2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
Functionality of Collaterals:
A Comparison Between Unsecured and Mutual Guaranteed
Loans
Lorenzo Gai
University of Florence
Department of Business and Economics
Mail: lorenzo.gai@unifi.it
Phone: +39-328/9153073
Federica Ielasi
University of Florence
Department of Business and Economics
Mail: federica.ielasi@unifi.it
Phone: +39-339/8510987
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2014 Cambridge Conference Business & Economics
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Functionality of Collaterals:
A Comparison Between Unsecured and Mutual Guaranteed
Loans
Abstract
The mutual guarantee is playing an increasingly important role as an instrument of economic
policy in Europe. The Mutual Loan-Guarantee Institutions (MGIs – in Italy Confidi) commit
to granting a collective guarantee to credits issued to their members, who in turn take part
directly or indirectly in the formation of the equity and the management of the scheme. The
paper aims at understanding which characteristics of bank loans are widespread among the
transactions guaranteed by MGIs, in order to verify whether the operation of this Consortia is
functional to mitigate the risk of the loans.
The study relates to the performing loan portfolio of 32 Italian banks. The total sample is
composed of 124,267 loans, relating to the period 2008-2009. Loans guaranteed by MGIs are
14,832. They refer to guarantees granted by 28 MGIs.
The empirical analysis has been conducted through the development of a stepwise logistic
regression, which aims to investigate the profiles of loans related with the granting of an
external guarantee.
The hypothesis tested highlights the opportunities for an optimization of the guaranteed loan
portfolio, through the elimination of overlapping between types of collaterals, in order to not
incur in the main negative effects of an excessive level of guarantees.
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The results of this study are based on an original and reserved dataset, not available in public
financial statements or in public statistics, but collected directly from banks that joined the
research.
1. Introduction
The Mutual Loan-Guarantee Institutions (MGIs – in Italy Confidi) are “collective initiatives
of a number of independent businesses or their representative organizations. They commit to
granting a collective guarantee to credits issued to their members, who in turn take part
directly or indirectly in the formation of the equity and the management of the scheme”
(European Commission, 2005).
The collective guarantees issued by these societies are now assuming an increasingly
important role as an instrument of economic policy, aimed at mitigating the effects of credit
rationing (Columba et al., 2010).
The small business lending is the segment in which the intervention of MGIs is more
effective (Beck et al., 2010). SMEs have relatively more difficulties in accessing credit,
because the most significant information asymmetries that characterize the relationship with
the bank, due to low reporting requirements and scarcity of public information (Tucker and
Lean, 2003; Esperanca et al., 2003; Chen, 2006; Busetta and Zazzaro, 2012).
Information asymmetries are typically mitigated by financial intermediaries by building
relationships based on the so-called relationship lending or by asking adequate collaterals on
issued loans. Both tools are more difficult to be activated towards SMEs, which, especially if
they are young, have limited guarantees and a short credit history (Manove et al., 2001; Beck
et al., 2005; Beck and Demirguc-Kunt, 2006).
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The intervention of MGIs should fill the inefficiencies generated by adverse selection, when
debtors do not have sufficient assets to provide adequate financial guarantees or when their
size does not allow the bank to carry out an in-depth screening and monitoring. In other
words, MGIs “are a wealth-pooling mechanism that allows otherwise inefficiently rationed
borrowers to obtain credit” (Busetta and Zazzaro, 2012).
The presence of mutual guarantees is able to make a positive impact in terms of increase in
functionality and liquidity of credit market, with a consequent reduction of the phenomena of
credit rationing (Barro, 1976; Stiglitz and Weiss, 1981).
However, the financial literature has also provided several evidences about the possible
adverse effects caused by the presence of collateral for bank loans, which could negatively
affect the operation of the parties involved, as well as the economic system as a whole
(Jackson and Kronman, 1979, Stiglitz, 1985; Bester, 1987; Rajan and Winton , 1995; Manove
and Padilla , 1999; Manove et al., 2001; Tagliavini and Lanzavecchia , 2005; Beck et al.,
2005; Beck and Demirguc - Kunt, 2006; Chen, 2006).
This contribution is part of the debate on the role of mutual guarantees, verifying which
characteristics of the bank loans are popular among the operations guaranteed by MGIs and,
consequently, the functionality of mutual guarantees in reducing potential credit rationing.
The study relates to the performing loan portfolio of 32 Italian banks. The total sample is
composed of 124,267 reports, relating to the period 2008-2009. The loans guaranteed by
MGIs are 14,832. They refer to guarantees granted by 28 first-level mutual guarantee
consorzia.
The empirical analysis has been conducted through the development of a stepwise logistic
regression, aimed at investigating the profiles of loans related to a significant extent to the
granting of a mutual guarantee.
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The conclusions highlight the opportunities for an optimization of the guaranteed loan
portfolio, in particular through the elimination of the overlap between types of guarantees, in
order to avoid the negative effects generated by excessive levels of collateral.
