provides a robust and informative solution. As it is sensitive to the magnitude of overages, it provides a clear benefit in comparison to traditional VaR TLT backtests that are only sensitive to overage frequency. Although it is not a regulatory requirement, regulators are starting to discuss and recommend ES backtesting. For example, the PRA, EBA and FED have all recommended ES backtesting in some of their latest publications. However, despite the fact that regulation currently only requires banks to perform VaR backtesting, banks should strive to implement ES backtesting as it supports better risk management.

For more information on this topic, contact Dilbagh Kalsi (Partner) or Hardial Kalsi (Manager).

Across the whole of Europe, banks apply different techniques to model their IFRS9 Expected Credit Losses on a best estimate basis. The diverse spectrum of modelling techniques raises the question: what can we learn from each other, such that we all can improve our own IFRS 9 frameworks? For this purpose, Zanders hosted a webinar on the topic of IFRS 9 on the 29^th of May 2024. This webinar was in the form of a panel discussion which was led by Martijn de Groot and tried to discuss the differences and similarities by covering four different topics. Each topic was discussed by one  panelist, who were Pieter de Boer (ABN AMRO, Netherlands), Tobia Fasciati (UBS, Switzerland), Dimitar Kiryazov (Santander, UK), and Jakob Lavröd (Handelsbanken, Sweden).

The webinar showed that there are significant differences with regards to current IFRS 9 issues between European banks. An example of this is the lingering effect of the COVID-19 pandemic, which is more prominent in some countries than others. We also saw that each bank is working on developing adaptable and resilient models to handle extreme economic scenarios, but that it remains a work in progress. Furthermore, the panel agreed on the fact that SICR remains a difficult metric to model, and, therefore, no significant changes are to be expected on SICR models.

Covid-19 and data quality

The first topic covered the COVID-19 period and data quality. The poll question revealed widespread issues with managing shifts in their IFRS 9 model resulting from the COVID-19 developments. Pieter highlighted that many banks, especially in the Netherlands, have to deal with distorted data due to (strong) government support measures. He said this resulted in large shifts of macroeconomic variables, but no significant change in the observed default rate. This caused the historical data not to be representative for the current economic environment and thereby distorting the relationship between economic drivers and credit risk. One possible solution is to exclude the COVID-19 period, but this will result in the loss of data. However, including the COVID-19 period has a significant impact on the modelling relations. He also touched on the inclusion of dummy variables, but the exact manner on how to do so remains difficult.

Dimitar echoed these concerns, which are also present in the UK. He proposed using the COVID-19 period as an out-of-sample validation to assess model performance without government interventions. He also talked about the problems with the boundaries of IFRS 9 models. Namely, he questioned whether models remain reliable when data exceeds extreme values. Furthermore, he mentioned it also has implications for stress testing, as COVID-19 is a real life stress scenario, and we might need to think about other modelling techniques, such as regime-switching models.

Jakob found the dummy variable approach interesting and also suggested the Kalman filter or a dummy variable that can change over time. He pointed out that we need to determine whether the long term trend is disturbed or if we can converge back to this trend. He also mentioned the need for a common data pipeline, which can also be used for IRB models. Pieter and Tobia agreed, but stressed that this is difficult since IFRS 9 models include macroeconomic variables and are typically more complex than IRB.

Significant Increase in Credit Risk

The second topic covered the significant increase in credit risk (SICR). Jakob discussed the complexity of assessing SICR and the lack of comprehensive guidance. He stressed the importance of looking at the origination, which could give an indication on the additional risk that can be sustained before deeming a SICR.

Tobia pointed out that it is very difficult to calibrate, and almost impossible to backtest SICR. Dimitar also touched on the subject and mentioned that the SICR remains an accounting concept that has significant implications for the P&L. The UK has very little regulations on this subject, and only requires banks to have sufficient staging criteria. Because of these reasons, he mentioned that he does not see the industry converging anytime soon. He said it is going to take regulators to incentivize banks to do so. Dimitar, Jakob, and Tobia also touched upon collective SICR, but all agreed this is difficult to do in practice.

Post Model Adjustments

The third topic covered post model adjustments (PMAs). The results from the poll question implied that most banks still have PMAs in place for their IFRS 9 provisions. Dimitar responded that the level of PMAs has mostly reverted back to the long term equilibrium in the UK. He stated that regulators are forcing banks to reevaluate PMAs by requiring them to identify the root cause. Next to this, banks are also required to have a strategy in place when these PMAs are reevaluated or retired, and how they should be integrated in the model risk management cycle. Dimitar further argued that before COVID-19, PMAs were solely used to account for idiosyncratic risk, but they stayed around for longer than anticipated. They were also used as a countercyclicality, which is unexpected since IFRS 9 estimations are considered to be procyclical. In the UK, banks are now building PMA frameworks which most likely will evolve over the coming years.

Jakob stressed that we should work with PMAs on a parameter level rather than on ECL level to ensure more precise adjustments. He also mentioned that it is important to look at what comes before the modelling, so the weights of the scenarios. At Handelsbanken, they first look at smaller portfolios with smaller modelling efforts. For the larger portfolios, PMAs tend to play less of a role. Pieter added that PMAs can be used to account for emerging risks, such as climate and environmental risks, that are not yet present in the data. He also stressed that it is difficult to find a balance between auditors, who prefer best estimate provisions, and the regulator, who prefers higher provisions.

Linking IFRS 9 with Stress Testing Models

The final topic links IFRS 9 and stress testing. The poll revealed that most participants use the same models for both. Tobia discussed that at UBS the IFRS 9 model was incorporated into their stress testing framework early on. He pointed out the flexibility when integrating forecasts of ECL in stress testing. Furthermore, he stated that IFRS 9 models could cope with stress given that the main challenge lies in the scenario definition. This is in contrast with others that have been arguing that IFRS 9 models potentially do not work well under stress. Tobia also mentioned that IFRS 9 stress testing and traditional stress testing need to have aligned assumptions before integrating both models in each other.

Jakob agreed and talked about the perfect foresight assumption, which suggests that there is no need for additional scenarios and just puts a weight of 100% on the stressed scenario. He also added that IFRS 9 requires a non-zero ECL, but a highly collateralized portfolio could result in zero ECL. Stress testing can help to obtain a loss somewhere in the portfolio, and gives valuable insights on identifying when you would take a loss. 

Pieter pointed out that IFRS 9 models differ in the number of macroeconomic variables typically used. When you are stress testing variables that are not present in your IFRS 9 model, this could become very complicated. He stressed that the purpose of both models is different, and therefore integrating both can be challenging. Dimitar said that the range of macroeconomic scenarios considered for IFRS 9 is not so far off from regulatory mandated stress scenarios in terms of severity. However, he agreed with Pieter that there are different types of recessions that you can choose to simulate through your IFRS 9 scenarios versus what a regulator has identified as systemic risk for an industry. He said you need to consider whether you are comfortable relying on your impairment models for that specific scenario.

This topic concluded the webinar on differences and similarities across European countries regarding IFRS 9. We would like to thank the panelists for the interesting discussion and insights, and the more than 100 participants for joining this webinar.

Interested to learn more? Contact Kasper Wijshoff, Michiel Harmsen or Polly Wong for questions on IFRS 9.

In October 2023, the European Banking Authority (EBA) published a report[1] with recommendations for enhancements to the Pillar 1 prudential framework to reflect environmental and social (E&S) risks, distinguishing between actions to be taken in the short term and in the medium to long term. The short-term actions are to be taken into account over the next three years as part of the implementation of the revised Capital Requirements Regulation and Capital Requirements Directive (CRR3/CRD6).

The EBA report follows a discussion paper on the same topic from May 2022[2], on which it solicited input from the financial industry. In this note, we provide an overview of the recommended actions by the EBA that relate to the prudential framework for banks. The EBA report also contains recommended actions for the prudential framework applying to investment firms, but these are not addressed here.

