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

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

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.

One of the key subjects in this re-assessment is the implementation of tangible and transparent Environment, Social, and Governance (ESG) factors into the business.

Treasury can drive sustainability throughout the company from two perspectives, namely through initiatives within the Treasury function and initiatives promoted by external stakeholders, such as banks, investors, or its clients. When considering sustainability, many treasurers first port of call is to investigate realizing sustainable financing framework. This is driven by the high supply of money earmarked for sustainable goals. However, besides this external focus, Treasury can strive to make its own operations more sustainable and, as a result, actively contribute to company-wide ESG objectives.

Figure 1: ESG initiatives in scope for Treasury

Treasury holds a unique position within the company because of the cooperation it has with business areas and the interaction with external stakeholders. Treasury can leverage this position to drive ESG developments throughout the company, stay informed of latest updates and adhere to regulatory standards. This article shows how Treasury can become a sustainable support function in its own right, highlights various initiatives within and outside the Treasury department and marks the benefits for Treasury – on top of realising ESG targets.

Internal initiatives

Automation and digitalization drive certain environmental initiatives within the Treasury department. Full digitalized records and bank statement management and the digitalization of form processes reduce the adverse environmental impact of the Treasury department. Besides reducing Treasury’s environmental footprint, digitalization improves efficiency of the Treasury team. By reducing the number of manual, cumbersome operational activities, time can be spent on value-adding activities rather than operational tasks.

Another great example of how Treasury can contribute to the ESG goals of the company, is to incorporate ESG elements in the capital allocation process. This can be done by adding ESG related risk factors to the weighted average cost of capital (WACC) or hurdle investment rates. By having an ESG linked WACC, one can evaluate projects by measuring the real impact of ESG on the required return on equity (ROE). By adjusting the WACC to, for example, the level of CO2 that is emitted by a project, the capital allocation process favours projects with low CO2 emissions.

An additional internal initiative is the design of a mobility policy with the objective to lower CO2 emissions. On one hand, this relates to decreasing the amount of business trips made by the Treasury department itself. On the other hand, it relates to the reduction of business travel by stakeholders of treasury such as bankers, advisors and system vendors. A framework that offsets the added value of a real-life meeting against the CO2 emission is an example of a measure that supports CO2 reduction on both sides. Such a framework supports determination whether the meeting takes place online or in person.

Furthermore, embedding ESG requirements into bank selection, system selection and maintenance processes is a valuable way of encouraging new and existing partners to undertake ESG related measures.

When it comes to social contributions, the focus could be on the diversity and inclusion of the Treasury department, which includes well-being, gender equality and inclusivity of the employees. Pursuing these policies can increase the attractiveness of the organization when hiring talent and make it easier to retain talent within the company, which is also beneficial to the Treasury function.

The development of a structured model that defines the building blocks for Treasury to support the achievement of companywide ESG objectives is a governance initiative that Treasury could undertake. An example of such a model is the Zanders Treasury and Risk Maturity Model, which can be integrated in any organization. This framework supports Treasury in keeping track of its ESG footprint and its contribution to company-wide sustainable objectives. In addition, the Zanders sustainability dashboard provides information on metrics and benchmarks that can be applied to track the progress of several ESG related goals for Treasury. Some examples of these are provided in our ‘Integration of ESG in treasury’ article.

External initiatives

Besides actions taken within the Treasury department, Treasury can boost company-wide ESG performance by leveraging their collaboration with external stakeholders. One of these external initiatives is sustainability linked financing, which is a great tool to encourage the setting of ambitious, company-wide ESG targets and link these to financing arrangements. Examples of sustainability linked financing products include green loans and bonds, sustainability-linked loans, and social bonds. To structure sustainability-linked financing products, corporates often benefit from the guidance of external parties when setting KPIs and ambitious targets and linking these to the existing sustainability strategy. Besides Treasury’s strong relationships with banks, retaining good relationships with (ESG) rating agencies and financial institutions is critical to stay abreast of the latest updates and adhere to regulatory standards. Additionally, investing excess cash in a sustainable manner, using green money market funds or assessing the ESG rating of counterparties, is an effective way of supporting sustainability.

Apart from financing instruments, Treasury can drive the ESG strategy throughout the organization in other ways. Treasury can seek collaborations with business partners to comply with ESG targets, which is another effective manner to achieve ESG related goals throughout the supply chain. An increasing number of corporates is looking to reduce the carbon footprint of their supply chain, for which collaboration is essential. Treasury can support this initiative by linking supplier onboarding on its supply chain finance program to the sustainability performance of suppliers.