The analyzes are based on an original and reserved dataset, not available in the banks’
balance sheets or in the statistics of the Supervisory Authorities, but collected directly from
banks that have joined this research.
The work continues with a review of the literature (Section 2) and the description of the
sample and the methodology applied (paragraph 3). The fourth paragraph presents the main
results obtained from the survey, while Section 5 presents the conclusions and some
indications for the rationalization of banks’ guaranteed loan portfolio.
2. Literature review
A large literature has discussed the role of collateral in bank loans and the relationship
between the demand for guarantees and the credit risk of the company. The results are not in
agreement and often not consistent with market practice.
In general, it is recognized that the guarantees not only reduce the loss in the event of default,
but they can promote the access to credit at the stage of granting the loan, reducing
information asymmetries between funded and funder.
The relationship between the level of guarantee provided and the risk of the borrower is,
however, sensitive to the nature of these information asymmetries.
When the risk of default of borrower is not readily observable by the bank, the guarantee is
mainly used as a screening tool: granting the guarantee, the company communicates to the
bank its trust and its commitment on the success of projects funded. Borrowers with less risky
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projects are willing to grant guarantees, paying a lower interest rate, while debtors with
projects characterized by a higher level of risk are willing to pay a higher interest rate, not
providing collaterals (Bester, 1985; Kanatas and Chan, 1985; Bester, 1987; Besanko and
Thakor, 1987; Bester, 1994). According to this literature, consequently, the guarantees are
most frequently granted by safer debtors.
On the other hand, when the type of borrower is more readily observable by the bank, the
guarantee is primarily used to control the problem of moral hazard. Riskier is the counterpart,
the most serious are these problems and, consequently, greater the demands for collateral by
banks (Hempel et al., 1986; Morsman, 1986; Leeth and Scott, 1989; Berger and Udell, 1990;
de Meza and Southey, 1996; Jemenez and Saurina, 2004; Chen, 2006). The secured loans are
therefore those characterized by a higher probability of default. According to this literature,
consistent with market practice, are the most risky borrowers to provide more guarantees.
Berger and Udell (1990) also investigated the relationship between the presence of collateral
and the degree of risk of the bank, concluding that the guarantee is most often associated with
banks with a higher risk.
The same positive correlation was found between the provided guarantees and the risk
premium on loans’ pricing, as well as between collateral and the total interest rate on loans
(Berger and Udell , 1990; Coco, 1999).
This contribution is part of the debate on the role of collateral, verifying the correlation
between the presence of a mutual guarantee and some characteristics of the firm-bank
relationship (the amount of the loan, its maturity, the presence of additional collateral, the
technical form of the loans…).
The analysis of the potential overlap between the mutual guarantees and other forms of
collateral, such as mortgages, is of primary importance in the evaluation of the lending
process. In fact, the literature agrees on the existence of an equilibrium threshold in the level
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of guarantees required to debtors (Bester, 1987; Manove and Padilla, 1999; Manove et al.,
2001; Chen, 2006). An excessive amount of guarantees can produce significant negative
effects: “There is an economic-efficiency case in favor of collateral limitations” (Manove et
al., 2001).
Regarding the positive effects, the practice of secured financing allows banks, other
conditions being equal, to deal with riskier loans, facilitating access to credit for households
and businesses (Barro, 1976; Stiglitz and Weiss, 1981).
The presence of secured loans is not only a condition imposed by the banking system, but it is
often a condition preferred from the same debtor, especially when this is a business. In fact,
the instrument of external guarantee allows the entrepreneur to hold assets outside the
company, pursuing the strategy summed up in the slogan “rich family-poor business”,
widespread among Italian SMEs, especially in the past, for fiscal and governance reasons.
However, when the guarantees required are redundant, such beneficial effects can produce
disadvantages for both the lender and the borrower, as well as for the whole economic
system, as discussed by extensive international literature.
First, the presence of collateral may reduce the incentive for banks to achieve effective
information about the counterpart (asset screening and selection) and to adequately monitor
the trend of the relationship (loan monitoring) (Jackson and Kronman, 1979; Stiglitz, 1985;
Rajan and Winton, 1995; Manove et al., 2001).
The practice of guarantees can reduce the sensitivity of the banker to the real prospects of the
financed projects, with possible negative consequences on the performance of its mediumterm loans portfolio, as witnessed by the numerous failures of the Italian special credit
institutions, characterized by widely backed portfolios (Tagliavini and Lanzavecchia, 2005).
The presence of substantial guarantees, moreover, does not encourage banks to forge solid,
in-depth and long-term relationships with counterparts. The so-called relationship lending
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represents an alternative to the application of guarantees to mitigate the problem of
asymmetric information (Berger and Udell, 1995; Harhoff and Korting, 1998; Jemenez and
Saurina, 2004; Cotugno et al., 2013). As a result, the practice of secured loans is not alien to
the phenomenon of multiple bank relationships (Ongena and Smith, 2000; Tagliavini and
Lanzavecchia, 2005).
