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MARCH/APRIL 1997
Announcement
ECONOMIC PERSPECTU
1997 Conference on
Bank Structure
& Competition
A review from the
Federal Reserve Bank
of Chicago
Value at risk for a mixture
of normal distributions:
The use of quasi-Bayesian
estimation techniques
Banking reform in a transition
economy: The case of Poland
FEDERAL RESERVE BANK
OF CHICAGO
Contents
Value at risk for a mixture
of normal distributions:
The use of quasi-Bayesian
estimation techniques..................................................................................................2
Subu Venkataraman
This article proposes a methodology for measuring value at
risk for fat-tailed asset return distributions. Simulation-based
results indicate that this approach provides better estimates
of risk than one based on the assumption that asset returns
are normally distributed.
Bank Structure Conference
announcement................................................................................ .............................. 14
Banking reform in a transition
economy: The case of Poland................................................................................. 16
Thomas S. Mondschean
and Timothy P. Opiela
Polish banks have been weakened by large loan losses and low
capital. Recapitalization and better economic conditions have
strengthened the industry, but problems remain, especially among
some large state-owned banks. Continued improvement of the
banking system will require additional domestic or foreign capital
to strengthen reserves and to modernize operations.
ECONOMIC PERSPECTIVES
President
Michael H. Moskow
Senior Vice President and Director of Research
William C. Hunter
Research Department
Financial Studies
Douglas Evanoff, Assistant Vice President
Macroeconomic Policy
Charles Evans, Assistant Vice President
Microeconomic Policy
Daniel Sullivan, Assistant Vice President
Regional Programs
William A. Testa, Assistant Vice President
Administration
Anne Weaver, Manager
Editor
Helen O’D. Koshy
Production
Rita Molloy, Kathryn Moran, Yvonne Peeples,
Roger Thryselius, Nancy Wellman
March/April 1997, Volume XXI, Issue 2
ECONOMIC PERSPECTIVES is published by
the Research Department of the Federal Reserve
Bank of Chicago. The views expressed are the
authors’ and do not necessarily reflect the views of
the management of the Federal Reserve Bank.
Single-copy subscriptions are available free of
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ECONOMIC PERSPECTIVES is also
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copy of the published material.
ISSN 0164-0682
Value at risk for a mixture
of normal distributions:
The use of quasi-Bayesian
estimation techniques
Subu Venkataraman
Rapid globalization, innova
tions in the design of deriva
tive securities, and examples
of spectacular losses associat------------------ ed with derivatives over the
past decade have made firms recognize the
growing importance of risk management. This
increased focus on risk management has led to
the development of various methods and tools
to measure the risks firms face.
One popular risk-measurement tool is
value at risk (VaR), which is defined as the
minimum loss expected on a portfolio of assets
over a certain holding period at a given confi
dence level (probability). For example, consider
a trader who is concerned about the risk, over
the next ten days, associated with holding a
specific portfolio of assets. A statement that, at
the 95 percent confidence level, the VaR of this
portfolio is $100,000 implies that 95 percent of
the time, losses over the 10-day holding period
should not exceed $100,000 (or losses should
exceed $100,000 only 5 percent of the time).
The use of value at risk techniques in risk
management has exploded over the last few
years. Financial institutions now routinely use
VaR techniques in managing their trading risk
and nonfmancial firms have started adopting the
technology for their risk-management purposes
as well. In addition, regulators are beginning to
design new regulations around it. Examples of
these regulations include the determination of
bank capital standards for market risk and the
reporting requirements for the risks associated
with derivatives used by corporations.
Proponents of VaR argue that the ability to
quantify risk exposure into a single number
represents the single most powerful advantage
of the technique.1 Despite its simplicity, how
ever, the technique is only as good as the in
puts into the VaR model.2 Many implementa
tions of VaR assume that asset returns are
normally distributed. This assumption simpli
fies the computation of VaR considerably.
However, it is inconsistent with the empirical
evidence of asset returns, which finds that asset
returns axz fat tailed. This implies that extreme
events are much more likely to occur in prac
tice than would be predicted based on the
assumption of normality. Take, for example,
the stock market crash of October 1987. The
assumption of normality would imply that such
an extreme market movement should occur
only once in approximately 5,900 years. As we
know, however, there have been worse stock
crashes than that of October 1987 even in this
century. This suggests that the normality as
sumption can produce VaR numbers that are
inappropriate measures of the true risk faced
by the firm.
While alternative return distributions have
been proposed that better reflect the empirical
evidence, any replacement for the normality
assumption must confront the issue of the sim
plicity of computations, which is one of the
2
ECONOMIC PERSPECTIVES
Subu Venkataraman is a senior economist in the
research department at the Federal Reserve Bank
of Chicago. The author would like to thank Mike
Sterling for exceptional research assistance.
primary benefits of VaR. In this article, I exam
ine one such alternative assumption that simul
taneously allows for asset returns that are fat
tailed and for tractable estimations of VaR.
This distribution, based on a mixture of normal
densities, has also been proposed by Zangari
(1996). First, I relate the mixture of distribu
tions approach to alternatives that have been
presented in the academic literature on the
stochastic processes governing asset returns.
Second, I use an estimation technique for the
parameters of the mixture of distributions that
is computationally simpler than the techniques
suggested by Zangari—the quasi-Bayesian
maximum likelihood estimation (QB-MLE)
approach (first suggested by Hamilton, 1991).3
Third, using simulated data, I show that the
QB-MLE combined with the mixture of nor
mals assumption provides better measures of
value at risk for fat-tailed distributions (like the
Student’s t) than the traditional normality as
sumption. I then establish that the technique
does not suffer from the problems associated
with the traditional maximum likelihood ap
proach and that it is effective in recovering the
parameters from simulated data.
Finally, this methodology is applied to
foreign exchange data for eight currencies from
1978 to 1996. It is well known that returns in
the foreign exchange market show dramatic
violations of the assumption of normality by
exhibiting fat tails (Jorion, 1995). I compute
VaR estimates under both the assumption of
normality and the mixture of normals approach
for each of the eight currencies. I show that the
mixture of normals assumption combined with
QB-MLE outperforms the traditional normality
assumption. First, the traditional normality
assumption leads to a significantly larger
number of violations of VaR than the mixture
of normals. Moreover, the number of viola
tions of VaR observed over the sample period
under the QB-MLE is consistent with the
stated goals of VaR.
To evaluate the performance of portfolio
VaRs (as in Hendricks, 1996), I examine how
information on the parameters governing indi
vidual currencies can be aggregated in the con
text of portfolios of these currencies. In con
trast to the normality assumption, however, the
use of the mixture of normals complicates this
aggregation considerably. I propose a specific
algorithm for computing portfolio statistics
FEDERAL RESERVE RANK OF CHICAGO
from the individual components that keeps the
analysis computationally simple. The effective
ness of the approximations underlying this
algorithm is judged by examining the magni
tude of violations from simulated portfolios of
currencies. Again, I compare the results under
the QB-MLE approach with the normalitybased results and the expected outcomes. I find
that, despite the simplifying aggregation assump
tions, the QB-MLE technique again outper
forms the normality-based approach and provides
VaR estimates consistent with what one would
expect. These results suggest that combining
the mixture of normals approach and the QBMLE estimation technique allows us to capture
fat-tailed distributions, while maintaining a
computationally tractable approach to VaR
computations.
VaR estimation under normality
Below, I review the concept of VaR under the
assumption of normality and how this assump
tion simplifies the computation of VaR consid
erably. Suppose that the return for any asset, z,
(z=l to AO at a given point in time, t, is normal
ly distributed, that is, Rjt~ N(p., cf). Moreover,
assume that asset returns are uncorrelated over
time, that is, cov (Rjt, R ) = 0,j = 1,2..., but
could be contemporaneously correlated across
assets, that is, cov (R. t, R. () = o Vz ©z, t, with
the covariance matrix being denoted by . For
any portfolio of these assets, with portfolio
weights given by co = [(Oj ro2... rov], with . co. = 1,
the returns can be written as the weighted aver
age of the returns on the individual assets, that
is, Rp = (fR,- The returns on this portfolio are
also normally distributed, with mean p^ =
co. p. and variance o3 = co cor. This represents
the first major advantage of assuming normali
ty. If individual asset returns are normally
distributed, then the returns on any portfolio of
these assets has a normal distribution as well.
At a critical probability of ex, the VaR is the
solution to
1)
l^RJp^a)dRp = a,
where <8>.) is the normal density for portfolio
returns. Typical values of ex range from 1 per
cent to 10 percent. The second advantage of
the normality assumption is that the compu
tation of VaRs at different critical values
3
(that is, solving equation 1) is relatively
straightforward.
However, these simplifying assumptions
have two drawbacks. First, many derivative
securities have payoffs that are nonlinear func
tions of the underlying assets. The fact that the
asset satisfies the normality assumption does
not imply that the derivative has a normal
distribution. This raises questions about whether
this version of VaR analysis can be applied
universally. This has been the focus of much
concern (Beder, 1995) and several variants
have been proposed to alleviate the problem
(Fallon, 1996).4 Second, there is considerable
evidence in the academic literature to suggest
that security returns are non-normal, typically
exhibiting fat tails and volatility clustering
(Kim and Kon, 1994, and the references cited
therein).