To conclude

As developments in ESG are rapidly unfold9ing, Zanders has started an initiative to continuously update our clients to stay ahead of the latest trends. Through the knowledge and network that we have built over the years, we will regularly inform our clients on ESG trends via articles on the news page on our website. The first article will be devoted to the revision of the Sustainability Linked Loan Principles (SLLP) by the Loan Market Association (LMA) and its American and Asian equivalents.

We are keen to hear which topics you would like to see covered. Feel free to reach out to Joris van den Beld or Sander van Tol if you have any questions or want to address ESG topics that are on your agenda.

The Bank of England is even of the opinion that climate change represents the tragedy of the horizon: “by the time it is clear that climate change is creating risks that we want to reduce, it may already be too late to act” [1]. This article provides a summary of the type of financial risks resulting from climate change, various initiatives within the financial industry relating to the shift towards a low-carbon economy, and an outlook for the assessment of climate change risks in the future.

At the December 2015 Paris Agreement conference, strict measures to limit the rise in global temperatures were agreed upon. By signing the Paris Agreement, governments from all over the world committed themselves to paving a more sustainable path for the planet and the economy. If no action is taken and the emission of greenhouse gasses is not reduced, research finds that per 2100, the temperature will have increased by 3°C to 5°C².. Climate change affects the availability of resources, the supply and demand for products and services and the performance of physical assets. Worldwide economic costs from natural disasters already exceeded the 30-year average of USD 140 billion per annum in seven out of the last ten years. Extreme weather circumstances influence health and damage infrastructure and private properties, thereby reducing wealth and limiting productivity. According to Frank Elderson, Executive Director at the DNB, this can disrupt economic activity and trade, lead to resource shortages and shift capital from more productive uses to reconstruction and replacement³.

According to the Bank of England, financial risks from climate change come down to two primary risk factors⁴:

Increasing concerns about climate change has led to a shift in the perception of climate risk among companies and investors. Where in the past analysis of climate-related issues was limited to sectors directly linked to fossil fuels and carbon emissions, it is currently being recognized that climate-related risk exposures concern all sectors, including financials. Banks are particularly vulnerable to climate-related risks as they are tied to every market sector through their lending practices.

Financial risks

• Physical risks. The first risk factor concerns physical risks caused by climate and weather-related events such as droughts and a sea level rise. Potential consequences are large financial losses due to damage to property, land and infrastructure. This could lead to impairment of asset values and borrowers’ creditworthiness. For example, as of January 2019, Dutch financial institutions have EUR 97 billion invested in companies active in areas with water scarcity⁵. These institutions can face distress if the water scarcity turns into water shortages. Another consequence of extreme climate and weather-related events is the increase in insurance claims: in the US alone, the insurance industry paid out USD 135 billion from natural catastrophes in 2017, almost three times higher than the annual average of USD 49 billion.
• Transition risks. The second risk factor comprises transition risks resulting from the process of moving towards a low-carbon economy. Revaluation of assets because of changes in policy, technology and sentiment could destabilize markets, tighten financial conditions and lead to procyclicality of losses. The impact of the transition is not limited to energy companies: transportation, agriculture, real estate and infrastructure companies are also affected. An example of transition risk is a decrease in financial return from stocks of energy companies if the energy transition undermines the value of oil stocks. Another example is a decrease in the value of real estate due to higher sustainability requirements.

These two climate-related risk factors increase credit risk, market risk and operational risk and have distinctive elements from other risk factors that lead to a number of unique challenges. Firstly, financial risks from physical and transition risk factors may be more far-reaching in breadth and magnitude than other types of risks as they are relevant to virtually all business lines, sectors and geographies, and little diversification is present. Secondly, there is uncertainty in timing of when financial risks may be realized. The possibility exists that the risk impact falls outside of current business planning horizons. Thirdly, despite the uncertainty surrounding the exact impact of climate change risks, combinations of physical and transition risk factors do lead to financial risk. Finally, the magnitude of the future impact is largely dependent on short-term actions.

Initiatives

Many parties in the financial sector acknowledge that although the main responsibility for ensuring the success of the Paris Agreement and limiting climate change lies with governments, central banks and supervisors also have responsibilities. Consequently, climate change and the inherent financial risks are increasingly receiving attention, which is evidenced by the various recent initiatives related to this topic.

