2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Mutual Guaranteed Loans In Italy: The Determinants Of Defaults
Lorenzo Gai - University of Florence, Italy
Phone: +39 055/4374705
Federica Ielasi – University of Florence, SDA Bocconi Milan, Italy
Phone: +39 055/4374731
Federico Rossi - University of Florence, Italy
Phone: +39 055/4374705
July 2-3, 2013
Cambridge, UK
1
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Mutual Guaranteed Loans In Italy: The Determinants Of Defaults
ABSTRACT
The mutual guarantee is playing an increasingly important role as an instrument of economic
policy in Europe: it aims at mitigating the effects of credit rationing and promoting the
revitalization of SMEs. However, the growth of insolvent companies had led to an increase in
the rate of defaults of guaranteed loans, resulting in profitability and liquidity tensions for
many Mutual Guarantee Institutions (MGIs).
The paper intends to verify the relevance of some qualitative determinants for the
classification of mutual guaranteed loans into different categories of risk. The analysis aims
to investigate whether the default depends on elements relating to the MGI, the lending bank
and the type of loan and guarantee, in addition to drivers related to the company financed.
The analysis, focuses on the overall guaranteed portfolio from 19 Italian MGIs. Based on the
study of the total stock of performing loans of these institutions at the end of 2010, we
investigate the determinants of the defaulted positions at the end of June 2011. The analyzed
sample is composed of 167,777 guaranteed loans, of which 11,349 in default. The amount of
guarantees under study is approximately € 7.5 billion, corresponding to over € 15 billion of
bank loans.
Using regression models we find out which variables are significantly associated with the
default of mutual guaranteed loans. The expected result is a significant correlation of the
degree of risk of the loan with some qualitative characteristics of the MGI and the bank, the
type of loan and issued guarantee.
July 2-3, 2013
Cambridge, UK
2
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
INTRODUCTION
The economic and financial crisis that has unfolded since 2007 has significantly impacted on
the Italian Mutual Guarantee Institutions (MGIs) system. Increased difficulties for SME's in
accessing bank loans due to the dramatic deterioration in general solvency conditions, have
led to an increase in demand for mutual guarantees. Against this background, there has been a
significant increase in the default rates of guaranteed loans; this, in turn, resulted in additional
liquidity needs for MGIs as well as higher capital requirements to adequately manage risks
and cope with income imbalances.
The Bank of Italy has stressed the need for MGIs to improve their risk analysis and
management processes: “Initial inspections on MGI's and collected documentary information
have shown that there are widespread shortcomings in risk assessment techniques, both in the
preliminary investigation stage of the lending process and in the subsequent monitoring of
loans”. More specifically, “in the preliminary investigation stage, there is the need to adopt
classification systems capable of accurately classifying enterprises; when issuing a guarantee,
business development goals must be balanced against the need to operate in a situation where
risks are constantly controlled” (Baldinelli, 2012). There is a need for MGIs to define
appropriate screening criteria and informed risk taking policies.
This paper aims to investigate some of the key drivers that may impact on the performance of
mutual guaranteed loans, focusing in particular on factors external to the guaranteed entity,
such as the characteristics of the MGI, the lending bank as well as the loan and the guarantee
itself.
The analysis of factors external to the guaranteed entity is particularly significant with respect
to the MGIs’ system. These institutions are often called upon to guarantee loans granted to
firms that are not required to prepare financial statements. In addition, access to data from the
July 2-3, 2013
Cambridge, UK
3
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Italian Credit Register relating to enterprises' behaviour in the financial system is currently
limited to larger supervised MGIs.
After reviewing the literature on the difficulties faced by MGIs in effectively assessing the
risk of default by guaranteed counterparties, this paper presents the analysed sample and the
methodology used to evaluate the risk factors that may be applied in the construction of a
specific scoring model for MGIs. The third section presents the results obtained from an
empirical analysis of the performing and defaulted loans under investigation, while in the
fourth section we outline the main conclusions while offering some suggestions for further
research.
1.
REVIEW OF THE LITERATURE
The overall beneficial effects produced by MGIs on guaranteed SMEs have been thoroughly
investigated in several research works on MGIs (Berger and Udell, 1995; Levitsky, 1997;
Riding and Haines, 2001; Bennet et al., 2005; European Commission, 2005; Freixas and
Rochet, 2008; Honohan, 2010; Canaan, 2011; Gai, 2011; KPMG, 2011; OECD, 2011; Leone
and Vento, 2012; De Vincentiis and Nicolai, 2012).
These effects can be divided into direct and indirect effects.
Direct effects refer to SMEs’ improved opportunities in accessing bank credit. SMEs
typically face more difficulties compared with large-sized enterprises in raising funds, due to
the higher risk perceived by lenders, the high administration costs associated with small
loans, information asymmetry and the widespread lack of adequate guarantees.
According to the literature, all these shortcomings can be mitigated by mutual guarantees,
thus promoting an increase in the amount of credit available to SMEs. In this respect,
July 2-3, 2013
Cambridge, UK
4
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Mistrulli and Vacca (2011) found that between 2007 and 2009 SMEs with loans backed by
mutual guarantees were much less affected by the credit crunch.
The lower credit risk perceived by banks as measured by the product of the probability of
default (PD) and the loss given default (LGD), is the major factor that explains the above
results. The mutual guarantee in fact reduces the LGD as measured by banks, and, under
certain circumstances, it also contributes to a reduction in banks' capital requirements.
Some empirical analyses have also shown that the reduction in credit risk associated with
mutual guaranteed loans also results from a decrease in the PD of borrowing enterprises
(Columba et al ., 2009, 2010; Bartoli et al., 2012). The literature points out that the PD for a
