Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs

Dietsch, Michel; Petey, Joël

doi:10.1016/j.jbankfin.2003.10.006

Cited by 100 publications

(87 citation statements)

References 7 publications

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“…But we find, in contrast with Kolari and Shin (2004), that lending to SMEs is riskier than to large corporates (see also Saurina and Trucharte (2004), Dietsch and Petey (2004)). As a consequence, we demonstrate that banks should develop credit risk models specifically addressed to SMEs in order to minimize their expected and unexpected losses.…”

Section: Sme Studiescontrasting

confidence: 82%

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Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

124

217

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Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs. JEL classification: G21; G28

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Section: Sme Studiescontrasting

confidence: 82%

“…From a credit risk point of view, SMEs are different from large corporates for many reasons. For example, Dietsch and Petey (2004) analyze a set of German and French SMEs and conclude that they are riskier but have a lower asset correlation with each other than large businesses.…”

Section: Introductionmentioning

confidence: 99%

Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

124

217

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“…SMEs constantly play a vital role in the US economy where statistics from the "US Small Business Administration opened the door for more in-depth and adequate research on failure 5 prediction models for all firms. However, the financial distress definition of Basel II, 90 days overdue on credit agreement payments, which are considered as the operational definition, failed to distinguish between large and small firms which have different structures from the credit risk point of view (Dietsch and Petey, 2004;and Altman and Sabato, 2007).…”

Section: Introductionmentioning

confidence: 99%

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

Kalak

Hudson

2016

International Review of Financial Analysis

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(2016) The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model. AbstractThis paper investigates the extent to which the size affects the SMEs probabilities of bankruptcy.Using a dataset of (11,117) US non-financial firms, of which (465) filed for insolvency under chapters 7/11 between 1980 and 2013. We forecast the bankruptcy probabilities by developing four discretetime duration-dependant hazard models namely SMEs, Micro, Small, and Medium. A comparison of the default prediction models for medium firms and SMEs suggest that almost an identical set of explanatory variables affect the default probabilities leading us to believe that there is no material impact on the decision-making process by treating each of these groups separately. However, comparisons between the micro and small firms with the SMEs firms strongly suggest that they need to be considered separately while modelling credit risk for them.JEL classification:

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“…∂π(x i ) ∂β j = −x ij (1 + τ β x i ) −( Substituting the former results in the equations (20), the score functions (7) and (8) are obtained. The second order partial derivatives of the log-likelihood function with respect to parameters β, τ are…”

Section: Appendixmentioning

confidence: 99%

Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model

Calabrese

Osmetti

2013

Journal of Applied Statistics

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A pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of the event. The most widely used model to estimate the Probability of Default (PD) is the logistic regression model. Since the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks, for example underestimation of the default probability, which could be very risky for banks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure used is the maximum likelihood method. This model accommodates skewness and it presents a generalisation of GLMs with complementary log-log link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to empirical data on Italian Small and Medium Enterprises (SMEs).

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Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs

Cited by 100 publications

References 7 publications

Modeling Credit Risk for SMEs: Evidence from the US Market

Modeling Credit Risk for SMEs: Evidence from the US Market

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model

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