Prototype risk rating system

Crouhy, Michel; Galai, Dan; Mark, Robert

doi:10.1016/s0378-4266(00)00117-5

Cited by 130 publications

(62 citation statements)

References 10 publications

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“…Their results parallel those of Mark (2000, 2001) in that PD correlations among US non-financial public firms over the period January 1987 to October 2000 are estimated to be higher when markets move down (i.e., PD levels are high on average) in contrast to when markets move up (PD levels are low). Moreover, Table 6 shows that their results differ from many of the papers presented in Section 3.1.2 [e.g., Barnhill and Maxwell (1999), Gersbach and Lipponer (2000), Erlenmaier and Gersbach (2001), Crouhy et al (2000Crouhy et al ( , 2001) and Zhou (2001)] in that default correlations increase as credit quality improves.…”

Section: Insert Table 5 Around Herementioning

confidence: 67%

“…Crouhy, Galai and Mark (2001) contend that "through-the-cycle" ratings are a more appropriate input into lending decisions, whereas "point-in-time" ratings are more appropriate for the purposes of capital allocation. Bangia, Diebold and Schuermann (2000) and Nickell, Perraudin and Varotto (2000) find evidence of macroeconomic and industry effects on rating transitions.…”

Section: Cyclical Effects On the Probability Of Default (Pd)mentioning

confidence: 99%

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A Survey of Cyclical Effects in Credit Risk Measurement Models

2002

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We survey both academic and proprietary models to examine how macroeconomic and systematic risk effects are incorporated into measures of credit risk exposure. Many models consider the correlation between the probability of default (PD) and cyclical factors. Few models adjust loss rates (loss given default) to reflect cyclical effects. We find that the possibility of systematic correlation between PD and LGD is also neglected in currently available models. 3 A Survey of Cyclical Effects in Credit Risk Measurement ModelsIt has long been recognized that banking is a procyclical business. That is, banks tend to contract their lending activity when business turns down because of their concern about loan quality and repayment probability. This exacerbates the economic downturn as credit constrained businesses and individuals cut back on their real investment activity.In contrast, banks expand their lending activity during boom periods, thereby contributing to a possible overheating of the economy that may transform an economic expansion into an inflationary spiral.The proliferation of credit risk measurement models in banking may accentuate the procyclical tendencies of banking, with potential macroeconomic consequences. That is, the models' overly optimistic estimates of default risk during boom times reinforces the natural tendency of banks to overlend just at the point in the business cycle that the central bank prefers restraint. Moreover, if credit risk models are unduly pessimistic during recessions, then even the most expansionary monetary policy may not encourage banks to lend to obligors that are perceived to be poor credit risks. Recent BIS proposals to utilize credit risk models such as CreditMetrics as a basis for bank capital requirements may further accentuate the procyclical nature of banking unless the credit cycle and its effect on credit risk are appropriately recognized in the model structure. If banks are constrained by risk sensitive (as measured by internal models) capital allocations and regulatory requirements, they may be unable to lend during low points in the business cycle and overly encouraged to lend during boom periods.1 This is because risk sensitive 1 To the extent that external credit ratings provide "through the cycle" estimates of default risk smoothed across the entire business cycle, it is the internal ratings-based approaches of the New Basel Capital Accord In this paper, we examine the treatment of cyclical factors in both academic and proprietary credit risk measurement models. 4 In section 2, we begin by discussing what is meant by procyclicality. We then divide our survey of credit risk measurement models into four sections. Section 3 surveys how various credit risk measurement models incorporate cyclical effects into the estimation of default probability (PD). In Section 4, that is most likely to exacerbate the procylical tendencies of banking. However, if credit ratings behave procyclically [as shown by Ferri, Liu and Majnoni (2000), Monfort and Mulder (2000) and...

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Section: Insert Table 5 Around Herementioning

confidence: 67%

Section: Cyclical Effects On the Probability Of Default (Pd)mentioning

confidence: 99%

A Survey of Cyclical Effects in Credit Risk Measurement Models

2002

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“…A report by the Basel Committee on Banking Supervision (2000) provides a comprehensive overview of rating practices. Crouhy, Galai and Mark (2001) describe the rating system of Moody's and Standard & Poor's and propose a prototype rating system for bank internal ratings.…”

Section: Introductionmentioning

confidence: 99%

Avoiding the rating bounce: why rating agencies are slow to react to new information

Löffler

2005

Journal of Economic Behavior & Organization

127

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Rating agencies state that they take a rating action only when it is unlikely to be reversed shortly afterwards. Based on a formal representation of the rating process, I show that such a policy provides a good explanation for the puzzling empirical evidence: Rating changes occur relatively seldom, exhibit serial dependence, and lag changes in the issuers' default risk. In terms of informational losses, avoiding rating reversals can be more harmful than monitoring credit quality only twice per year.

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“…four years before the failed firms went into bankruptcy. An initial group of 19 financial ratios was selected (Table 2) as possible predictors (independent variables) considering the frequency with which they had previously proved to be effective in the context of the existing literature (Altman, 1968;Altman, 1993;Altman, Brady, Resti, & Sironi, 2005;Altman, Haldeman, & Narayanan, 1977;Altman & Sabato, 2005, 2006Beaver, 1966;Blum, 1974;Crouhy, Mark, & Galai, 2001;Edmister, 1972), as well as the opportunity to cover the three fundamental areas of company balances: liquidity, leverage and profitability. The initial number of these potential failure predictors was progressively reduced based on the results of the variance inflation factor (VIF) method and the stepwise method (SM), separately applied to the above described training SE sample (consisting of 2,200 firms) and SMLE sample (consisting of 3,200 firms).…”

Section: Default Predictorsmentioning

confidence: 99%

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Ciampi¹

2017

IJBM

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The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically constructed to capture the specificities of SEs' risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following: 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.

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Prototype risk rating system

Cited by 130 publications

References 10 publications

A Survey of Cyclical Effects in Credit Risk Measurement Models

A Survey of Cyclical Effects in Credit Risk Measurement Models

Avoiding the rating bounce: why rating agencies are slow to react to new information

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

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