Multiple banking relationships and exposure at default. Evidence from the Italian market

Author:Lucia Gibilaro, Gianluca Mattarocci
Position:Department of Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy
Pages:2-19
SUMMARY

Purpose This paper aims to analyse the exposure at default (EAD) in the event of multiple banking relationships to understand the differences with respect to solo banking relationships and forecast the banks risk exposure. Design/methodology/approach The paper uses a unique database provided by the Italian public credit register representative of the full Italian market before... (see full summary)

 
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Multiple banking relationships
and exposure at default
Evidence from the Italian market
Lucia Gibilaro
Department of Management, Economics and Quantitative Methods,
University of Bergamo, Bergamo, Italy, and
Gianluca Mattarocci
Department of Management and Law, University of Rome Tor Vergata, Rome, Italy
Abstract
Purpose This paper aims to analyse the exposure at default (EAD) in the event of multiple banking
relationships to understandthe differences with respect to solo banking relationships and forecast the banks
risk exposure.
Design/methodology/approach The paper uses a unique database provided by the Italian public
credit register representative of the full Italian market before the nancial crisis. The analysis compares
different EAD risk proxiesfor debtors with unique and multiple banking relationshipsto underline the main
differencesamong the two groups.
Findings Results show that EAD forecastcould be improved considering the existence of exposures with
other lendersand banks that consider such type of information can reduce the risk of underestimatingthe risk
exposureof a debtor.
Originality/value The paper is the rst attempt to model the EAD on the basis of the existence of
multiple lending exposures.Results demonstrate a different lenders risk exposure for debtorswith multiple
credit risk exposure and show the usefulness of the information about the overall system exposure in
evaluatingthe risk exposure related to this type of customers.
Keywords Credit risk, Relationship lending, Exposure at default, Multiple lending
Paper type Research paper
1. Introduction
Multiple banking relationships are common almost all countries, even if the number of
lenders normally used by European borrowers is higher than that used by the Americans
(Ongena and Smith, 2000). The standard monitoring theory proposed by Diamond (1984)
does not justify this business practice because it implies a duplication of monitoring costs
that could be saved ifeach borrower obtained lending from only one bank.
The literature on relationship lending suggests that borrowers and lenders establish forms
of commitment that are conductive to the provision of long-term nance that implies repeated
interactions (Mayer, 1988). Such literature demonstrates that normally rmsthathavealower
number of lenders and establish long-term relationships with them collect money at a lower
interest rate (Berger and Udell, 1995) and with lower collateral requirements (Boot and Thakor,
1994) due to the decreased opaqueness and borrower risk (Petersen and Rajan, 1994). The
Authors are grateful to the referees for the useful suggestion provided. The paper is the result of the
authorscombined eorts and continuous exchange of idea. The introduction and literature review
are ascribed to Lucia Gibilaro and other sections to Gianluca Mattarocci.
JFRC
26,1
2
Received18 April 2016
Revised26 December 2016
Accepted29 January 2017
Journalof Financial Regulation
andCompliance
Vol.26 No. 1, 2018
pp. 2-19
© Emerald Publishing Limited
1358-1988
DOI 10.1108/JFRC-04-2016-0031
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1358-1988.htm
choice of multiple lending exposure is normally justied by the risk that superior available
information enables a single bank to extract monopoly rents (Sharpe, 1990) and generally rms
with greater growth opportunities and more opaque assets are more interested in this choice
(Farinha and Santos, 2002). Because a unique lending relationship is very special, rms adopt a
model that allows for multiple but asymmetric bank nancing, where the concentration of
lending exposures is affected by the level of the expected cash-ows or liquidation values
(Elsas et al.,2004) and attesting that rms seek a mix of relationship and transaction lending
(Bolton et al.,2016).
Because the structure of bank relationships is associated with the protability of the rm
(Degryse and Ongena, 2001), the analysis of the role of multiple lending contributes to predict
the risk of borrowersnancing (Foglia et al., 1998), even though it is focused prevalently on the
probability of default and the loss given default, leaving aside the issue of the risk driver
determining the amount of the maximum credit loss, that is the EAD. Regarding the
probability of default, there is no consensus in the literature on the impact of multiple lending
on the risk drivers: some propose the thesis that the greater the number of lenders, the lower the
probability of default will be due to the lack of information monopoly and, therefore, the lower
the incentives to nance high-risk projects (Jimenez and Saurina, 2004). Others, however,
demonstrate that a longer-term relationship with a prominent bank will ensure the lenders
support in managing liquidity problems (Elsas and Krahnen, 1998). For the loss give default,
the role of collateral is normally higher for transaction lending in the medium and long term,
while it is higher for relationship lending in the short term; therefore, the loss given default will
be lower in the medium to long term for a single lending relationship and in the short term for
multiple lending solutions (Jimenez et al.,2006). Switching to the third driver of the expected
loss, EAD, it has received limited attention in a relationship with the role of single or multiple
lending exposure, even if credit limits are affected by the debtors access to other loan nancial
services (Chakrarborty et al., 2010) and credit lines usage is jointly determined (Su, 2009)and
unaffected by cash ows for rms relying mostly on them (Campello et al., 2011) while it is
affected by banksmonitoring and control activities (Zhao et al., 2011). The intensity of bank
monitoring activity can be inuenced by the structure of relationships (Foglia et al., 1998)
because the private information a nancial institution generates about a rm is less valuable
when the rm deals with multiple sources of nancial services (Cole, 1998) and the informative
content is affected by the type of the exposure (Chakrarborty and Hu, 2006).
By analysing the behaviour of defaulted borrowers with respect to their principal and other
lenders through the data provided by the Italian Credit Register for the period 2006-2010, the
paper is part of the studies on credit risk prediction and it delves into the impact of multiple
lending on EAD considering the impact of multiple lending on EAD, looking at the behaviour
of defaulted borrowers with respect to their principal and other lenders. Coherently with Jacobs
(2010), the results show that an higher number of creditors is associated with less risk close to
the default event for used credit lines, but they evidence that a lower number of creditors is
more effective in controlling the banks expected EAD and to nance in bonis customers in
light of the ability of relationship banks to solve information asymmetries (Boot, 2000). Novel
empirical evidences show that the effectiveness of single versus multiple lenders to limit
exposure risk is affected by the default denition, stating that the capability of multiple lenders
to limit risk is more signicant for past due denition with respect to restructured credits. The
lender characteristics appear relevant in determining the EAD, and the choice of considering
the borrowing exposures by multiple lenders allows an increase in the predictability of EAD
and a reduction in the probability of underestimating the risk exposure.
The paper contributes to the existingliterature in many directions. First, the paper adds
insight into credit risk prediction by extending the knowledge on EAD determinants
Multiple
banking
relationships
3

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