Investigating the Determinants of Nonperforming Loans
The key motivation for this paper
is to improve our understanding of the credit risk determinants by focusing on the Romanian banking system while
casting a vigilant eye on potential contagion effects from
neighbouring countries.
This is particularly important given
that the Romanian financial system is dominated by foreignowned commercial banks. Among them, the Greek banks’
subsidiaries have a substantial presence as they hold 30.7%
of aggregate foreign capital while they account for the second
largest market share in the Romanian banking system [1].
Therefore, any attempt of exploring the deterministic factors
of the Romanian banks’ credit risk should not be limited
solely on endogenous variables of the respective economy. Using time series modelling techniques, this paper
empirically investigates the determinants of ex post credit
risk as reflected on the loss loan provisions to total loans
ratio for the Romanian banking system.
Related to a growing
body of literature, the purpose of the study is to offer an
insight into the factors that determine the quality of the loan
portfolio of Romanian banks. In this direction, it utilizes a
broad dataset that spans from December 2001 to November
2010.
The explanatory power of macroeconomic, Romanian
bank-specific, monetary, interest rates and financial markets’
variables is investigated. The key contribution of the paper lies in the introduction of proxies for the Greek debt crisis
and the subsequent Greek banks’ financial distress.
The aim
is to examine a potential transmission channel or spillover
effects to the Romanian banking system. As the twin Greek
crises unfold, any repercussion for the neighbours is possibly
the most important issue in the minds of policy makers,
regulatory bodies, and bankers.
Furthermore, the time series
utilised include both the booming period as well as the
downturn following the financial crisis and the ensuing
manifestation of Greece’s structural weaknesses.
Overview of the Romanian Banking System
The Romanian economy has evolved from a long history of
defaults on sovereign debt, periods of high inflation, and
banking crises. During the Great Depression period, many
local and foreign banks in Romania collapsed or experienced
heavy runs [2].
The crisis historical database indicates that
in 1933 the redemption for domestic and foreign debt was
suspended. In the post-World War II period the country
experiences a debt crisis during the 1980s.
Barisitz [3] indicates that until 1998 the Romanian
banking system was overwhelmingly state owned.
Credit
institutions granted loans to an unrestructured real sector dominated by inefficient state-owned factories, subject
to “quasiautomatic” refinancing by the Central Bank of
Romania, which conducted an accommodative monetary
policy.
Thus, there is no surprise in the fact that bad
loans were a serious problem for all economies in the SEE
region due to inherited legacies but also to continuing
lending practices [4]. In Romania, the dominant state-owned
banks accumulated large portfolios of defaulted loans and
required massive capital injections from the government.
Furthermore, severe macroeconomic shocks led to banking
crises and economic growth resumed only after these crises
were resolved.
By the end of the 1990s the Romanian government
carried out legal reforms through the new central bank law.
Retrospectively, the year 1999 proved to be a sort of structural
turning point for the Romanian economy as the authorities
initiated the first privatizations of major Romanian banks.
Given the size of the country, the Romanian financial sector
offered an impressive growth potential for foreign strategic
investors.
Figures 1 and 2 depict the current situation in the
Romanian banking system.
The following years up to the burst of the global financial
crisis were characterised by rapid credit growth. That was
thought to be part of the ongoing process of financial
deepening given that the credit to GDP ratio still remains
at relatively low levels.
On the other hand concerns were raised whether the economy was experiencing a credit boom,
a situation where credit expands at an unsustainable pace.
It has been argued that the presence of foreign banks in
Romania has increased the efficiency of financial intermediation and the availability of credit to the real economy.
Yet, there are indications that financial stress originating in
Euro area-based parent banks may have been transmitted
to Romania.
The lending survey of the National Bank of
Romania (NBR) indicates that the risk profile of almost all
industries rose with the riskiest sectors being construction
and real estate, thus, reflecting the adverse impact of
the global financial crisis.