firm affiliated with an MGI is lower compared to non-affiliated firms. This would be
consistent with the view that MGIs can improve banks’ performances in screening and
monitoring SMEs, thus also reducing the administrative costs of individual loans.
The lower PD associated with guaranteed loans could be mainly attributed to the MGIs’
ability in dealing with and mitigating the problems of information asymmetry that
characterise bank loans (Levitsky, 1997; Freixas and Rochet, 2008; Columba et al. 2009,
2010; OECD, 2011). This ability is related to better knowledge, especially compared to
larger banks, of the local business and peer pressures exerted among affiliated firms (Stiglitz,
1990; Varian, 1990).
Bartoli et al. (2010, 2012) argue that the information role played by MGIs with respect to
banks is even more significant during downturns, when bank scoring and rating systems,
based on numerous pro-cyclical variables, become less important.
The literature has shown that MGIs’ guarantees not only increase the amount of available
credit (Honohan, 2010), but they also reduce the cost of credit (Bennet et al., 2005; Kang and
Heshmati, 2008). With regard to the Italian market, Columba et al. (2009, 2010), as well as
July 2-3, 2013
Cambridge, UK
5
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Mistrulli and Vacca (2011) highlighted the MGIs’ positive contribution in reducing the
interest rate firms pay on their loans.
Studies on the role of MGIs have shown that the benefits these institutions provide by
facilitating the relationship between banks and firms have produced positive effects on
SMEs’ productivity and, more generally, on the real economy of the countries involved, with
an impact, for example, on technological development and employment rates (OECD, 2011).
These effects may be classified as indirect effects.
In this regard, Kang and Heshmati (2008) evaluated the effect of guarantees on the SMEs’
performance; Roper (2009) analysed the effectiveness of guarantee schemes in promoting the
growth of innovative and technologically advanced small and medium-sized enterprises; on
the basis of data from MGIs of different European countries, AECM (2010) found a
correlation between the issue of new mutual guarantees and the ability to retain and create
new jobs; Schmidt and Elkan (2010) quantified the macroeconomic effects of bank
guarantees in Germany.
The recent period of intense economic and financial turbulence and the increased rate of nonperforming firms, however, have also brought to light the weaknesses entrenched in the role
of MGIs and have revived doubts about the effectiveness of their interventions: “The extent
to which credit guarantee schemes (CGSs) actually provide these benefits is a major area of
debate. Experience suggests that credit guarantee schemes do play a role in expanding credit
to SMEs. However, empirical evidence on the exact nature and size of the impact of CGSs is
inconclusive” (OECD, 2011).
It has been verified that while the engagement of an MGI may reduce information
asymmetries between banks and firms, yet it does not allow for an effective control of moral
hazard situations. Mutual guarantees are an outside collateral that is not directly provided by
the borrower, and accordingly more subject to a moral hazard effect, which is further
July 2-3, 2013
Cambridge, UK
6
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
intensified when public funds supplement the guarantee (Jimenez and Saurina, 2004;
Mistrulli and Vacca, 2011). In addition, since the cost of the guarantee provided by the MGI
is only partially offset by a decrease in the interest rate paid on the loan (Camino and
Cardone, 1999), it has been argued that the firms that make use of MGIs do not represent a
random sample of SMEs but rather a subset characterised by a higher risk profile compared
with the overall average. Only firms that would otherwise have their loan application rejected
from banks are apparently willing to bear the guarantee cost.
These considerations seem to be confirmed by evidence of a deterioration in loans credit
quality that is more significant for loans secured by MGIs than for those granted to other
firms, especially during economic downturns. According to Mistrulli and Vacca (2011), the
rate of loans becoming non-performing during the recent economic and financial crisis was
higher for firms backed by mutual guarantees than for similar enterprises that were not
assisted by mutual guarantees. This situation, where MGIs appear to provide guarantees to
riskier SMEs, is exacerbated by the general higher vulnerability of SMEs ascertained during
the most adverse periods and their relatively high mortality rates (OECD, 2011). Recent
empirical analyses seem to contradict the conclusions reached by other studies which, on the
contrary, show that SMEs that are members of MGIs have a lower PD even at the height of
the crisis (Bartoli et al., 2012).
The deterioration in the quality of loans backed by MGIs and the consequent need to control
moral hazard situations call for improved methods for screening and monitoring the
guaranteed firms, allowing for an accurate assessment of counterparty creditworthiness. This
would not only keep the economic and financial performance of MGIs under control, but
would also limit the fees payable by their members (Gai, 2011).
This study is part of a line of research that aims to explain the determinants of the PD for a
mutual guaranteed firm, by verifying the drivers contributing to the defaults recorded by
July 2-3, 2013
Cambridge, UK
7
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
MGIs during the recent crisis. Such analysis aims to identify some statistically significant
variables in order to subsequently construct a scoring model that can be used by specialised
financial institutions when providing mutual guarantees.
The model is built using the methodologies suggested by the literature on the expected PD,
discussed in the following section (Siddiq, 2005; Engelmann and Rauhmeier, 2006; Gai,
2008; Loeffler and Posch, 2011).
2. SAMPLE AND METHODOLOGY
The sample is composed of a secured portfolio from 19 Italian MGIs at the end of 2010.
Starting from that date we observed the defaulted positions at the end of June 2011.
The overall sample consists of 167,777 guaranteed loans, including 11,349 defaulted; the
total amount of guarantees is equal to about 7.5 billion euro.
The sample corresponds to about two-thirds of the stock of total guarantees issued by the
Italian MGIs and could be considered statistically very robust.
This sample analysis aims to investigate the materiality of a set of independent variables in
explaining customers’ default:

