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Targeted Review of Internal Models (TRIM): Review of observations and findings for Traded Risk

May 2021
5 min read

Discover the significant deficiencies uncovered by the EBA’s TRIM on-site inspections and how banks must swiftly address these to ensure compliance and mitigate risk.


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.

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