The outlook for the Romanian
banking system remains negative, driven mainly by the
tough economic conditions in the country following a
severe recession in 2009 [5]. The deteriorating operating
environment in Romania is characterised by a contracting
economy, widened fiscal deficit, and rising unemployment.
In particular, the country’s macroeconomy appears to the
main source of concern for Romanian banks given the sharp
increase in the level of nonperforming loans. Furthermore,
the high proportion of foreign currency lending mainly to households elevates their credit risk profile while the stressed
liquidity as reflected in the system’s loan-to-deposit ratio
(the loan-to-deposit ratio is at relatively high levels reflecting
the Romanian banks’ reliance on wholesale-parent bank
funding) may lead to a further tightening on the supply side
of credit.
Empirical Literature Review
This section reviews the empirical work on the relation
between macroeconomic variables and the loan portfolio
quality or credit risk (the framework for studying the impact
of macroeconomic variables or the business cycle on credit
risk is represented by two competitive theories.
The first one
stresses that credit risk is procyclical whereas the second one
defends the countercyclical view). Many studies investigate
the factors that induce financial crises by examining potential
links between bank-specific variables and macroeconomic
factors.
Delving into the specifics of the crises literature, Gavin
and Hausmann [6] argue that excessive credit growth
is a primary factor behind banking crises as usually it
reflects a decline in the credit standards.
Examining the
macroeconomic factors that contributed to banking crises
in Latin America during the 1990s, the authors find that
interest rates, expected inflation, terms of trade, domestic
income, credit growth and the monetary and exchange rate
regime are important constraints on loan servicing capacity.
Similar results can be found in Diamond and Rajan [7].
Demirguc¨¸-Kunt and Detragiache [8] theorize that banks face
insolvency due to falling asset values when bank borrowers
are unable to repay their debt as a result of adverse shocks
to economic activity.
Using a multivariate logistic model
for a large sample of developing and developed countries
during 1980–1994, the authors find that inflation and the real
interest rate are positively associated with a banking crisis
whereas the GDP has an inverse relationship.
Furthermore,
the study by Hardy and Pazarbasioglu [9] strongly suggests
that the likelihood of banking system distress is largely in
accord with declining economic growth. The authors also
find that capital inflows and credit expansion to private
sector, associated with rising consumption and real interest
rates, typically precede banking crises.
An increasingly popular method of assessing financial
sector vulnerabilities is the macro stress-testing approach
(the term refers to a range of techniques used to assess the
vulnerability of a financial system to “exceptional but plausible” macroeconomic shocks).
Relevant studies examine the
link between banks’ loan losses, or Nonperforming loans,
and macroeconomic factors. The most common approach
in similar studies involves estimating on historical data the
sensitivity of banks’ balance sheets to adverse changes in
macro fundamentals.
Then the estimated coefficients can
be used to simulate the impact on the financial system of
possible stress scenarios in the future. The focus is on credit
risk, which by large represents the most significant risk faced
by banking systems worldwide.
Two main strands of the literature can be identified in
this area, building on the seminal works by Wilson [10, 11]
and Merton [1]. Merton [1] models initially the response
of equity prices to macro fundamentals and then maps
asset price movements into default probabilities.
On the
other hand, Wilson’s [10, 11] framework allows the direct
modelling of sensitivity of default probabilities in various
industrial sectors to the evolution of a set of macroeconomic
variables.
Studies analysing the macroeconomic determinants of banks’ loan losses or Nonperforming loans include
Pesola [12] for the Nordic countries, Kalirai and Scheicher
[13] for Austria, and Delgado and Saurina [14] for Spain.
Typically, these studies find that loan loss provisions are
negatively related to GDP growth and positively related to
interest rates. Kalirai and Scheicher [13] estimate a time
series model of aggregate loan loss provisions in the Austrian
banking system as a function of an extensive array of
macroeconomic variables.
Results indicate that a rise in
short rates, a fall in business confidence, a decline in the
stock market, and a decline in industrial production have an
impact on the loss loan provisions.