size of the lending bank;

business sector of the enterprise;

geographical area where the MGI is located;

geographical area where the firm is located;

type of loan granted by the bank;

percentage ratio of the issued guarantee to the amount of the loan;

whether a reinsurance/counter guarantee is provided;

guaranteed amount.
July 2-3, 2013
Cambridge, UK
8
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
The impact of the independent variables on SME’s default was analysed according to a
process entirely analogous to that used for the construction of a rating model (Bren et al.,
2002; Cangemi et al., 2003; Basel Committee on Banking Supervision, 2005):
Step 1:
Univariate analyses
Hosmer-Lemenshow test and Wald test
Estimation of the coefficients
Receiver Operating Character
(ROC)
curve
Step 2:
Multivariate analysis
Correlation analysis for pairs of variables
Stepwise binary logistic regression
ROC curve
On the basis of the independent variables that are a priori suitable for use as drivers of
default, these two steps are intended to identify a limited number of such variables that have
the best theoretical and ‘qualitative’ characteristics.
Univariate analysis aims to study the behaviour of independent variables as separately
considered and to verify whether they are suitable for inclusion in the model to estimate
default. The tests carried out for each variable (with the results being shown in the appendix)
are illustrated as follows.
a) Hosmer-Lemenshow test and Wald test. The observations are divided into deciles based on
the expected probability of the default event. To check the goodness of the model, the event
and non-event observations are compared with the expected values under the assumption of a
model that accurately predicts the response variable.
July 2-3, 2013
Cambridge, UK
9
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
b) Estimation of the coefficients. Through this estimation, the sign and the ‘intensity’ of the
i coefficient for each xi variable are observed, calculated using the logistic regression model.
Variables that show a coefficient with an ‘inverted’ sign with respect to expected results are
excluded: this feature may in fact point to poor quality of the input data and in any case a
response by the model that is not clear with respect to expected default. Therefore only those
variables that have a unique direct or indirect relation with the logistic score are selected (the
score increases when the variable value increases or decreases when the variable value
increases throughout the entire range of that variable values).
c) ROC Curve. This method evaluates how the logistic regression performs in correctly
classifying one of two groups (performing/default). The summary statistics of the curve known as Area Under the Curve (AUC) - was chosen as the method for selecting the
variables: therefore those variables that show a higher AUC level are preferred. We preferred
to use the ROC curve instead of a similar tool - CAP - as the former does not require that the
composition of the sample reflects the actual performing/default proportion.
With regard to the second step of the scoring model construction, the binary logistic
regression was developed using the ‘loan status’ as the dependent variable, which takes the
value 1 if the loan went into default or 0 if the loan is still performing at the relevant date.
Therefore, the expected PD, which in this case coincides with the logistic score as the sample
used reflects the actual performing/default composition of the population, is given by the
following equation:
yi = 1/ [1+exp(-(β0 + βixi)]
(1)
or
July 2-3, 2013
Cambridge, UK
10
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
PDi = 1/ [1+exp(-(β0 + β1MGIarea + β2BankSize + β3TypeGuar + β4SMEsect +
β6TypeLoan
β5SMEarea
+ β7Amount))]
+
(2)
The coefficients 1, ... 8 and the intercept 0 are estimated by iterative procedure (stepwise)
through the maximum likelihood method. The independent variables xi have the following
meanings:

MGIarea: is the geographical area where the MGI is located. Italian provinces were
divided into three main geographical areas: North, Centre and South/Islands;

BankSize: is the size of the bank that granted the loan. This variable may have three
values: Large, Medium and Small;

