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IASB/FASB Meeting:             March 2009, London
Subject:                       Credit Growth, Problem Loans and Credit Risk
Provisioning in Spain (Agenda paper 7D)
CREDIT GROWTH, PROBLEM
LOANS AND CREDIT RISK
PROVISIONING IN SPAIN
Santiago Fern&#225;ndez de Lis,
Jorge Mart&#237;nez Pag&#233;s
and Jes&#250;s Saurina
Banco de Espa&#241;a
Banco de Espa&#241;a — Servicio de Estudios
Documento de Trabajo n.&#186; 0018
CREDIT GROWTH, PROBLEM LOANS AND
CREDIT RISK PROVISIONING IN SPAIN (*)
Santiago Fern&#225;ndez de Lis, Jorge Mart&#237;nez Pag&#233;s and Jes&#250;s Saurina
(*) Paper prepared for the BIS Autumn Central Bank Economists’ Meeting, October 2000. We
thank Alicia Sanch&#237;s for helpful research assistance. We thank Pilar Alvarez, Juan
Ayuso, Anselmo D&#237;az, Juan Gonz&#225;lez, Nieves Rodr&#237;guez, Luis Javier Rodr&#237;guez and
participants in an internal seminar in the Banco de Espa&#241;a for very helpful comments.
This paper analyses the cyclical behaviour of bank credit, loan losses and provisions for
loan losses in Spain. These three variables are strongly cyclical in Spain -as in many other
countries- and this poses some problems to bank supervisors and regulators. In a context of
strong competitive pressures, there is a tendency for loose bank credit conditions in an upturn in
view of the low level of contemporaneous non-performing loans. This may contribute to an over-
extension of credit. The low quality of these loans will only become apparent with the ex post
emergence of default problems, which will tend to appear during downturns, with an estimated
lag of approximately three years in the case of Spain. On the other hand, loan loss provisions in
Spain have traditionally had a pro-cyclical bias as they were largely linked to the volume of
contemporaneous problem assets. Low provisioning in the upturn reveals that latent risks are
not properly acknowledged and hence book profits are biased upwards during the upturn and
downward during the downturn. The paper explains in detail the rationale and expected effects
of the new loan loss provision –the so-called statistical provision- introduced recently by the
Banco de Espa&#241;a and aimed at an appropriate recording and recognition of expected losses.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                               1
The purpose of this paper is to analyse the growth of bank credit and its prudential
implications in Spain. This is an ever-present item on the agenda of banking supervisors,
since most banking crises have had as a direct cause the inadequate management of credit
risk by institutions.
Disaster myopia, herding behaviour, perverse incentives and principal-agent
problems explain mistakes in bank credit policy in an expansionary phase. Banks could be
forced into an excessive credit expansion as a result of an informational externality that
makes bank credit policies interdependent. Short-term concerns of bank managers coupled
with the fact that the market is more forgiving if mistakes are made by many players at the
same time force bank managers into an overly expansionary credit policy that will increase
borrowers’ debt levels excessively and that will result in an increase in problem loans.
Bank supervisors are well aware of this problem. However, it is very difficult to
persuade bank managers to follow more prudent credit policies during an economic upturn,
especially in a highly competitive environment. Even conservative managers might find
market pressure for higher profits very difficult to overcome. This is compounded by the fact
that for many countries loan loss provisions are cyclical, increasing during the downturn and
reaching their lowest level at the peak. To a large extent, this reflects an inadequate ex post
accounting of credit risk. As a result, book profits follow the opposite pattern. Many credit
risk mistakes are made during the expansionary phase of the economic cycle although they
only become apparent ex post in the downturn. In the paper we present a regulatory device
recently adopted in Spain that could contribute to correct this problem.
The new loan loss provision introduced by the Banco de Espa&#241;a, the so-called
statistical provision, is explained in detail in this paper. The statistical provision is aimed at a
proper accounting recognition of ex ante credit risk. Expected loan losses exist from the
moment a loan is granted. This should be reflected in the risk premium included in the price
of credit and hence in the income stream coming from the loan since its very beginning.
Therefore it seems logical to build up the corresponding provision for loan losses also at
that time. This change is expected to reduce the cyclical behaviour of loan loss provisions,
correcting the resulting bias in the profit and loss account, decreasing bank profit volatility
and improving bank managers&#39; awareness of credit risk. The statistical provision should
also be regarded as a mechanism to overcome the co-ordination problems of individual
banks at the peak of the cycle and to reinforce medium-term bank solvency.
The paper is organised as follows. The following section analyses the patterns of
bank lending in Spain while section three looks into the determinants of ex post credit risk
from an individual bank level perspective. Section four is devoted to the analysis of past
2                                                BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
loan loss provisioning policies in Spain. Section five presents recent changes in provision
requirements, in particular the introduction of the statistical provision. Its functioning is
analysed and its expected effects explained. The last section makes some concluding
2. THE CYCLICAL PATTERN OF BANK LENDING IN SPAIN
As Chart 1A clearly shows, the growth of bank lending is characterised by alternate
periods of expansion and stagnation. Despite the profound structural changes undergone
by the Spanish economy over a period as long as that considered 1, bank lending has
maintained its cyclical pattern. This growth cycle matches very precisely the business cycle.
Credit is not only pro-cyclical, but tends to grow faster than GDP during expansions
and more slowly during recessions, which is reflected in the behaviour of the bank lending-
to-GDP ratio (see Chart 1B). This ratio displays a tendency to grow over time, consistent
with the progressive financial development of the economy, which is temporarily interrupted
by periods of economic and credit stagnation.
This behaviour can be explained by demand and/or supply factors. On the demand
side, the composition of expenditure is an important determinant of credit. Different types of
household and firm expenditures are financed to a differing extent with bank loans. For
example, business investment, residential investment and durable consumption are
expenditure decisions requiring a higher resort to external finance than non-durable
consumption. Moreover, debt can finance not only real expenditure but also financial
acquisitions, which are not included in GDP and show a particularly intense cyclical pattern.
On the other side, real interest rates are also an important determinant of credit demand.
Finally, relative prices can also have an impact on credit demand. Thus, for example,
demand for mortgages depends on housing prices. Since bank loans are deflated by CPI to
obtain a measure of real credit, an increase in housing prices would lead to an increase in
our measure of real credit.
In particular, the Spanish financial system was characterised in the sixties by bank rates fixed administratively, mandatory
investment ratios, restrictions on the opening of branches and on the setting up of new banks, poorly developed financial
markets and the practical absence of an active monetary policy. It was gradually transformed into a liberalised, internationally
integrated financial system in the early nineties. Some of the main landmarks in this process were: the implementation of an
active monetary policy from 1973, the progressive liberalisation of bank interest rates throughout the period from 1974 to
1987, the elimination of mandatory ratios and restrictions on the opening of new branches or banks between 1974 and 1988,
the creation of an efficient government debt secondary market in 1987 and the securities markets reform in 1988.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                          3
On the supply side, there is a growing literature on credit rationing that relates its
level to borrowers’ net wealth. Hence, the cyclical behaviour of net wealth is an essential
element for explaining the cyclical behaviour of credit 2. The effect on the business cycle is
to increase cyclical fluctuations in what has been denominated the “financial accelerator
effect”3. Apart from that, misalignments in asset prices may lead to inadequate lending and
borrowing decisions and to financial fragility.