Chen (2006) highlights another possible adverse effects of excessive guarantees: when the
amount of collateral exceeds a critical threshold, and the bank cannot request additional
coverages during the renegotiation of the debt, there may be inefficiencies in the incentives of
the debtor to solve its financial imbalances.
In general, literature and political debate agree on the opportunity of an adequate protection
of the creditor’s rights, by the posting of guarantees and/or in the context of court
proceedings, but paying attention to the correct balance with the rights of the debtor,
especially in terms of efficient access to credit.
If the banking practice focuses on the guaranteed loans, a relative advantage is generated for
the asset-based projects. A credit policy based on the guarantee with tangible goods creates a
comparative disadvantage for intangible investments and innovative ones, which are not
suitable to being incorporated into guaranteed schemes.
Overall, the culture of secured financing may reduce opportunities for resource allocation to
borrowers unable to provide adequate guarantees, including SMEs: “The difficulty of banks
in assessing the creditworthiness of small borrowers often goes hand-in-hand with inadequate
availability of collateralizable wealth from the latter. Lack of information and collateral
therefore are universally seen as the main structural features explaining the reluctance of
banks to lend to small enterprises, especially during economic downturns, with negative
effects on industry dynamics, competitiveness and growth” (Busetta and Zazzaro, 2012).
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The discrimination towards small, young or innovative companies, may therefore result in the
medium-term negative effects on the competitiveness of the economy (Beck et al., 2005;
Beck and Demirguc - Kunt, 2006).
The demand for collateral also reduces the incentive for the lender to demand proper
capitalization of the company, generating potential adverse effects on the stability of the
economic system, as emphasized by the literature on the relationship between the
effectiveness of the instruments of credit protection and the financial structure of firms (La
Porta et al., 1998; Rajan and Zingales, 1995).
As noted above, the presence of collateral reduces the incentive for banks to carry out actions
of screening and monitoring on the company and the family, with potential negative effects
on the economic system: “collateral and screening are substitutes from the point of view of
banks. Yet they are not equivalent from the social standpoint. Because of their superior
expertise in project evaluation, the screening activity of banks is a value-enhancing activity
for society, whereas the posting of collateral is not, since it merely allows a transfer of wealth
from the borrower to the bank when things go badly” (Manove et al., 2001). The economic
system as a whole, in the presence of excessive collateral, cannot rely on the fact that
financial resources are invested in the best business projects, reducing the potential allocative
efficiency of the loans.
Finally, the presence of redundant guarantees is able to generate a negative impact on other
stakeholders of the borrower, including creditors other than banks.
In fact, the practice of secured loans undermines the basis of the principle of equal treatment
of creditors. Outside collateral produces similar effects to an increase in share capital, but for
the sole benefit of the guaranteed party. It creates a separate capitalization in favor of a
particular creditor (Bolton and Scharfstein, 1996). The principle of equal treatment (par
condicio creditorum) allows a wider splitting of the consequences of corporate insolvencies
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and it is then effective for the development of the economy. However, the objective of
protecting requires adequate coverage of the banks against losses resulting from business
failures.
The mutual guarantees is then able to mitigate some of the negative effects of excessive
financial guarantees. MGIs demonstrated ability to carry out a thorough screening of the
guaranteed companies, which would complement the loans selection process held by banks
(Columba et al., 2010; Busetta and Zazzaro, 2012; Bartoli et al., 2012 , Gai et al., 2013) .
This ability derives from an in-depth knowledge of the local business and peer pressures
exerted between the different associated.
However, it appears appropriate that the demand for such forms of guarantee is carefully
assessed by banks and MGIs. In fact, the cost of mutual guarantee is typically only partially
offset by a decrease in the rate of interest paid by the borrower (Arping, 2010).
3 . Sample and methodology
The sample of this research is made up of a total of 124,267 performing loans, of which
14,832 backed by mutual guarantees.
The analysed positions are included in the loans portfolio of 32 Italian cooperative banks;
guarantees are issued by 28 MGIs. Since the objective of the analysis was to understand the
profile of bank loans backed by mutual guarantees, in order to assess the effectiveness of the
guarantee process, it was considered appropriate to analyse the loans portfolio of small banks,
characterized by a similar bargaining power with respect to MGIs. The data refer to the
period 2008-2009.
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We studied a series of independent variables aimed to distinguish the sub-sample of
unsecured loans from the sub-sample of mutual guaranteed loans.
First, the model includes variables related to the legal status of the borrower, distinguishing
between households, legal entities, individual firms and co-holders. Empirical studies show a
greater number of loans secured by MGIs among small and medium-sized enterprises. By
consequence, it is expected that the sub-sample of guaranteed loans is particularly
concentrated among legal entities and individual firms.
Second, we included among independent variables the several technical forms of loans
granted by banks, distinguishing between overdrafts on current accounts, advances, signature
loans, instalment loans, active grants and portfolio. The different technical forms of loan
enable the beneficiary to meet specific needs, such as those related to investments in fixed
assets, investments in working capital or management of corporate liquidity. The presence of
a significant correlation between the technical form of financing and the grant of a mutual
guarantee allow to assess the effectiveness of the MGIs in supporting specific financial needs
of enterprises.