If the EBA’s recommendations are implemented in the prudential framework, in our view the most immediate implications for banks would be:

• When using external ratings to determine own fund requirements for credit risk under the standardized approach (SA) of Pillar 1, ensure that E&S risks are explicitly considered when evaluating the appropriateness of the external ratings as part of the due diligence requirements.
• When calculating own fund requirements for credit risk under the internal-ratings-based (IRB) approach, embed E&S risks in the rating assignment, risk quantification (for example through a margin of conservatism or the downturn component) and/or expert judgment and overrides.
• To assess E&S risks at a borrower level, establish a process to obtain and update material E&S-related information on the borrowers’ financial condition and credit facility characteristics, as part of due diligence during onboarding and ongoing monitoring of the borrowers’ risk profile.
• For IRB banks, embed E&S risks in the credit risk stress testing programs.
• Ensure that E&S risks are considered in the valuation of collateral, specifically for financial and real estate collateral.
• For market risk, embed environmental risks in trading book risk appetite, internal trading limits and the new product approval process. Furthermore, for banks aiming to use the internal model approach (IMA) of the Fundamental Review of the Trading Book (FRTB) regulation, environmental risks need to be considered in their stress testing program.
• For operational risk, identify whether E&S risks constitute triggers of operational risk losses.

We note that many of these implications align with the ECB’s expectations in the ECB Guide on climate-related and environmental risks[3].

Background

The EBA report considers both environmental and social risks, which the EBA characterizes as follows:

• As drivers of environmental risks, EBA distinguishes physical and transition (climate) risks. It does not explicitly refer in the report to other environmental risks, such as a loss of biodiversity or pollution, but in an earlier report the EBA considered these as part of chronic physical risks[4].
• EBA considers social factors to be related to the rights, well-being and interests of people and communities, including factors such as decent work, adequate living standards, inclusive and sustainable communities and societies, and human rights. As drivers of social risks, EBA distinguishes environmental factors (as materialization of physical and transition risks may change living standards and the labor market and increase social tensions, for example) as well as changes in policies and market sentiment. These may in part be driven by actions taken to meet the United Nation’s sustainable development goals (SDGs) in 2030.

In line with the ECB Guide on climate-related and environmental risks[5], the EBA does not view E&S risks as stand-alone risks, but as drivers of traditional banking risks. This is depicted in Figure 1. The report considers the impact on credit, market, operational, liquidity and concentration risks and reviews to what extent E&S risks can be reflected in capital buffers and the macro-prudential framework. It does not explicitly consider the securitization framework, although this will be implicitly affected by impacts on credit risk. The EBA does not see an impact of E&S risks on the (risk-insensitive) leverage ratio, and therefore does not consider it in the report.

Figure 1: Examples of transmission channels for environmental and social risks (source: EBA).

The EBA notes that the Pillar 1 framework has been designed to capture the possible financial impact of cyclical economic fluctuations, but not to capture the manifestation of long-term environmental risks. It is therefore important to keep the main principles that form the basis of the prudential framework in mind when contemplating adjustments to reflect E&S risks in the prudential framework. The main principles as highlighted by the EBA are summarized below.

Main principles of the prudential framework and the relation to the horizon for E&S risks  

With repect to the framework in general:

• Own fund requirements are intended to cover potential unexpected losses. In contrast, expected losses are directly deducted from own funds, and are generally captured in the accounting rules through provisions, impairments, write-downs and appropriate valuation of assets.
• The purpose of own fund requirements is to ensure resilience of an institution to unexpected adverse circumstances, before appropriate mitigating actions and strategy adjustments can be implemented. Therefore, environmental factors that can affect institutions in the short to medium term are expected to be reflected in the prudential framework. However, for those with an impact in the longer term, institutions are expected to take appropriate mitigating actions in their strategy.
• The high confidence level used in the Pillar 1 framework to protect institutions from risks over the short to medium horizon may no longer be achievable and appropriate if longer horizons would be considered.
• To the extent that institutions are exposed to E&S risks in relation to their specific strategy and business model, coverage of these risks in the Pillar 2 own-fund requirements instead of Pillar 1 could be appropriate. In addition, reflection of these risks in the Pillar 2 guidance for stress testing may be considered.

With respect to the internal-ratings-based (IRB) approach for credit risk:

• The Probability of Default (PD) represents a one-year default probability, which is required to be calibrated based on long-run average (‘through-the-cycle’) default rates. As such, longer-term risk characteristics of the obligor may be taken into account.
• The Credit Conversion Factor (CCF) as an estimate of potential additional drawdowns before default naturally relates to the one-year time horizon for the PD, but is expected to reflect the situation of an economic downturn.
• The time horizon for the Loss Given Default (LGD) extends to the full maturity of the exposure and/or the collection process and its calibration is also expected to reflect the situation of an economic downturn.  

In the following sections we summarize the EBA recommendations by risk type.

Credit risk

The recommendations of the EBA largely put the burden on financial institutions to take E&S risks into account in the inputs for the existing Pillar 1 framework and/or to apply conservatism or overrides to the outputs. It does not recommend to include explicit E&S risk-related elements in the determination of risk weights for rated and unrated exposures in the SA or in the risk-weight formulas of the IRB. The main reasons for not doing so are that it is not clear what common and objective E&S-related factors should be used as input, what the proper functional form would be, a lack of evidence on which the size of an adjustment could be based so that it results in proper risk differentiation, and the risk of double counting with the reflection of E&S risks in the inputs to the existing own funds calculations under Pillar 1 (external ratings in the SA and PD, LGD and CCF in the case of IRB). However, the EBA will continue to evaluate this possibility in the medium to long term. The EBA also does not recommend introducing an environment-related adjustment factor to the risk weights resulting from the existing Pillar 1 framework[6].

Recommended actions for credit risk

Short term

• SA) The EBA encourages rating agencies to integrate environmental and social factors as drivers in the external credit risk assessments and to provide enhanced disclosures and transparency about the rating methodologies.
• (SA) Financial institutions to explicitly consider environmental factors in the due diligence that they are required to perform when using external credit risk assessments.
• (IRB) Financial institutions to reflect E&S risks in the rating assignment, risk quantification (for example through a margin of conservatism or the downturn component) and/or expert judgment and overrides, without affecting the overall performance of the rating system. In this context:
  • Quantification of risks must be based on sufficient and reliable observations;
  • Overrides should be for specific, individual cases where the institution believes there is material exposure to E&S risks but it has insufficient information to quantify it. Such overrides need to be regularly assessed and challenged;
  • If an institution derives PDs for internal rating grades by a mapping to a scale from a credit rating institution, it needs to consider whether the default rates associated with the external scale reflect material E&S risks.
• To assess E&S risks at a borrower level, institutions need to have a process to obtain and update material E&S-related information on the borrowers’ financial condition and on credit facility characteristics, as part of the due diligence during onboarding and ongoing monitoring of borrowers’ risk profile.
• (IRB) Financial institutions to consider E&S risks in their stress testing programs.
• (SA, IRB) Financial institutions to ensure prudent valuation of immovable property collateral, considering climate-related physical and transition risks as well as other environmental risks. The prudent valuation should be considered at origination, re-valuation and during monitoring.

Medium to long term

• (SA) Financial institutions to monitor that environmental factors are reflected in financial collateral valuations through market values under Pillar 1 and valuation methodologies under Pillar 2.
• (SA) The EBA to consider whether benefits from the Infrastructure Supporting Factor (ISF) should only be applied to high-quality specialized lending corporate exposures that meet strong environmental standards.
• (SA) The EBA to consider adjusting risk weights, both in general and specifically for those assigned to real estate exposures.
• (IRB) As E&S risks materialize in defaults and loss rates over time, institutions need to redevelop or recalibrate their PD and LGD estimates.