TypeGuar: is the type of guarantee issued by the MGI. It may be broken down into four
kinds, depending on how the guarantee can be enforced by the bank and the MGI
financial liability.
In particular, MGIs’ operations may be segregated, subsidiary,
segmented or Basel compliant, as shown in Table 1 below;

SMEsect: is the business sector in which the firm operates. A distinction is made between
agriculture/forestry/fisheries, trade, mining, industry/construction and services;

SMEarea: is the geographical area where the borrowing SME's is located. Italian
provinces were divided into three main geographical areas: North, Centre and
South/Islands;

TypeLoan: is the type of loan granted by the bank to the SME. The numerous types of
loans available were aggregated in four broad categories: current account overdrafts,
advances on invoices, unsecured term loans, mortgage loans;

Amount: the amount of the guarantee provided by the MGI in favour of the SME (as
detailed in euro).
July 2-3, 2013
Cambridge, UK
11
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Table 1: MGIs’ operations broken down by mode of enforcement/financial liability
3. ANALYSIS OF RESULTS
Before analysing the variables that have a significant relationship with customers default, a
test was conducted to ascertain the existence of significant differences between the
characteristics of performing loans compared with those in default. In particular, we
examined differences in some aspects of the lending bank, the MGI, the borrower, the loan
and the guarantee.
The following figures show the results obtained, highlighting, for almost all of the variables
considered, a significant distinction between the two sub-samples (performing and default).
The most significant differences are found in particular with regard to the characteristics of
the bank, the loan and the guarantee.
Figure 1: Performing and defaulted guarantees by bank size
Figure 2: Performing and defaulted guarantees by MGI geographical area
Figure 3: Performing and defaulted guarantees by firm’s geographical area and sector
Figure 4: Performing and defaulted guarantees by type of loan
Figure 5: Performing and defaulted guarantees by type of guarantee
Default situations are more frequently associated with loans granted by large banks to firms
operating in Northern Italy, in the form of unsecured term loans, backed by a segregated-type
guarantee, issued by MGIs established in the northern regions.
July 2-3, 2013
Cambridge, UK
12
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
We subsequently carried out the univariate logistic regression analyses for the available
variables, in order to verify the robustness of those variables for the functioning of the model.
The fit tests performed and the regression coefficient estimations, which are detailed in the
appendix, confirm the validity of the variables selected a priori.
The final syntax of the model was defined by excluding those variables characterised by
significant pair correlation. As shown in the correlation matrix (Table 2 below), the
geographical area of the firm's location is strongly correlated with that of the MGI. This
result turns out to be consistent with expectations. Excluding the larger MGIs, in fact, we
observe that the majority of members reside in the same region as the relevant MGI.
Table 2: Correlation matrix
Between the two strongly related variables, the ‘MGI location’ was excluded from the model,
as this variable showed a smaller area below the curve in the relevant ROC graph.
After completing the variables selection process we went on with the construction of the
analysis model, using stepwise logistic regression. In particular, a step forward regression
was used, which, starting from a model with the intercept only, adds the significant variables
one after the other, in accordance with the Wald statistic.
The differentiation between the two performing/default sub-samples is confirmed by the
results provided by the Logit Regression (see appendix). The model, by strengthening the
statistical validity of the analysis, provide again a situation where all the selected variables
are significant (characterised by a significance coefficient of less than 5%). In general, the
variables selected a priori appear as crucial in discriminating between the two loan samples
and contribute to the definition of the binary dependent variable - performing/default considered in this analysis.
July 2-3, 2013
Cambridge, UK
13
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
The classification table in the appendix shows that the percentages of correct classification
between the two sub-samples resulting from the model are quite high. Considering the overall
correct classification, we can see that the higher figure is related to step four of the regression
model. The model whose prediction depends on the variables available at that step, i.e. the
type of guarantee provided, the bank size, the type of loan and the guaranteed amount,