Finally, we included as independent variables some factors that may affect the degree of risk
faced by the bank in granting the loan. The higher is the risk of collateralised transactions, the
greater is the effectiveness of the MGI’s intervention. In fact, the operation of a mutual
guarantee consortia should aim to increase the number of borrowers financed by the banking
system, reducing the credit risk of companies otherwise excluded from access to credit,
thanks to the guarantee. In particular, among the independent variables related to the credit
risk of the bank we included the presence of additional forms of guarantee, with specific
reference to mortgage collateral, the maturity of the loan and the amount of the granted loan.
As shown in the literature on the analysis of bank credit risk, the greater is the amount of
funding and the smaller the forms of guarantee obtained, the higher are the potential losses
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given the insolvency of the borrower. The maturity of loans affects the degree of risk faced
by the bank, as in the long-term the assumptions underlying the assessments of the loans are
more uncertain. In the paper, we didn’t analyse variables related to the qualitative and
quantitative characteristics of the borrowers, as not available.
From the methodological point of view, the goal of the analysis is realized verifying the
existence of significant differences between the profile of unsecured loans and the profile of
mutual guaranteed ones.
First of all, this analysis was done by carrying out the t-test for equality of means, in order to
verify the following null hypothesis: the averages of the two sub-samples, unsecured and
mutual guaranteed loans, are equal.
The correlations between pairs of variables were then analysed, and finally a regression step
forward model has been developed, in order to verify the variables related more significantly
with the presence of a mutual guarantee. The regression model adopted, starting from a
function with only the intercepts, adds the significant variables one after the other, according
to the Wald statistic.
In performing regression we used as the dependent variable “MG” (mutual guarantee), that
takes the value 1 in the presence of a loan secured by a MGI or 0 otherwise.
The objective of the analysis is to explain the different characteristics of mutual guaranteed
loans, analysing three groups of independent variables, related to the legal characteristics of
the borrower, the technical form of the loan granted by the bank and the degree of risk of the
loan.
MG = 1 / [1 + exp (- (β0 + β1 House + β2 Legal + β3 Indiv + β4 Co-holder + β5 Overdraft + β6
Advance + β7 Signat + β8 Instal + β9 Grants + β10 Portf + β11 Mortg + β12 Matur + β13
Amount))]
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The coefficients 1, ... 13 and the intercept 0 are estimated by iterative procedure (stepwise)
through the maximum likelihood method.
Table 1 provides a breakdown of the variables considered.
Table 1: Regression Variables
Dependent Variable
MG
Features
Dummy variable, with value 1 if the loan is guaranteed by a MGI,
0 otherwise.
Independent Variable
Features
First category: legal
status
House
Dummy variable , with value 1 if the loan counterpart is a
household, 0 otherwise.
Legal
Dummy variable with value 1 if the borrower is a legal entity, 0
otherwise.
Indiv
Dummy variable, with value 1 if borrower is an individual firm, 0
otherwise.
Co-holder
Dummy variable with value 1 if the loan has co-holders, 0
otherwise .
Second category:
technical form
Overdraft
Dummy variable, with value 1 if the loan was paid in the form of
current account, 0 otherwise.
Advance
Dummy variable, with value 1 if the loan was paid in the form of
advance, 0 otherwise.
Signat
Dummy variable, with value 1 if the loan was paid in the form of
signed credit, 0 otherwise.
Instal
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Dummy variable, with value 1 if the loan was paid in the form of
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long term loan, 0 otherwise.
Grants
Dummy variable , with value 1if the loan was paid in the form of
active grant and 0 otherwise.
Portf
Dummy variable , with value 1if the loan was paid in the form of
portfolio, 0 otherwise.
Third category:
degree of risk
Mortg
Dummy variable, with value 1 if the loan is secured by a
mortgage, 0 otherwise.
Matur
Variable expressed in years that states the maturity of the loan.
The overdrafts are considered a with a maturity equal to 0.
Amount
Variable expressed in thousands of Euros that states the amount of
funding approved.
For further details on the characteristics of the variables, the main descriptive statistics are
shown in the statistical appendix.
4 . Main results
The t-test of the averages, included in the statistical appendix, shows a good divergence
between the two sub-samples of mutual guaranteed and unsecured loans.
As shown in the two charts below, there are significant differences the two sub-samples,
especially with reference to type of borrowers. As expected, the portfolio of guaranteed
positions consist almost exclusively of loans granted to legal entities and individual firms,
while over a third of unsecured loans are granted to households. The principal form of loan is
the instalment one in both the analysed sub-samples. The composition of secured and
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unsecured portfolios is different with respect to other forms of loans: advances are most
common among mutual guaranteed loans, while overdrafts on current accounts and signature
loans have a relatively lower incidence.