Several alternatives to normality have
been proposed in the literature. For example,
in their comprehensive survey of alternative
definitions for the stochastic process for stock
returns, Kim and Kon classify the return pro
cesses as time-dependent and time-independent
models of conditional heteroscedasticity (that
is, of changes in the volatility of asset returns).5
While the time-dependent models are more
successful as models of asset returns, they are
also considerably more complicated. Moreover,
when firms are attempting to forecast the risk
of losses over short holding periods (ranging
from one day to two weeks), simpler models
might be adequate. This seems to have been the
justification behind the RiskMetrics™ frame
work developed by JP Morgan, as well as the
variant proposed by Zangari (1996), which
uses a simple version of the mixture of normals
approach.6 Clearly, the trade-off between hav
ing a procedure that accurately reflects the risk
of the portfolio and one that is not too compu
tationally intensive for the end user needs to
be considered. Below, I discuss this mixture of
distributions approach, relate it to the existing
academic findings, and discuss problems with
conventional estimation techniques. I then
describe the alternative approach that over
comes these problems—the QB-MLE tech
nique (Hamilton, 1991).
VaR estimation for a discrete
mixture of normals
is inappropriate and that returns are actually fat
tailed. One way to model such a distribution is
to assume that returns are generated from a
mixture of normal distributions. Specifically,
suppose the stochastic process for the returns
for security z is defined by
2) VM’,+(i-W
where R" ~ Mfi„, tf), R* ~ MO, oQ. and A.(
takes on a value of 1 with probability p, being
equal to zero otherwise. The three random
variables {R"t, R&, A.,} are assumed to be uncor
related with each other and over time.7
Intuitively, the return on an asset at any
given time can be drawn from one of two nor
mal distributions, with the outcome, A, deter
mining which distribution is chosen. For exam
ple, most of the time (with probability p) the
returns might be from the first distribution, that
is, A = 1. Occasionally (with probability 1 -p),
something unusual might happen (like the stock
market crash of October 1987) that significantly
increases volatility. This would be reflected
in equation 2 by returns generated from the
second (potentially higher variance) distribu
tion, that is, A = 0. The benefit of such a spec
ification is that it allows for the possibility
that occasionally the return is generated from
a distribution with a higher variance, while
simultaneously maintaining the structure of
normal densities, conditional on the realiza
tion of A (a jump from one distribution to
another).8
The first issue of concern, then, is the
estimation of the parameters {p, p;i, a (, oj
for individual assets (since the realization of
A is not typically observed by the research
er). I discuss three alternative methodologies
for estimating {p,pj;,a ,,aB}. First, I consider
traditional maximum likelihood. It turns out
that there are problems associated with this
approach in the context of mixtures. These
problems motivate the next approach, which
is the QB-MLE technique. I discuss alterna
tive interpretations of the approach, and assess
its effectiveness estimating parameters in
simulated data. For completeness, I briefly
compare the QB-MLE approach to the Baye
sian (Gibbs-sampling based) approach pro
posed by Zangari (1996).
Empirical evidence suggests that the assump
tion that asset returns are normally distributed
4
ECONOMIC PERSPECTIVES
Traditional maximum likelihood approach
This approach would require the research
er to select the parameters that maximize the
following log-likelihood function (dropping
subscript z for convenience) for the mixture of
normal densities
3)
t((P,K,a,ap)|{JRj) =
Unfortunately, as pointed out by Hamilton
(1991), a global maximum does not exist for
this function.9 Consequently, attempting to
use this approach to parameter estimation leads
to instability, local solutions, and nonconver
gence problems.
Quasi-Bayesian maximum likelihood estimation
Hamilton (1991) points out that the esti
mation problem would have been simplified
considerably if the researcher had observations
on the realization of X available directly. More
over, even if one had some observations, or
some priors, this estimate could be improved.
Second, while technical restrictions get around
the problem of the failure of the existence of a
global maximum, this still leaves the question
how to deal with these problems in the small
sample case. The method suggested by Hamil
ton is to maximize the following variant to the
likelihood function:
4)
t((p,|i„,a,aB)|{^})-|log(at)
a„
b
b„
-^log(o2)-4--^2
p a- o2
C (ZZZ
Li )2
2o2
where C(.) is the likelihood function defined
in equation 3 and {an, bn, cn, mn, aR, bB,} are
(nonnegative) constants that reflect one’s prior
beliefs about the parameters that are being
estimated.10 Hamilton presents four alternative
interpretations for the functional form that he
has suggested and the manner in which the
constants reflect the researcher’s prior beliefs
about the parameters.11 Under three of these
four approaches, the estimator can be interpreted
as being based on Bayesian updating of the
researcher’s prior beliefs.
FEDERAL RESERVE RANK OF CHICAGO
Zangari’s methodology is based on the
Bayesian updating of the densities for the rele
vant parameters using the observed return
series. Since the computation of this posterior
distribution is difficult in practice, Zangari
suggests the use of the Gibbs sampler instead.12
This procedure is time consuming; consequent
ly, Zangari proposes that the mixture-related
parameters be reestimated only once a month.
Moreover, as pointed out earlier, the QB-MLE
technique also has several Bayesian interpreta
tions and the method is relatively straightfor
ward to implement.
A direct comparison of the QB-MLE
with the Bayesian estimation technique is
beyond the scope of this article. Instead, the
analysis focuses on how well the mixture of
normals assumption combined with QB-MLE
does relative to the traditional normality
assumption.
Results based on simulated data
To examine both the effectiveness of the
estimation technique and the ability of the
mixture of normals to capture fat tails, I pro
vide two sets of results. First, I examine how
well the QB-MLE performs in estimating the
parameters in simulated data generated from
a mixture of normals data-generating process.
Then, I compare the implications of assuming
normality with those of the mixture of normals
when the underlying density has a fat-tailed
distribution.
Returns generatedfrom a mixture of normals
To examine the robustness of the QBMLE technique, I generate a variety of samples
and examine the ability of the algorithm to
estimate the parameters. Specifically, I consid
er mixtures drawn from two normals with zero
means, variances
= 2, oB = 10, andp rang
ing from 0.10 to 0.90. For each set of parame
ter inputs, I generate 100 samples of size 1,000
and estimate the parameters for each subsample.
The results of this process, the mean and the
standard deviation of the parameter estimates,
are presented in table 1. The estimation rou
tines are stable and do a good job in estimating
the underlying parameters. The next step is to
evaluate the effectiveness of this technique
when the return-generating process exhibits fat
tails (without necessarily being drawn from a
mixture of normal distributions).
5
Table 2 illustrates that the
mixture of normals has smaller
Estimates from simulated data
errors than the normal approach
Probability (p)
Estimates
at higher percentile levels. More
over,
when it has higher absolute
A
A
p
errors than the normal approach,
it errs toward conservative (high)
0.10
0.10741
1.95615
10.02450
(0.03919))
(0.62693)
(0.32751)
VaR estimates. This is in contrast to
the normal approach, which tends to
0.20
0.20857
1.99397
10.01270
(0.03249)
(0.30232)
(0.32215)
generate low VaR estimates. Look
ing first at the columns labeled
0.30
0.29628
1.98501
10.02834
(0.02919)
(0.21278)
(0.31111)
error relative to theoretical, we
see that both the normal and the
0.40
0.39521
1.98489
9.93684
(0.02837)
(0.15897)
(0.40044)
mixture of normals approach do
a better job of measuring VaR at
0.50
0.50197
1.99356
10.02182
(0.02800)
(0.10294)
(0.39484)
higher degrees of freedom. This
is not a surprise, since the distri
0.60
0.60376
2.00225
9.98221
(0.02981)
(0.11112)
(0.39651)
bution begins to more closely
resemble a normal density. More
0.70
0.70108
1.99523
9.95835
(0.02322)
(0.08838)
(0.48623)
over, the normal approach under
states (in absolute terms) the VaR
0.80
0.80018
1.99578
10.05155
(0.021877)
(0.07755)
(0.64645)
relative to the true value at very
high levels of confidence and
0.90
0.89690
1.99648
9.87647
(0.01380)
(0.05238)
(0.90500)
overstates it at lower levels. In
contrast, the mixture of normals
Notes: The maximum likelihood estimates are based on equation 4,
with a. = b. = 0.20, c. = 0.10, mj = 0, for / = n,p (as in Hamilton, 1991).
approach reflects the opposite
Averages for 100 samples (of size 1,000) drawn from a mixture of normals
distribution with a„=2, a„=10, and p varying from 0.10 to 0.90 across the runs.
behavior, understating VaRs only
Standard deviations of tne estimates are in parentheses.
under very low levels of confi
dence. While the percentage error
under the mixture can be quite high (as much as
Returns generated from a
Student’s t distribution
36.10 percent), it is generally biased toward
A distribution that exhibits the typical
being higher than the normals when a high level
property of fat tails seen in asset returns is the
VaR is required. This represents a desirable
Student’s t distribution, which is characterized
characteristic of such a risk measure. Contrary
by its degrees offreedom. Fat-tailed behavior
to conventional wisdom, assuming normality
is more pronounced at lower degrees of free
when the distribution is fat tailed need not result
dom, with the distribution resembling a normal
in VaRs that are consistently understated. Simi
density at higher degrees of freedom. I generate
lar patterns are also observed if one compares
simulated data from Student’s t distributions
the computed VaRs to the sample VaR, which
with 2, 4, 10, and 100 degrees of freedom. For
is defined as the critical return, in the simulated
each of these, I generate a sample of size 10,000.
sample, such that p percent of the returns lie
The VaR for each simulation is computed in
below this threshold.
two ways. The theoretical VaR is computed
Estimation results for foreign
based on the parameters used in the simulation.
exchange data
The sample VaR is based on the appropriate
To assess the ability of the mixture of normals
percentile from the sample itself.
and the QB-MLE technique to estimate parame
Then, I estimate parameters under the
ters and measure VaR more accurately than the
assumption of normality as well as the assump
normal distribution, I examine how well it does
tion that the data have been generated from a
with a sample of daily foreign exchange returns
mixture of normals. Based on these parameters,
for eight currencies—the Canadian dollar, French
I compute the VaRs at different probability
franc, German mark, Italian lira, Japanese yen,
levels for the normal and mixture of normals
Swiss franc, British pound, and Dutch guilder.
approach and compare them to the theoretical
Returns are measured from January 1, 1978, to
and sample VaRs.
