Credit risk in banks’ exposures to non‐financial firms

• Date: 01 November 2018
• Author: Giuseppe Cascarino,Fabio Parlapiano,Alberto Maria Sorrentino,Matteo Accornero,Roberto Felici
• DOI: http://doi.org/10.1111/eufm.12138
• Published date: 01 November 2018

DOI: 10.1111/eufm.12138

ORIGINAL ARTICLE

Credit risk in banks’exposures to non-financial

firms

Matteo Accornero

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Giuseppe Cascarino

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Roberto Felici

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Fabio Parlapiano

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Alberto Maria Sorrentino

Bank of Italy, Department of Economics,

Statistics and Research, Via Nazionale

91, 00182, Rome, Italy

Emails:

Matteo.Acconero@bancaditalia.it;

Giuseppe.Cascarino@bancaditalia.it;

Roberto.Felici@bancaditalia.it;

Fabio.Parlapiano@bancaditalia.it;

AlbertoMaria.Sorrentino@bancaditalia.it

Abstract

This paper outlines a framework based on microdata and a

structural model to gauge credit risk in banks’exposures 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.

KEYWORDS

credit risk, sectoral risk, systemic risk, structural multi-factor model

JEL CLASSIFICATION

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;24:775–791. wileyonlinelibrary.com/journal/eufm © 2017 John Wiley & Sons, Ltd.

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INTRODUCTION

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 banks’exposures 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 banks’exposures 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

sector.

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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