Graph 1 : Breakdown by type of legal status – mutual guaranteed and unsecured portfolio
Graph 2: Breakdown by type of loan – mutual guaranteed and unsecured portfolio
The two sub-samples have an heterogeneous composition with reference to the independent
variables related to the degree of risk of the loans. Over 30% of the positions backed by
mutual guarantees have a guarantee of mortgage type too. On the other hand, the sub-sample
of non-guaranteed financing doesn’t have mortgage guarantees.
With regard to the maturity of the loans, the unsecured portfolio is characterized by an
average maturity of approximately 9 years, compared with an average maturity of less than 5
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years for secured loans. In order to further analyse this aspect, we eliminated from the sample
all the overdrafts, for which we assumed a maturity equal to zero. Even eliminating these
positions, the average maturity of unsecured loans is much higher than those backed by
mutual guarantees (approximately 12 years, compared with less than 7 years).
Finally, the analyses show an approved average amount much higher for non-guaranteed
financing with respect to the guaranteed portfolio (approximately EUR 108,000 against EUR
68,000).
These first tests show a very different profile in the two sub-samples.
The results of the descriptive analysis are confirmed by the subsequent development of the
regression model.
The stepwise regression was interrupted at step 8, excluding from the model the legal entities
and individual firms, signature loans, active grants and portfolios.
Taking into account the overall percentage of correct classification and the degree of balance
between the correct prediction of the two sub-samples, the step 8 of the model is considered
as the most calibrated, as shown in the following table 2. The independent variables available
at this step are “households”, “co-holders”, “overdrafts”, “advances”, “instalment loans”,
“mortgages”, “maturity” and “amount”.
Table 2: Classification table
Observed
Estimated
Mutual guarantee
0
Step 1
Correct %
1
Mutual
0
109362
0
100,0
guarantee
1
10119
4713
31,8
Total %
Step 2
91,9
Mutual
0
39509
69853
36,1
guarantee
1
352
14480
97,6
Total %
Step 3
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43,5
Mutual
0
58645
50717
53,6
guarantee
1
404
14428
97,3
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Total %
Step 4
58,8
Mutual
0
84411
24951
77,2
guarantee
1
3445
11387
76,8
Total %
Step 5
77,1
Mutual
0
93278
16084
85,3
guarantee
1
3647
11185
75,4
Total %
Step 6
84,1
Mutual
0
87542
21820
80,0
guarantee
1
2547
12285
82,8
Total %
Step 7
80,4
Mutual
0
87542
21820
80,0
guarantee
1
2547
12285
82,8
Total %
Step 8
80,4
Mutual
0
89281
20081
81,6
guarantee
1
2701
12131
81,8
Total %
81,7
Reference value: 0,119
On the basis of the empirical analysis, the final selected model is the following:
MG = 1 / [ 1 + exp ( - ( β0 + + β1 House + β2 Co-holder + β3 Overdraft + β4 Advance + β5
Instalm + β6 Mortg + β7 Matur + β8 Amount))]
The following Table 3 shows the main results of the regression model.
Table 3: Stepwise logistic regression - step 8
B
E.S.
Wald
df
Sig.
Exp(B)
Households
-3,185
,056
3267,354
1
,000
,041
Co-holders
-3,657
,141
676,588
1
,000
,026
Overdrafts
1,287
,150
73,861
1
,000
3,622
Advances
1,853
,152
149,362
1
,000
6,377
Instalment loans
3,736
,149
627,381
1
,000
41,927
24,565
490,496
,003
1
,960
46606411717,364
Maturity
-,207
,005
2045,710
1
,000
,813
Amount
-,002
,000
188,979
1
,000
,998
Constant
-3,332
,148
507,902
1
,000
,036
Mortgages
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It is interesting to see how the final model included all the variables associated with the
degree of risk of the loans granted by the bank.
Since the objective of the analysis is to verify the level of functionality of the intervention of
MGIs, it is appropriate to carry out a study on the three independent variables related to the
degree of credit risk: the presence of mortgage collaterals, the maturity and the amount of
loans.
An analysis of the coefficients of the regression, reported in Table 3, shows that, with
reference to mortgages the sign is positive: the presence of a mortgage collateral is positively
correlated with the issuing of a mutual guarantee.
On the other hand, the maturity and the amount have negative signs: they are negatively
correlated with the presence of a mutual guarantee. As a result, the longer is the maturity of
the loans, and the greater the relative amount, much less widespread are the guarantees
provided by MGIs.
5. Conclusions
The analysis does lead to important conclusions, especially with regard to the degree of
functionality of the mutual guarantees respect to expected loss given default for banks.
Regarding the profile of counterparties backed by a mutual guarantee, the results of the
empirical analysis allow us to confirm the expectations: secured loans are negatively related
to loans granted to individuals and co-holders, while they are focused almost exclusively
between legal entities and individual entrepreneurs.
With regard to the technical forms of the loans, the analysis shows that the operation of MGIs
does not affect significantly the access to credit through a particular form of financing.
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Therefore, it is not supported the fulfilment of a specific financial need rather than others.
The mutual guaranteed loans are mainly provided in the form of instalment loans, bank
overdraft and advances, with a relative incidence not particularly dissimilar compared to
unsecured loans.