Banks and regulators

The Network of Central Banks and Supervisors for Greening the Financial System (NGFS) is an international cooperation between central banks and regulators⁶. NGFS aims to increase the financial sector’s efforts to achieve the Paris climate goals, for example by raising capital for green and low-carbon investments. NGFS additionally maps out what is needed for climate risk management. DNB and central banks and regulators of China, Germany, France, Mexico, Singapore, UK and Sweden were involved from the start of NGFS in 2017. The ECB, EBA, EIB and EIOPA are currently also part of the network. In the first progress report of October 2018, NGFS acknowledged that regulators and central banks increased their efforts to understand and estimate the extent of climate and environmental risks. They also noted, however, that there is still a long way to go.

In their first comprehensive report of April 2019, NGFS drafted the following six recommendations for central banks, supervisors, policymakers and financial institutions, which reflect best practices to support the Paris Agreement⁷:

1. Integrating climate-related risks into financial stability monitoring and micro-supervision;
2. Integrating sustainability factors into own-portfolio management;
3. Bridging the data gaps by public authorities by making relevant data to Climate Risk Assessment (CRA) publicly available in a data repository;
4. Building awareness and intellectual capacity and encouraging technical assistance and knowledge sharing;
5. Achieving robust and internationally consistent climate and environment-related disclosure;
6. Supporting the development of a taxonomy of economic activities.

All these recommendations require the joint action of central banks and supervisors. They aim to integrate and implement earlier identified needs and best practices to ensure a smooth transition towards a greener financial system and a low-carbon economy. Recommendations 1 and 5, which are two of the main recommendations, require further substantiation.

• The first recommendation consists of two parts. Firstly, it entails investigating climate-related financial risks in the financial system. This can be achieved by (i) mapping physical and transition risk channels to key risk indicators, (ii) performing scenario analysis of multiple plausible future scenarios to quantify the risks across the financial system and provide insight in the extent of disruption to current business models in multiple sectors and (iii) assessing how to include the consequences of climate change in macroeconomic forecasting and stability monitoring. Secondly, it underlines the need to integrate climate-related risks into prudential supervision, including engaging with financial firms and setting supervisory expectations to guide financial firms.
• The fifth recommendation stresses the importance of a robust and internationally consistent climate and environmental disclosure framework. NGFS supports the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD⁸) and urges financial institutions and companies that issue public debt or equity to align their disclosures with these recommendations. To encourage this, NGFS emphasizes the need for policymakers and supervisors to take actions in order to achieve a broader application of the TCFD recommendations and the growth of an internationally consistent environmental disclosure framework.

Future deliverables of NGFS consist of drafting a handbook on climate and environmental risk management, voluntary guidelines on scenario-based climate change risk analysis and best practices for including sustainability criteria into central banks’ portfolio management.

Asset managers

To achieve the climate goals of the Paris Agreement, €180 billion is required on an annual basis⁵. It is not possible to acquire such a large amount from the public sector alone and currently only a fraction of investor capital is being invested sustainably. Research from Morningstar shows that 11.6% of investor capital in the stock market and 5.6% in the bond market is invested sustainably⁹. Figure 1 shows that even though the percentage of capital invested in sustainable investment funds (stocks and bonds) is growing in recent years, it is still worryingly low.

Figure 1: Percentage of invested capital in Europe in traditional and sustainable investment funds (shares and bonds). Source: Morningstar [9].

The current levels of investment are not enough to support an environmentally and socially sustainable economic system. As a result, the European Commission (EC) has raised four initiatives through the Technical Expert Group on sustainable finance (TEG) that are designed to increase sustainable financing¹⁰. The first initiative is the issuance of two kinds of green (low-carbon) benchmarks. Offering funds or trackers on these indices would lead to an increase in cash flows towards sustainable companies. Secondly, an EU taxonomy for climate change mitigation and climate change adaptation has been developed. Thirdly, to enable investors to determine to what extent each investment is aligned with the climate goals, a list of economic activities that contribute to the execution of the Paris Agreement has been drafted. Finally, new disclosure requirements should enhance visibility of how investment firms integrated sustainability into their investment policy and create awareness of the climate risks the investors are exposed to.

Insurance firms

Within the insurance sector, the Prudential Regulation Authority (PRA) requires insurers to follow a strategic approach to manage the financial risks from climate change. To support this, in July 2018, the Bank of England (BoE) formed a joint working group focusing on providing practical assistance on the assessment of financial risks resulting from climate changes. In May 2019, the working group issued a six-stage framework that helps insurers in assessing, managing and reporting physical climate risk exposure due to extreme weather events¹¹. Practical guidance is provided in the form of several case studies, illustrating how considering the financial impacts can better inform risk management decisions.

