How to measure single-name credit risk concentrations

Uberti, Pierpaolo; Figini, Silvia

doi:10.1016/j.ejor.2009.05.001

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…In the future, however, this information will certainly be used by the supervisory authority for determining the capital surcharge for assumed risk. 9 For simplicity we will refer to this kind of concentration as sectorial. We must, however, be aware that it also comprises geographical concentration.…”

Section: Concentration Risk Under a Supervisory Approachmentioning

confidence: 99%

“…9 In this area and within the simplified approach, the Spanish 7 See [14]. 8 No capital surcharge for concentration risk is now required from Spanish banks.…”

Section: Concentration Risk Under a Supervisory Approachmentioning

confidence: 99%

“…Bonti et al [8] also stress the importance of these relationships: they propose using a multifactor model for estimating economic capital, which should take into account the relationships between economic (or geographical) sectors. Stemming from a different research path, Uberti and Figine [9] propose an index for quantifying name concentration in a credit portfolio. This index may be used within Pillar II for determining the capital surcharge required for this kind of concentration.…”

Section: Introductionmentioning

confidence: 99%

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Sector concentration risk: A model for estimating capital requirements

Semper¹,

Beltrán²

2011

Mathematical and Computer Modelling

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a b s t r a c tThe 2004 Basel Committee on Banking Supervision Accord (known as Basel II) provides a common framework for banks to determine their minimum capital requirements for solvency purposes. For credit risk (the most important one for banking) Basel II uses an asymptotic single risk factor (ASRF) model and, as we demonstrate in the paper, assumes two fundamental hypotheses: Firstly, that there is only one risk factor common to all banks; and secondly, that the number of debtors in bank portfolios is high enough to ensure that no single debtor's behaviour can have a significant impact on the portfolio value as a whole. This allows capital requirements to be estimated by using a model based on the percentage of defaulting borrowers (x).The model only requires values for two variables: the probability of default and loss if default occurs. Using a 99% likelihood and assuming that all sectors are equally correlated, the model estimates x through the cumulative distribution function for the Gaussian distribution.But many bank portfolios do not fit these hypotheses, and therefore the ASRF model underestimates actual capital requirements. Thus, a surcharge for concentration risk is required.There are two kinds of concentration risk (sector and name concentration risk), each one corresponding to the violation of one of the above mentioned hypotheses. Supervisory authorities are currently developing models to incorporate this surcharge into banking solvency rules. In Spain, the Spanish Central Bank bases its surcharge proposal for sectorial concentration on the Herfindahl-Hirschman Index (HHI). In this paper we show that HHI treats all sectors as equally risky and propose an alternative index (CI) in which sectors are weighted according to risk. Moreover, our index also incorporates the relations between each pair of sectors (in the HHI framework no sectorial relationship is considered). Our proposal is based on an adjusted variance-covariance matrix, in which negative covariances have been equalled to 0. We demonstrate the HHI is a particular case of our proposed index, by means of simplifying hypotheses.As we will show, the proposed index has two fundamental properties: it is lower and upper bounded; and it decreases as concentration and/or risk decreases. These properties allow the index to be incorporated into bank risk management models. In this way bank estimations can improve upon those based on the supervisory model and, according to banking rules, can also be used for determining the capital surcharge for sectorial concentration.

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Section: Concentration Risk Under a Supervisory Approachmentioning

confidence: 99%

“…9 In this area and within the simplified approach, the Spanish 7 See [14]. 8 No capital surcharge for concentration risk is now required from Spanish banks.…”

Section: Concentration Risk Under a Supervisory Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sector concentration risk: A model for estimating capital requirements

Semper¹,

Beltrán²

2011

Mathematical and Computer Modelling

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“…The increased regulatory interest in credit concentration risk has prompted the studies of Diamond and Rajan (2009) and Gurtler et al (2006) that confirmed the importance of the contribution of this type of risk to economic capital. Furthermore, credit concentration risk related to single-name, sector and geographical exposures have been acknowledged and analysed by the empirical studies of Dullmann and Masschelein (2007), Gurtler et al (2010), Vincenzo (2010) and Uberti and Figini (2010). Recognising the importance of credit concentration risk, the PRA has recently proposed several changes to the Pillar 2 framework, introducing methodologies for calculating Pillar 2 capital add-on for credit concentration risk (PRA, 2015b; KPMG, 2015).…”

