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 Delft University of Technology (TU) aims to be a world-class institution with excellent research in specific disciplines. In order to achieve this, it needs good research facilities. A substantial part of the current facilities is up for renovation. How can this be financed in times of cost cutting?

More than 5,000 people work at TU Delft, and 17,000 students study there, preparing for professional life. “The TU is a city in a city,” Rianne van der Slot explains. She is the controller of the real estate management team at the University of Delft. “We own 36 buildings with a floor space of approximately 550,000 square meters. We manage all real estate ourselves, as well as the land. We even own the sewerage and have to maintain it ourselves.”

In 1999, the government donated all university real estate to the TU. Most buildings date from the 1960s and ’70s and are in need of thorough, large-scale maintenance or replacement. “The TU Delft will have to finance this itself,” says Mariëlle Vogt, director of finance at the TU. “The estimated costs of possible new buildings, renovation, and large-scale maintenance for the next 10 years amount to approximately half a billion euros.”

The Ministry of Education, Culture, and Science gives a Government Contribution to the TU on a yearly basis, as it does to all Dutch universities. This amount varies as a consequence of different government decisions and adjusted ministerial budgets and is more likely to decrease than to increase despite the growing number of students.

Vogt says: “Unfortunately, we receive no extra government contribution for these investments in real estate. All universities struggle with the combination of real estate in need of renovation and little resources of their own, but for a technical university like ours, it is even more essential. Real estate is a core asset in our primary process. You need a specific building in order to build a sophisticated lab. At universities, you won’t attract renowned scientists with a high salary unless you also have top-rate facilities, so infrastructure is essential. Only then will you be able to attract the right people.”

Role-play

At the end of 2009, the university decided to make extra savings in order to be able to put funds aside for renovations in real estate, education, and science. The plan was to borrow a limited amount and, in addition, put some money aside every year. From 2010 onwards, however, considerable cutbacks were made in the contribution of the government. “As far as revenues are concerned, The Hague is now an uncertain factor,” Vogt says. “It is really difficult to make funding prognoses for the next 30 years. We have to engage in scenario planning and perform sensitivity analyses to ensure that we can pay the loans back in time. That was a reason to look for external help, from Zanders.”

TU Delft preferred funding from the government: Treasury Banking (in Dutch: ‘schatkistbankieren’). Apart from the fact that it is cheaper, it makes more sense for us, as a university, to borrow from another public body. In addition, the government has sufficient resources available, says Ronald van den Bosch, senior business controller of the TU. “We did not know, however, if we could meet the conditions of treasury paper.”

Together with Zanders, a role-play was developed. Vogt explains: “We prepared everything as if we were going to a commercial bank and then asked Koen Reijnders and Hendrik Pons to take the critical position that a bank would take. By doing this, we wanted to submit ourselves to the discipline of a commercial bank – then you know that you are acting in a prudent manner.”

Infrastructure is essential. Only then, will you be able to attract the right people.

Mariëlle Vogt, director of finance at the TU.

Scenarios

It became clear that the TU was able to meet the conditions of Treasury Banking. The business case that Zanders developed with the TU departments for Real Estate and Finance led to a model with which one could calculate the outcome of all kinds of scenarios. An extra investment in one of the buildings, an unexpected interest development, or a higher indexation of building costs: the consequences of all these occurrences will become clear from the model.

“Together we built a toolbox with which we can – so it seems – anticipate developments,” Vogt says. “It is a custom-made model that extends to 2030 and contains a number of scenarios – different financial prognoses in which there is a constant connection between the overall financial prognosis of the TU Delft and its real estate plans.”

The interests of the two departments differ. Real estate feels the pressure of users that require certain facilities. Finance supervises the prudent use of limited funds. “Zanders has connected these two interests,” Van den Bosch states. “In the case of real estate, one argues on project level, whereas the finance department thinks on a balance sheet level. With the model, the consequences on an aggregated level became clear for both sides – a good joint effort of the departments. By constantly setting the costs of certain investments against the funding of those investments, one can decide what the possibilities are within a certain period of time.”

Towards the future

It happens all too often that such a model is built to support funding but afterwards disappears in a drawer. The TU chose to use the model as part of the process to make timely adjustments, when necessary. Vogt says: “Twice a year – also to inform our supervisory council – we update the investment and maintenance plan, including all financial prognoses at TU level. We have subjected ourselves to this discipline; normally you would leave that to the bank. The model safeguards that this happens in a well-thought-out manner.”

At an earlier stage, it was not necessary to use a “model with scenario buttons,” as the costs coincided mostly with the revenues. The real estate investments were the direct cause. Now the model will be used in future. “Together with Zanders, they have the up-to-date knowledge of the market,” Vogt says. “Our two focus areas this year are the finance and risk policy. Of the EUR 500 million that we spend each year, approximately EUR 350 million comes from the government. Every time, you have to carefully consider which investments you will make that year. The model indicates per year the effect of such an investment on your liquidity, amortization, and maintenance costs.”

No vibrations

“In the case of investments in large-scale maintenance, we look for opportunities to reduce costs toward the future,” Van der Slot says. “Certain investments will lead to energy savings or lower CO2 emissions. Renovation will also reduce certain maintenance costs. It is very interesting to see that in such a model.”

Growth is not the purpose of renovation. It is more likely to see fewer TU buildings than more in the future. With the renovations, the ‘New Way of Working’ will be introduced. BK City, the housing complex for the TU faculty of Architecture, has many open spaces and flexible workplaces. We will introduce renewed concepts of education, such as offering digital classes.

All sorts of rankings exist that indicate the relative position of universities. These are not just based on the number of students, but decisive factors are primarily the amount of research and the number of publications. These, then, depend again on the infrastructure that one can offer. Vogt notes: “Some buildings have to satisfy very high standards, like the building of Applied Sciences. In the Nano labs, the passing of a truck should not cause any vibration whatsoever. It is an interesting but complicated matter. We are not a cookie factory.”