On the other hand, substantial differences are related to the degree of risk of the two subsamples of loans. Lacking detailed information on individual borrowers, able to express the
attended probability of default, the analysis focused on the elements of the loan able to affect
the level of credit risk faced by the bank, especially in terms of attended loss given default.
The analysis showed a profile of the secured loans contrary to expectations. Compared to
loans not secured by mutual guarantee, in fact, guaranteed loans are often covered by
mortgages and they are loans of shorter maturity and lower amount.
With reference to the analysed risk profiles, certainly partial, the survey shows a major
operation of MGIs in the coverage of loans characterized, other conditions being equal, by a
lower degree of risk for the bank. In contrast to the recommendations of the literature, then, it
seems that MGIs do not go to guarantee those loans characterized by a higher risk profile,
which would not have been access to finance in the absence of external guarantee.
It should be emphasized that this results can be influenced by the degree of risk of the
counterparties in terms of probability of default. In other words, the concentration of mutual
guarantees among loans characterized by a lower loss in the event of default could be
motivated by the widespread presence of mutual guarantees among counterparties
characterized by a particularly high probability of default. Because the credit risk is the
product of the borrower’s probability of default and the loss given default, it is possible that
the MGIs wish to compensate for the higher risk of default of counterparties with a greater
portfolio diversification and a specific attention to loss given default.
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However, this modus operandi is ineffective for the banking system and it penalizes
businesses.
Particular attention should be given to the combined presence of mortgages and mutual
guarantees. In the development of stepwise regression, the independent variable “mortgage”
is the one selected at the first step of the model construction. It is therefore an important
factor that affect the profile of the mutual guaranteed loans.
Out of a total of approximately 500 million Euros of exposure guaranteed by MGIs on
31.12.2009, about 198 million were secured by real estate mortgage too. This condition is
inefficient for the banking system, as it weakens the effects of the presence of MGIs in terms
of capital allocation.
As shown in Table 4, starting in 2009, the banks included in the sample have chosen to focus
on eligible collaterals, for the purposes of limiting capital absorption for the credit risk. In
fact, first demand guarantees (Basel compliant) show a strong growth from 2008 and 2009.
Table 4: Total stock broken down by type of guarantee
Stock 2008 (€)
Stock 2008
Stock 2009 (€)
(%)
Stock 2009
(%)
First demand guarantee
42.097.909
17%
208.783.658
42%
Subsidiary/ segregated
205.480.148
83%
291.701.264
58%
247.578.057
100%
500.484.922
100%
guarantee
Total
However, since typically the mortgage guarantee covers all or a significant portion of the
exposure, such form of “double coverage” is inefficient from the point of view of capital
requirements. In this case, the mutual guarantee, even if released at first demand from a
supervised MGI subject, it doesn’t act as an additional tool for reducing capital absorption for
the beneficiary bank. As a result, the banks make a sub-optimal management of credit risk
July 1-2, 2014
Cambridge, UK
20
2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
mitigation instruments. On the contrary, they would take advantage of the mortgages only in
the absence of an eligible mutual guarantee issued by a supervised Consortia.
The optimization of the guaranteed portfolio would also allow customers to not pay costs,
explicit and implicit, of the provision of a double guarantee, reducing the overall pricing of
loans and facilitating access to credit for those individuals who do not have mortgage
collaterals.
A solution to the problem of “double coverage” would also improve the functionality of
MGIs, whose operations would be directly channeled towards firms which are otherwise
excluded from bank financing.
References
AECM, Guarantees and the recovery: the impact of anti-crisis guarantee measures. Tech.
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2010
Arping S., The pricing of bank debt guarantees, Journal of Financial Stability, No. 108, 2010.
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21
2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
Bester H., Screening vs. rationing in credit markets with imperfect information, American
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Bester H., The role of collateral in credit market with imperfect information, European
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July 1-2, 2014
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22
2014 Cambridge Conference Business & Economics
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Jemenéz G., Saurina J., Collateral, type of lender and relationship banking as determinants
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July 1-2, 2014
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Statistical Appendix
Descriptive Statistics
N
July 1-2, 2014
Cambridge, UK
Minimum
Maximum
Average
Std. deviation
Mutual guarantee
124267
0
1
,12
,324
Household
124267
0
1
,32
,468
Legal entities
124267
0
1
,32
,467
Individual firms
124267
0
1
,20
,401
Co-holders
124267
0
1
,15
,362
Overdrafts
124267
0
1
,21
,405
Advances
124267
0
1
,06
,236
Signature loans
124267
0
1
,02
,144
Instalment loans
124267
0
1
,71
,456
Active grants
124267
0
1
,00
,024
Portfolio
124267
0
1
,01
,079
Mortgages
124194
0
1
,04
,191
Maturity
124267
0
40
8,55
7,691
Amount
124267
,2419
66750,0000
103,411637
296,5764993
24
2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
Levene's test for
equality of
variances
F
Sig.
