Credit risk in banks’ exposures to non‐financial firms

Date01 November 2018
Published date01 November 2018
DOI: 10.1111/eufm.12138
Credit risk in banksexposures to non-financial
Matteo Accornero
Giuseppe Cascarino
Roberto Felici
Fabio Parlapiano
Alberto Maria Sorrentino
Bank of Italy, Department of Economics,
Statistics and Research, Via Nazionale
91, 00182, Rome, Italy
This paper outlines a framework based on microdata and a
structural model to gauge credit risk in banksexposures to
non-financial firms. Sectoral risk factors are accounted for
using a multi-factor model. We use expected and
unexpected losses as indicators of credit risk stemming
from the corporate sector as a whole, and we put forward a
measure of systemic risk relevance of economic sectors. We
apply the model to the Italian economy, showing the
sensitivity of credit risk indicators to different character-
istics of default risk, cyclicality and concentration of
economic sectors.
credit risk, sectoral risk, systemic risk, structural multi-factor model
G21, G32
The authors are thankful to Giorgio Gobbi, Valerio Vacca, Antonio Di Cesare and Francesco Columba for their time and
effort in reviewing the paper, and to Carmelo Salleo and Alessio De Vincenzo, who co-ordinated the ESRB Expert Group
on Sectoral Risk where a prototype of the work was prepared. Special thanks to Marco Orlandi for providing the authors
with constructive comments and references, and to participants at the 2015 Banking Research Network workshop at the
Bank of Italy in Rome and participants at the 2016 EFMA Annual Meeting in Basel. Furthermore, the authors are grateful
to two anonymous referees and to the Editor John Doukas for helpful suggestions and comments. The views expressed in
this paper are solely those of the authors and do not necessarily reflect those of the Bank of Italy or of the Eurosystem. All
errors are our own.
Eur Financ Manag. 2018;24:775791. © 2017 John Wiley & Sons, Ltd.
Past episodes of banking crises have highlighted the role that credit risk plays in undermining the
stability of the banking system (BCBS, 2004). In downturn scenarios, increased correlation of defaults
across different borrowers may cause severe losses in credit portfolios, hampering the lending capacity
of banks and possibly imposing capital adjustments. Consequently, modelling credit risk has become
an increasingly important topic, prompting new research into forward looking indicators that provide
estimates of losses in adverse scenarios.
Dependence between defaults of different firms can be caused by fundamental factors, including:
direct links between firms such as trade credit, and indirect links such as the exposure to the same
markets. Moreover, sectoral or geographical factors may influence the default risk of otherwise
unrelated firms (Lucas, 1995). In a diversified economy, it is therefore desirable to model default risk
as being driven by multiple risk factors, as opposed to assuming a single risk factor driving correlations
across borrowers (Gordy, 2003).
This paper outlines a framework to measure credit risk stemming from banksexposures to the
corporate sector as a whole. We account for the role played by sectoral risk factors using a multi-factor
model with microdata. In our framework, we group banksexposures into portfolios by economic
sector and estimate the distribution of the potential losses for each sectoral portfolio. We characterize
the loss distributions using two indicators: the expected loss and the unexpected loss, which capture
losses in the average and adverse scenarios, respectively. In addition, the share of unexpected loss
attributable to a sector is suggested as an indicator to assess the contribution to systemic risk of that
The structural modelling approach used in this paper has found wide consensus in risk
management, given its flexibility in the modelling of default dependence, i.e., the probability of joint
defaults, which is key in financial stability analysis due to its role in determining the severity of losses
in adverse scenarios. However, to date the lack of data has posed a challenge for the use of structural
modelling in financial stability surveillance.
In the empirical section, we apply the model to the case of the Italian corporate sector as a whole
and to a set of sectoral sub-portfolios. We show that expected losses in sectoral portfolios are not
homogeneous, and that differences across sectors arise mostly from probabilities of default, which
display a greater variability than recovery rates. Exposures in the property sectors display a remarkably
higher default risk compared to other sectors. Unexpected losses also display great variability across
sectors, and they are positively associated with the cyclicality of a sector.
Large sectoral portfolios (in term of exposure), contribute more than others to the credit losses of
the aggregate corporate portfolio. However, consideration of other metrics, such as cyclicality and
concentration, is also important to assess the systemic risk relevance of a sector. For instance some
sectors, such as Construction, show a contribution to the unexpected loss of the aggregate Italian
corporate portfolio which is markedly higher than its share of debt. To sum up, the analysis shows that
the size of a sectoral portfolio is an insufficient metric to assess the systemic risk relevance of a sector,
as the impact of a sector on losses in adverse scenarios depends crucially on its correlation with the rest
of the economy.
Our work relates to a number of studies on credit risk. Using a macroeconomic approach Virolainen
(2004) proposes a credit risk model that links sectoral default rate series with common macroeconomic
factors, such as GDP, interest rate and corporate debt. The model is employed to stress test the impact
of adverse shocks in macroeconomic factors on the aggregate Finnish corporate credit portfolio.
Duellmann & Masschelein (2006) estimate the potential impact of sector concentration on the
economic capital using German data. As in our work, credit risk is measured via a structural

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