(SA = standardized approach; IRB = Internal-rating-based approach)

Market risk

Within market risk, the EBA sees the main interaction of E&S risks with the equity, credit spread and commodity markets, in which E&S risks may cause additional volatility. In line with the existing regulatory guidance, the EBA expects E&S risks not to be treated as separate risk factors but as drivers of existing risk factors, with the exception of products for which cash flows depend specifically on ESG factors (‘ESG-linked products’).

The EBA does not recommend changes at this point to the standardized approach (SA) and the internal model approach (IMA) under the FRTB regulation, which will come into effect in the EU in 2025. The primary reason is the lack of sufficient evidence on the impact of E&S risks to enable a data driven approach, which forms the basis of the FRTB.

When calculating the expected shortfall (ES) measure under the IMA based on last 12 months' market data, the materialization of E&S risks will automatically be reflected in the market data that is used. When using market data from a stress period, either to calculate ES in the IMA or to calibrate risk factor shocks for the sensitivity-based measure (SbM) at a risk class level in the SA, the reflection of E&S risks will depend on the choice of stress period. To include E&S risks fully in the IMA but avoid overlap with the (partial) presence of E&S risks in historical data, the EBA views the consideration of E&S risks in a separate ‘risk not in the model engine’ (RNIME) add-on as most promising option for the medium to long term, leveraging the framework described in the ECB Guide to internal models[7].

Recommended actions for market risk

Short term

• (SA, IMA) Financial institutions to consider environmental risks in relation to their trading book risk appetite, internal trading limits and new product approval.
• (IMA) Financial institutions to consider environmental risk as part of their stress testing program that is required to get internal model approval.

Medium to long term

• (SA, IMA) Competent authorities to consider how to treat ESG-linked products for the residual risk add-on in the SA and in the IMA.(SA) The EBA to consider including a dimension for ESG risks in the existing equity and credit spread risk classes, or including a separate environmental risk class.
• (IMA) Financial institutions to consider ESG risks when monitoring risks that are not included in the model, for which the ECB’s RNIME framework could be used as a basis.

(SA = standardized approach; IRB = Internal-rating-based approach)

Operational risk

The EBA notes that various types of operational risks can increase as a result of E&S risks, including damage to physical assets, disruption of business processes and litigation. However, the new standardized approach (SA) for operational risk in the Basel III framework, which will come into effect in the EU in 2025, does not have a forward-looking component – it only considers historical loss experience (besides business indicators). Historical losses are unlikely to fully reflect the potential future impact of E&S risks, but there is as of yet insufficient evidence and data to quantify and consider this in an amendment of the SA.

Recommended actions for operational risk

Short term

• Financial institutions to identify whether E&S risks constitute triggers of operational risk losses.

Medium to long term

• Following evidence of E&S risk factors to trigger operational risk losses, the EBA to consider whether revisions to the BCBS SA methodology are warranted.

Liquidity risk

The EBA report describes three ways in which E&S risks may affect the liquidity coverage ratio (LCR) calculation. First, liquid assets that are specifically exposed to E&S risks may become less liquid and/or decrease in value. As a consequence, they may no longer satisfy the eligibility criteria for liquid assets. If they still do, then the decrease in market value would reflect the lower liquidity and reduce the LCR. Second, contingent liabilities arising from environmentally harmful investments would need to be included as outflows in the LCR calculation, thereby lowering the LCR. Third, a decrease in credit quality of receivables that are particularly exposed to E&S risks will decrease the inflows that can be taken into account in the LCR calculation. The EBA concludes that the existing LCR framework can capture the impact of E&S risks on the definition of liquid assets, outflows and inflows, so that no amendments are needed.

Regarding the existing framework for the net stable funding ratio (NSFR), the EBA notes that a reduction in the creditworthiness and/or liquidity of loans and securities exposed to E&S risks would lead to a higher requirement for stable funding and thereby negatively impact the NSFR. In this way, the existing NSFR framework can capture the impact of E&S risks on the definition of stable assets.

In summary, the EBA does not propose changes to the LCR and NSFR frameworks in relation to E&S risks. In case of excessive exposure to E&S risks for individual institutions, it notes that supervisors can set specific liquidity or funding requirements as part of the Pillar 2 framework for LCR and NSFR.

Concentration risk

The SA and IRB of the Pillar 1 framework for credit risk assume that a bank’s loan portfolio has full diversification of name-specific (idiosyncratic) risk and is well diversified across sectors and geographies. Because of these assumptions, the framework is not able to capture concentration risks, including those arising from E&S risks. In the current framework, single-name concentration risk is separately captured in Pillar 1 using the large exposure regime. Sector and geographic concentrations are considered in the SREP process under Pillar 2.

Recommended actions for concentration risk

Short term

• The EBA to develop a definition of environment-related concentration risk as well as exposure-based metrics for its quantification (e.g., ratio of exposures sensitive to a given environmental risk driver in a specific geographical area or in a specific industry sector over total exposures, total capital or RWA). These metrics will be part of supervisory reporting and, when relevant, external disclosure. In addition, they should be considered as part of Pillar 2 under SREP and/or supplement Pillar 3 disclosures on ESG risks.The EBA does not recommend to change the existing large exposure regime.

Medium to long term

• Based on the experience obtained with initial environment-related concentration risk metrics and quantification, the EBA may consider enhanced metrics and the appropriateness to introduce it in the Pillar 1 framework.
  • This would entail the design and calibration of possible limits and thresholds, add-ons or buffers, as well as the specification of possible consequences if there are breaches.

Capital buffers and macroprudential framework

An alternative to amending the calculation of capital requirements to capture E&S risks in the prudential framework would be to increase the minimum required level of capital and/or to implement ‘borrower-based measures’ (BMM). Such BMMs aim to prevent a build-up of risk concentrations, for example by setting upper bounds on loan-to-value or loan-to-income for mortgage lending. Of the various possibilities, the EBA deems the use of a systemic risk capital buffer as the most suitable, although a double counting with the inclusion of E&S risks in the calculation of capital requirements under Pillar 1 and 2 needs to be avoided.

Recommended actions for capital buffers and macroprudential framework

Short term

• The EBA to asses changes to the guidelines on the appropriate subsets of sectoral exposures to which a systematic risk buffer may be applied.

Medium to long term

• The EBA to coordinate with other ongoing initiatives and assess the most appropriate adjustments.

Conclusion

The EBA considers E&S risks as a new source of systemic risk, which may not be adequately captured in the existing prudential framework. At the same time, the EBA recognizes the challenges in assessing the impact of these risks on regulatory metrics. The challenges range from a lack of granular and comparable data, varying definitions of what is environmentally and socially sustainable, historic data not being representative of what can be expected in the future, to the high uncertainty about the probability of future materialization of E&S risks. Moreover, the time horizon considered in the existing Pillar 1 framework is much shorter than the long horizon over which environmental risks are likely to fully materialize, with an exception of short-term acute physical and transition risks.

Against this background, the EBA does not recommend concrete quantitative adjustments to the existing Pillar 1 framework at this point. Nonetheless, it does expect financial institutions to take E&S risks into account in the inputs to the existing Pillar 1 framework or to apply overrides based on expert judgment. The EBA further proposes actions that should provide more clarity over time about the drivers and materiality of E&S risks. In due time, this can provide the basis for quantitative amendments to the Pillar 1 framework.

If you are interested to discuss this topic in more detail or would like support to embed E&S risks in your organization, please contact Pieter Klaassen at p.klaassen@zandersgroup.com or +41 78 652 5505.

[1]EBA (2023), Report on the role of environmental and social risks in the prudential framework (link), October.

[2] EBA (2022), Discussion paper on the role of environmental risks in the prudential framework (link), May. For a summary, see the article (link) on the Zanders website.

[3] ECB (2020), Guide on climate-related and environmental risks (link), November.