correctly classifies 71% of analysed loans. The breakdown between correct prediction of
performing positions (71.1%) and defaulted positions (69.9%) is strongly balanced. This step
is therefore deemed to be the best calibrated step of the model.
Based on the empirical analysis carried out, the final model selected for estimating defaults
on transactions guaranteed by the MGIs is as follows:
PDi = 1/ [1+exp(-(β0 + β1BankSize + β2TypeGuar + β3TypeLoan + β4Amount))]
(3)
The results produced by the ROC curve constructed from this model are quite high. The area
below the curve, shown in Figure 6, has a value of 73.6%.
Figure 6: ROC curve regression model
It is interesting to see that all variables representing the characteristics of the borrowing firm
were excluded from the model. The actual deterioration of mutual guaranteed loans does not
have a clear relationship with the sector of the firm or its geographical location, but rather
with the size of the lending bank, the kind of guarantee given, the type of loan and the
guaranteed amount.
By analysing both the variables selected by the model at the various steps, and the estimated
coefficients for the independent variables, we can see that the type of bank and the type of
guarantee are particularly significant drivers of the PD of mutual guaranteed loans.
July 2-3, 2013
Cambridge, UK
14
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
An analysis of the coefficients, as detailed in the appendix, shows that with reference to the
type of bank the correlation is negative. Given the classification made with regard to such
variable, this means that defaults are more frequently associated with loans granted by large
banks.
On the contrary, with respect to the form of guarantee, the correlation is positive. Thus, given
the classification of the types of guarantees, the lowest PD is recorded for Basel compliant
guarantees, while segregated operations are characterised by a high risk of default.
It should be noted that the developed model has mainly statistical and descriptive purposes
and only intends to illustrate the conditional relations between variables. The estimated
parameters are to be interpreted as correlations and not as a cause and effect relationship. In
other words, it is not possible to infer that either the bank size or the form of guarantee bring
about higher PD of the guaranteed firm. It is instead accurate to say that, other things being
equal, the default in analysed loans is more frequently associated with loans granted by large
banks and backed by specific forms of mutual guarantees.
These conditional relations may conceal some shortcomings in MGIs’ processes to screen
and monitor customer creditworthiness.
Firms requesting a guarantee are often introduced to the MGI by the lending bank, especially
when the financial institution is large. The MGI is not often involved in the customer search
and selection, which could increase the risks taken by the MGI, if the in-house evaluation
process is not sufficiently rigorous and independent.
The risk taken by the MGI when granting the guarantees can also be influenced by its
financial liability and the way in which the bank enforces the guarantee in the event of
borrower's default. When the guarantee is issued in the segregated form, which is associated
with a high incidence of defaults, the MGI is exposed to the least financial and liquidity risks,
as opposed to the Basel compliant guarantee, which is associated with a limited incidence of
July 2-3, 2013
Cambridge, UK
15
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
defaults. The MGI's unlimited financial liability and the banks immediate enforcement of the
guarantee against it, upon occurrence of the firm's default, might have led to more rigorous
screening processes in the event of Basel compliant operations.
More specifically, the segregated operation is carried out by issuing collateral that is linked to
the Provision for Possible Loan Losses recognised as liabilities in the MGI's Balance Sheet
and which are invested in cash balances and portfolio securities (which are not available as
they are pledged in favour of the bank). Unlike the Basel compliant operation, any possible
enforcement by the bank against the MGI only occurs after recovery from the principal
debtor has failed. Furthermore, with respect to the issued guarantees, the MGI has only a
limited liability (up to the amount of the mentioned provision) for the losses incurred by the
bank. Unlike the Basel compliant operation, the MGI’s net assets are ‘unassailable’ even
when the Provisions are insufficient to cover the losses incurred by the lending banks as a
result of borrowers' default. Also with respect to the supervised MGIs, the guarantees issued
in this form do not require additional regulatory capital against the credit risk taken.
However, we cannot rule out that there may be other reasons justifying the lower
concentration of defaults in the Basel compliant mode of operation, especially when