For our purposes the most important supply factor is banks’ lending policy. If this is
relaxed during the upturn, an accumulation of risk is built up and this potentially affects
banks’ solvency in the downturn. This is compatible with the financial-instability hypothesis
of Kindleberger (1978) and Minsky (1982), according to which the financial system is
inherently unstable. There is a tendency for “excessive” accumulation of debt in times of
plenty, when borrowers appear able to bear higher levels of expenditure and debt. This
“excess” is then corrected during recessions through deflation and economic crisis. The
result is again an increase in business cycle fluctuations 4.
Table 1 summarises the available data on the behaviour of credit and its above-
mentioned determinants in the Spanish economy during the period from 1963 to 19995. All
explanatory factors display a pro-cyclical behaviour, except the real interest rate on loans 6,
thus contributing to explaining the pro-cyclical pattern of the credit-to-GDP ratio.
Expenditure items more dependent on credit, such as residential and non-residential
investment and durable-goods consumption, exhibit a stronger pro-cyclical behaviour than
GDP; the same is true for financial acquisitions. Real and financial asset prices also tend to
grow faster in periods of economic expansion7, boosting private-sector wealth. Finally, net
financial assets of non-financial firms and households, although showing a long-run upward
trend, are comparatively higher at the beginning of the cyclical upturn, before deteriorating
progressively over the expansionary phase. The absence of adequate information on banks’
lending policy does not allow us to draw any conclusion in respect to this potential
explanatory factor. Nevertheless, some anecdotal evidence can be drawn from the analysis
of the various credit cycles in the period considered.
See, for example, Kiyotaki and Moore (1997) or Su&#225;rez and Sussman (1999).
See, for example, Bernanke et al (1998). However, net wealth not only affects credit supply, but also credit demand.
This kind of behaviour has been explained by disaster myopia (Herring, 1999), herd behaviour (Rajan, 1994) or as a result
of perverse incentives, e.g. the existence of a safety net.
Although an appropriate indicator of total (real and financial) wealth of the non-financial private sector in Spain is not
available, there are some incomplete but potentially useful indicators, which are presented in Table 1.
Structural changes in the Spanish economy (essentially liberalisation in the early period and EMU in the late period) mask
the cyclical pattern of real interest rates.
In the case of share prices, they also tend to lead changes in GDP growth.
4                                                                  BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
The first credit cycle is particularly remarkable. Before the mid-seventies oil crisis,
the Spanish economy grew strongly and government-controlled interest rates were set at
very low levels. The loose monetary environment, compounded by the absence of good
incentives and management skills among bank managers, contributed to the strong growth
of bank lending and to an excessive indebtedness on the part of Spanish non-financial
firms. The total debt (bank loans, fixed-income securities, loans from non-residents and
trade credit) of Spanish non-financial firms as a percentage of GDP reached a historical
peak at the beginning of the 1980s (see Chart 2). This must have contributed to the impact
of the oil price shocks of 1973 and 1979 on the Spanish economy and to the severe
banking crisis that affected half of Spain’s commercial banks (which accounted for around
25-30% of the total capital of the sector) between 1977 and 1985. This banking crisis is
likely to have exacerbated the economic stagnation, pushing back economic recovery until
the mid-eighties. Between 1976 and 1986, despite low real interest rates, average annual
bank lending growth was just 0.3%. That is, the bubble pattern of non-financial firms’ debt
seems to point to banks&#39; lending policies contributing to exacerbate the business cycle.
An important explanatory factor of the 1987-1991 boom in bank lending was the
increase in housing prices, which rose more than 100% during the second half of the 1980s.
Owing partly to the huge increase in housing prices and partly to the shift by commercial
banks towards the business of lending to households, the volume of mortgage loans
granted by Spanish banks grew strongly from 1985 8 (see Chart 3). In the stagnation period
between 1992 and 1996 bank lending was almost flat, but this occurred against the
background of a very tight monetary policy and without a generalised banking crisis.
The current bank lending expansion is not independent of the greater
macroeconomic stability (low inflation and real interest rates) stemming from EMU. Other
potentially important elements are the increase in asset prices and in the acquisitions of
financial assets, particularly abroad, by Spanish non-financial firms and households (see
Table 1). Also, growing competition among banks may be boosting credit growth. This
growing competition is reflected in declining banks’ margins, although not in bottom-line
profits due to the very low level of loan loss provisions in recent years.
To sum up, bank lending in Spain is strongly pro-cyclical. There are several potential
explanations for high credit growth during economic expansions, not all of them having the
same implications. Since we are interested in credit growth as an indicator of risk, we are
particularly concerned about the possibility of an excessive accumulation of debt resulting
Mortgages were traditionally the niches of savings banks. Since the late eighties, commercial banks have competed
intensively to gain a higher share of this market. While mortgages accounted for 20% of total loans in 1985, they now (first half
of 2000) stand at around 45% of total loans.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                            5
from a systematic easing of bank credit conditions during periods of strong economic
activity. Although some anecdotal evidence and some theoretical arguments point in that
direction, the incomplete quantitative information available, the important structural changes
the Spanish economy has undergone and the identification problems at an aggregate level
render the estimation of the relative impact of each factor extremely difficult. One potential
way of casting some light on this issue is to analyse the relationship between credit growth
and problem loans at the level of individual institutions, which is the aim of the next section.
3. CREDIT POLICY OF INDIVIDUAL BANKS AND PROBLEM LOANS
There is a very close relationship between problem loans and the economic cycle.
During recessions problem loans increase as a result of firms’ and households’ financial
distress. When the economy grows strongly, the income of non-financial firms and
households expands and they can repay loans easily, contributing to the decline in banks’
problem loans ratios.
Chart 4 depicts the strong correlation between the problem loans ratio of Spanish
deposit institutions and the GDP growth rate. Of course, this relationship has a negative
sign as problem loans increase when GDP growth rates slow, and vice versa.
Different types of banks show a very similar relationship between bad loans and
economic activity. Chart 5 plots the ratio of problem loans for Spanish commercial and
savings banks, which represent more than 95% of the total assets of credit institutions.
Although the level of the ratio differs, the cyclical pattern is very similar.
The ratio of problem loans also differs by type of loan. Households and firms have
different levels of bad loans. On average, the former is lower than the latter. Among
households, mortgages have very low delinquency levels compared to consumer loans,
credit card loans or overdrafts. Among firms, there are substantial differences in problem
loans ratios by economic sector: for instance, real estate developers show, on average
during the cycle, more problem loans than extensions to public utilities as indicated by data
from the Banco de Espa&#241;a Credit Register (CIR).
In addition to these macroeconomic factors, the credit policy of each individual
institution is crucial for understanding its level of problem loans. Chart 6 gives the number of
banks (both commercial and savings banks) whose ratio of problem loans deviates from the
simple annual average ratio by a certain amount of percentage points. It is shown that there
6                                               BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
is a lot of dispersion among problem loans ratios for the same cyclical or macroeconomic
position 9. At the same point in the cycle, some banks have significantly below-average
problem loans ratios while others have much more ex post credit risk.