Authorities

Another initiative is the Climate Financial Risk Forum (CFRF), a joint initiative of the PRA and the Financial Conduct Authority (FCA)¹². The forum consists of senior representatives of the UK financial sector from banks, insurers and asset managers. CFRF aims to build capacity and share best practices across financial regulators and the industry to enhance responses to the financial climate change risks. The forum set up four working groups focusing on risk management, scenario analysis, disclosure and innovation. The purpose of these working groups, which consist of CFRF members as well as other experts such as academia, is to provide practical guidance on each of the four focus areas.

Current status and outlook

On 5 June 2019, the TCFD published a Status Report assessing a disclosure review on the extent to which 1,100 companies included information aligned with these TCFD recommendations in their 2018 reports. The report also assessed a survey on companies’ efforts to live up to TCFD recommendations and users’ opinion on the usefulness of climate-related disclosures for decision-making¹³. Based on the disclosure review and the survey, TCFD concluded that, while some of the results were encouraging, not enough companies are disclosing climate change-linked financial information that is useful for decision-making. More specifically, it was found that:

• “Disclosure of climate-related financial information has increased, but is still insufficient for investors;
• More clarity is needed on the potential financial impact of climate-related issues on companies;
• Of companies using scenarios, the majority do not disclose information on the resilience of their strategies;
• Mainstreaming climate-related issues requires the involvement of multiple functions.”

Further, the BoE finds that despite the progress, there is still a long way to go: while many banks are incorporating the most immediate physical risks to their business models and assess exposures to transition risks, many of them are not there yet in their identification and measurement of the financial risks. They stress that governments, financial firms, central banks and supervisors should work together internationally and domestically, private sector and public sector, to achieve a smooth transition to a low-carbon economy. Mark Carney, Governor of the BoE, is optimistic and argues that, conditional on the amount of effort, it should possible to manage the financial climate risks in an orderly, effective and productive manner⁴.

With respect to the future, Frank Elderson made the following claim: “Now that European banking supervision has entered a more mature phase, we need to retain a forward-looking strategy and develop a long-term vision. Focusing on greening the financial system must be a part of this.”³.

References

¹ https://www.bankofengland.co.uk/-/media/boe/files/speech/2019/avoiding-the-storm-climate-change-and-the-financial-system-speech-by-sarah-breeden.pdf
² https://public.wmo.int/en/media/press-release/wmo-climate-statement-past-4-years-warmest-record
³ https://www.bankingsupervision.europa.eu/press/interviews/date/2019/html/ssm.in190515~d1ab906d59.en.html
⁴ https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/report/transition-in-thinking-the-impact-of-climate-change-on-the-uk-banking-sector.pdf
⁵ https://fd.nl/achtergrond/1294617/beleggers-moeten-met-de-billen-bloot-over-klimaatrisico-s
⁶ https://www.dnb.nl/over-dnb/samenwerking/network-greening-financial-system/index.jsp
⁷ https://www.banque-france.fr/sites/default/files/media/2019/04/17/ngfs_first_comprehensive_report_-_17042019_0.pdf
⁸ https://www.fsb-tcfd.org/publications/final-recommendations-report/
⁹ http://www.morningstar.nl/nl/
¹⁰ https://ec.europa.eu/info/publications/sustainable-finance-technical-expert-group_en
¹¹ https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/publication/2019/a-framework-for-assessing-financial-impacts-of-physical-climate-change.pdf
¹² https://www.bankofengland.co.uk/news/2019/march/first-meeting-of-the-pra-and-fca-joint-climate-financial-risk-forum
¹³ https://www.fsb-tcfd.org/wp-content/uploads/2017/06/FINAL-2017-TCFD-Report-11052018.pdf

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.

Regulatory Landscape

Despite a delay of one year, many banks are struggling to be ready for FRTB in January 2023. Alongside the FRTB timeline, banks are also preparing for other important regulatory requirements and deadlines which share commonalities in implementation. We introduce several of these below.

SIMM

Initial Margin (IM) is the value of collateral required to open a position with a bank, exchange or broker.  The Standard Initial Margin Model (SIMM), published by ISDA, sets a market standard for calculating IMs. SIMM provides margin requirements for financial firms when trading non-centrally cleared derivatives.

BCBS 239

BCBS 239, published by the Basel Committee on Banking Supervision, aims to enhance banks’ risk data aggregation capabilities and internal risk reporting practices. It focuses on areas such as data governance, accuracy, completeness and timeliness. The standard outlines 14 principles, although their high-level nature means that they are open to interpretation.

SA-CVA

Credit Valuation Adjustment (CVA) is a type of value adjustment and represents the market value of the counterparty credit risk for a transaction. FRTB splits CVA into two main approaches: BA-CVA, for smaller banks with less sophisticated trading activities, and SA-CVA, for larger banks with designated CVA risk management desks.