[4] See section 2.3.2 in EBA (2021), Report on management and supervision of ESG risks for credit institutions and investment firms (link), June.

[5] ECB (2020), Guide on climate-related and environmental risks (link), November.

[6] In the current EU Pillar 1 framework, adjustments are included that result in lower risk weights for small- and medium-sized enterprises (SME) and infrastructure lending. As the EBA notes, these adjustments are not risk-based but have been included in the EU to support lending to SMEs and for infrastructure projects.

[7] See ECB (2019), ECB Guide to internal models (link).

In this report, biodiversity loss ranks as the fourth most pressing concern after climate change adaptation, mitigation failure, and natural disasters. For financial institutions (FIs), it is therefore a relevant risk that should be taken into account. So, how should FIs implement biodiversity risk in their risk management framework?

Despite an increasing awareness of the importance of biodiversity, human activities continue to significantly alter the ecosystems we depend on. The present rate of species going extinct is 10 to 100 times higher than the average observed over the past 10 million years, according to Partnership for Biodiversity Accounting Financials[i]. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) reports that 75% of ecosystems have been modified by human actions, with 20% of terrestrial biomass lost, 25% under threat, and a projection of 1 million species facing extinction unless immediate action is taken. Resilience theory and planetary boundaries state that once a certain critical threshold is surpassed, the rate of change enters an exponential trajectory, leading to irreversible changes, and, as noted in a report by the Nederlandsche Bank (DNB), we are already close to that threshold[ii].

We will now explain biodiversity as a concept, why it is a significant risk for financial institutions (FIs), and how to start thinking about implementing biodiversity risk in a financial institutions’ risk management framework.

What is biodiversity?

The Convention on Biological Diversity (CBD) defines biodiversity as “the variability among living organisms from all sources including, i.a., terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part.”[iii] Humans rely on ecosystems directly and indirectly as they provide us with resources, protection and services such as cleaning our air and water.

Biodiversity both affects and is affected by climate change. For example, ecosystems such as tropical forests and peatlands consist of a diverse wildlife and act as carbon sinks that reduce the pace of climate change. At the same time, ecosystems are threatened by the accelerating change caused by human-induced global warming. The IPBES and Intergovernmental Panel on Climate Change (IPCC), in their first-ever collaboration, state that “biodiversity loss and climate change are both driven by human economic activities and mutually reinforce each other. Neither will be successfully resolved unless both are tackled together.”[iv]

Why is it relevant for financial institutions?

While financial institutions’ own operations do not materially impact biodiversity, they do have impact on biodiversity through their financing. ASN Bank, for instance, calculated that the net biodiversity impact of its financed exposure is equivalent to around 516 square kilometres of lost biodiversity – which is roughly equal to the size of the isle of Ibiza in Spain[v]. The FIs’ impact on biodiversity also leads to opportunities. The Institute Financing Nature (IFN) report estimates that the financing gap for biodiversity is close to $700 billion annually[vi]. This emphasizes the importance of directing substantial financial resources towards biodiversity-positive initiatives.

At the same time, biodiversity loss also poses risks to financial institutions.

The global economy highly depends on biodiversity as a result of the increasedglobalization and interconnectedness of the financial system. Due to these factors, the effects of biodiversity losses are magnified and exacerbated through the financial system, which can result in significant financial losses. For example, approximately USD 44 trillion of the global GDP is highly or moderately dependent on nature (World Economic Forum, 2020). Specifically for financial institutions, the DNB estimated that Dutch FIs alone have EUR 510 billionof exposure to companies that are highly or very highly dependent on one or more ecosystems services[vii]. Furthermore, in the 2010 World Economic Forum report worldwide economic damage from biodiversity loss is estimated to be around USD 2 to 4.5 trillion annually. This is remarkably high when compared to the negative global financial damage of USD 1.7 trillion per year from greenhouse gas emissions (based on 2008 data), which demonstrates that institutions should not focus their attention solely on the effects of climate change when assessing climate & environmental risks[viii].

Examples of financial impact

Similarly to climate risk, biodiversity risk is expected to materialize through the traditional risk types a financial institution faces. To illustrate how biodiversity loss can affect individual financial institutions, we provide an example of the potential impact of physical biodiversity risk on, respectively, the credit risk and market risk of an institution:

Credit risk:

Failing ecosystem services can lead to disruptions of production, reducing the profits of counterparties. As a result, there is an increase in credit risk of these counterparties. For example, these disruptions can materialize in the following ways:

• A total of 75% of the global food crop rely on animals for their pollination. For the agricultural sector, deterioration or loss of pollinating species may result in significant crop yield reduction.
• Marine ecosystems are a natural defence against natural hazards. Wetlands prevented USD 650 million worth of damages during the 2012 Superstorm Sandy [OECD, 2019), while the material damage of hurricane Katrina would have been USD 150 billion less if the wetlands had not been lost.

Market risk:

The market value of investments of a financial institution can suffer from the interconnectedness of the global economy and concentration of production when a climate event happens. For example:

• A 2011 flood in Thailand impacted an area where most of the world's hard drives are manufactured. This led to a 20%-40% rise in global prices of the product[ix]. The impact of the local ecosystems for these type of products expose the dependency for investors as well as society as a whole.

Core part of the European Green Deal

The examples above are physical biodiversity risk examples. In addition to physical risk, biodiversity loss can also lead to transition risk – changes in the regulatory environment could imply less viable business models and an increase in costs, which will potentially affect the profitability and risk profile of financial institutions. While physical risk can be argued to materialize in a more distant future, transition risk is a more pressing concern as new measures have been released, for example by the European Commission, to transition to more sustainable and biodiversity friendly practices. These measures are included in the EU biodiversity strategy for 2030 and the EU’s Nature restoration law.

The EU’s biodiversity strategy for 2030 is a core part of European Green Deal. It is a comprehensive, ambitious, and long-term plan that focuses on protecting valuable or vulnerable ecosystems, restoring damaged ecosystems, financing transformation projects, and introducing accountability for nature-damaging activities. The strategy aims to put Europe's biodiversity on a path to recovery by 2030, and contains specific actions and commitments. The EU biodiversity strategy covers various aspects such as:

• Legal protection of an additional 4% of land area (up to a total of 7%) and 19% of sea area (up to a total of 30%)
• Strict protection of 9% of sea and 7% of land area (up to a total of 10% for both)
• Reduction of fertilizer use by at least 20%
• Setting measures for sustainable harvesting of marine resources

A major step forwards towards enforcement of the strategy is the approval of the Nature restoration law by the EU in July 2023, which will become the first continent-wide comprehensive law on biodiversity and ecosystems. The law is likely to impact the agricultural sector, as the bill allows for 30% of all former peatlands that are currently exploited for agriculture to be restored or partially shifted to other uses by 2030. By 2050, this should be at least 70%. These regulatory actions are expected to have a positive impact on biodiversity in the EU. However, a swift implementation may increase transition risk for companies that are affected by the regulation.

The ECB Guide on climate-related and environmental risks explicitly states that biodiversity loss is one of the risk drivers for financial institutions[x]. Furthermore, the ECB Guide requires financial institutions to asses both physical and transition risks stemming from biodiversity loss. In addition, the EBA Report on the Management and Supervision of ESG Risk for Credit Institutions and Investment Firms repeatedly refers to biodiversity when discussing physical and transition risks[xi].

Moreover, the topic ‘biodiversity and ecosystems’ is also covered by the Corporate Sustainability Reporting Directive (CSRD), which requires companies within its scope to disclose on several sustainability related matters using a double materiality perspective.[1] Biodiversity and ecosystems is one of five environmental sustainability matters covered by CSRD. At a minimum, financial institutions in scope of CSRD must perform a materiality assessment of impacts, risks and opportunities stemming from biodiversity and ecosystems. Furthermore, when biodiversity is assessed to be material, either from financial or impact materiality perspective, the institution is subject to granular biodiversity-related disclosure requirements covering, among others, topics such as business strategy, policies, actions, targets, and metrics.