Since the seminal work of Sims [15], the VAR approach
to empirical investigation of monetary policy shocks has
gained momentum. Several studies have used the VAR
models (studies that employ VAR models include Blaschke
et al. [16], Hoggarth et al.
[17], Delgado and Saurina [14],
Gambera [18], and Baboucek and Jancar [19]) to investigate
the macro fundamentals transmission mechanism in the
United States and other countries (these models are used
in the studies developed at the central banks of the UK,
Japan, Spain, the Netherlands, and at the European Central
Bank).
These models include various macroeconomic factors, ranging from a number from two to five depending on
the country. In some cases variables more directly related to
the creditworthiness of firms are added, such as measures
of indebtedness.
In other cases, market-based indicators of
credit risk, such as equity prices and corporate bond spreads
are used (introducing market variables such as interest
rates, foreign exchange rates, and equity and real estate
price indices into credit risk models is a way of explicitly
integrating the analysis of market and credit risks).
Foglia’s
[20] survey indicates that researchers increasingly adopt
models that are more flexible and easier to use, such as VARs
and other strictly statistical rather than structural models.
The estimation process normally requires the selection of a
set of macroeconomic and financial variables that, according
to economic theory and empirical evidence, affect credit
risk.
In this regard, variables such as economic growth,
unemployment, interest rates, equity prices, and corporate
bond spreads contribute to default risk [20].
Several recent papers [21, 22] analyse the impact of
macro fundamentals on the credit quality of banks’ debtors
using the framework of Wilson [10, 11].
Virolainen [23]
estimates a macroeconomic credit risk model for the Finnish
corporate sector over the period from 1986 to 2003 (a
distinguishing feature of the study is that the sample period
used to estimate the model includes a severe recession
and a banking crisis).
The SUR model results suggest a
significant relationship between corporate sector default
rates and key macroeconomic factors including GDP, interest
rates, and corporate indebtedness. As in most studies, the estimated model is employed to analyse corporate credit risks
conditional on current macroeconomic conditions, that is,
stress testing.
The findings are in line with previous studies
using observed bankruptcies for default rate measures.
Following Virolainen [23] methodology, Trenca and
Benyovszki [22] estimate a macroeconomic credit risk model
for the Romanian corporate sector over the period 2002–
2008.
Results suggest a significant relationship between
industry-specific default rates and macroeconomic factors
such as GDP growth rate, consumer price index, real interest
rate charged on loans, the exchange rate, and industryspecific indebtedness.
Boss [21] estimates a macroeconomic
credit risk model for the aggregate corporate default rate
to analyse stress scenarios for the Austrian banking sector.
Findings suggest that industrial production, inflation, the
stock index, the nominal short-term interest rate, and the
oil price are the most important determinants of corporate
default rates.
A leading role in the development of aggregate stress
tests has been performed by the IMF, in cooperation with
the World Bank. In 2005 the IMF conducted for the
first time in Greece a financial sector assessment program
[24].
Similar to Boss [21], Kalfaoglou [24] emphasises that
credit risk remains the most important risk in the Greek
banking sector.
Despite the satisfactory stress tests’ results,
the author indicates that the cross-border expansion of banks
increases their vulnerability to external shocks which, in
turn, requires better and more intensive risk management
practices.
The IMF’s [5] Romanian stress tests indicate that
the financial system is particularly vulnerable to the effects of
further slowing or reversal of capital inflows and associated
downward pressure on the exchange rate.
The stress tests
were based on data to end June 2008 (it is worth noting
that the 2010 EU-wide stress test exercise did not consider
the Romanian banks). Thus, the exercise takes no account of
developments in macrofinancial variables and balance sheets
since then.
Furthermore, it does not explicitly assess the
impact of the sharp slowing of lending, either as a result of tightening credit standards or in response to reduced funding
from foreign parent banks.
The above-mentioned studies, in general, corroborate
theoretical postulates with respect to the macroeconomic
influences on loan portfolio quality and, consequently, on
banking sector stability. In effect, good economic conditions
seem to be commensurate with good loan quality measured
by either the nonperforming loans’ ratio or loan loss
provisions.
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