August 26, 1996.13
TABLE 1
6
ECONOMIC PERSPECTIVES
TABLE 2
Simulation results comparing VaR estimates for Student’s t distributions
t dist
(theoretical)
t dist
(actual)
Normal
0.5
1.0
2.5
5.0
-9.9248
-6.9646
-4.3027
-2.9200
-9.6126
-6.7726
-4.3153
-2.9249
-6.7562
-6.1018
-5.1408
-4.3143
0.5
1.0
2.5
5.0
-4.6041
-3.7469
-2.7764
-2.1318
-4.4062
-3.5614
-2.6265
-2.0445
-3.5883
-3.2407
-2.7303
-2.2914
0.5
1.0
2.5
5.0
-3.1693
-2.7638
-2.2281
-1.8125
-3.1248
-2.7663
-2.2517
-1.8064
-2.8916
-2.6115
-2.2002
-1.8465
0.5
1.0
2.5
5.0
-2.6259
-2.3642
-1.984
-1.6602
-2.6388
-2.3869
-2.0292
-1.6896
-2.6105
-2.3576
-1.9863
-1.6670
Percentile
Mixture of
normals
Error
Error
Error
Error
relative to
theoretical
(normal)
relative to
theoretical
(mixture)
relative to
sample
(normal)
relative to
sample
(mixture)
19.14%
36.10%
31.34%
-4.94%
-29.72%
-9.90%
19.13%
47.50%
23.01%
39.96%
30.96%
-5.10%
7.82%
7.67%
-1.00%
-3.64%
-18.56%
-9.00%
3.95%
12.08%
12.66%
13.28%
4.65%
0.48%
1.36%
0.03%
-0.76%
-0.48%
-7.46%
-5.60%
-2.29%
2.22%
2.80%
-0.06%
-1.80%
-0.14%
0.42%
0.57%
0.66%
0.59%
-1.07%
-1.23%
-2.11%
-1.34%
-0.08%
-0.39%
-1.59%
-1.16%
Student's f with 2 degrees of freedom
-31.93%
-12.39%
19.48%
47.74%
-11.8241
-9.4787
-5.6513
-2.7758
Student's f with 4 degrees of freedom
-22.06%
-13.51%
-1.66%
7.49%
-4.9642
-4.0344
-2.7487
-2.0543
Student's t with 10 degrees of freedom
-8.76%
-5.51%
-1.25%
1.88%
-3.2123
-2.7647
-2.2112
-1.8038
Student's f with 100 degrees of freedom
-0.59%
-0.28%
0.12%
0.41%
-2.6368
-2.3777
-1.9970
-1.6700
Notes: Errors are computed based on the (percent) difference between the VaR based on either the normal or the mixture
of normals assumption and a benchmark VaR. This benchmark is computed using the known degrees of freedom
for the t distribution {theoretical VaR) as well as the appropriate percentile in the sample (sample VaR).
Summary statistics for the currency re
turns are provided in table 3. The hypothesis
that these returns are drawn from a normal
distribution is strongly rejected.14
First, I evaluate the difference between
VaR measures based on the normal versus the
mixture of normals for each currency. I com
pute VaRs for each currency on a daily basis
TABLE 3
Sample statistics
Daily foreign exchange returns
Italian
lira
British
pound
Swiss
franc
Dutch
guilder
Canadian
dollar
French
franc
German
mark
3.37
-7.63
.807
10.2
-16.0
-7.03
-11.0
-3.99
0
0
0
0
0
0
0
0.000123
Maximum
0.01728
0.058678
0.058746
0.066893
0.035571
0.063879
0.103479
0.045885
Minimum
-0.01864
-0.04141
-0.03876
-0.03672
-0.05155
-0.03985
-0.09723
-0.03843
Std. deviation
0.002621
0.006929
0.006784
0.006542
0.006621
0.006884
0.008205
0.006643
Skewness
0.11387
0.035704
0.173338
0.531506
-0.39195
0.0313
0.097693
-0.08654
Kurtosis
6.62272
6.29632
7.666992
10.25995
6.501476
6.82522
15.23094
6.239683
2,471.592
2,039.186
4,108.262
10,098.9
2,415.103
2,745.512
28,068.85
1,974.41
0
0
0
0
0
0
0
0
4,502
4,502
4,502
4,502
4,502
4,502
4,502
4,502
Mean
(X 10A5)
Median
Jarque-Bera
statistic
Probability
Number of
observations
Japanese
yen
Notes: The sample consists of daily returns from January 1, 1978, to August 26, 1996. A normal distribution should have a skewness (S) of 0 and
kurtosis (K) of 3. The Jarque-Bera statistic is
Tr
1
,
— [S2 +— (K-3)2J, where 7" is the number of observations. The test statistic has a %2 distribution with 2 degrees of freedom.
FEDERAL RESERVE BANK OF CHICAGO
7
and examine the frequency and the magnitude
of the violations that occur. These are com
pared to what one would have expected if the
VaRs had been correctly computed.
I use a 250-day estimation window and
compute VaRs based on a one-day holding
period, at the 97.5 percent confidence level,
based on a recent survey of typical assumptions
underlying VaR models used by firms.15 The
survey found that the confidence interval used
by firms ranges from 95 percent to 99 percent,
the one-day holding period VaR is typically
computed, an observation period of one year
(250 trading days) is used, and the historical
data are equally weighted. The original sample
consists of 4,502 daily (log) return observa
tions. The initial estimation window and the
need to compare VaRs with the next day’s
outcome reduce the VaR comparison to 4,251
observations. On any given day, /, I use the
return series l ^z,! /1250 to compute the parame
ters and, therefore, VaRt. This is compared with
Rt and a violation is said to occur whenever
|R | > | E4RJ.16 If the VaR is computed correct
ly, the expected number of violations is 0.05
times the number of observations, implying
212.5 violations. I examine the implications of
both the assumption of normality and the mix
ture of normals approach. Figure 1 shows the
time variation in the parameter estimates over
the sample period for the German mark.17
There is considerable time variation in the
volatility measures (panels A, C, and D) under
both approaches. Interestingly, there is also
considerable variation over time in the estimate
ofp, the probability that returns are drawn from
one distribution in the mixture.
The results from comparing VaR estimates
for the eight currencies with the actual number
of violations are summarized in table 4. In a
sample size of 4,251, one would expect 212.5
violations. The number of violations that occur
under the assumption of normality is signifi
cantly higher than one would expect and a
likelihood ratio test rejects the hypothesis that
the true underlying probability of a violation is
5 percent. In contrast, the number of violations
of the VaR estimated under the mixture of
normals is much lower than under the normal.
In addition, one cannot reject the hypothesis
that the model has a probability of violations
equal to 5 percent.
TABLE 4
Violations of VaR under alternative methodologies
Number of violations
Average size of
violations (percent)
Expected size of
violations (percent)
Normal
Mixture
Normal
Mixture
Normal
Mixture
256
(8.801)**
233
(2.011)
0.7113
0.7421
0.0428
0.0407
245
(4.981)*
227
(1.012)
1.7722
1.7975
0.1021
0.0960
263
(11.757)**
224
(0.639)
1.7481
1.7463
0.1082
0.0920
Italian lira
251
(6.938)**
223
(0.533)
1.6692
1.7694
0.0986
0.0928
Japanese yen
251
(6.938)**
215
(0.030)
1.4333
1.4052
0.0846
0.0711
248
(5.921)*
222
(0.436)
1.8809
1.8906
0.1097
0.0987
British pound
279
(19.995)**
226
(0.879)
1.4411
1.4932
0.0946
0.0794
Dutch guilder
261
(10.873)**
237
(2.859)
1.7545
1.7319
0.1077
0.0966
Currency
Canadian dollar
French franc
German mark
Swiss franc
‘Significant at the 5 percent level.
"Significant at the 1 percent level.
Notes: The log-likelihood test statistic (reported in parentheses) is LR = 2[1n[(a‘)x(1 - a‘)r_x] - 1n[ax(1 - <x)r-x]], T = sample size (4,251), x =
number of violations, a = 0.05, and a* = x/Tis the sample fraction of violations. The test statistic has an asymptotic %2 distribution with 1 degree
of freedom. The critical values are 6.6349 and 3.841 at the 99 percent and 95 percent confidence levels, which translates into violations of 250
and 241, respectively.