Where to start?

In line with regulatory requirements, financial institutions should already be integrating biodiversity into their risk management practices. Zanders recognizes the challenges associated with biodiversity-related risk management, such as data availability and multidimensionality. Therefore, Zanders suggests to initiate this process by starting with the following two steps. The complexity of the methodologies can increase over time as the institution’s, the regulator’s and the market’s knowledge on biodiversity-related risks becomes more mature.  

1. Perform materiality assessment using the double materiality concept. This means that financial institutions should measure and analyze biodiversity-related financial materiality through the identification of risks and opportunities. Institutions should also assess their impacts on biodiversity, for example, through calculation of their biodiversity footprint. This can start with classifying exposures’ impact and dependency on biodiversity based on a sector-level analysis.
2. Integrate biodiversity-related risks considerations into their business strategy and risk management frameworks. From a business perspective, if material, financial institutions are expected to integrate biodiversity in their business strategy, and set policies and targets to manage the risks. Such actions could be engagement with clients to promote their sustainability practices, allocation of financing to ‘biodiversity-friendly’ projects, and/or development of biodiversity specific products. Moreover, institutions are expected to adjust their risk appetites to account for biodiversity-related risks and opportunities, establish KRIs along with limits and thresholds. Embedding material ESG risks in the risk appetite frameworks should include a description on how risk indicators and limits are allocated within the banking group, business lines and branches.

Considering the potential impact of biodiversity loss on financial institutions, it is crucial for them to extend their focus beyond climate change and also start assessing and managing biodiversity risks. Zanders can support financial institutions in measuring biodiversity-related risks and taking first steps in integrating these risks into risk frameworks. Curious to hear more on this? Please reach out to Marije Wiersma, Iryna Fedenko, or Jaap Gerrits.

[1] CSRD applies to large EU companies, including banks and insurance firms. The first companies subject to CSRD must disclose according to the requirements in the European Sustainability Reporting Standards (ESRS) from 2025 (over financial year 2024), and by the reporting year 2029, the majority of European companies will be subject to publishing the CSRD reports. The sustainability report should be a publicly available statement with information on the sustainability-matters that the company considers material. This statement needs to be audited with limited assurance.

[i] PBAF. (2023). Dependencies - Pertnership for Biodiversity Acccounting Financials (PBAF)

[ii] De Nederlandche Bank. (2020). Indepted to nature - Exploring biodiversity risks for the Dutch Financial Sector.

[iii] CBD. (2005). Handbook of the convention on biological diversity

[iv] IPBES. (2021). Tackling Biodiversity & Climate Crises Together & Their Combined Social Impacts

[v] ASN Bank (2022). ASN Bank Biodiversity Footprint

[vi] Paulson Institute. (2021). Financing nature: Closing the Global Biodiversity

[vii] De Nederlandche Bank. (2020). Indepted to nature - Exploring biodiversity risks for the Dutch Financial Sector

[viii] PwC for World Economic Forum. (2010). Biodiversity and business risk

[ix] All the examples related to credit and market risk are presented in the report by De Nederlandsche Bank. (2020). Biodiversity Opportunities and Risks for the Financial Sector

[x] ECB. (2020). Guide on climate-related and environmental risks.

[xi] EBA. (2021). EBA Report on Management and Supervision of ESG Risk for Credit Institutions and Investment Firms

Under FRTB regulation, PLA requires banks to assess the similarity between Front Office (FO) and Risk P&L (HPL and RTPL) on a quarterly basis. Desks which do not pass PLA incur capital surcharges or may, in more severe cases, be required to use the more conservative FRTB standardised approach (SA).​

What is the purpose of PLA?​

PLA ensures that the FO and Risk P&Ls are sufficiently aligned with one another at the desk level.​ The FO HPL is compared with the Risk RTPL using two statistical tests.​ The tests measure the materiality of any simplifications in a bank’s Risk model compared with the FO systems.​ In order to use the Internal Models Approach (IMA), FRTB requires each trading desk to pass the PLA statistical tests.​ Although the implementation of PLA begins on the date that the IMA capital requirement becomes effective, banks must provide a one-year PLA test report to confirm the quality of the model.

Which statistical measures are used?​

PLA is performed using the Spearman Correlation and the Kolmogorov-Smirnov (KS) test using the most recent 250 days of historical RTPL and HPL.​ Depending on the results, each desk is assigned a traffic light test (TLT) zone (see below), where amber desks are those which are allocated to neither red or green.​

What are the consequences of failing PLA?

Capital increase: Desks in the red zone are not permitted to use the IMA and must instead use the more conservative SA, which has higher capital requirements. ​Amber desks can use the IMA but must pay a capital surcharge until the issues are remediated.

Difficulty with returning to IMA: Desks which are in the amber or red zone must satisfy statistical green zone requirements and 12-month backtesting requirements before they can be eligible to use the IMA again.​

What are some of the key reasons for PLA failure?

Data issues: Data proxies are often used within Risk if there is a lack of data available for FO risk factors. Poor or outdated proxies can decrease the accuracy of RTPL produced by the Risk model.​ The source, timing and granularity also often differs between FO and Risk data.

Missing risk factors: Missing risk factors in the Risk model are a common cause of PLA failures. Inaccurate RTPL values caused by missing risk factors can cause discrepancies between FO and Risk P&Ls and lead to PLA failures.

Roadblocks to finding the sources of PLA failures

FO and Risk mapping: Many banks face difficulties due to a lack of accurate mapping between risk factors in FO and those in Risk. ​For example, multiple risk factors in the FO systems may map to a single risk factor in the Risk model. More simply, different naming conventions can also cause issues.​ The poor mapping can make it difficult to develop an efficient and rapid process to identify the sources of P&L differences.

Lack of existing processes: PLA is a new requirement which means there is a lack of existing infrastructure to identify causes of P&L failures. ​Although they may be monitored at the desk level, P&L differences are not commonly monitored at the risk factor level on an ongoing basis.​ A lack of ongoing monitoring of risk factors makes it difficult to pre-empt issues which may cause PLA failures and increase capital requirements.

Our approach: Identifying risk factors that are causing PLA failures

Zanders’ approach overcomes the above issues by producing analytics despite any underlying mapping issues between FO and Risk P&L data. ​Using our algorithm, risk factors are ranked depending upon how statistically likely they are to be causing differences between HPL and RTPL.​ Our metric, known as risk factor ‘alpha’, can be tracked on an ongoing basis, helping banks to remediate underlying issues with risk factors before potential PLA failures.

Zanders’ P&L attribution solution has been implemented at a Tier-1 bank, providing the necessary infrastructure to identify problematic risk factors and improve PLA desk statuses. The solution provided multiple benefits to increase efficiency and transparency of workstreams at the bank.

Conclusion

As it is a new regulatory requirement, passing the PLA test has been a key concern for many banks. Although the test itself is not considerably difficult to implement, identifying why a desk may be failing can be complicated. In this article, we present a PLA tool which has already been successfully implemented at one of our large clients. By helping banks to identify the underlying risk factors which are causing desks to fail, remediation becomes much more efficient. Efficient remediation of desks which are failing PLA, in turn, reduces the amount of capital charges which banks may incur.

Challenges with VaR models in a turbulent market​

With recent periods of market stress, including COVID-19 and the Russia-Ukraine conflict, banks are finding their VaR models under strain. A failure to adhere to VaR backtesting requirements can lead to pressure on balance sheets through higher capital requirements and interventions from the regulator.