Chart 7 shows the distribution of the ratio of problem loans in two different positions
of the cycle: downturn (1992-1994) and strong growth (1997-1999). As expected, the mean
and the dispersion of problem loan ratios are higher at the trough than at the peak of
economic activity. The distribution of problem loans is asymmetric with a long tail on the
right hand side, indicating that the number of banks with significantly above-average
problem loans is larger than those significantly below average.
The above indicators underline that microeconomic variables at the level of each
bank must play a determining role in explaining bank problem loans. Besides, the cyclical
position of the economy affects the level and dispersion of the problem loans ratio.
A rapid credit expansion is deemed one of the most important causes of problem
loans . During economic expansions many banks are engaged in fierce competition for
market share in loans, resulting in strong credit growth rates. The easiest way to gain
market share is to lend to borrowers of lower credit quality. This market share strategy is
even more dangerous if the bank is a new entrant in a product or regional market. Initially,
banks selling new products will probably have more problem loans in their new business
simply because they lack the necessary expertise. Banks entering a distinct regional market
will be subject to adverse selection. Incumbents will allow the riskiest customers to leave
the bank but will retain the best ones. The risk profile of a client becomes known only with
time. The informational disadvantage of new entrants together with their appetite for market
share might be a recipe for later loan portfolio problems11.
Principal-agent problems could fuel credit expansion because bank managers focus
more on gaining market share than on shareholder profitability. Managers poorly monitored
by shareholders might be willing to increase risk in order to bolster short-term profitability.
Therefore, managers could have incentives to overextend credit in order to maximise their
The comparison is calculated with the simple average of each year. Although certain institutions have a regional basis,
Spanish regions are not different enough to have independent economic cycles, and it can be concluded therefore that most of
them share the same cyclical position.
Clair (1992), Salas and Saurina (1999b) and Solttila and Vihri&#228;l&#228; (1994) find evidence that past credit growth explains the
current level of problem loans, after controlling for the composition of the bank loan portfolio.
Shaffer (1998) shows that adverse selection has a persistent effect on new entrants.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                         7
utility 12. It can take a long time to realise the danger of these risky strategies because
managers may be engaged in income smoothing practices13.
Shareholders of banks with very low solvency levels might be tempted to increase
credit risk as a bet on resurrection. A subtler case of dangerous incentives appears in banks
that are experiencing slow but steady declines in their charter values 14. Crockett (1997)
points out that increasing competition may encourage disaster myopia. Similarly, herd
behaviour can fuel overly liberal credit policies because the penalties for being wrong in
company are much lower than for being wrong in isolation 15.
Several additional factors could affect the level of bank problem loans. First of all,
loan portfolio composition plays an important role as an indicator of bank risk profile16.
Besides, risk concentration is an additional source of concern as many banking crises have
pointed out. Secondly, inefficient banks performing poor screening and monitoring of
borrowers will have lower portfolio quality17. Thirdly, the overall competitive environment in
which banks operate could also affect the level of credit risk the bank is willing to take. If the
bank has some degree of monopoly power, it has the possibility of charging higher interest
rates in the future. Therefore, a higher number of firms of lower quality could obtain funds
from the bank. This would not happen in a competitive market where it is not possible to
recover in the future the present losses because the firm, after solving its difficulties, would
not pay an interest rate above the market rate 18.
Salas and Saurina (1999b) have modelled the problem loans ratio of Spanish banks
in order to gauge the impact of loan growth policy on bad loans. They were interested in
capturing the lag between credit expansion and the emergence of problem loans.
Gorton and Rosen (1995) show that when bank managers receive private benefits of control and are imperfectly
monitored, managers will take on excessive risk if the industry is unhealthy.
Fudenberg and Tirole (1995) analyse the theoretical foundations for income smoothing. The empirical literature has
widely confirmed the existence of such practices among banks. Saurina (1998) contains a survey of the theoretical and
empirical literature on earnings management.
Keeley (1990) shows how the deregulation of the American banking industry brought about an increase in competition
that eroded bank charter values, giving incentives to managers to shift to riskier policies (more credit risk and less capital).
Salas and Saurina (1999a) find similar results for Spanish commercial banks.
See for instance Rajan (1994).
See Boyd and Gertler (1993), Davis (1993), Domowitz and Sartain (1999), Keeton and Morris (1987), Murto (1994),
Pensala and Solttila (1993) and Randall (1993). Berger and Udell (1998) and Freixas and Rochet (1997) discuss, from a
theoretical point of view, the role of collateral.
Berger and DeYoung (1997) and Kwan and Eisenbeis (1997) find that inefficient banks are more prone to risk taking.
Petersen and Rajan (1995) find that a higher percentage of young firms are financed in a concentrated banking market than in
a competitive one.
8                                                                 BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
Macroeconomic developments, regulatory changes and portfolio composition, size and the
incentives bank managers and shareholders face were controlled19.
Table 2 shows their empirical estimation results using a panel data of commercial
and savings banks from 1985-1997. As expected, the cycle (measured through the current
and lagged-one-year GDP growth rates) has a negative and significant impact on problem
loans. The current impact is much more important. Additionally, increases in non-financial
firms&#39; indebtedness raise problem loans.
Regarding the bank specific variables, there is a strongly significant and positive
impact of credit growth on problem loans but with a lag of around three years. Therefore, an
increase in credit today will have a negative impact on problem loans three years hence.
Branch growth also has a positive impact on problem loans with a three-year lag underlining
the importance of adverse selection in bank expansion strategies.
Other results from the same paper confirm that inefficient banks hold riskier
portfolios, collateralised loans are less risky and large banks have fewer problem loans
probably as a result of their better portfolio diversification opportunities. These results are
quite robust to many specification changes.
The finding that credit growth affects problem loans with a relatively long lag is a
matter of concern for supervisors. If bank managers are interested in short-term targets they
will not take proper measures to limit medium-term exposures to credit risk. Given that
credit expansion occurs usually during favourable economic periods where optimism is
widespread, it is easy to understand how difficult it is for supervisors to convince bank
managers of the need to be cautious. Furthermore, conservative bank managers are under
strong pressure to act like their riskier colleagues in order to reach higher short-term profits
(based on increased volumes and riskier borrower profiles). Things are even more worrying
when the book value of the loan portfolio and profits are not properly adjusted –through the
related provision- by the expected future losses. Hence the importance of provisioning
The literature on problem loan determinants is scarce. Some authors (Brookes et al (1994) or Davis (1992)) have only
focused on macro variables whereas others (Keeton and Morris (1988) or Solttila and Vihri&#228;l&#228; (1994)) use only micro data.
Very few papers analyse both macro and micro determinants of problem loans. Among these are Clair (1992) and Gonz&#225;lez-
Hermosillo et al (1997).
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                  9
4. LOAN LOSS PROVISIONS
There is no harmonisation of asset classification rules at an international level. The
definition of problem loans or asset impairment varies across countries. Some countries
allow bank managers and/or external auditors to establish the amount of bad loans instead
of having a definition of impaired assets. These practices differ from those of other countries
where a precise definition of impaired assets is provided by regulators. However, even in
this latter group of countries, asset classification criteria differ. 90 days overdue is a quite
standard period to classify a loan as non-performing but some countries use different
overdue dates depending on the credit product. Some national regulations classify as
doubtful those credit exposures that, although not yet overdue, are already showing signs of
a very low repayment probability.