8
ECONOMIC PERSPECTIVES
FE DE RA L RE SE RV E BA NK OF CHICAG O
FIGURE 1
Parameter estimates of the German mark
Notes: The estimates are computed using rolling 250-day windows [f—250,f—1 ]. The maximum likelihood estimates for
panels B-D are based on equation 4, with
= 0.20, c. = 0.10, m. = 0, for / = n, p (as in Hamilton, 1991).
Table 4 also shows the average size of a
violation and the expected size of a violation
(defined as the average size times the frequency
of a violation). The average size of the viola
tion is larger under the mixture than under the
pure normal assumption. While this might
seem surprising, recall (from the results of
table 2) that the rank ordering of the VaRs
under the normal versus mixture of normals
depends critically on the level of confidence
as well as the shape of the distribution, and
this could explain the results in table 4.
The expected size of the violations is
uniformly smaller under the mixture (the last
two columns of table 4). This suggests that for
individual assets, the mixture of normals pro
vides superior VaR estimates than the conven
tional normality assumption because both the
number and expected size of violations are
lower under the mixture of normals approach.
Next, I examine how well this process works in
the context of portfolios.
Portfolio results
As mentioned earlier, the two benefits of
the normality assumption are that it is relative
ly simple to calculate the VaRs associated with
different confidence levels and to aggregate
individual parameters to develop the parame
ters of a portfolio. The mixture of normals
shares the first property. But how would one
aggregate these parameters in the context of a
portfolio? I assume that the covariance across
10
assets is independent of X.. This implies that
there are only two covariance matrices that
could be generating the returns. The off-diagonal
terms of these matrices are independent of X
realizations, while the diagonals are either
or
depending on the realization of X.. The
second issue is whether these realizations are
independent across assets. The assumption of
independence would be consistent with the
large literature on jump diffusion models,
which typically assumes that the jump risk is
fully diversifiable (Merton, 1976). However, it
is not immediately clear that this assumption is
reasonable in the context of the risk-manage
ment activities of a bank, since the prospect of
bankruptcy could make the bank worry about
risk that might seem diversifiable in an asset
pricing context. Moreover, this assumption
complicates the mapping between confidence
levels and VaRs considerably. For example,
with eight assets, one would have to consider
28 = 256 possibilities for the realizations of X.,
with the process quickly becoming intractable.
The assumption of perfect correlation is
not valid either, since one would then expect
identical values ofp for all eight currencies.
Here, I adopt a computationally simpler alter
native and test to see whether the approxima
tion works. For a portfolio co, I use as inputs
p = X co » and the two covariance matrices, Z
and ZB, which are identical along the off-diagonals and contain the relevant variances on the
diagonals. These assumptions approximate the
distribution of portfolio returns
by a mixture of normals distri
bution. To assess how good an
approximation this represents, I
form 30 random portfolios of
the eight currencies and evaluate
how well the portfolio VaR
estimates do relative to the prof
its and losses on the portfolio.
In figure 2,1 plot the num
ber of violations (the simulation
is for 4,251 daily returns for 30
different portfolios) under the
mixture of normals approach
relative to the conventional nor
mality assumption. The portfoli
os have been sorted based on
their VaR estimates under the
assumption of normality. Again,
the fraction of violations under
the mix is much lower than under
ECONOMIC PERSPECTIVES
TABLE 5
VaR violations for portfolios of currencies
Normal
Mixture of
normals
249
(5.5460)
217.4
(7.7442)
0.0586
(0.013)
0.0511
(0.0018)
Magnitude of violations
1.0970
(0.1766)
1.1293
(0.1845)
Average violations
0.0643
(0.0109)
0.0580
(0.0109)
Number of violations
Proportions of violations
Notes: The statistics are based on 30 random portfolios of
currencies constructed over the entire sample period.
Standard deviations are in parentheses.
the normality assumption. Moreover, the critical
number of violations to reject the hypothesis
that the VaR is consistent with a 2.5 percent
confidence level is 250 (at the 1 percent level)
or 241 (at the 5 percent level). For the normal
density, the hypothesis can be rejected in 26 of
the 30 portfolios at the 5 percent level and in
13 of the portfolios at the 1 percent level. One
cannot reject the mixture-based VaRs in any of
the portfolios.
Table 5 indicates that, similar to the find
ings for individual currencies, the magnitude of
the violations under the mixture tends to
be larger than the magnitude under pure
normality. However, the expected size of
the violations is smaller under the mixture
of normals approach.18 Thus, in the context
of portfolios as well as individual assets,
the mixture of normals provides superior
VaR estimates relative to the conventional
normality assumptions, because the num
ber of violations is lower and consistent
with expectations and the ex ante expected
size of violations is smaller.
Conclusion
The analysis in this article highlights
the critical nature of the existing assump
tions underlying VaR computations and
the complications that result when the method
ology is used for assets that exhibit fat-tailed
return distributions. The mixture of normals
approach combined with QB-MLE is shown to
perform significantly better, in the context of
both individual assets and portfolios. Further
research is needed on the number of compo
nents to include in the mixture, more compli
cated intertemporal dependencies, and the
development of computationally feasible
aggregation algorithms.
NOTES
JIn fact, the concept of VaR was motivated by this ability
to capture risk by one number. Dennis Weatherstone, the
chief executive officer of JP Morgan at the time (and also
the chairman of the influential Group of Thirty study on
derivatives), insisted that such a single measure of the
firm’s exposure be made available to him every morning,
resulting in the development of the underlying quantita
tive techniques (Financial Engineering, Ltd., Risk, special
supplement, 1996).
2The inputs into the model include 1) assumptions (and
estimation techniques) for the stochastic processes that
determine the returns on individual assets; 2) a methodol
ogy for mapping the return distributions for individual
assets into the aggregate return distribution for the portfo
lio (and hence to profits and losses [P&L]); and 3) a
computationally simple process for evaluating VaR at
different probability levels for this aggregate P&L distri
bution. All these steps obviously also depend on the
relevant holding period over which the analysis is con
ducted. In this article, my primary focus is not on the
second aspect (refer to JP Morgan’s RiskMetrics™ docu
ment on position mappings for greater detail). I focus
instead on the estimation of the underlying stochastic
processes and the difficult trade-off between the need for
FEDERAL RESERVE BANK OF CHICAGO
an approach that is accurate and the need for one that is
easy to implement. I discuss some aggregation issues later
in this article.
3I also briefly review the problems associated with stan
dard estimation techniques (such as maximum likelihood),
and the Bayesian approach using Gibbs sampling pro
posed by JP Morgan.
4Most techniques try to approximate these nonlinearities
based on Taylor series expansion, leading to methods
based on the delta and gamma of security-type approach
es, for example.
5Under the former, they consider ARMA, GARCH, and
EGARCH models (Bollerslev, Engle, and Nelson [1994])
and the Glosten, Jagannathan, and Runkle (1993) specifi
cations for asset returns. Under the latter, they consider
Student’s t models, generalized mixtures of normals,
Poisson jump models, and stationary normal models.
6See Kon (1984) for a comparison of a general version of
the mixture of normals with the Student’s t density, for
example.
11
7The mixture of normals also allows for more complex
structures where X could follow a Markov process. See
Engel and Hamilton (1990) for an application.
posterior distribution, 3) an analogy with a Bayes estima
tor, or 4) a penalized maximum likelihood function.
12 For full details of this methodology, see Zangari (1996).
8In fact, the traditional jump diffusion model can be inter
preted as allowing for the possibility of a jump between an
infinite number of normal distributions (Kon [1984]).
specifically, a singularity arises whenever one of the
observations is attributed entirely to a single distribution,
since the mean is then imputed to be the value of the
observation and the variance approaches zero. However,
focusing attention on just the largest local maximum with
positive variances leads to consistent estimates. The
problem at that stage is one of ensuring that the numerical
algorithms are bounded away from zero, which is difficult
to do in practice. Moreover, as pointed out by Lehmann
(1983) and Robert (1994), in any finite sample, the proba
bility that none of the observations was generated from
one of the mixture components is strictly positive. They
argue that this also contributes to the instability of the
maximum likelihood estimation process. Consequently,
estimation under this approach is not recommended.
10Notice that in the special case where these constants are
all set to zero, the functional form suggested by Hamilton
(1991) collapses to the traditional maximum likelihood
function. The constants are identical in our estimation to
those used by Hamilton. Moreover, perturbing the param
eters had no effect on the estimates. Refer to table 1 for
additional details.
13The currencies selected here are identical to those in
Hendricks (1996). The start of the time period is also
identical, while the ending point reflects when this re
search project was started.
14A11 the currencies reflect significant skewness and
kurtosis relative to what one would expect if the sam
ples had been drawn from normal distributions. In
addition, the Jarque-Bera statistic rejects normality for
all currencies.
15“Amendment to the Capital Market Accord to incorpo
rate market risks: The use of internal models for supervi
sory purposes,” a study conducted by a joint ISDA/LIBA
task force which surveys its members to assess the as
sumptions underlying their use of VaR.
16I focus attention on both excessive losses and gains
throughout this analysis. Disaggregating these two does
not change the nature of the results.
17The patterns for the other currencies are substantially
similar and are therefore not included.
18These simulation runs are computationally intensive, but
increasing the number of portfolios to 50 did not change
the nature of the results.