VaR backtesting

VaR is integral to the capital requirements calculation and in ensuring a sufficient capital buffer to cover losses from adverse market conditions.​ The accuracy of VaR models is therefore tested stringently with VaR backtesting, comparing the model VaR to the observed hypothetical P&Ls. ​A VaR model with poor backtesting performance is penalised with the application of a capital multiplier, ensuring a conservative capital charge.​ The capital multiplier increases with the number of exceptions during the preceding 250 business days, as described in Table 1 below.​

Table 1: Capital multipliers based on the number of backtesting exceptions.

The capital multiplier is applied to both the VaR and stressed VaR, as shown in equation 1 below, which can result in a significant impact on the market risk capital requirement when failures in VaR backtesting occur.​

Pro-cyclicality of the backtesting framework​

A known issue of VaR backtesting is pro-cyclicality in market risk. ​This problem was underscored at the beginning of the COVID-19 outbreak when multiple banks registered several VaR backtesting exceptions. ​This had a double impact on market risk capital requirements, with higher capital multipliers and an increase in VaR from higher market volatility.​ Consequently, regulators intervened to remove additional pressure on banks’ capital positions that would only exacerbate market volatility. The Federal Reserve excluded all backtesting exceptions between 6th – 27th March 2020, while the PRA allowed a proportional reduction in risks-not-in-VaR (RNIV) capital charge to offset the VaR increase.​ More recent market volatility however has not been excluded, putting pressure on banks’ VaR models during backtesting.​

Historical simulation VaR model challenges​

Banks typically use a historical simulation approach (HS VaR) for modelling VaR, due to its computational simplicity, non-normality assumption of returns and enhanced interpretability. ​Despite these advantages, the HS VaR model can be slow to react to changing markets conditions and can be limited by the scenario breadth. ​This means that the HS VaR model can fail to adequately cover risk from black swan events or rapid shifts in market regimes.​ These issues were highlighted by recent market events, including COVID-19, the Russia-Ukraine conflict, and the global surge in inflation in 2022.​ Due to this, many banks are looking at enriching their VaR models to better model dramatic changes in the market.

Enriching HS VaR models​

Alternative VaR modelling approaches can be used to enrich HS VaR models, improving their response to changes in market volatility. Volatility scaling is a computationally efficient methodology which can resolve many of the shortcomings of HS VaR model, reducing backtesting failures.​

Enhancing HS VaR with volatility scaling​

The Volatility Scaling methodology is an extension of the HS VaR model that addresses the issue of inertia to market moves.​ Volatility scaling adjusts the returns for each time t by the volatility ratio σT/σt, where σt is the return volatility at time t and σT is the return volatility at the VaR calculation date.​ Volatility is calculated using a 30-day window, which more rapidly reacts to market moves than a typical 1Y VaR window, as illustrated in Figure 1.​ As the cost of underestimation is higher than overestimating VaR, a lower bound to the volatility ratio of 1 is applied.​ Volatility scaling is simple to implement and can enrich existing models with minimal additional computational overhead.​

Figure 1: The 30-day and 1Y rolling volatilities of the 1-day scaled diversified portfolio returns. This illustrates recent market stresses, with short regions of extreme volatility (COVID-19) and longer systemic trends (Russia-Ukraine conflict and inflation). 

Comparison with alternative VaR models​

To benchmark the Volatility Scaling approach, we compare the VaR performance with the HS and the GARCH(1,1) parametric VaR models.​ The GARCH(1,1) model is configured for daily data and parameter calibration to increase sensitivity to market volatility.​ All models use the 99th percentile 1-day VaR scaled by a square root of 10. ​The effective calibration time horizon is one year, approximated by a VaR window of 260 business days.​ A one-week lag is included to account for operational issues that banks may have to load the most up-to-date market data into their risk models.​

VaR benchmarking portfolios​

To benchmark the VaR Models, their performance is evaluated on several portfolios that are sensitive to the equity, rates and credit asset classes. ​These portfolios include sensitivities to: S&P 500 (Equity), US Treasury Bonds (Treasury), USD Investment Grade Corporate Bonds (IG Bonds) and a diversified portfolio of all three asset classes (Diversified).​ This provides a measure of the VaR model performance for both diversified and a range of concentrated portfolios.​ The performance of the VaR models is measured on these portfolios in both periods of stability and periods of extreme market volatility. ​This test period includes COVID-19, the Russia-Ukraine conflict and the recent high inflationary period.​

VaR model benchmarking

The performance of the models is evaluated with VaR backtesting. The results show that the volatility scaling provides significantly improved performance over both the HS and GARCH VaR models, providing a faster response to markets moves and a lower instance of VaR exceptions.​

Model benchmarking with VaR backtesting​

A key metric for measuring the performance of VaR models is a comparison of the frequency of VaR exceptions with the limits set by the Basel Committee’s Traffic Light Test (TLT). ​Excessive exceptions will incur an increased capital multiplier for an Amber result (5 – 9 exceptions) and an intervention from the regulator in the case of a Red result (ten or more exceptions).​ Exceptions often indicate a slow reaction to market moves or a lack of accuracy in modelling risk.​

VaR measure coverage​

The coverage and adaptability of the VaR models can be observed from the comparison of the realised returns and VaR time series shown in Figure 2.​ This shows that although the GARCH model is faster to react to market changes than HS VaR, it underestimates the tail risk in stable markets, resulting in a higher instance of exceptions.​ Volatility scaling retains the conservatism of the HS VaR model whilst improving its reactivity to turbulent market conditions. This results in a significant reduction in exceptions throughout 2022.​

Figure 2: Comparison of realised returns with the model VaR measures for a diversified portfolio.

VaR backtesting results​

The VaR model performance is illustrated by the percentage of backtest days with Red, Amber and Green TLT results in Figure 3.​ Over this period HS VaR shows a reasonable coverage of the hypothetical P&Ls, however there are instances of Red results due to the failure to adapt to changes in market conditions.​ The GARCH model shows a significant reduction in performance, with 32% of test dates falling in the Red zone as a consequence of VaR underestimation in calm markets.​ The adaptability of volatility scaling ensures it can adequately cover the tail risk, increasing the percentage of Green TLT results and completely eliminating Red results.​ In this benchmarking scenario, only volatility scaling would pass regulatory scrutiny, with HS VaR and GARCH being classified as flawed models, requiring remediation plans.

Figure 3: Percentage of days with a Red, Amber and Green Traffic Light Test result for a diversified portfolio over the window 29/01/21 - 31/01/23.

VaR model capital requirements​

Capital requirements are an important determinant in banks’ ability to act as market intermediaries. The volatility scaling method can be used to increase the HS capital deployment efficiency without compromising VaR backtesting results.​

Capital requirements minimisation​

A robust VaR model produces risk measures that ensure an ample capital buffer to absorb portfolio losses. When selecting between robust VaR models, the preferred approach generates a smaller capital charge throughout the market cycle. Figure 4 shows capital requirements for the VaR models for a diversified portfolio calculated using Equation 1, with 𝐴𝑑𝑑𝑜𝑛𝑠 set to zero. Volatility scaling outperforms both models during extreme market volatility (the Russia-Ukraine conflict) and the HS model in period of stability (2021) as a result of setting the lower scaling constraint. The GARCH model underestimates capital requirements in 2021, which would have forced a bank to move to a standardised approach.

Figure 4: Capital charge for the VaR models measured on a diversified portfolio over the window 29/01/21 - 31/01/23.

Capital management efficiency

Pro-cyclicality of capital requirements is a common concern among regulators and practitioners. More stable requirements can improve banks’ capital management and planning. To measure models’ pro-cyclicality and efficiency, average capital charges and capital volatilities are compared for three concentrated asset class portfolios and a diversified market portfolio, as shown in Table 2. Volatility scaling results are better than the HS model across all portfolios, leading to lower capital charges, volatility and more efficient capital allocation. The GARCH model tends to underestimate high volatility and overestimate low volatility, as seen by the behaviour for the lowest volatility portfolio (Treasury).