T-test of means for independent samples
t-test for equality of means
t
df
Sig. (2-
Difference
Standard error
Confidence interval for the
tailed)
between
difference
difference at 95%
averages
Equal
Households
variances
98418,304
,000
Not equal
variances
Equal
Legal entities
variances
2838,962
,000
Not equal
variances
Equal
Individual firms
variances
8917,017
,000
Not equal
variances
Equal
Co-holders
variances
18054,308
,000
Not equal
variances
Equal
Overdrafts
variances
368,767
,000
Not equal
variances
Advances
July 1-2, 2014
Cambridge, UK
Equal
variances
2050,109
,000
Lower
Upper
80,424
124265
,000
,321
,004
,313
,329
147,578
44445,354
,000
,321
,002
,317
,325
-64,616
124265
,000
-,259
,004
-,267
-,252
-60,210
18325,994
,000
-,259
,004
-,268
-,251
-66,426
124265
,000
-,229
,003
-,236
-,222
-54,717
17317,035
,000
-,229
,004
-,237
-,221
53,623
124265
,000
,168
,003
,162
,174
125,194
103127,248
,000
,168
,001
,165
,170
9,204
124265
,000
,033
,004
,026
,040
9,662
19686,492
,000
,033
,003
,026
,039
-23,446
124265
,000
-,048
,002
-,052
-,044
25
2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
Not equal
variances
Equal
Signature loans
variances
1132,820
,000
Not equal
variances
Equal
Instalment loans
variances
31,970
,000
Not equal
variances
Equal
Active grants
variances
22,501
,000
Not equal
variances
Equal
Portfolio
variances
254,058
,000
Not equal
variances
Equal
Mortgages
variances
713842,489
,000
Not equal
variances
Equal
Maturity
variances
12020,184
,000
Not equal
variances
Amount
July 1-2, 2014
Cambridge, UK
Equal
variances
243,758
,000
-18,768
17125,474
,000
-,048
,003
-,053
-,043
16,483
124265
,000
,021
,001
,018
,023
32,546
55472,189
,000
,021
,001
,019
,022
-2,771
124265
,006
-,011
,004
-,019
-,003
-2,794
19177,676
,005
-,011
,004
-,019
-,003
2,371
124265
,018
,000
,000
,000
,001
4,068
37527,808
,000
,000
,000
,000
,001
7,928
124265
,000
,005
,001
,004
,007
13,609
37566,713
,000
,005
,000
,005
,006
-225,689
124192
,000
-,318
,001
-,321
-,315
-83,112
14831,000
,000
-,318
,004
-,325
-,310
64,837
124265
,000
4,291
,066
4,162
4,421
102,564
31710,639
,000
4,291
,042
4,209
4,373
15,547
124265
,000
40,3044733
2,5924863
35,2232457
45,3857009
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2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
Not equal
29,209
variances
47519,876
,000
40,3044733
1,3798689
37,5999118
43,0090347
Correlations between pairs of variables
Mutual
Households
guarantee
Households
Legal
entities
Legal
Individual
Co-
Overdrafts Advances Signature Instalment Active Portfolio Mortgages Maturity Amount
entities
firms
holders
loans
grants
-,222
1,000
-,474
-,347
-,296
-,126
-,167
-,063
,224
-,011
-,025
-,115
,190
-,073
,180
-,474
1,000
-,345
-,294
,204
,235
,106
-,339
,029
,006
,085
-,341
,130
Individual
Pearson’s
Correlation firms
Co-holders
,185
-,347
-,345
1,000
-,215
,075
,018
-,001
-,084
-,012
,048
,105
-,157
-,069
-,150
-,296
-,294
-,215
1,000
-,183
-,107
-,054
,241
-,009
-,029
-,079
,368
,004
Overdrafts
-,026
-,126
,204
,075
-,183
1,000
-,128
-,075
-,791
-,012
-,041
-,026
-,534
-,052
Advances
,066
-,167
,235
,018
-,107
-,128
1,000
-,037
-,389
-,006
-,020
,022
-,278
,007
Signature
-,047
-,063
,106
-,001
-,054
-,075
-,037
1,000
-,228
-,004
-,012
-,025
-,067
,018
,008
,224
-,339
-,084
,241
-,791
-,389
-,228
1,000
-,037
-,123
,023
,653
,034
-,007
-,011
,029
-,012
-,009
-,012
-,006
-,004
-,037
1,000
-,002
-,003
-,020
,122
-,022
-,025
,006
,048
-,029
-,041
-,020
-,012
-,123
-,002
1,000
-,013
-,068
-,019
,539
-,115
,085
,105
-,079
-,026
,022
-,025
,023
-,003
-,013
1,000
-,059
-,008
Maturity
-,181
,190
-,341
-,157
,368
-,534
-,278
-,067
,653
-,020
-,068
-,059
1,000
,144
Amount
-,044
-,073
,130
-,069
,004
-,052
,007
,018
,034
,122
-,019
-,008
,144
1,000
,000
.
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
.
,000
,000
,000
,000
,000
,000
,000
,013
,000
,000
,000
,000
,000
,000
.