11 Specific ally, he suggests that one could interpret the
estimator as 1) representing prior information which is
equivalent to observed data, 2) the mode of a Bayesian
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Beder, Tanya Styblo, “VAR: Seductive but
dangerous,” Financial Analysis Journal, Sep
tember-October 1995, pp. 12-24.
Financial Engineering, Ltd., Risk Publica
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Risk, June 1996.
Bollerslev, T., R. Engle, and D. Nelson,
“ARCH models,” in Handbook ofEconomet
rics, Robert F. Engle and D. McFadden (eds.),
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Glosten, L., R. Jagannathan, and D. Runkle,
“On the relationship between the expected
value and the volatility of the normal excess
return on stocks,” Journal ofFinance, Vol. 48,
No. 5, December 1993, pp. 1779-1801.
Casella, G., and E. George, “Explaining the
Gibbs Sampler,” The American Statistician,
Vol. 46, No. 3, August 1992, pp. 167-174.
Engel, C., and J. Hamilton, “Long swings in
the dollar: Are they in the data and do the mar
kets know it?,” American Economic Review,
Vol. 80, 1990, pp. 689-713.
Fallon, William, “Calculating value-at-risk,”
Wharton Financial Institutions, working paper,
No. 96-49, 1996.
12
Hamilton, James, “A quasi-Bayesian approach
to estimating parameters for mixtures of normal
distributions,” Journal ofBusiness & Economic
Statistics, Vol. 9, No. 1, 1991, pp. 27-39.
Hendricks, Darryll, “Evaluation of value-atrisk models using historical data,” Economic
Policy Review, Federal Reserve Bank of New
York' Vol. 2, April 1996, pp. 39-69.
ECONOMIC PERSPECTIVES
Hopper, Gregory, “Value at risk: A new
methodology for measuring portfolio risk,”
Business Review, Federal Reserve Bank of
Philadelphia, July/August 1996, pp. 19-29.
Merton, R., “Option pricing when under un
derlying stock returns are discontinuous,”
Journal ofFinancial Economics, Vol. 3, No. 1/2,
1976, pp. 125-144.
Hsieh, David, “Nonlinear dynamics in finan
cial markets: Evidence and implications,”
Financial Analysts Journal, Vol. 51, JulyAugust 1995, pp. 55-62.
Morgan Guaranty Trust Company, Global
Research, RiskMetrics , technical document,
New York, 1995.
Jorion, P., “Predicting volatility in the foreign
exchange markets,” Journal ofFinance, Vol.
50, No. 2, 1995, pp. 507-528.
Kim, Dongcheol, and Stanley Kon, “Alterna
tive models for the conditional heteroscedasticity of stock returns,” Journal ofBusiness, Vol.
67, No. 4, 1994, pp. 563-598.
Kon, Stanley, “Models of stock returns—A
comparison,” Journal ofFinance, Vol. 39, No.
1, March 1984, pp. 147-165.
Lehmann, E.L., Theory ofPoint Estimation,
New York: John Wiley and Sons, 1983.
Robert, Christian, “Mixture of distributions:
inference and estimation,” Universite de Rouen
and Crest, Department de Math, Insee, Paris,
working paper, 1994.
Titterington, D.M., A. Smith, and U. Makov,
Statistical Analysis ofFinite Mixture Distribu
tions, Wiley Series in Probability and Mathe
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Turner, Chris, “VAR as an industrial tool,”
Risk, Vol. 9, No. 3, March 1996.
Zangari, Peter, “An improved methodology
for measuring VAR,” RiskMetrics Monitor,
Reuters/JP Morgan, 1996.
Leong, Kenneth, “The right approach,” Risk,
Special Supplement, June 1996.
FEDERAL RESERVE RANK OF CHICAGO
13
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Technology
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Conference on Bank Structure and Competition
April 30-May 2,1997
Federal Reserve Bank of Chicago
The major theme of the 33rd annual Conference on Bank Structure and
Competition will be the changing role of technology in the financial services
industry. The discussions will evaluate the opportunities and potential concerns
raised by the incorporation of recent technological innovations. Special attention
will be given to public policy implications.
In recent years, technology has had a major impact on the activities and
processes of financial institutions. The traditional Norman Rockwell image of a
banker providing basic services at the teller window has long since passed.
Technology has altered the means by which banks deliver and market services,
evaluate and manage portfolio risk, and respond to supervisors and regulators. It
has significantly altered back-office operations and has brought into question the
appropriateness of regulations concerning service offerings, geographic and
ownership restrictions, traditional antitrust enforcement, and the very framework
of the existing regulatory structure.
There is also significant disagreement on just how quickly new
An additional session to discuss the impact of technology on
technology will be incorporated and, as a result, how quickly the
issues related to risk management, payments, and antitrust
industry will evolve. Has the bank branch become obsolete just
enforcement will include Susan Phillips, Board of Governors of
as banks have obtained the legal right to branch on an interstate
the Federal Reserve System, James Culberson, Jr., President,
basis? Will consumers embrace new methods of utilizing
American Bankers Association, and Brian Smith, Partner,
financial services? Will banks continue to be “special,” or will
Mayer, Brown, and Platt.
technology further homogenize all financial institutions? Will
banks maintain their dominant position in the financial sector by
There will also be sessions addressing the following:
developing alliances with the providers of technology, or will
■ Incentive-compatible regulation,
providers of the evolving technology make significant inroads
■ The opaqueness of bank assets,
into current bank markets? What are the consumer security
■ Modeling bank risk,
issues associated with new electronic delivery systems, and
■ Corporate control in banking,
how can they be addressed?
■ Bank activity in over-the-counter markets,
■ Credit scoring models,
The 1997 conference will focus on these and related policy issues.
■ Bank-firm and bank-regulator relationships, and
Featured speakers include Alan Greenspan, Chairman of the Board
■ Bank performance.
of Governors of the Federal Reserve System, Jerry McElhatton,
President, MasterCard International, Scott Cook, Chairman, Intuit
The first day of the conference has traditionally been intended
Inc., and John Grundhofer, President and Chief Executive Officer,
primarily for an academic audience and has emphasized more
First Bank System, Inc.
technical research papers. This year we are also scheduling a
bankers round table the first day. The Thursday and Friday
To discuss issues directly related to the conference theme, we
sessions are for a more general audience.
have arranged a special panel of industry experts including
Dominick Cavuoto, Principal, KPMG Peat Marwick, Bill
Invitations to the conference will be mailed in March. If you are
Randle, Senior Vice President, Huntington Bancshares, Inc.,
not currently on the conference mailing list or have changed
Cathy Minehan, President, Federal Reserve Bank of Boston,
your address and would like to receive an invitation, please
Christine Varney, Commissioner, Federal Trade Commission,
contact Regina Langston at the Federal Reserve Bank of Chicago
and Charles Goodhart, London School of Economics. Each will
at 312-322-5641, or send your request to the Federal Reserve
bring a unique perspective to issues concerning technology
Bank of Chicago, Public Affairs Department, 3rd Floor, P.O. Box
and the future of the financial services industry.
834, Chicago, Illinois, 60690-0834.
Banking reform in a transition
economy: The case of Poland
Thomas S. Mondschean
and Timothy P. Opiela
The demise of several Com
munist-led governments in
Central and Eastern Europe
has given way to an economic
____________ transformation of these nations
that may be as important to the people who live
there as the political transformation has been.
These countries are trying to reduce the use of
central planning and rely more on the behavior
of firms and households operating in open
markets to improve economic decisionmaking
and resource allocation. However, the transfor
mation has not progressed as quickly or as
smoothly as originally hoped. Moreover, basic
policy disagreements continue over the pace of
privatization, the conditions under which foreign
firms should be allowed to enter a nation’s
market or buy its existing firms, and other issues
central to the process of economic reform.
To understand better the difficulties poli
cymakers face in reforming these economies,
we focus on one aspect of the economic transi
tion, banking reform, in one transition economy,
Poland. A country’s banking system exists to
collect funds from savers and lend them to
borrowers, as well as to provide an efficient
payments mechanism. A system’s ability to
allocate hinds as efficiently as possible to finance
productive investment and consumption expen
ditures is crucial in producing a high and sus
tainable rate of economic growth. Under central
planning, however, the state directed the distri
bution of funds throughout the economy with
no regard for their most productive use. The
institutional infrastructure and incentive structure
necessary for decentralized credit allocation
decisions based on rational economic criteria
never developed.
The issues Poland has faced in reforming
its banking system are similar to those con
fronting other transition economies. The banking
system emerged from the Communist era with
little capital, a large portfolio of nonperform
ing loans, no meaningful system of accounting,
little recourse for lenders in the event of default,
technologically backward operations, and inad
equately trained staff. Prudential regulatory
and supervisory capabilities to address moral
hazard incentives and corruption were almost
nonexistent. In addition, by the end of the 1980s,
the country was on the brink of hyperinflation,
16
ECONOMIC PERSPECTIVES
Thomas S. Mondschean is associate professor
of economics at DePaul University and a con
sultant to the Economic Research Department
of the Federal Reserve Bank of Chicago. Timothy
P. Opiela is associate professor of economics
at the University of the Pacific and served as a
visiting economist at the National Bank of
Poland from 1995 to 1996. The authors espe
cially thank the director of research, Ryszard
Kokoszczynski, Marta Gotajewska, Jan Krzysztof
Solarz, Pawet Wyczanski, and other staff mem
bers of the research department at the National
Bank of Poland for providing data and comments
on earlier drafts. Anna Siwy and Urszula Zapolska
provided excellent research assistance. Financial
support from DePaul University and the University
of the Pacific is gratefully acknowledged. This
article is dedicated to the memory of Herbert L.