The differences among countries increase when examining loan loss provisioning
rules and practices. There are specific and general provisions with different requirements in
each country, sometimes set by the regulators, sometimes left to the choice of bank
managers (although reviewed by external auditors)20. Besides, tax treatment of loan loss
provisions also differs widely.
There is a contrast between the considerable efforts made to harmonise capital
requirements at an international level and the lack of such harmonisation in terms of asset
classification and provisioning rules 21. The issue is important, since some apparently safe
capital ratios can suddenly disappear in a banking crisis when the loan portfolio has not
been properly classified and provisioned.
Spain has a very detailed regulatory framework for asset classification and loan loss
provisions, which limits bank managers&#39; discretion. The accuracy of both is checked
thoroughly by on-site inspections carried out regularly by the Banco de Espa&#241;a. The
traditional Spanish regulatory system distinguishes between specific and general
provisions. A third category of provisions has recently been created, the so-called statistical
provision. The general provision is a fixed one, while the specific provision aims at covering
impaired assets (ex post credit risk). The statistical provision is intended to acknowledge
expected losses, as explained below.
Before describing the characteristics of the statistical provision and the reasons for it
being set, it is worth quickly reviewing the asset classification and “old” provisioning rules.
The differences are highlighted in Beattie et al (1995).
The European Union is currently focusing on the convergence of regulatory practices regarding loan loss provisioning
10                                                                BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
Annex 1 contains a brief summary of asset classification criteria and loan loss provision
requirements. Note that provisions calculated following Annex 1 criteria are generally
treated as an expense from a tax perspective 22. Banks are obviously free to provision above
the legal requirements, but an excess over the minimum does not benefit from this tax
Chart 8 plots the loan loss provision ratio (provisions of the year over total loans). It
can be seen that the loan loss provision in Spain shows a strong cyclical behaviour: the
ratio of provisions to total loans falls during periods of economic growth and rises
considerably during recessions.
Since 1994 the ratio of provisions has continuously decreased reaching an all-time
low last year as a result of the economic expansion and the strong decline of problem loans.
Loan portfolios have, at the same time, been showing strong rates of growth over the last
two or three years. Given the positive (although considerably lagged) relationship between
credit growth and problem loans, these developments are worrying. In the downturn, the
increase in impaired assets and demanding pro-cyclical loan loss provisions could threaten
the profits of the riskiest institutions.
From a conceptual standpoint, it is important to keep in mind that credit risk appears
at the very beginning of the loan operation when the borrower receives the money. Of
course the bank cannot know whether a particular loan will default, but it knows that a
certain proportion of the loans in its portfolio will certainly default 23. This should be reflected
appropriately by the bank by charging the borrower with a risk premium. The income
stemming from the risk premia should cover the expected losses resulting from problem
loans. These are an ex post realisation of credit risk and tend to concentrate in the trough of
the business cycle, resulting in a different accounting recognition pattern of income and
costs along time.
As a result of the provisioning accounting rules discussed in Annex 1, the latent risk
of loan portfolios was not properly recognised in the profit and loss account under the old
system. In periods of economic expansion the fall in doubtful loans goes hand in hand with
the decrease in provisions, which in turn allows bank managers to improve bottom-line
profits. However, there is something wrong in the level of profits shown if the latent credit
risk in the loan portfolio is not properly taken into account. Every loan intrinsically has an
expected (or potential) loss that should be recognised as a cost by means of an early
provision. Otherwise, the picture of the true profitability and solvency of the bank over time
The only exception is the general provision for mortgages (0.5%) which is not considered as a tax-deductible expense.
The situation is similar to that of insurance products.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                          11
could be distorted. More dangerously, the overvaluation of profits might lead to an increase
in dividends that could undermine the solvency of the bank. Therefore, the
acknowledgement of latent losses is a prudent valuation principle (similar to the
mathematical reserves set aside by insurance companies) that contributes to correcting the
cyclical bias that currently exists in the profit and loss account.
If the total cost of the loan is not properly recognised and accounted for, bank
managers willing to gain market share may be tempted during economic expansions to
underprice loans. More conservative managers will face strong incentives to follow this
aggressive pricing behaviour in order to protect market shares. This herding behaviour is
very dangerous for the stability of the whole banking system.
All these facts and potential or real problems seem to point at the same direction:
there is a need for a statistical provision that covers the expected loss inherent to the loan
portfolio. This statistical provision should be considered as a cost for the bank and should
be taken into account in the pricing of the operation.
5. THE STATISTICAL PROVISION FOR INSOLVENCY
The new regulation on provisions was approved at the end of 1999 but came into
effect on July 1st 2000. Poveda (2000) explains the rationale and the mechanism
underlying the so-called statistical provision.
There are two approaches to comply with this provision that differ in the way the
expected or latent loss is estimated. First, banks can use their own internal models in order
to determine the statistical provision. Internal models use the bank own-loss experience to
determine the provision. However, they must be integrated into a proper system of credit
risk measurement and management, have to use the bank own historical database
spanning at least an entire economic cycle and must be verified by the supervisor. The loan
portfolio should be segmented in homogeneous groups. If the bank only has internal models
for one or some of these groups, the inspectors of Banco de Espa&#241;a will verify that the bank
is not practising a cherry picking strategy. The internal model approach has been accepted
by the regulator so as to stimulate banks to measure and manage their credit risk more in
line with the new BIS proposal to reform the Capital Accord. Those banks that have started
to use these models for their own internal purposes will be rewarded with its use for the
statistical provision.
Alternatively, for those banks that have not developed yet their own internal models,
there is a standard approach based on a set of coefficients established by the regulator.
12                                            BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
The standard approach establishes six risk categories with the corresponding coefficients.
Such coefficients are multiplied by the exposure 24.
1) Without risk (0%): those risks involving the public sector.
2) Low risk (0.1%): mortgages with outstanding risk below 80% of the property
value as well as risks with firms whose long-term debts are rated at least A.
3) Medium-low risk (0.4%): financial leases and other collateralised risks (different
from the former in point 2).
4) Medium risk (0.6%): risks not mentioned in other points.
5) Medium-high risk (1%): personal credits to finance purchases of durable
6) High risk (1.5%): credit card balances, current account overdrafts and credit
account excesses.
These categories correspond roughly to the different levels of credit risk in the
portfolio. Our historical experience shows that credit cards, overdrafts and consumer loans
are far riskier than mortgages or public-sector loans. The coefficients reflect the average net
specific provision over the economic cycle. They are based on figures for the period 1986-
1998, but also take into account the improvements in credit risk measurement and
management made by Spanish credit institutions during these years. The statistical
provision is obviously intended to anticipate the next economic cycle rather than to reflect
past ones.
The fund of the statistical provision for insolvency will be charged quarterly in the
profit and loss account by the positive difference between one-quarter of the estimate of
latent global losses in the different portfolios (using the standard or internal model
approach) and the net charges for specific provisions in the quarter. If the difference is
negative the amount will be written as income in the P &amp; L account deducting the fund of the
statistical provision for insolvency (as long as there is an available balance). The fund built
in this way has an upper bound set equal to three times the result of multiplying the
Loans to credit institutions are excluded.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                         13
coefficients by the exposure 25. It should be borne in mind that the statistical provision is not
a tax-deductible expense.
Some simple algebra will help illustrate the working of the old and new system of
Balance: GF = g*L, where L stands for total loans and g for the parameter
(between 0.5% and 1%).