compared to segregated operations:
a) Basel compliant guarantees have recently been introduced when the MGIs' new
regulatory regime has come into effect. As the PD increases over time since a loan was
granted, this may have led to slower growth in the stock of defaulted loans associated
with Basel compliant operations compared with the other types of operations;
b) in the event of segregated guarantees (as well as subsidiary guarantees) the payment
arrangements require that the bank complete the enforcement process against the
guaranteed borrower before the MGI pays the bank. Given the long time this process
usually requires, the positions in question are more likely to ‘stratify’ over time.
July 2-3, 2013
Cambridge, UK
16
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
4. CONCLUSIONS
According to the analyses performed, the defaulted positions held by the MGIs are
significantly correlated with the size of the bank granting the loan and the form of guarantee
issued by the MGI. Such results are not intended to assert any cause-effect relationship
between these variables and customer default, they rather provide a description of the
phenomenon in order to identify any critical issues in both the MGIs’ and the lending banks’
risk-taking processes.
First, the empirical analysis has shown that PD is strongly correlated with bank size. Despite
this figure may be partially biased by the inclusion in the sample of some large MGIs, this
evidence deserves some remarks. First, it raises a doubt as to the effectiveness of the internal
rating models used by large banks in screening and monitoring customers compared to less
massive and more ‘personalised’ assessment processes. Secondly, the principles in support of
local banking seem to be increasingly more important as a source of competitive advantage in
banks loan portfolio management, especially with regard to smaller size enterprises.
The analyses have also shown that the type of guarantee issued affects not only the amount of
MGIs’ and banks’ capital requirements as well as the financial liability and available liquidity
of MGIs, but also the rate of default recorded on these loans.
The empirical analysis therefore points to the need for a more proactive approach by MGIs in
screening enterprises eligible for mutual guarantees, as well as the opportunity for
strengthening risk assessment and monitoring models, even when the form of guarantee
involves limited financial liability and moderate liquidity risk.
July 2-3, 2013
Cambridge, UK
17
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
These needs are all the more pressing in current times, as MGIs’ portfolios have become
significantly riskier, affecting the amount of capital required to adequately manage risks as
well as the financial results for the period.
REFERENCES
AECM (2010). Guarantees and the Recovery: the impact of anti-crisis guarantee measures,
Secretariat General AECM, Brussels.
Altman, E. (2002). Revisiting Credit Scoring Models in a Basel 2 Environment, Working
Paper, Stern School of Business, New York University, no. 5.
Bartoli, F., Ferri, G., Murro, P., & Rotondi, Z. (2012). Bank-firm relations and the role of
Mutual Guarantee Institutions at the peak of the crisis, Journal of Financial Stability, April.
Basel Committee on Banking Supervision (2005). Working Paper No. 14 Studies on the
Validation of Internal Rating Systems, May.
Bennet, F., Doran, A., & Billington, H. (2005). Do Credit Guarantees Lead to Improved
Access to Financial Services? Recent Evidence from Chile, Egypt, India and Poland,
Commission Financial Sector Team, Policy Division Department for International
Development, London, February.
Berger, A.N., & Udell, G.F. (1995). Relationship Lending and Lines of Credit in Small Firms
Finance, Journal of Business, no. 68.
Bren, D., Kocagil, A.E., Wingate, A., & Wright, D. (2002). Moody's RiskCalc™ For Private
Companies: Italy, October.
Canaan, S.P. (2011). The Determinants of Expansion of SMEs under a Partial Credit
Guarantee Scheme: The Case of Lebanon, ERF 17th annual conference, March, 20-22.
July 2-3, 2013
Cambridge, UK
18
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Cangemi, B., De Servigny, A., & Friedman, C. (2003). Standard & Poor’s Credit Risk
Tracker for Private Firms. Technical Document, November.
Camino, D., & Cardone, C. (1999). The Valuation and Cost of Credit Insurance Schemes for
SMEs: The Role of the Loan Guarantee Associations, in International Small Business Journal,
vol. 17, n. 4, July.
Columba, F., Gambacorta, L., & Mistrulli P.E. (2009). The effects of mutual guarantee
consortia on the quality of bank lending, in Revue Bancaire et Financière, Vol. 4.
Columba, F., Gambacorta, L., & Mistrulli P.E. (2010). Mutual Guarantee Institutions and
Small Business Finance, in Journal of Financial Stability, no. 6.
De Vincentiis, P., Nicolai, M. (by) (2012). I Confidi di nuova generazione. Gli impatti della
crisi e delle nuove regole, Bancaria Editrice, Rome.