Baer, Jr., former assistant vice president and
senior economist at the Federal Reserve Bank
of Chicago, who was conducting research on
the Polish banking system at the time of his
death in 1995.
which was eroding public confidence in the
Polish currency, the zloty.
Poland’s banking problems also bear simi
larities to those of developed economies. Even
though Poland has been making the transition
toward a market economy for several years, the
majority of its banking system assets are still
controlled by the state. Thus, deciding how and
when to privatize commercial banks is an impor
tant issue. However, some of Poland’s largest
banks are undercapitalized and have inadequate
resources to address their nonperforming loan
problems. Privatizing poorly capitalized banks
can create a moral hazard incentive that would
raise the cost of resolving bank failures in the
future, a situation we have seen develop in many
countries in recent years. For example, the
problems in the U.S. banking and savings and
loan industries in the 1980s—inadequately
capitalized institutions, insufficient regulatory
oversight, and an unwillingness to address the
moral hazard incentives caused by generous
deposit insurance guarantees—led to a large
taxpayer-financed bailout and congressional
reform. The knowledge gained in the U.S.
can help policymakers in Poland to avoid simi
lar mistakes.
Another topic of current interest in Poland
is whether the banking industry should consoli
date to improve efficiency and better serve larger
firms. Some believe that existing Polish banks
are too small and too regional in nature to com
pete effectively; hence, they favor merging
regional banks to form larger banking groups.
Given that the U.S. has also been undergoing a
period of banking consolidation and expansion
across state lines, an understanding of the pros
and cons of consolidation in Poland broadens
our understanding of the issue.
A third issue concerns policy on foreign
financial institutions wishing to operate in
Poland. Some foreign banks have entered the
Polish market by acquiring an equity stake in
an existing bank, while others have built their
operations from scratch. On the one hand,
Poles recognize that foreign banks bring in
modern technology, management techniques,
and additional capital, which can enhance the
quality and sophistication of the financial ser
vices offered to the public. On the other hand,
they fear that domestic institutions will be
unable to compete effectively and that foreign
banks will dominate the Polish banking system.
FEDERAL RESERVE RANK OF CHICAGO
Given Poland’s history of being dominated by
foreign countries, there is a strong feeling that
allowing foreign banks to gain the upper hand
would not be in the country’s best interests in
the long run. Many countries, including the
U.S., have confronted this issue.
While Poland has come a long way in
reforming its banking system, in our view
more progress needs to be made before Polish
banks can operate efficiently. We believe the
key problem facing Polish banks today is not
that they are too small but that they have too
little capital. Without adequate capital, these
banks are constrained to hold large amounts
of government securities instead of making
commercial loans. As a result, less credit is
available to businesses and households than
would otherwise be the case. Consolidating
banks without infusing capital would not improve
the situation; indeed, the cost of consolidation
would reduce capital adequacy even further.
Improving capital adequacy, in our opinion,
should be a higher priority than encouraging
consolidation.
Below, we present an overview of the
banking reform program and the impact of
economic conditions on the banking industry
in the first few years of the transition. We
analyze the performance of Polish banks during
the 1990s. Then we discuss the most pressing
issues facing both the government and the
industry in the years to come.
Banking reform at the beginning
of the transition
Under Communist control from the end of
World War II to the end of the 1980s, Poland’s
banking system became highly centralized and
primarily served as a conduit for transferring
funds between the central government and the
various state enterprises that controlled the
country’s economic life. The most important
financial institution, the National Bank of
Poland (NBP), served as both central bank
and supplier of credit to key industries.
Decisions on monetary policy, the allocation
of credit to borrowers, and the scope of the
NBP’s operations were made by the central
government. The NBP was directly respon
sible to the Ministry of Finance, with the
president of the NBP serving as Undersecretary
of State at the ministry.
17
During the 1980s, the Polish government
began reforming the banking system. The
Banking Act of 1982 separated the NBP from
the Ministry of Finance and required parlia
mentary approval for the appointment of the
president of the NBP. This act also legalized
the formation of private banks as joint stock
companies with or without foreign equity par
ticipation. However, the NBP continued to
perform the functions of both a central and a
commercial bank until 1989, when the Parlia
ment passed a new Banking Act and the National
Bank of Poland Act. Approximately 400 regional
branch offices of the NBP were converted into
nine regional, state-owned commercial banks,
as listed in table 1. These banks, centered in
major cities, inherited a substantial part of the
NBP’s commercial loan portfolio, consisting
primarily of loans to existing state-owned enter
prises (SOEs). As the first step in the ultimate
privatization of these banks, in September and
October 1991 the nine banks were converted
into joint stock companies wholly owned by
the Ministry of Finance. To date, four of the
nine have been privatized, with their stocks
trading on the Warsaw Stock Exchange.
The remainder of the NBP became a tradi
tional central bank in the western sense, holding
reserves, issuing currency, advancing credit to
the banking system, overseeing the payments
system, and holding part of the debt of the Polish
government. The independence of the central
bank was reinforced by law, with the president
of the NBP now nominated by the President of
Poland and confirmed by Parliament. In addi
tion to its monetary policy functions, the reorga
nized NBP is responsible for supervision and
regulation of the banking system.1
By 1990, the government owned six other
specialized banks (also listed in table 1). PKO BP
was separated from the NBP in 1988. Its pri
mary functions were to accept household
deposits and advance loans to finance public
housing construction. The bank has a nation
wide network of branches and other outlets and
the largest share of total deposits (26.8 percent
TABLE 1
Structure of the Polish banking industry
Total
assets
City
Percent of total
banking assets
(million zioty)
The nine commercial banks
Bank Depozytowo-Kredytowy
Bank Gdariski
Bank Przemystowo-Handlowy (BPH)
Bank Zachodni
Pomorski Bank Kredytowy (PBKS)
Powszechny Bank Gospodarczy (PBG)
Powszechny Bank Kredytowy
Wielkopolski Bank Kredytowy (WBK)
Bank Slgski
Lublin
Gdansk
Krakow
Wroclaw
Szczecin
Lodz
Wa rsaw
Poznan
Katowice
Total for the nine banks
3,658.8
4,636.6
7,448.3
5,048.7
3,661.6
9,181.0
8,373.5
5,035.9
8,683.7
2.1
2.7
4.3
2.9
2.1
5.4
4.9
2.9
5.1
55,728.1
32.5
35,839.1
21,679.3
12,731.4
13,153.1
1,263.0
3,621.5
20.9
12.6
7.4
7.7
0.7
2.1
88,287.4
51.5
27,475.7
16.0
The specialist banks
Powszechna Kasa Oszcz^dnosci Bank Paristwowy (PKO BP)
Polska Kasa Opieki SA (Pekao SA)
Bank Handlowy
Bank Gospodarki Zywnosciowej (BGZ)
Polski Bank Rozwoju (PBR)
Bank Rozwoju Eksportu (BRE)
Total for the specialist banks
Other banks
Nationwide
Nationwide
Warsaw
Wa rsaw
Wa rsaw
Wa rsaw
Note: Total assets data are for September 30, 1996.
Source: Gazeta Bankowa, December 8, 1996.
18
ECONOMIC PERSPECTIVES
as of the end of June 1996) of any bank in
Poland. Pekao SA offers deposit accounts
denominated in foreign currencies through its
nationwide branch network, and serves as a
vehicle for overseas Poles to remit funds to
their relatives in Poland. As of June 30, 1996,
it held 16.8 percent of Poland’s total deposits.
Bank Handlowy, which was started in 1870, is
a major corporate bank providing a wide range
of financial services, including foreign trade
financing. BGZ, the Bank for Food Economy,
is the primary supplier of credit to the agricul
tural sector. The bank is owned partly by the
national government and partly by over 1,200
local cooperative banks, which offer deposit
accounts and loans to private farmers and selfemployed craftsmen. BRE, the Export Devel
opment Bank, was established in 1987 to pro
vide trade financing and competition for Bank
Handlowy and Pekao SA. This bank was priva
tized in 1992. Finally, PBR, the Polish Develop
ment Bank, was established in 1990. It operates
primarily as a banker’s bank, channeling funds
to other banks from foreign credit lines or its
own resources. It has also been involved in the
organization and development of the Polish
interbank money market.
During the early part of the transition, the
growth of privately owned banks was encour
aged. In an effort to increase competition among
banks, the government liberalized entry require
ments for the establishment of new banks. For
example, the minimum amount of capital need
ed to secure a banking license at the end of 1989
was 400,000 doty, approximately $61,500 at
the prevailing exchange rate.2 Moreover, the
rules concerning the background and experience
of bank owners and managers were not rigor
ously enforced. As a result of the liberal entry
policy, the number of banks in Poland expanded
from six in 1988 to 75 by the end of 1990.