Annual provision: GP = g*∆L
Specific provision:
Balance: SF = e*M, where M stands for problem loans and e for the
parameter (between 10% and 100%).
Annual provision: SP = e*∆M
Annual total provision in the old system (general + specific):
AP = GP + SP = g*∆L + e*∆M
General and specific provisions: as before.
Statistical provision:
Latent risk measure Lr = s*L, where s stands for the average coefficient
(between 0% and 1.5% in the standard approach).
Annual provision: StP = Lr – SP
If SP &lt; Lr (low problem loans) =&gt; StP &gt; 0 (building up of the statistical fund)
The limit takes into account the maximum non-specific deduction (4%) set by the European Union Directive (86/635/EEC).
14                                                            BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
If SP&gt; Lr (high problem loans) =&gt; StP &lt; 0 (depletion of the statistical fund)
Balance of the statistical fund: StF = StPt + StFt-1, with a limit: 0 ≤ StF ≤ 3*Lr
Annual total provision in the new system (generic + specific + statistical), assuming
that limits are not reached:
AP = GP + SP + StP= g*∆L + SP + (Lr – SP) = g*∆L + s*L
The expected effects of the new statistical provision
The statistical provision was designed not to substitute but to complement the
specific provision. Hence it is expected to have a counterbalancing effect on the strong
cyclical behaviour of loan loss provisions in Spain. The statistical provision increases
precisely during the expansionary phase. During recessions the specific provisions increase
while the use of the statistical fund smoothes its impact on the profit and loss account of the
bank. The combined effect of both provisions will be a better accounting recognition of both
income and costs stemming from bank loan portfolios and hence an improved
measurement of bank profits.
The volatility of bank book profits will decrease. The extent to which this lower
volatility would have real effects is a much more complex issue that goes beyond the aims
of this paper 26. The main stabilising effect of the statistical provision will be seen in the next
recession when banks will be able to use the statistical fund to cover the specific loan loss
provisions requirements 27. Managers pursuing very aggressive credit growth strategies
have to set aside more provisions.
From a theoretical point of view, the establishment of the statistical provision might
be viewed as a device provided by the regulator to facilitate the co-ordination of individual
banks to sidestep the trap indicated by Rajan (1994). Forcing a general increase in loan
loss provisions during the expansionary period contributes to reinforcing medium-term bank
solvency, to better match income and expenses from an accounting point of view, to
decrease earnings volatility and probably to make bank managers more aware of credit risk.
Without this “external” intervention, if loan loss provisions were left to the discretion of bank
If bank stockholders perceive the lower profit volatility as a measure of lower risk, they could fund the bank at cheaper
Note that when the statistical provision requirement is below the specific one, the difference is credited to the profit and
loss account and therefore has the net effect of decreasing the amount necessary to comply with the specific loan loss
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                          15
managers, we could end up ex post with an over-extension of credit and an excessive build-
up of imbalances in the financial sector that might result in financial fragility and distress on
A simulation exercise
Table 3 shows a simulation of the impact of the new statistical provision and its
interaction with the profit and loss account. For the sake of simplicity, the calculations have
been made annually although the provision is required on a quarterly basis.
It is necessary to make hypotheses about the growth of the loan portfolio
(normalised at 1000), the statistical provision parameter value (set at 0.5%) 28 and the ratio
of specific loan loss provisions to total loans (for the general provision, a 0.75% parameter
is set). Additionally, a hypothesis about profits before taxes is needed.
Chart 9 shows the impact of the old and new system of provisions. Under the old
system the joint effect of the specific plus the general provisions was strongly cyclical. The
introduction of the statistical provision has a counterbalancing effect as it has the opposite
cycle profile. The joint effect of the old system plus the statistical provision is to smooth
provisions during the cycle. As shown in Chart 10, the statistical fund builds up during the
expansionary period (low problem loans) and decreases in the downturn.
The scenario depicted in Table 3 allows us to illustrate the impact of the statistical
provision over time. Credit risk grows strongly during the first two years. From the third year
a relatively abrupt economic landing starts, profits before provisions decline at the lowest
point of the cycle (year 6) and resume thereafter in line with outstanding loans recovery.
The statistical fund is built up during the first four years as long as the statistical provision is
above the specific one. Two-thirds of the statistical fund limit are reached in year 4. As soon
as the specific provision requirements outpace the statistical ones (year 5), the statistical
fund is depleted reaching its lowest level in year 8 when it is almost exhausted. From year 9
onwards, the build-up resumes as the cyclical position of the economy improves.
Table 3 shows that the joint effect of the specific plus the general loan loss
provisions (those existing in Spain until last year) was slightly below 15% of profits before
provisions until the economy turned down. When economic conditions deteriorated profits
decreased strongly. In year 6 almost three-quarters of profits before provisions were wiped
The 0.5% is a rough average of the six risk category parameters (from 0% to 1.5%), weighted according to the share of
each risk category in the portfolio.
16                                                          BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
Under the new system, the statistical provision represents more than 15% of profits
up till the recession. Later on, it becomes negative as the statistical fund is used. When all
the loan loss provisions are considered together, the picture is much more reassuring:
during almost all the period simulated the joint impact of the three types of provisions on
profits is around 35%, although it increases slightly over time as a result of the hypothesis
made on the course of profits before provisions and total loans. Contrary to the “old system”
scenario, in the recession there is no abrupt fall in profits after provisions.
Other scenarios changing one or several of the hypotheses used in Table 3 have
been tested. For instance, if instead of using a 0.5% parameter for the statistical provision
we set its value at 0.4%, the statistical fund is completely exhausted in year 6 and does not
start to build up again until year 10. On the other hand, if we use 0.6% as the value of the
parameter, the statistical fund never approaches the lower limit and the impact on the level
of profits is higher.
The real impact of the statistical provision will depend on the coefficient applied by
each bank (the standard one or the result of internal models), the course of profits before
provisions and credit growth, and the future specific provisions that the bank will need. Loan
portfolio composition affects the value of the standard coefficients used and, therefore,
changes the amount of the statistical fund.
In this paper the rationale for a proper accounting of provisions for bad loans is
discussed and analysed. Bank lending is strongly pro-cyclical in Spain, as it is in many other
countries. In a context of strong competitive pressures, there is a tendency for loose bank
credit conditions in an upturn in view of the low level of contemporaneous non-performing
loans. This may contribute to the build-up of financial imbalances in the non-financial sector.
The low quality of these loans will only become apparent with the ex-post emergence of
default problems, which will tend to appear during downturns, with an estimated lag of
approximately three years in the case of Spain.
Provisions in Spain have traditionally had a pro-cyclical bias as they were largely
linked to the volume of contemporaneous problem assets. The potential coincidence at the
peak of loose credit policies and low provisions is an important concern for bank
supervisors. Low provisioning reveals that latent risks are not properly acknowledged. As a
result, book profits tend to overstate true profits in periods of low non-performing loans and
high credit growth (upturn) and understate them in periods of high problem loans and low
credit growth (downturn).
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                           17
The new statistical provision introduced recently attempts to fill the gap, as it aims at
covering expected losses. The statistical provision is an increasing function of portfolio risk.