,000
,000
,000
,354
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
.
,000
,000
,000
,000
,001
,000
,000
,000
,092
Instalment
loans
Active
grants
Portfolio
Mortgages
Households
Sig. (1 tail) Legal
entities
Individual
firms
Co-holders
July 1-2, 2014
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27
2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
Overdrafts
,000
,000
,000
,000
,000
.
,000
,000
,000
,000
,000
,000
,000
,000
Advances
,000
,000
,000
,000
,000
,000
.
,000
,000
,017
,000
,000
,000
,011
Signature
,000
,000
,000
,354
,000
,000
,000
.
,000
,108
,000
,000
,000
,000
,003
,000
,000
,000
,000
,000
,000
,000
.
,000
,000
,000
,000
,000
,009
,000
,000
,000
,001
,000
,017
,108
,000
.
,251
,146
,000
,000
Portfolio
,000
,000
,013
,000
,000
,000
,000
,000
,000
,251
.
,000
,000
,000
Mortgages
,000
,000
,000
,000
,000
,000
,000
,000
,000
,146
,000
.
,000
,004
Maturity
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
.
,000
Amount
,000
,000
,000
,000
,092
,000
,011
,000
,000
,000
,000
,004
,000
.
Instalment
loans
Active
grants
Variables in the equation
Step
1a
Step 2b
B
E.S.
Wald
df
Sig.
Mortgages
23,583
585,466
,002
1
,968
Constant
-2,380
,010
52474,685
1
,000
,093
Households
-2,753
,055
2541,297
1
,000
,064
Mortgages
23,641
556,476
,002
1
,966
Constant
-1,967
,011
33166,696
1
,000
,140
Households
-3,068
,055
3149,353
1
,000
,047
Co-holders
-4,255
,139
933,192
1
,000
,014
Mortgages
23,728
534,203
,002
1
,965
Constant
-1,653
,011
22266,896
1
,000
,192
Households
-3,315
,055
3623,445
1
,000
,036
Co-holders
-4,566
,140
1071,216
1
,000
,010
Step 3c
Step 4d
July 1-2, 2014
Cambridge, UK
28
Exp(B)
17459389244,3
83
18508483846,8
27
20185947240,0
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2014 Cambridge Conference Business & Economics
Instalment loans
,814
,023
1204,784
1
,000
Mortgages
23,708
527,777
,002
1
,964
Constant
-2,134
,019
12523,377
1
,000
,118
Households
-3,131
,056
3177,026
1
,000
,044
Co-holders
-3,554
,140
641,098
1
,000
,029
2,507
,035
5098,450
1
,000
12,271
24,729
488,926
,003
1
,960
Maturity
-,240
,004
3122,116
1
,000
,787
Constant
-2,030
,019
11219,755
1
,000
,131
Households
-3,117
,056
3141,892
1
,000
,044
Co-holders
-3,552
,140
640,245
1
,000
,029
,553
,040
186,268
1
,000
1,738
2,661
,038
5015,195
1
,000
14,313
24,706
489,216
,003
1
,960
Maturity
-,238
,004
3083,415
1
,000
,788
Constant
-2,196
,024
8550,986
1
,000
,111
Households
-3,117
,056
3145,102
1
,000
,044
Co-holders
-3,562
,140
643,693
1
,000
,028
Overdrafts
1,264
,150
71,309
1
,000
3,540
Advances
1,757
,151
134,762
1
,000
5,796
Instalment loans
3,842
,149
662,950
1
,000
46,616
24,690
489,708
,003
1
,960
Maturity
-,234
,004
2992,352
1
,000
,791
Constant
-3,401
,148
529,527
1
,000
,033
Households
-3,185
,056
3267,354
1
,000
,041
Instalment loans
Step
5e
Mortgages
Advances
Step 6f
Instalment loans
Mortgages
Step
7g
Mortgages
Step 8h
July 1-2, 2014
Cambridge, UK
ISBN : 9780974211428
29
2,256
19779694765,5
89
54889291025,0
43
53685892858,5
93
52813576837,7
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2014 Cambridge Conference Business & Economics
ISBN : 9780974211428
Co-holders
-3,657
,141
676,588
1
,000
,026
Overdrafts
1,287
,150
73,861
1
,000
3,622
Advances
1,853
,152
149,362
1
,000
6,377
Instalment loans
3,736
,149
627,381
1
,000
41,927
24,565
490,496
,003
1
,960
Maturity
-,207
,005
2045,710
1
,000
,813
Amount
-,002
,000
188,979
1
,000
,998
Constant
-3,332
,148
507,902
1
,000
,036
Mortgages
a. Variable included in the step 1: Mortgages.
b. Variable included in the step 2: Households.
c. Variable included in the step 3: Co-holders.
d. Variable included in the step 4: Instalment loans.
e. Variable included in the step 5: Maturity.
f. Variable included in the step 6: Advances.
g. Variable included in the step 7: Overdrafts.
h. Variable included in the step 8: Amount.
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30
46606411717,3
64