The economic environment in the early
years of the transition
As Poland’s first non-Communist govern
ment since the end of World War II assumed
power in September 1989, the economy was in
serious difficulty. To curry favor with the elec
torate prior to the 1989 parliamentary elections,
the previous government had increased govern
ment spending and paid for it by increasing the
money stock. As a result, the budget deficit
soared to 7.4 percent of gross domestic product
(GDP) in 1989, and the inflation rate rose from
FEDERAL RESERVE RANK OF CHICAGO
an already high 60.2 percent in 1988 to 251.1
percent in 1989. (Selected economic statistics
are presented in table 2.) After some discussion
of what kind of economic reform program to put
in place, the Polish government implemented
what came to be known as the Balcerowicz Plan,
a bold program of “shock therapy” designed to
speed the process of economic liberalization
and make it extremely difficult for a future
government to go back to the previous system.3
Almost all prices in the economy were decon
trolled in 1990, while at the same time consumer
and producer subsidies were cut from 12.9
percent of GDP in 1989 to 7.3 percent in 1990,
5.1 percent in 1991, and 3.3 percent in 1992.
As a result of the lifting of price controls and
the lagged effects of the expansionary mone
tary policy, inflation worsened in 1990 to
585.8 percent.
Another aspect of the Balcerowicz Plan
was to promote greater competition among
Polish industries. As a result of central plan
ning, most Polish industries were highly con
centrated, and the fear was that decontrolling
prices would lead to monopolistic pricing poli
cies that would reduce overall social welfare.
The government addressed this issue by elimi
nating all nontariff restrictions on imports and
reducing the average tariff rate from 13.3 percent
to 8 percent. Foreign competition, it was hoped,
would hold in check the desire of large indus
trial enterprises to raise prices and also give
these firms an incentive to improve quality and
service to their customers. At the same time,
the Polish zloty was devalued by 31.6 percent
from 0.65 to 0.95 doty per dollar to give Polish
firms an initial competitive advantage over
their foreign competitors.
The initial effects of the Balcerowicz Plan
were positive. The government budget actually
showed a surplus of 2.8 percent of GDP in
1990. The quantity and variety of goods avail
able for sale expanded, and lines to purchase
scarce consumer goods, a fact of life under
Communism, disappeared. The currency deval
uation initially helped Polish exporters. A spirit
of optimism pervaded the country and was
bolstered by the fall of Communism in neigh
boring countries. The initial euphoria over
political and economic reform, however, gave
way to a severe recession, with declines in real
GDP of 11.6 percent in 1990 and 7.6 percent in
1991. There were several causes. First, the
19
rapid change in relative prices brought about
by deregulation forced businesses to restruc
ture quickly or close their doors. Unemploy
ment grew rapidly and industrial production
fell. Second, the shift in the government budget
from deficit in 1989 to surplus in 1990 was, in
effect, a substantial tightening of fiscal policy,
which in time would have a dampening effect
on the growth of aggregate demand. Third, in
an effort to contain inflation, the NBP adopted
a more restrictive monetary policy and interest
rates soared. Thus, even if firms could gain
access to credit, the price of credit was very
high. Fourth, with the collapse of the Soviet
Union, Poland lost its largest export market.
Finally, Western Europe was undergoing a
recession of its own during this period, which
further reduced demand for Polish exports. Not
surprisingly, the rate of unemployment rose
dramatically from almost zero in 1988 to 11.8
percent by the end of 1991.
Effect of economic reform on the
banking system
The volatile conditions that persisted in
the economy also affected the banking sector.
In terms of reported income, bank profits were
positive at the beginning of the transition. In
particular, 1990 was an excellent year, with the
industry earning 1.66 billion zloty, which rep
resented a return on assets of 7.2 percent. The
high rate of net income was primarily due to
the banks’ ability to hold deposit interest rates
below the rate of inflation while earning a
positive real return on loans; hence, the indus
try recorded a net interest margin of 17 percent
of total assets during 1990. Net income fell in
nominal terms by 13.5 percent in 1991 to 1.44
billion zloty, and rose in 1992 by 4.3 percent to
1.5 billion zloty. Adjusted for inflation, how
ever, net income fell by 49 percent in 1991 and
by 27.1 percent in 1992. The deterioration in
the industry’s net profit position was based in
part on greater competition in both the loan
TABLE 2
Selected economic indicators, 1989-96
Units
1989
1990
1991
1992
1993
1994
1995
1996
11.8
59.2
80.9
114.9
155.8
210.4
286.0
351.7“
Inflation (CPI)
O//o
251.1
585.8
70.3
43.0
35.3
32.2
27.8
19.9
Real GDP growth
GDP (current prices)
bil. ztoty
%
0.2
-11.6
-7.6
1.5
3.8
5.3
7.0
6.0“
Government budget
surplus
% of GDP
-7.4
2.8
-2.0
-4.9
-2.3
-2.2
-1.8
-2.4
Unemployment rate
% (yearend)
6.1
11.5
11.8
13.6
15.7
16.0
14.9
14.0“
Current account balance
bil. $
-1.8
0.7
-2.2
-0.3
-2.3
0.9
-2.3
-1.0“
External debt
bil. $
49.0
48.0
47.6
48.4
47.3
42.2
43.9
42.9“
Exchange rate
ztoty/$
(yearend)
0.7
1.0
1.2
1.6
2.1
2.4
2.5
2.9
Total currency in
circulation
bil. ztoty
(yearend)
1.0
3.9
5.6
7.8
10.0
12.3
19.5
23.6
Annual growth rate
Currency plus domestic
deposits
Annual growth rate
Currency plus domestic
and foreign deposits
Annual growth rate
O//o
91.5
298.1
42.8
38.8
28.0
23.0
59.1
20.6
bil. ztoty
(yearend)
NA
13.1
19.7
30.9
39.8
55.2
83.0
111.1
O//o
NA
NA
50.3
57.3
12.9
38.7
50.2
34.0
bil. ztoty
(yearend)
NA
19.1
26.1
41.1
55.9
77.3
104.3
134.5
O//o
NA
NA
36.9
57.5
36.0
38.2
34.2
29.0
a1996 figures are estimates.
bFor 11 months of 1996.
cThrough September 1996.
Note: NA is not available.
Sources: GDP figures are from the Central Statistics Office in Warsaw. All other figures are from the
National Bank of Poland, Information Bulletins, various years.
20
ECONOMIC PERSPECTIVES
and deposit markets, which reduced the net
interest rate spread to 3.7 percent of total assets
by 1992.
However, these profit data do not reflect
the true economic deterioration of Polish banks
during the 1990-92 period for several reasons.
First, in 1990 and 1991 banks were required to
record interest accrued on loans but not actually
paid by borrowers as income. This had the
effect of overstating the actual income that
banks were receiving, as well as depleting the
industry’s capital since banks had to pay in
come tax on profits they did not actually receive.
The Ministry of Finance finally rectified this
situation in 1992, leading to lower reported
interest income in 1992 and subsequent years.
A second reason bank profit figures over
stated the sector’s performance was the deteri
orating condition of the economy and the
banks’ response. Real GDP fell 18.3 percent
from 1989 to 1991. Much of the decline was
concentrated among the large SOEs, but these
firms were unable or unwilling to restructure
their operations in response to falling demand
for their output. As a result, these firms were
unable to service their loans and needed addi
tional credit to cover their losses. Because they
had always been bailed out by the government
in the past and their size meant they could not
be closed without a huge increase in local
unemployment, they had little incentive to
change. For the most part, the banks chose to
TABLE 3
Nonperforming loans
As a percent of total loans
Category
1991
1992
1993
1994
1995
Substandard
Doubtful
Loss
Total
8.4
4.8
2.6
15.8
9.2
9.2
11.6
30.0
7.1
6.0
17.9
31.0
5.7
5.3
17.7
28.7
5.0
3.4
12.8
21.2
Provision coverage as a percent of required
Category
1991
1992
1993
Substandard
Doubtful
Loss
Total
31.6
16.9
26.7
62.1
11.8
6.6
36.8
33.1
16.3
25.0
87.1
82.6
1994
25.8
55.4
100.1
103.1
1995
26.1
59.5
100.2
103.8
Note: Total loan provision coverage is calculated based on
required coverage of 20 percent, 50 percent, and 100 percent
for loans classified as substandard, doubtful, and loss, respectively.
Sources: Data for 1991 and 1992 are from the National Bank
of Poland. Data for 1993 through 1995 are from OECD (1996).
FEDERAL RESERVE RANK OF CHICAGO
extend the loan repayment period and convert
the unpaid interest into principal rather than
declare the loan to be in default or initiate
other workout procedures. This increased these
banks’ overall risk exposure. Moreover, be
cause they were among the largest firms in
Poland and at the time there were no restric
tions on the amount a bank could lend to one
customer, their solvency could be jeopardized
by the default of a small number of borrowers.
A third reason the accounting data masked
the deterioration of bank capital was that banks
did not add enough to their loan loss reserves
as the amount of nonperforming loans was
increasing. One reason for this was that only
provisions made for loans classified as lost
were tax deductible; provisions for loans clas
sified as doubtful or substandard are not tax
deductible. In addition, the degree of regulatory
oversight was low because the NBP did not
have legal authority to enforce provisioning
standards until March 1992. Thus, banks had
little incentive to provision against potential
loan losses, so the amount of reported capital
on their balance sheets overstated their true
net worth.
An examination of problem loans reported
by Polish banks sheds some light on the extent
of the bad loan problem in the 1990-92 period.