It is inversely related to the specific loan loss provision. When the later decreases the
statistical provision increases, building up a statistical fund. When specific provisions rise
again in the downturn, the statistical fund is progressively depleted and the impact on profits
is smoothed. As a result, there is a better matching of income and expenses stemming from
loan portfolios throughout the cycle and hence, a better measurement of bank profits.
From a theoretical point of view, the new provision could also be seen as a device
that corrects the effects of certain inefficiencies that arise in the banking sector as a result of
disaster myopia, herd behaviour, asymmetric information and short-term concerns of bank
The introduction of the statistical provision is expected to improve bank managers&#39;
awareness of credit risk, leading to a proper recording and recognition of ex ante credit risk,
reducing the pro-cyclical behaviour of loan loss provisions and correcting cyclical biases
and volatility in banking profits as a result of the improved accounting acknowledgement of
expected losses in bank loan portfolios.
18                                               BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
Asset classification criteria
There are two main criteria for classifying an asset as doubtful:
1) Outstanding debts more than three months overdue. Additionally, in relation to a
single risk: the accumulation of unrepaid matured sums for an amount of over
25% of the outstanding debt will entail the classification of the entire loan as
doubtful. In relation to a customer: the accumulation of sums classified as
doubtful of over 25% of the outstanding risks will classify the total risk with that
customer as doubtful.
2) Debts, matured or not, which do not meet the first criteria are classified as
doubtful if there are reasonable doubts concerning their repayment. There are
some objective circumstances: if the borrower has negative equity, continuous
losses, general delays in payment, an inadequate assets/liabilities or
equity/liabilities ratio, cash-flow problems or the impossibility of obtaining
additional financing. At the same time, debts that are in the process of legal
recovery, debts of borrowers who are for the time being illiquid, etc 29.
A debt, matured or not, will be classified as very doubtful and written off when the
borrower is declared bankrupt or if it has been classified as doubtful for 3 years (6 years for
Provisioning criteria
1) For assets classified as doubtful because they are in arrears, the provisions will
be provided according to percentages of their value based on the time elapsed
since the maturity of the first quota.
This is meant to further curtail managers&#39; discretion.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                           19
1.1)   In general:
Between 3 and 6 months: 10%
Between 6 and 12 months: 25%
Between 12 and 18 months: 50%
Between 18 and 21 months: 75%
Over 21 months: 100%
1.2)   Mortgages:
Between 3 and 4 years: 25%
Between 4 and 5 years: 50%
Between 5 and 6 years: 75%
Over 6 years: 100%
While the amount of the outstanding risk is greater than 80% of the value
of the property, the general percentages (1.1) shall be applied.
Risks with the public sector do not require provision.
In the case of assets classified as doubtful for reasons other than insolvency
(existence of arrears), provisions should be created up to the estimated value of the non-
recoverable amounts. In general they cannot be less than 25% of the balances classified as
doubtful. Credits of over €25,000 which are not classified as doubtful, and are not
adequately documented, will be provisioned at 10%.
Regardless of the funds considered for provision previously, credit exposures,
contingent liabilities and doubtful assets for which there is no obligation to make specific
insolvency provision (with the exception of public-sector exposures), will require funds to be
set aside applying the following percentages:
0.5% for mortgages (with outstanding risk below 80% of the property value)
1% for all other risks
20                                             BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
Arellano, M. and S. Bond (1991): “Some Test of Specification for Panel Data: Monte
Carlo Evidence and Application to Employment Equations”, Review of Economic Studies
58, pp. 277-297.
Beattie, V. A.; P. D. Casson; R. S. Dale; G. W. McKenzie; C. M. S. Sutcliffe and M.
J. Turner (1995): Banks and Bad Debts: Accounting for International Losses in International
Banking. John Wiley &amp; Sons.
Berger, A. N. and R. DeYoung (1997): “Problem Loans and Cost Efficiency in
Commercial Banks”, Journal of Banking and Finance Vol 21, no. 6, June, pp. 849-870
Berger, A. N. and G. F. Udell (1998): &quot;The economics of small business finance: The
roles of private equity and debt markets in the financial growth cycle&quot;, Journal of Banking
and Finance 22, pp. 613-673.
Bernanke, B.S., M. Gertler and S. Gilchrist (1998): “The financial accelerator in a
quantitative business cycle framework“, NBER Working Paper 6455.
Boyd, J. and M. Gertler (1993): “U. S. Commercial Banking: Trends, Cycles, and
Policy”, National Bureau of Economic Research Working Paper 4404
Brookes, M., M. Dicks and M. Pradhan (1994): “An empirical model of mortgage
arrears and repossessions”, Economic Modelling 11 (2), pp. 134-144.
Clair, R. T. (1992): “Loan Growth and Loan Quality: Some Preliminary Evidence
from Texas Banks”, Economic Review, Federal Reserve Bank of Dallas, Third Quarter, pp.
Crockett, A. (1997): “The theory and practice of financial stability”. Essays in
international finance 203, April. Princeton University.
Davis, E. P. (1993): “Bank Credit Risk”, Working Paper Series No 8, Bank of
Davis, E. P. (1992): Debt, Financial Fragility, and Systemic Risk. Clarendon Press,
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                       21
Domowitz, I. and R. L. Sartain (1999): &quot;Determinants of the Consumer Bankruptcy
Decision&quot;. The Journal of Finance, Vol LIV, No 1, february, pp. 403-420.
Freixas, X. and J. C. Rochet (1997): Microeconomics of Banking. The MIT Press.
Fudenberg D. and Tirole, J. (1995): “A Theory of Income and Dividend Smoothing
Based on Incumbency Rents”. Journal of Political Economy, vol 103, no. 1, pp. 75-93.
Gonz&#225;lez-Hermosillo, B., C. Pazarba&#231;ioglu and R. Billings (1997): “Determinants of
Banking System Fragility: A Case Study of Mexico”, IMF Staff Papers Vol 44, n. 3,
Gorton, G. and R. Rosen. &quot;Corporate Control, Portfolio Choice, and the Decline of
Banking.&quot; The Journal of Finance Vol L, No 5, (December 1995), 1377-1418.
Herring, R. (1999): “Credit Risk and Financial Instability”, Oxford Review of
Economic Policy, vol. 15, no. 3, Autumn, pp. 63-79.
Keeley, M. C. (1990): “Deposit insurance, risk, and market power in banking”.
American Economic Review 5, 1183-1200.
Keeton, W. R. and Ch. S. Morris (1988): “Loan Losses and Bank Risk-Taking: Is
There a Connection?”, Research Working Paper 88-04, Federal Reserve Bank of Kansas
Keeton, W. R. and Ch. S. Morris (1987): “Why Do Banks&#39; Loan Losses Differ?”,
Economic Review may, Federal Reserve Bank of Kansas City, pp. 3-21.
Kindleberger, C. (1978): “Manias, Panics and Crashes: A History of Financial
Crises”, Basingstoke, Macmillan.
Kiyotaki. N. and J. Moore (1997): “Credit Cycles”, Journal of Political Economy, v.
105, n.2, April, pp. 211-248.
Kwan, S. and R A. Eisenbeis. &quot;Bank risk, capitalization, and operating efficiency.&quot;
Journal of Financial Services Research 12:2/3 (1997), 117-131.