Table 2: Average capital requirement and capital volatility for each VaR model across a range of portfolios during the test period, 29/01/21 - 31/01/23.

Conclusions on VaR backtesting

Recent periods of market stress highlighted the need to challenge banks’ existing VaR models. Volatility scaling is an efficient method to enrich existing VaR methodologies, making them robust across a range of portfolios and volatility regimes.

VaR backtesting in a volatile market

Ensuring VaR models conform to VaR backtesting will be challenging with the recent period of stressed market conditions and rapid changes in market volatility. Banks will need to ensure that their VaR models are responsive to volatility clustering and tail events or enhance their existing methodology to cope. Failure to do so will result in additional overheads, with increased capital charges and excessive exceptions that can lead to additional regulatory scrutiny.

Enriching VaR Models with volatility scaling

Volatility scaling provides a simple extension of HS VaR that is robust and responsive to changes in market volatility. The model shows improved backtesting performance over both the HS and parametric (GARCH) VaR models. It is also robust for highly concentrated equity, treasury and bond portfolios, as seen in Table 3. Volatility scaling dampens pro-cyclicality of HS capital requirements, ensuring more efficient capital planning. The additional computational overhead is minimal and the implementation to enrich existing models is simple. Performance can be further improved with the use of hybrid models which incorporate volatility scaling approaches. These can utilise outlier detection to increase conservatism dynamically with increasingly volatile market conditions.

Table 3: Percentage of Green, Amber and Red traffic Lights test results for each VaR model across a range of portfolios for dates in the range: 13/02/19 - 31/01/23.

Zanders recommends

Banks should invest in making their VaR models more robust and reactive to ensure capital costs and the probability of exceptions are minimised. VaR models enriched with a volatility scaling approach should be considered among a suite of models to challenge existing VaR model methodologies. Methods similar to volatility scaling can also be applied to parametric and semi-parametric models. Outlier detection models can be used to identify changes in market regime as either feeder models or early warning signals for risk managers

Non-modellable risk factors (NMRFs) have been shown to be one of the largest contributors to capital charges under FRTB. The use of proxies is one of the methods that banks can employ to increase the modellability of risk factors and reduce the number of NMRFs. Other potential methods for improving the modellability of risk factors is using external data sources and modifying risk factor bucketing approaches.

Proxies and FRTB

A proxy is utilised when there is an insufficient historical data for a risk factor. A lack of historical data increases the likelihood of the risk factor failing the Risk Factor Eligibility Test (RFET). Consequently, using proxies ensures that the number of NMRFs is reduced and capital charges are kept to a minimum. Although the use of proxies is allowed, regulation states that their usage must be limited, and they must have sufficiently similar characteristics to the risk factors which they represent.

Banks must be ready to provide evidence to regulators that their chosen proxies are conceptually and empirically sound. Despite the potential reduction in capital, developing proxy methodologies can be time-consuming and require considerable ongoing monitoring. There are two main approaches which are used to develop proxies: rules-based and statistical.

Proxy decomposition

FRTB regulation allows NMRFs to be decomposed into modellable components and a residual basis, which must be capitalised as non-modellable. For example, credit spreads for small issuers which are not highly liquid can be decomposed into a liquid credit spread index component, which is classed as modellable, and a non-modellable basis or spread.  

To test modellability using the RFET, 12-months of data is required for the proxy and basis components. If the basis between the proxy and the risk factor has not been identified and properly capitalised, only the proxy representation of the risk factor can be used in the Risk Theoretical P&L (RTPL). However, if the capital requirement for a basis is determined, either: (i) the proxy risk factor and the basis; or (ii) the original risk factor itself can be included in the RTPL.

Banks should aim to produce preliminary analysis on the cost benefits of proxy development – does the cost and effort of developing proxies outweigh the capital which could be saved by increasing risk factor modellability? For example, proxies which are highly volatile may also result in increasing NMRF capital charges.

Approaches for the development of proxies

Both rules-based and statistical approaches to developing proxies require considerable effort. Banks should aim to develop statistical approaches as they have been shown to be more accurate and also more efficient in reducing capital requirements for banks.

Rules-based approach

Rules-based approaches are more simplistic, however are less accurate than the statistical approaches. They find the “closest fit” modellable risk factor using somewhat more qualitative methods. For example, picking the closest tenor on a yield curve (see below), using relevant indices or ETFs, or limiting the search for proxies to the same sector as the underlying risk factor.

Similarly, longer tenor points (which may not be traded as frequently) can be decomposed into shorter-tenor points and cross-tenor basis spread.

Statistical approach

Statistical approaches are more quantitate and more accurate than the rules-based approaches. However, this inevitably comes with computational expense. A large number of candidates are tested using the chosen statistical methodology and the closest is picked (see below).

For example, a regression approach could be used to identify which of the candidates are most correlated with the underlying risk factor. Studies have shown that statistical approaches not only produce the more accurate proxies, but can also reduce capital charges by almost twice as much as simpler rules-based approaches.

Conclusion

Risk factor modellability is a considerable concern for banks as it has a direct impact on the size of their capital charges. Inevitably, reducing the number of NMRFs is a key aim for all IMA banks. In this article, we show that developing proxies is one of the strategies that banks can use to minimise the amount of NMRFs in their models. Furthermore, we describe the two main approaches for developing proxies: rules-based and statistical. Although rules-based approaches are less complicated to develop, statistical approaches show much better accuracy and hence have the potential to better reduce capital charges.

Under the FRTB internal models approach (IMA), the capital calculation of risk factors is dependent on whether the risk factor is modellable. Insufficient data will result in more non-modellable risk factors (NMRFs), significantly increasing associated capital charges.

NMRFs

Risk factor modellability and NMRFs

The modellability of risk factors is a new concept which was introduced under FRTB and is based on the liquidity of each risk factor. Modellability is measured using the number of ‘real prices’ which are available for each risk factor. Real prices are transaction prices from the institution itself, verifiable prices for transactions between arms-length parties, prices from committed quotes, and prices from third party vendors.

For a risk factor to be classed as modellable, it must have a minimum of 24 real prices per year, no 90-day period with less than four prices, and a minimum of 100 real prices in the last 12 months (with a maximum of one real price per day). The Risk Factor Eligibility Test (RFET), outlined in FRTB, is the process which determines modellability and is performed quarterly. The results of the RFET determine, for each risk factor, whether the capital requirements are calculated by expected shortfall or stressed scenarios.

Consequences of NMRFs for banks

Modellable risk factors are capitalised via expected shortfall calculations which allow for diversification benefits. Conversely, capital for NMRFs is calculated via stressed scenarios which result in larger capital charges. This is due to longer liquidity horizons and more prudent assumptions used for aggregation. Although it is expected that a low proportion of risk factors will be classified as non-modellable, research shows that they can account for over 30% of total capital requirements. 

There are multiple techniques that banks can use to reduce the number and impact of NMRFs, including the use of external data, developing proxies, and modifying the parameterisation of risk factor curves and surfaces. As well as focusing on reducing the number of NMRFs, banks will also need to develop early warning systems and automated reporting infrastructures to monitor the modellability of risk factors. These tools help to track and predict modellability issues, reducing the likelihood that risk factors will fail the RFET and increase capital requirements.

Methods for reducing the number of NMRFs

Banks should focus on reducing their NMRFs as they are associated with significantly higher capital charges. There are multiple approaches which can be taken to increase the likelihood that a risk factor passes the RFET and is classed as modellable.

Enhancing internal data

The simplest way for banks to reduce NMRFs is by increasing the amount of data available to them. Augmenting internal data with external data increases the number of real prices available for the RFET and reduces the likelihood of NMRFs. Banks can purchase additional data from external data vendors and data pooling services to increase the size and quality of datasets.