IBOR

Interbank Offered Rates (IBORs) are benchmark reference interest rates. As they have been subject to manipulation and due to a lack of liquidity, IBORs are being replaced by Alternative Reference Rates (ARRs). Unlike IBORs, ARRs are based on real transactions on liquid markets rather than subjective estimates.

Synergies With Current Regulation

Existing SIMM and BCBS 239 frameworks and processes can be readily leveraged to reduce efforts in implementing FRTB frameworks.

SIMM

The overarching process of SIMM is very similar to the FRTB Sensitivities-based Method (SbM), including the identification of risk factors, calculation of sensitivities and aggregation of results. The outputs of SbM and SIMM are both based on delta, vega and curvature sensitivities. SIMM and FRTB both share four risk classes (IR, FX, EQ, and CM). However, in SIMM, credit is split across two risk classes (qualifying and non-qualifying), whereas it is split across three in FRTB (non-securitisation, securitisation and correlation trading). For both SbM and SIMM, banks should be able to decompose indices into their individual constituents. 

We recommend that banks leverage the existing sensitivities infrastructure from SIMM for SbM calculations, use a shared risk factor mapping methodology between SIMM and FRTB when there is considerable alignment in risk classes, and utilise a common index look-through procedure for both SIMM and SbM index decompositions.

BCBS 239

BCBS 239 requires banks to review IT infrastructure, governance, data quality, aggregation policies and procedures. A similar review will be required in order to comply with the data standards of FRTB. The BCBS 239 principles are now in “Annex D” of the FRTB document, clearly showing the synergy between the two regulations. The quality, transparency, volume and consistency of data are important for both BCBS 239 and FRTB. Improving these factors allow banks to easily follow the BCBS 239 principles and decrease the capital charges of non-modellable risk factors. BCBS 239 principles, such as data completeness and timeliness, are also necessary for passing P&L attribution (PLA) under FRTB.

We recommend that banks use BCBS 239 principles when designing the necessary data frameworks for the FRTB Risk Factor Eligibility Test (RFET), support FRTB traceability requirements and supervisory approvals with existing BCBS 239 data lineage documentation, and produce market risk reporting for FRTB using the risk reporting infrastructure detailed in BCBS 239.

Synergies With Future Regulation

The IBOR transition and SA-CVA will become effective from 2023. Aligning the timelines and exploiting the similarities between FRTB, SA-CVA and the IBOR transition will support banks to be ready for all three regulatory deadlines.

SA-CVA

Four of the six risk classes in SA-CVA (IR, FX, EQ, and CM) are identical to those in SbM. SA-CVA, however, uses a reduced granularity for risk factors compared to SbM. The SA-CVA capital calculation uses a similar methodology to SbM by combining sensitivities with risk weights. SA-CVA also incorporates the same trade population and metadata as SbM. SA-CVA capital requirements must be calculated and reported to the supervisor at the same monthly frequency as for the market risk standardised approach.

We recommend that banks combine SA-CVA and SbM risk factor bucketing tasks in a common methodology to reduce overall effort, isolate common components of both models as a feeder model, allowing a single stream for model development and validation, and develop a single system architecture which can be configured for either SbM or SA-CVA.

IBOR Transition

Although not a direct synergy, the transition from IBORs will have a direct impact to the Internal Models Approach (IMA) for FRTB and eligibility of risk factors. As the use of IBORs are discontinued, banks may observe a reduction in the number of real-price observations for associated risk factors due to a reduction in market liquidity. It is not certain if these liquidity issues fall under the RFET exemptions for systemic circumstances, which apply to modellable risk factors which can no longer pass the test. It may be difficult for banks to obtain stress-period data for ARRs, which could lead to substantial efforts to produce and justify proxies. The transition may cause modifications to trading desk structure, the integration of external data providers, and enhanced operational requirements, which can all affect FRTB.

We recommend that banks investigate how much data is available for ARRs, for both stress-period calculations and real-price observations, develop any necessary proxies which will be needed to overcome data availability issues, as soon as possible, and Calculate IBOR capital consequences through the existing FRTB engine.

Conclusion

FRTB implementation is proving to be a considerable workload for banks, especially those considering opting for the IMA. Several FRTB requirements, such as PLA and RFET, are completely new requirements for banks. As we have shown in this article, there are several other important regulatory requirements which banks are currently working towards. As such, we recommend that banks should leverage the synergies which are seen across this regulatory landscape to reduce the complexity and workload of FRTB.

The current trend to operate a ‘data-driven business’ and the fact that regulators are increasingly focused on data quality and data availability, could give an extra impulse to the use of ML models.