Minsky, H. (1982): “The Financial-Instability Hypothesis: Capitalist Processes and
the Behaviour of the Economy”, in C. Kindleberger (ed.), Financial Crises, Cambridge,
22                                           BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
Murto, R. (1994): &quot;Finnish banking crisis: Can we blame bank management?&quot;,
Finnish Economic Papers, Vol 7, No 1, Spring, pp. 56-68.
Pensala, J. and H. Solttila (1993): “Banks&#39; Nonperforming Assets and Write-Offs in
1992”, Bank of Finland Discussion Papers 10/93.
Petersen, M. A. and R. Rajan (1995): “The Effect of Credit Market Competition on
Lending relationships”, Quarterly Journal of Economics Vol CX No 2 May, pp. 407-443.
Poveda, R. (2000): “La reforma del sistema de provisiones de insolvencia”. Bolet&#237;n
Econ&#243;mico. Banco de Espa&#241;a, pp 79-91.
Rajan, R. G. (1994): “Why bank credit policies fluctuate: a theory and some
evidence”, Quarterly Journal of Economics Vol CIX No 2 May, pp. 399-441.
Randall, R. E. (1993): “Lessons from New England Bank Failures”, New England
Economic Review May/June, pp.13-38.
Salas, V. and J. Saurina (1999a): “Deregulation, market power and risk behaviour in
Spanish Banks”. Unpublished paper.
Salas, V. and J. Saurina (1999b): “Credit risk in two institutional settings: Spanish
commercial and savings banks”. Unpublished paper.
Saurina, J. (1998): “Is there income smoothing in the Spanish savings banks?”.
Shaffer, S. (1998): &quot;The Winner&#39;s Curse in Banking&quot;, Journal of Financial
Intermediation, 7, pp. 359-392.
Solttila, H. and V. Vihri&#228;l&#228; (1994): “Finnish Bank&#39;s Problem Assets: Result of
Unfortunate Asset Structure or Too Rapid Growth?”, Bank of Finland Discussion Papers
Su&#225;rez, J. and O. Sussman (1999): “Financial Distress and the Business Cycle”,
Oxford Review of Economic Policy, vol. 15, no. 3, Autumn, pp. 39-51.
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                      23
TABLE 1: SUMMARY OF THE BEHAVIOUR OF BANK LOANS AND THEIR MAIN DETERMINANTS
Average              Average            Average            Average     Average
1963-1975           1976-1986          1987-1991          1992-1996    1997-1999
* Bank loans to non-financial
(annual real growth rate)                      11.4                  0.3                9.2              0.5         13.1
- Mortgage loans (a)                        13.7                  1.2              15.8               8.1         16.4
- Other loans                               10.9                  0.0               7.1              -3.3         10.7
(annual real growth rate)                       5.8(e)               1.8                4.3              1.4          3.8
- Residential investment                     5.0(e)              -2.2               4.6               1.6          6.0
- Non-residential investment                 9.2(e)              -0.6              11.2               0.3          8.5
- Durable-goods consumption                  7.7(e)               0.7               6.5               0.0         11.6(f)
* Net acquisition of financial assets
---                18.2(i)           19.1              13.6         20.4
* Average real interest rate of
loans(b)                                         -2.4(g)               0.0              10.1               7.6          3.9
* Wealth indicators
- Housing price index
(annual real growth rate)                   ---                  ---             13.0(h)           -4.4          4.9
- Stock Exchange price index
(annual real growth rate)                -0.3                 -7.9              -0.5               5.5         29.6
(lagged 1 year)                          0.8                -14.1              11.6               1.4         29.2
- Net financial assets of Non-
financial firms &amp; Households
(% GDP)                                     ---                26.4(i)           47.6              54.7         85.9
* Annual inflation rate (CPI)                     8.9                 14.5                5.9              4.4          2.1
* 3-month interbank real interest
rate(b)                                            ---                 1.1(j)             8.3              5.3          2.1
* Indicators of bank health
- Non-performing ratio(c)                    0.9                  3.8                3.7              6.2          2.2
- Indicator of banking crisis(d)             ---                 27.7                0.3              7.0          0.0
- Real profitability before taxes
(over own funds)                           7.8(g)              -0.5              13.2               8.2         13.3
Sources: Banco de Espa&#241;a, INE, Ministerio de Obras P&#250;blicas, Transportes y Medio Ambiente.
(a) Loans secured with real assets.
(b) Nominal interest rate less current inflation rate.
(c) Doubtful and non-performing loans over total loans to non-financial residential sectors. This series is not homogeneous due
to regulatory changes. Prior to 1982 there was no strict definition of doubtful or non-performing loans and since then, more
regulatory changes have taken place, particularly in 1987.
(d) Sum of the percentages over total capital and reserves of capital and reserves of banks with solvency problems during the
period considered. It refers only to commercial banks since they represent the bulk of entities with solvency problems.
(e) Data from 1965.
(f) Data to 1998.
(g) Data from 1971.
(h) Data from 1988.
(i) Data from 1980.
(j) Data from 1977.
Estimation of the problem loans equation in first differences
(dependent variable ln(RMit/(1-RMit), with RM the ratio of problem loans to total loans)
VARIABLES                                                              PARAMETER VALUES
Dependent lagged 1 year (ln(RMit-1 /(1-RMit-1 ))                                 0.6681*** (13.64)
GDP growth rate (∆GDP t)                                                         -0.0799*** (-7.13)
GDP growth rate with 1 lag (∆GDP t-1 )                                           -0.0159** (-2.01)
Families&#39; indebtedness (DFAMt)                                                   -0.0092** (-1.83)
Debt-equity ratio (DEMP t)                                                        0.0017*** (2.83)
1988 Regulation (REG88)                                                           0.1482** (2,49)
Loan growth rate with 2 lags (∆LOANit-2 )                                          0.0005       (0.43)
Loan growth rate with 3 lags (∆LOANit-3 )                                         0.0020** (2.14)
Loan growth rate with 4 lags (∆LOANit-4 )                                          0.0002       (0.20)
Branch growth rate with 2 lags (∆BRAN it-2)                                       -0.0007      (-1.17)
Branch growth rate with 3 lags (∆BRAN it-3)                                       0.0007*** (2.60)
Branch growth rate with 4 lags (∆BRAN it-4)                                       -0.0001      (-0.09)
Inefficiency (INEFit)                                                              0.0029*      (1.64)
% Loans without collateral (NCOL it)                                               0.0128** (2.51)
% Assets over total assets (SIZEit)                                               -0.0811** (-2.38)
Net interest margin with 2 lags (INTMit-2 )                                        0.0422*      (1.69)
Net interest margin with 3 lags (INTMit-3 )                                        0.0120       (0.38)
Capital to total assets with 2 lags (SOLR )
it-2                                     -0.0297      (-1.43)
Capital to total assets with 3 lags (SOLR )
it-3                                     -0.0012      (-0.14)
Market share (MPOW it)                                                             0.0275       (1.17)
No. of observations and time period                                                 934; 1988-1997
Variance of residuals (σ2 )                                                                    0.0857
Sargan&#39;s Test (S)                                                                           45.61 (38)
Second order autocorrelation (m2 )                                                             -1.430
The equation is estimated using the DPD package written by Arellano and Bond (1991). RM it-1 and NCOLit are treated as endogenous,
using the Generalised Method of Moments with 2 and 3 lags to instrument these two variables. t-value in brackets, *** variable
significant at the 1% level, ** at the 5% and * at the 10%. In the Sargan test (which follows a χ2), the degrees of freedom are in
brackets; its theoretical value at the 95% level for 38 degrees of freedom is 53.36. m2 follows a N(0,1).
