Updated 20/11/2024
In force

Version from: 09/07/2024
Amendments (1)
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Article 179 - Overall requirements for estimation

Attention! This article will be amended on 01/01/2025. Please consult Regulation 2024/1623 to review the changes that will be made to the article.

Article 179

Overall requirements for estimation

1.  

In quantifying the risk parameters to be associated with rating grades or pools, institutions shall apply the following requirements:

(a) 

an institution's own estimates of the risk parameters PD, LGD, conversion factor and EL shall incorporate all relevant data, information and methods. The estimates shall be derived using both historical experience and empirical evidence, and not based purely on judgemental considerations. The estimates shall be plausible and intuitive and shall be based on the material drivers of the respective risk parameters. The less data an institution has, the more conservative it shall be in its estimation;

(b) 

an institution shall be able to provide a breakdown of its loss experience in terms of default frequency, LGD, conversion factor, or loss where EL estimates are used, by the factors it sees as the drivers of the respective risk parameters. The institution's estimates shall be representative of long run experience;

(c) 

any changes in lending practice or the process for pursuing recoveries over the observation periods referred to in Article 180(1)(h) and (2)(e), Article 181(1)(j) and (2), and Article 182(2) and (3) shall be taken into account. An institution's estimates shall reflect the implications of technical advances and new data and other information, as it becomes available. Institutions shall review their estimates when new information comes to light but at least on an annual basis;

(d) 

the population of exposures represented in the data used for estimation, the lending standards used when the data was generated and other relevant characteristics shall be comparable with those of the institution's exposures and standards. The economic or market conditions that underlie the data shall be relevant to current and foreseeable conditions. The number of exposures in the sample and the data period used for quantification shall be sufficient to provide the institution with confidence in the accuracy and robustness of its estimates;

(e) 

for purchased receivables the estimates shall reflect all relevant information available to the purchasing institution regarding the quality of the underlying receivables, including data for similar pools provided by the seller, by the purchasing institution, or by external sources. The purchasing institution shall evaluate any data relied upon which is provided by the seller;

(f) 

an institution shall add to its estimates a margin of conservatism that is related to the expected range of estimation errors. Where methods and data are considered to be less satisfactory, the expected range of errors is larger, the margin of conservatism shall be larger.

Where institutions use different estimates for the calculation of risk weights and for internal purposes, it shall be documented and be reasonable. If institutions can demonstrate to their competent authorities that for data that have been collected prior to 1 January 2007 appropriate adjustments have been made to achieve broad equivalence with the definition of default laid down in Article 178 or with loss, competent authorities may permit the institutions some flexibility in the application of the required standards for data.

2.  

Where an institution uses data that is pooled across institutions it shall meet the following requirements:

(a) 

the rating systems and criteria of other institutions in the pool are similar to its own;

(b) 

the pool is representative of the portfolio for which the pooled data is used;

(c) 

the pooled data is used consistently over time by the institution for its estimates;

(d) 

the institution shall remain responsible for the integrity of its rating systems;

(e) 

the institution shall maintain sufficient in-house understanding of its rating systems, including the ability to effectively monitor and audit the rating process.