Engelmann, B., & Rauhmeier, R. (by) (2006), The Basel II Risk Parameters: Estimation,
Validation, and Stress Testing, Springer-Verlag.
European Commission (2005). Guarantees and Mutual Guarantees. Report to the
Commission by an Independent Expert Group, January.
Freixas, X., Rochet, J.C. (2008). Microeconomics of Banking, 2nd Edition, The MIT Press.
Honohan, P. (2010). Partial credit guarantees: principles and practice, Journal of Financial
Stability, no. 6.
Gai, L. (2008). Il rating delle PMI, Franco Angeli, Milan.
Gai, L. (by) (2011). La nuova attività dei Confidi. Profili gestionali, contabili e
regolamentari, FrancoAngeli, Milan.
Gai L., & Rossi F. (2010). Public policies and venture capital in supporting SMEs: an
economic comparison of available instruments, in Strategic Change, Vol. 19, no.7.
July 2-3, 2013
Cambridge, UK
19
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Green A. (2003) Credit Guarantee Schemes for Small Enterprises: An Effective Instrument to
Promote Private Sector-Led Growth?, the United Nations Industrial Development
Organization (UNIDO) Working Paper, no. 10, August.
Jimenez, G., & Saurina, J. (2004). Collateral, type of lender and relationship banking as
determinants of credit risk, Journal of Banking and Finance, no. 28.
Kang, J.W., & Heshmati, A. (2008). Effect of credit guarantee policy on survival and
performance of SMEs in Republic of Korea, Small Business Economics, no. 31.
KPMG (2011). Credit access guarantees: a public asset between State and Market.
International survey on guarantee market players.
Lei, H., & Xi, G. (2005). Different Guarantee Institutions and Their Effects on the Financing
of Small and Medium-Sized Enterprises (SMEs), in Economic Research Journal, September.
Levitsky, J. (1997). Credit Guarantee Schemes for SMEs: an international review, Small
Enterprise Development, no. 2, June.
Loeffler, G., & Posch, P.N. (2011) Credit Risk Modeling using Excel and VBA, The Wiley
Finance Series, 2° Edition.
Mistrulli, P.E., & Vacca, V. (by) (2011). I confidi e il credito alle piccole imprese durante la
crisi, Questioni di Economia e Finanza della Banca d’Italia, no. 105, october.
Leone, P., & Vento, G.A. (2012). Credit Guarantee Institutions and SME finance, London
Palgrave Mcmillan, Studies in Banking and Finance Institutions.
Riding, A.L., & Haines, G. Jr. (2001). Loan Guarantees: Costs of Default and Benefits to
Small Firms, Journal of Business Venturing, Vol. 16, no. 6.
Roper, S. (2009). Credit Guarantee Schemes: a tool to promote SME growth and innovation
in the MENA Region, Warwick Business School, UK for the 3rd MENA-OECD Working
Group on SME Policy, 26th October.
OECD (2011). Facilitating access to finance, Discussion paper on credit guarantee schemes.
July 2-3, 2013
Cambridge, UK
20
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Siddiq, N. (2005). Credit Risk Scorecards: Developing and Implementing Intelligent Credit
Scoring, Wiley and SAS Business Series.
Stiglitz, J.E. (1990). Peer Monitoring and Credit Markets, World Bank Economic Review,
Vol. 4, no. 3.
Varian, H. (1990). Monitoring Agents with other Agents, Journal of Institutional and
Theoretical Economics, Vol. 146, no. 1
STATISTICAL APPENDIX
Table 3: Contingency table for the Hosmer-Lemeshow test - univariate analysis
Value = 0
Observed
Step 1
MGI geographical area
Step 1
Bank size
Step 1
Expected
Value = 1
Observed
Expected
Total
1
20581
20402,91
883
1061,092
21464
2
54239
54595,18
3931
3574,819
58170
3
81608
81429,91
6535
6713,091
88143
1
10591
10889,91
619
320,087
11210
2
64518
63915,35
2757
3359,647
67275
3
81319
81622,74
7973
7669,265
89292
1
83333
81931,59
1546
2947,413
84879
2
26703
27491,06
2569
1780,937
29272
3
32207
34835,11
6692
4063,886
38899
4
14185
12170,24
542
2556,764
14727
1
3889
3777,872
133
244,128
4022
2
42207
42474,2
3127
2859,805
45334
3
130
130,822
10
9,178
140
4
50086
49832,47
3389
3642,533
53475
5
60116
60212,64
4690
4593,357
64806
1
27379
27331,59
1165
1212,406
28544
2
45224
45322,85
2929
2830,151
48153
Type of guarantee
Step 1
Business sector
Step 1
Firm geographical area
July 2-3, 2013
Cambridge, UK
21
2013 Cambridge Business & Economics Conference
Step 1
Type of loan
Step 1
ISBN : 9780974211428
3
83825
83773,56
7255
7306,444
91080
1
474
462,481
11
22,519
485
2
46704
47410,26
3376
2669,742
50080
3
15828
15962,48
1174
1039,518
17002
4
40299
37957,34
517
2858,659
40816
5
53123
54635,44
6271
4758,561
59394
1
16107
16161,967
671
616,033
16778
2
16127
15832,068
685
979,932
16812
3
15714
15448,991
819
1084,009
16533
4
15958
15613,100
820
1164,900
16778
5
15624
15578,209
1155
1200,791
16779
6
15801
15723,792
1164
1241,208
16965
7
15433
15532,450
1345
1245,550
16778
8
14654
14874,176
1428
1207,824
16082
9
15350
15507,183
1428
1270,817
16778
10
15660
16156,064
1834
1337,936
17494
Amount
Table 4: Classification table - univariate analysis
Expected
Value
Observed
MGI geographical area
July 2-3, 2013
Cambridge, UK
Step 1
Value
0
0
1
74820
81608
Correct %