Source: Salas and Saurina (1999b).
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                            25
TABLE 3: SIMULATION EXERCISE
ITEMS                                              y0              y1             y2             y3             y4          y5          y6           y7           y8           y9          y10          y11
1. Total loans
Outstanding stock                                 1000.0         1160.0          1345.6        1507.1         1627.6      1725.3       1794.3      1902.0       2035.1       2197.9       2417.7      2707.8
Rate of growth (%)                                                 16.0            16.0          12.0            8.0         6.0          4.0         6.0          7.0          8.0         10.0        12.0
2. Profits before provisions
Level                                                21.5            23.2           25.0           26.0           26.5        26.5        26.0         26.5         27.6         29.3         31.6         34.8
Rate of growth                                                        8.0            8.0            4.0            2.0         0.0        -2.0          2.0          4.0          6.0          8.0         10.0
3. Net loan loss provision
Specific (a)                                                           1.7            2.0            3.0            5.7         9.5        17.9         14.3         11.2          8.8          7.3          5.4
General (b)                                                           1.2            1.4            1.2            0.9         0.7         0.5          0.8          1.0          1.2          1.6          2.2
Total                                                                 2.9            3.4            4.2            6.6        10.2        18.5         15.1         12.2         10.0          8.9          7.6
4. Statistical provision
Parameter value (%)                                    0.5
Estimated expected loss (c)                                            5.8            6.7            7.5            8.1         8.6          9.0          9.5        10.2         11.0         12.1         13.5
Actual statistical provision (d)                                       4.1            4.7            4.5            2.4        -0.9         -9.0         -4.8        -1.0          2.2          4.8          8.1
Statistical fund                                       0.0             4.1            8.8           13.3           15.7        14.9         5.9          1.1          0.1          2.3          7.2         15.3
Upper limit                                          15.0            17.4           20.2           22.6           24.4        25.9        26.9         28.5         30.5         33.0         36.3         40.6
5. Total loan loss provisions                                            7.0            8.1            8.7            9.0         9.4         9.5         10.3         11.2         12.2         13.7         15.7
6. Profits after provisions
Without statistical provision                                        20.2           21.6           21.8           19.9        16.3         7.6         11.5         15.4         19.2         22.7         27.2
With statistical provision                                           16.2           16.9           17.3           17.5        17.2        16.5         16.2         16.4         17.0         17.9         19.0
% OVER TOTAL LOANS
Net loan loss provision                                                 0.25           0.25           0.28           0.41        0.59        1.03         0.79         0.60         0.46         0.37         0.28
Specific                                                              0.15           0.15           0.20           0.35        0.55        1.00         0.75         0.55         0.40         0.30         0.20
General                                                               0.10           0.10           0.08           0.06        0.04        0.03         0.04         0.05         0.06         0.07         0.08
Statistical provision                                                   0.35           0.35           0.30           0.15    -0.05           -0.50        -0.25        -0.05        0.10         0.20         0.30
Statistical fund                                        0.00            0.35           0.65           0.88           0.97     0.86            0.33         0.06         0.01        0.11         0.30         0.56
Upper limit                                            1.50            1.50           1.50           1.50           1.50        1.50        1.50         1.50         1.50         1.50         1.50         1.50
Total loan loss provisions                                              0.60           0.60           0.58           0.56        0.54        0.53         0.54         0.55         0.56         0.57         0.58
Profits after provisions
Without statistical provision                                         1.74           1.61           1.45           1.23        0.95        0.42         0.60         0.76         0.88         0.94         1.00
With statistical provision                                            1.39           1.26           1.15           1.08        1.00        0.92         0.85         0.81         0.78         0.74         0.70
PROVISIONS OVER PROFITS BEFORE PROVISIONS (%)
Specific + general provisions                                         12.7           13.6           16.2           24.9        38.5        71.0         56.8         44.2         34.2         28.2         21.8
Statistical provision                                                 17.5           18.8           17.4            9.2        -3.2        -34.5        -17.9        -3.7          7.5         15.3         23.4
Total provisions                                                      30.2           32.4           33.6           34.1        35.3         36.5         38.9        40.5         41.7         43.5         45.2
(a) The specific provision is linked to problem loans and hence cyclical. Here we make a hypothesis about its behaviour along a complete cycle.
(b) 0.0075*Change in outstanding loans. The parameter 0.0075 is a weighted average of the coefficients applied to mortgages and other risks.
(c) Parameter of the statistical provision multiplied by the outstanding stock of loans.
(d) Estimated expected loss minus the specific provision, unless the statistical fund reaches its upper or lower limit.
CHART 1. LONG-TERM BEHAVIOUR OF CREDIT
A. CREDIT AND GDP ANNUAL REAL GROWTH RATES
1963   1966     1969   1972       1975       1978       1981       1984   1987   1990   1993   1996   1999
Bank credit to non-financial resident sector
B. CREDIT/GDP
1963   1966    1969    1972      1975       1978        1981       1984   1987   1990   1993   1996   1999
CHART 2. TOTAL DEBT/ GDP
1963   1966   1969    1972       1975      1978       1981      1984    1987   1990   1993   1996   1999
Non-financial firms               Households
CHART 3. MORTGAGES AND HOUSING PRICES
1963   1966   1969     1972      1975      1978       1981       1984   1987   1990   1993   1996   1999
Mortgages annual growth rate
Housing price index real annual growth rate
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CHART 4. PROBLEM LOANS RATIO AND GDP RATE OF GROWTH
10                                                                                                               -2
5                                                                                                                2
0                                                                                                                6
83    84    85    86   87    88      89   90   91    92     93    94    95    96    97     98    99
problem loans ratio (LHS)                inverted GDP growth rate (RHS)
CHART 5. PROBLEM LOANS RATIO
83    84    85    86    87   88      89    90   91    92      93    94    95    96    97     98        99
commercial banks          savings banks
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                            29
CHART 6. DISPERSION OF PROBLEM LOANS RATIO
-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
deviations in % points
CHART 7. DISPERSION OF PROBLEM LOANS RATIO
1992-1994 AND 1997-1999
0       2   4    6   8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Problem loans ratio 1992-1994              Problem loans ratio 1997-1999
30                                                                                     BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
CHART 8. LOANS LOSS PROVISION RATIO AND GDP GROWTH RATE
3                                                                                                           -2
1.5                                                                                                          2
0                                                                                                           6
83   84    85     86    87    88     89      90   91     92    93    94   95    96      97   98   99
loan loss provisions ratio (LHS)             inverted GDP growth rate (RHS)
BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018                                                                   31
CHART 9. PROVISIONS OVER TOTAL LOANS
(SIMULATIONS EXERCISE)
y1    y2     y3         y4   y5     y6         y7         y8   y9     y10        y11
old system             statistical provision             new system
CHART 10. STATISTICAL FUND OVER TOTAL LOANS
y1    y2     y3         y4   y5     y6         y7         y8   y9     y10    y11
32                                                    BANCO DE ESPA&#209;A / DOCUMENTO DE TRABAJO N. 0018
eIFRS International Accounting Standards Board