It is important for banks to initially investigate their internal data and understand where the gaps are. As data providers vary in which services and information they provide, banks should not only focus on the types and quantity of data available. For example, they should also consider data integrity, user interfaces, governance, and security. Many data providers also offer FRTB-specific metadata, such as flags for RFET liquidity passes or fails.

Finally, once a data provider has been chosen, additional effort will be required to resolve discrepancies between internal and external data and ensure that the external data follows the same internal standards.

Creating risk factor proxies

Proxies can be developed to reduce the number or magnitude of NMRFs, however, regulation states that their use must be limited. Proxies are developed using either statistical or rules-based approaches.

Rules-based approaches are simplistic, yet generally less accurate. They find the “closest fit” modellable risk factor using more qualitative methods, e.g. using the closest tenor on the interest rate curve. Alternatively, more accurate approaches model the relationship between the NMRF and modellable risk factors using statistical methods. Once a proxy is determined, it is classified as modellable and only the basis between it and the NMRF is required to be capitalised using stressed scenarios.

Determining proxies can be time-consuming as it requires exploratory work with uncertain outcomes. Additional ongoing effort will also be required by validation and monitoring units to ensure the relationship holds and the regulator is satisfied.

Developing own bucketing approach

Instead of using the prescribed bucketing approach, banks can use their own approach to maximise the number of real price observations for each risk factor.

For example, if a risk model requires a volatility surface to price, there are multiple ways this can be parametrised.  One method could be to split the surface into a 5x5 grid, creating 25 buckets that would each require sufficient real price observations to be classified as modellable. Conversely, the bank could instead split the surface into a 2x2 grid, resulting in only four buckets. The same number of real price observations would then need to be allocated between significantly less buckets, decreasing the chances of a risk factor being a NMRF.

It should be noted that the choice of bucketing approach affects other aspects of FRTB. Profit and Loss Attribution (PLA) uses the same buckets of risk factors as chosen for the RFET. Increasing the number of buckets may increase the chances of passing PLA, however, also increases the likelihood of risk factors failing the RFET and being classed as NMRFs.

Conclusion

In this article, we have described several potential methods for reducing the number of NMRFs. Although some of the suggested methods may be more cost effective or easier to implement than others, banks will most likely, in practice, need to implement a combination of these strategies in parallel. The modellability of risk factors is clearly an important part of the FRTB regulation for banks as it has a direct impact on required capital. Banks should begin to develop strategies for reducing the number of NMRFs as early as possible if they are to minimise the required capital when FRTB goes live.

The EBA has recently published the findings and observations from their TRIM on-site inspections. A significant number of deficiencies were identified and are required to be remediated by institutions in a timely fashion.

Since the Global Financial Crisis 2007-09, concerns have been raised regarding the complexity and variability of the models used by institutions to calculate their regulatory capital requirements. The lack of transparency behind the modelling approaches made it increasingly difficult for regulators to assess whether all risks had been appropriately and consistently captured.

The TRIM project was a large-scale multi-year supervisory initiative launched by the ECB at the beginning of 2016. The project aimed to confirm the adequacy and appropriateness of approved Pillar I internal models used by Significant Institutions (SIs) in euro area countries. This ensured their compliance with regulatory requirements and aimed to harmonise supervisory practices relating to internal models.

TRIM executed 200 on-site internal model investigations across 65 SIs from over 10 different countries. Over 5,800 deficiencies were identified. Findings were defined as deficiencies which required immediate supervisory attention. They were categorised depending on the actual or potential impact on the institution’s financial situation, the levels of own funds and own funds requirements, internal governance, risk control, and management.

The findings have been followed up with 253 binding supervisory decisions which request that the SIs mitigate these shortcomings within a timely fashion. Immediate action was required for findings that were deemed to take a significant time to address.

Assessment of Market Risk

TRIM assessed the VaR/sVaR models of 31 institutions. The majority of severe findings concerned the general features of the VaR and sVaR modelling methodology, such as data quality and risk factor modelling.

19 out of 31 institutions used historical simulation, seven used Monte Carlo, and the remainder used either a parametric or mixed approach. 17 of the historical simulation institutions, and five using Monte Carlo, used full revaluation for most instruments. Most other institutions used a sensitivities-based pricing approach.

VaR/sVaR Methodology

Data: Issues with data cleansing, processing and validation were seen in many institutions and, on many occasions, data processes were poorly documented.

Risk Factors: In many cases, risk factors were missing or inadequately modelled. There was also insufficient justification or assessment of assumptions related to risk factor modelling.

Pricing: Institutions frequently had inadequate pricing methods for particular products, leading to a failure for the internal model to adequately capture all material price risks. In several cases, validation activities regarding the adequacy of pricing methods in the VaR model were insufficient or missing.

RNIME: Approximately two-thirds of the institutions had an identification process for risks not in model engines (RNIMEs). For ten of these institutions, this directly led to an RNIME add-on to the VaR or to the capital requirements.

Regulatory Backtesting

Period and Business Days: There was a lack of clear definitions of business and non-business days at most institutions. In many cases, this meant that institutions were trading on local holidays without adequate risk monitoring and without considering those days in the P&L and/or the VaR.

APL: Many institutions had no clear definition of fees, commissions or net interest income (NII), which must be excluded from the actual P&L (APL). Several institutions had issues with the treatment of fair value or other adjustments, which were either not documented, not determined correctly, or were not properly considered in the APL. Incorrect treatment of CVAs and DVAs and inconsistent treatment of the passage of time (theta) effect were also seen.

HPL: An insufficient alignment of pricing functions, market data, and parametrisation between the economic P&L (EPL) and the hypothetical P&L (HPL), as well as the inconsistent treatment of the theta effect in the HPL and the VaR, was seen in many institutions.

Internal Validation and Internal Backtesting

Methodology: In several cases, the internal backtesting methodology was considered inadequate or the levels of backtesting were not sufficient.

Hypothetical Backtesting: The required backtesting on hypothetical portfolios was either not carried or was only carried out to a very limited extent

IRC Methodology

TRIM assessed the IRC models of 17 institutions, reviewing a total of 19 IRC models. A total of 120 findings were identified and over 80% of institutions that used IRC models received at least one high-severity finding in relation to their IRC model. All institutions used a Monte Carlo simulation method, with 82% applying a weekly calculation. Most institutions obtained rates from external rating agency data. Others estimated rates from IRB models or directly from their front office function. As IRC lacks a prescriptive approach, the choice of modelling approaches between institutes exhibited a variety of modelling assumptions, as illustrated below.

Recovery rates: The use of unjustified or inaccurate Recovery Rates (RR) and Probability of Defaults (PD) values were the cause of most findings. PDs close to or equal to zero without justification was a common issue, which typically arose for the modelling of sovereign obligors with high credit quality. 58% of models assumed PDs lower than one basis point, typically for sovereigns with very good ratings but sometimes also for corporates. The inconsistent assignment of PDs and RRs, or cases of manual assignment without a fully documented process, also contributed to common findings.

Modellingapproach: The lack of adequate modelling justifications presented many findings, including copula assumptions, risk factor choice, and correlation assumptions. Poor quality data and the lack of sufficient validation raised many findings for the correlation calibration.

Assessment of Counterparty Credit Risk

Eight banks faced on-site inspections under TRIM for counterparty credit risk. Whilst the majority of investigations resulted in findings of low materiality, there were severe weaknesses identified within validation units and overall governance frameworks.

Conclusion

Based on the findings and responses, it is clear that TRIM has successfully highlighted several shortcomings across the banks. As is often the case, many issues seem to be somewhat systemic problems which are seen in a large number of the institutions. The issues and findings have ranged from fundamental problems, such as missing risk factors, to more complicated problems related to inadequate modelling methodologies. As such, the remediation of these findings will also range from low to high effort. The SIs will need to mitigate the shortcomings in a timely fashion, with some more complicated or impactful findings potentially taking a considerable time to remediate.