Section: Study Background and Regulatory Framework Overviewmentioning

confidence: 99%

Reviewing Pillar 2 regulations: credit concentration risk

Prorokowski

Prorokowski²,

Nteh³

2019

JFRC

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Purpose This paper aims to analyse the recent changes to the Pillar 2 regulatory-prescribed methodologies to classify and calculate credit concentration risk. Focussing on the Prudential Regulation Authority’s (PRA) methodologies, the paper tests the susceptibility to bias of the Herfindahl–Hirscham Index (HHI). The empirical tests serve to assess the assumption that the regulatory classification of exposures within the geographical concentration is subject to potential misuse that would undermine the PRA’s objective of obtaining risk sensitivity and improved banking competition. Design/methodology/approach Using the credit exposure data from three global banks, the HHI methodology is applied to the portfolio of geographically classified exposures, replicating the regulatory exercise of reporting credit concentration risk under Pillar 2. In doing so, the validity of the aforementioned assumption is tested by simulating the PRA’s Pillar 2 regulatory submission exercise with different scenarios, under which the credit exposures are assigned to different geographical regions. Findings The paper empirically shows that changing the geographical mapping of the Eastern European EU member states can result in a substantial reduction of the Pillar 2 credit concentration risk capital add-on. These empirical findings hold only for the banks with large exposures to Eastern Europe and Central Asia. The paper reports no material impact for the well-diversified credit portfolios of global banks. Originality/value This paper reviews the PRA-prescribed methodologies and the Pillar 2 regulatory guidance for calculating the capital add-on for the single name, sector and geographical credit concentration risk. In doing so, this paper becomes the first to test the assumptions that the regulatory guidance around the geographical breakdown of credit exposures is subject to potential abuse because of the ambiguity of the regulations.

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“…Due to regulatory concern of Basel II, credit risk assessment has been the major focus of financial and banking industry. Considering credit risk forecasting process, banks must differentiate good customers from bad ones in terms of their creditworthiness (Uberti and Figini 2010). The need for reliable models that predict defaults accurately is imperative so that the interested parts can take either preventive or corrective action.…”

Section: Introductionmentioning

confidence: 99%

New Internal Rating Approach for Credit Risk Assessment / Naujas Vidaus Reitingu Modelis Kredito Rizikos Vertinimui

Boguslauskas

Mileris

Adlytė

2011

Technological and Economic Development of Economy

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The assessment and modeling of the credit risk is one of the most important topics in the field of financial risk management. In this investigation the credit risk assessment model was developed and tested for Lithuanian companies. 20 financial ratios of the companies were calculated for each year of the 3 year period of interest. The analysis of variance (ANOVA) and Kolmogorov-Smirnov test were applied and the set of variables reduced from 60 to 25. Logistic regression was used for the classification of the companies into reliable and not reliable ones. Financial ratios, having the highest correlation to the possibility of default were selected for further investigation and several credit ratings were attributed to the companies according to these variables’ values. The average values of Mahalanobis Distances calculated for the most reliable companies were the lowest and these values increased with a decreased reliability of the company. The differences between Mahalanobis Distances of the companies having different credit ratings confirmed the reliability of the model results. Santrauka Kredito rizikos vertinimas ir modeliavimas – viena iš aktualiausiu temų, kalbant apie finansinės rizikos valdymą. Atlikto tyrimo metu buvo sukurtas kredito rizikos modelis. šis modelis išbandytas 198 Įmonių aibėje, skaičiuojant po 20 finansinių rodiklių 3 analizuojamų metu laikotarpiu. Panaudojus ANOVA metodą ir Kolmogorovo – Smirnovo statistiką, kintamųjų kiekis buvo sumažintas nuo 60 iki 25 rodiklių. Įmonįu klasifikavimui į 2 grupes: patikimus ir nepatikimus banko klientus, atsižvelgiant į jų įsipareigojimų nevykdymo tikimybę, buvo naudojama logistinė regresija. 97 proc. patikimų (nebankrutavusių) ir 82 proc. nepatikimų (bankrutavusių) įmonių suklasifikuotos teisingai. Tolimesniam tyrimui atrinkti 7 finansiniai rodikliai, kurių koreliacinis ryšys su įsipareigojimų nevykdymo tikimybe buvo didžiausias. Atsižvelgiant į šių kintamųjų reikšmės, įmonėms buvo priskirti 9 kredito reitingai. Vidutines Mahalanobio atstumu reikšmes, apskaiČiuotos patikimiausioms kompanijoms buvo mažiausios; šios reikšmės didėjo, mažejantįmonių patikimumui. Skirtingį reitingį įmonėms apskaiČiuoti Mahalanobio atstumų skirtumai, pagrindė modelio rezultatų patikimumą.

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How to measure single-name credit risk concentrations

Cited by 8 publications

References 18 publications

Sector concentration risk: A model for estimating capital requirements

Sector concentration risk: A model for estimating capital requirements

Reviewing Pillar 2 regulations: credit concentration risk

New Internal Rating Approach for Credit Risk Assessment / Naujas Vidaus Reitingu Modelis Kredito Rizikos Vertinimui

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