47,8
22
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
1
4814
6535
Total %
Step 1
48,5
Value
Bank size
0
75109
81319
48
1
3376
7973
70,3
Total %
Step 1
49,5
Value
Type of guarantee
0
110036
46392
70,3
1
4115
7234
63,7
Total %
Step 1
57,6
69,9
Value
0
46226
110202
29,6
1
3270
8079
71,2
Business sector
Total %
Step 1
32,4
Value
Firm geographical area
0
72603
83825
46,4
1
4094
7255
63,9
Total %
Step 1
47,6
Value
Type of loan
0
63006
93422
40,3
1
4561
6788
59,8
Total %
Step 1
41,6
Value
Amount
0
46440
109988
29,7
1
2078
9271
81,7
Total %
33,2
Table 5: Variables in the equation - univariate analysis
B
Step 1 MGI geographical area
Constant
Step 1 Bank size
Constant
July 2-3, 2013
Cambridge, UK
E.S.
Wald
df
Sig.
Exp(B)
-0,23 0,015
247,149
1
0
0,794
-2,265 0,024
8799,666
1
0
0,104
-0,581 0,018
1029,976
1
0
0,559
-1,784 0,027
4524,747
1
0
0,168
23
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Step 1 Type of guarantee
0,588 0,009
4319,054
1
0
1,801
Constant
-3,913 0,024
25796,14
1
0
0,02
0,041 0,008
29,244
1
0
1,042
-2,78 0,031
8148,529
1
0
0,062
-0,334 0,014
563,141
1
0
0,716
-2,105 0,023
8424,022
1
0
0,122
0,145 0,008
324,962
1
0
1,156
-3,168 0,032
9534,923
1
0
0,042
Step 1 Amount
-,038
,002
351,962
1
,000
,962
Constant
-2,485
,012
46418,248
1
,000
,083
Step 1 Business sector
Constant
Step 1 Firm geographical area
Constant
Step 1 Type of loan
Constant
Table 6: Classification table - multivariate analysis
Expected
Value
Observed
0
1
Step 1 Value 0 110036 46392
1
4115
7234
Total %
70,3
63,7
69,9
Step 2 Value 0 103001 53427
1
3046
8303
Total %
65,8
73,2
66,3
Step 3 Value 0 108812 47616
1
July 2-3, 2013
Cambridge, UK
Correct %
3316
8033
69,6
70,8
24
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Total %
69,6
Step 4 Value 0 111242 45186
1
3411
7938
Total %
71,1
69,9
71,0
Step 5 Value 0 104181 52247
1
3248
8101
Total %
66,6
71,4
66,9
Step 6 Value 0 105700 50728
1
3305
8044
Total %
67,6
70,9
67,8
Table 7: Variables in the equation - multivariate analysis
Wald
df Sig.
Exp(B)
4319,054
1 ,000
1,801
-3,913 ,024 25796,136
1 ,000
,020
B
Step 1 Type of guarantee
Constant
,588 ,009
-,569 ,019
934,120
1 ,000
,566
,569 ,009
4194,558
1 ,000
1,767
-3,045 ,036
7328,495
1 ,000
,048
-,609 ,019
1051,013
1 ,000
,544
,613 ,009
4545,709
1 ,000
1,846
Amount
-,058 ,002
656,407
1 ,000
,944
Constant
-2,880 ,036
6318,092
1 ,000
,056
Step 2 Bank size
Type of guarantee
Constant
Step 3 Bank size
Type of guarantee
July 2-3, 2013
Cambridge, UK
E.S.
25
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
-,617 ,019
1076,141
1 ,000
,540
Type of guarantee
,609 ,009
4469,160
1 ,000
1,839
Type of loan
,148 ,009
300,178
1 ,000
1,159
Amount
-,057 ,002
642,381
1 ,000
,944
Constant
-3,417 ,049
4955,763
1 ,000
,033
-,622 ,019
1097,229
1 ,000
,537
,590 ,009
4087,573
1 ,000
1,804
-,237 ,014
286,948
1 ,000
,789
,146 ,009
290,208
1 ,000
1,157
Amount
-,058 ,002
651,784
1 ,000
,944
Constant
-2,989 ,055
2967,163
1 ,000
,050
-,626 ,019
1112,773
1 ,000
,535
,595 ,009
4098,263
1 ,000
1,813
Business sector
-,042 ,008
27,282
1 ,000
,959
Firm geographical area
-,250 ,014
309,611
1 ,000
,778
,145 ,009
287,568
1 ,000
1,156
Amount
-,057 ,002
641,500
1 ,000
,944
Constant
-2,814 ,064
1919,023
1 ,000
,060
Step 4 Bank size
Step 5 Bank size
Type of guarantee
Firm geographical area
Type of loan
Step 6 Bank size
Type of guarantee
Type of loan
TABLES AND FIGURES
July 2-3, 2013
Cambridge, UK
26
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Table 2: MGIs’ operations broken down by mode of enforcement/financial liability
Mode of the Bank’s enforcement of
payment against the MGI
First demand
After enforcement
of payment against
the principal
debtor
Limited
(without
capital  Segmented
 Segregated
operations
operations
absorption for the MGI)
MGI
financial
 Basel
liability
Unlimited
(with
 Subsidiary
capital
compliant
operations
absorption for the MGI)
operations
Table 2: Correlation matrix
MGIarea
BankSize
TypeGuar SMEsect
SMEarea
TypeLoan
Amount
MGIarea
100,000%
-7,382%
-20,269%
-24,475%
77,775%
2,381%
-6,434%
BankSize
-7,382%
100,000%
0,116%
-6,062%
-7,300%
5,330%
-3,169%
TypeGuar
-20,269%
0,116%
100,000% 8,909%
-12,748%
3,715%
4,466%
SMEsect
-24,475%
-6,062%
8,909%
100,000% -17,523%
-0,167%
4,779%
SMEarea
77,775%
-7,300%
-12,748%
-17,523%
100,000%
-4,840%
-0,249%
TypeLoan
2,381%
5,330%
3,715%
-0,167%
-4,840%
100,000%
0,315%
Amount
-6,434%
-3,169%
4,466%
4,779%
-0,249%
0,315%
100,000%
July 2-3, 2013
Cambridge, UK
27
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Figure 2: Performing and defaulted guarantees by bank size
Figure 2: Performing and defaulted guarantees by MGI geographical area
Figure 3: Performing and defaulted guarantees by firm’s geographical area and sector
July 2-3, 2013
Cambridge, UK
28
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Figure 4: Performing and defaulted guarantees by type of loan
Figure 5: Performing and defaulted guarantees by type of guarantee
Figure 6: ROC curve regression model
July 2-3, 2013
Cambridge, UK
29