During that time, the number of enterprises
estimated by banks as incapable of repaying
interest and principal on time grew more than
sevenfold from 548 to 4,448. As shown in
table 3, the proportion of nonperforming
loans increased from 1991 to 1992. Ac
cording to NBP bank supervision policy,
the loan provision requirements against
nonperforming loans (as a fraction of these
loans) were 20 percent for substandard,
50 percent for doubtful, and 100 percent
for loss. The data in table 3 show that
actual provisioning as of the end of 1992
was considerably below what was needed
to meet government standards. Clearly,
some banks did not have enough capital to
reserve fully against their nonperforming
loans. Although these firms were insolvent
in economic terms, they were allowed to
continue operations. In the case of privately
owned and operated banks, such a decision
would have created a moral hazard incen
tive to increase risk taking in the hope
of regaining solvency. Because the vast
21
majority of banking assets was still owned and
controlled by the government, the moral hazard
incentive could be contained.
The problems confronting Poland’s bank
ing industry required action on several fronts.
First, the government passed a revised banking
law in March 1992, giving the NBP the authority
to enforce capital adequacy and loss provision
ing standards. The law also set limits on the
amount a bank could lend to one borrower; no
loan could be for more than 10 percent of capi
tal and total loans to a single borrower could
not exceed 15 percent of capital. Second, to
address knowledge deficiencies among bank
employees and management about modern
bank practices, the International Monetary
Fund and the World Bank funded a program
in which a commercial bank from the West
would be “twinned” with one of the nine ex-NBP
banks. The western bank would send staff to
the Polish bank to introduce western banking
practices and technology and to train staff.
Seven of the nine banks chose to participate
and contracts were signed in mid-1992. A
similar program was set up to help train bank
examiners and provide technical assistance to
the NBP to modernize its operations.
Now that it had the legal authority to deal
with the banking crisis, the NBP, in coopera
tion with the Ministry of Finance, began to act.
First, international accounting firms were hired
in 1992 to conduct an audit of loan portfolios
as of the end of 1991. For the nine ex-NBP
banks, 9 percent of the best bank’s loan portfo
lio was considered doubtful or lost, while in the
worst bank the figure was as high as 60 percent.
All nine banks were instructed to establish
workout departments, assign to these depart
ments loans classified as doubtful or loss, and
take action to recover the loans. In November
1992, the NBP issued an order requiring banks
to provision fully against all lending to these
customers by the end of 1993 (later extended
to March 31, 1994). Finally, an Enterprise and
Bank Restructuring Program (EBRP) to address
the undercapitalization of the banks and the
causes of the bad loan problem went into effect
on March 19, 1993.
The EBRP initially applied to seven of
the nine ex-NBP banks. (Wielkopolski Bank
Kredytowy and Bank Slqski were shown by
the 1992 audit not to require restructuring
and were privatized in 1993 and 1994, respec
tively.) The key feature of the EBRP was a
one-time recapitalization of the banks, with the
size of the capital infusion based on the value
of each bank’s nonperforming loan portfolio at
the end of 1991. The recapitalization, totaling
approximately $520 million, would raise each
bank’s risk-based capital-asset ratio to 12 per
cent, well above the Basel norm of 8 percent,
to ensure adequate capitalization should loan
quality deteriorate and to make credible the
promise that this would be the last opportunity
to recapitalize. To qualify, the banks were
required to undergo another credit evaluation
by outside auditors, set up workout departments,
and take action to resolve all loans classified as
nonperforming at the end of 1991. By the end
of March 1994, each bank had to show that
either 1) a court or bank conciliation agreement
had been signed (similar to chapter 11 in the
U.S.); 2) the debtor had been fully servicing
its debt for at least the previous three months;
3) the debtor had been declared bankrupt;
4) liquidation had been initiated under the
Privatization Law (privatization is pending) or
under the law on SOEs (the enterprise is being
shut down); or 5) the debt had been sold on a
secondary market. The law also required that
no new loans be made to nonperforming bor
rowers, which reinforced a guideline put in
place by the NBP in 1992.
Gray and Hoile (1996) analyzed the effect
of the EBRP on creditors and borrowers. They
conclude that the program had many benefits.
It gave the banks a needed recapitalization and
forced them to develop the institutional capa
bility to deal with problem debtors. It required
them to resolve these loans through workouts,
loan sales, or forced liquidation. Gray and
Hoile report that larger and/or stronger firms
tended to repay their debt or enter bank concil
iation, while smaller and/or weaker firms tended
to go into bankruptcy or liquidation. However,
they also conclude that the program has not
achieved the level of borrower restructuring
its architects had hoped for. The restructuring
agreements that banks signed with borrowers
dealt primarily with financial conditions and
did not address fundamental management or
operational changes. Gray and Hoile contend
that the system of bankruptcy and, especially,
SOE liquidation does not give enough control
to creditors of distressed firms. They argue the
existing system leads to lenient treatment of
borrowers that may delay needed restructuring
of SOEs.
22
ECONOMIC PERSPECTIVES
Behavior and performance of Polish
banks since 1992
As shown in table 2, the economy recovered
strongly from the recession of the early 1990s,
with real GDP growth increasing from 3.8
percent in 1993 to 7 percent in 1995 and an
estimated 6 percent in 1996. Inflation has
continued to fall every year since 1990, reach
ing 19.9 percent in 1996.
The improving inflation picture has led to
a rapid decline in interest rates. Figure 1 illus
trates the decline in short-term interest rates
since the beginning of 1992. In January 1992,
the three-month Treasury bill yield was 45.6
percent. It declined steadily over the next four
years to 18.79 percent by December 1996.
Deposit and loan rates have also declined, but
the spread between Treasury bills and deposit
rates of similar maturity has remained positive.
The spread between loans and deposits has also
FEDERAL RESERVE RANK OF CHICAGO
remained large, though it has decreased some
what over the past two years. The large spreads
between interest-earning assets and the banks’
costs of funds have enabled banks to maintain
high net interest margins.
Table 4 presents aggregate balance sheets
for selected years from 1992 to 1996, and table
5 shows selected ratios. As shown in table 5,
the ratio of capital to total assets declined from
4.8 percent in 1992 to 4.3 percent in September
1996. However, the 1992 figure overstated the
true net worth position of the banking system
because provisions for loan losses were made
for only 33.1 percent of what was legally
required. By the end of 1994, according to
OECD (1996) data, the coverage ratio had
risen to over 100 percent, indicating that reserve
levels now appear to be adequate. The capitalasset ratio declined to 3.1 percent in 1995, but
due to improved profitability and a 700 million
zloty capital infusion into BGZ,
it rose in 1996.
Table 5 also shows the rapid
growth of holdings in govern
ment securities. The share of
securities in bank portfolios rose
from 15.6 percent at the end of
1992 to 30.1 percent at the end of
September 1996. There are three
reasons for the growth in govern
ment securities relative to other
asset categories. First, Treasury
spreads over deposit rates have
been positive, so they have repre
sented a low-risk method to
increase net interest income.
Second, since Treasury bills and
bonds are counted as only 10
percent and 20 percent, respec
tively, in the calculation of riskweighted assets, the return per
zloty of capital is extremely high,
especially adjusted for risk.
Moreover, the low level of
capital in the Polish banking
system implies that banks must
hold a significant quantity of
government securities to meet
the risk-based capital standard
of 8 percent of risk-weighted
assets. Finally, given the risky
commercial lending environ
ment in Poland, it made sense
23
TABLE 4
Aggregate balance sheet of commercial banks
1992
Assets
Cash and reserves at NBP
Due from other financial
institutions
1994
bil. zloty
% of total
assets
5.85
8.96
1996a
% of total
assets
bil. zloty
8.39
bil. zloty
% of total
assets
6.94
10.61
5.56
6.11
2.60
3.98
6.06
5.01
11.65
10.49
16.07
18.87
15.60
16.97
8.85
2.66
4.08
0.89
0.74
1.68
0.88
Total loans
24.33
37.28
41.54
34.34
70.59
37.01
Corporate
23.14
35.45
38.21
31.59
61.22
32.11
Personal
1.19
1.82
3.32
2.74
9.37
4.91
Securities
10.17
15.58
28.87
23.87
57.46
30.13
Due from abroad
Due from general government
Other assets
9.01
13.80
16.35
13.52
21.73
11.39
Total assets
65.27
100.00
120.95
100.00
190.70
100.00
Liabilities
Foreign liabilities
3.02
4.63
3.76
3.11
6.54
3.43
Due to financial institutions
7.97
12.21
13.86
11.46
21.95
11.51
Due to general government
3.91
5.99
3.69
3.05
8.00
4.19
Zloty deposits of
nonfinancial sector
23.06
35.33
42.97
35.53
77.89
40.84
Demand deposits
7.10
10.88
15.18
12.55
1.35
11.20
Savings deposits
0.95
1.46
1.45
1.20
2.37
1.24
15.01
23.00
26.35
21.79
54.16
28.40
11.74
Time deposits
Foreign currency deposits of
nonfinancial sector
9.08
13.91
22.05
18.23
22.39
Demand deposits
2.59
3.97
7.52
6.22
8.26
4.33
Time deposits
6.49
9.94
14.53
12.01
14.13
7.41
Other liabilities
14.90
22.83
28.47
23.54
45.71
23.97
Tier 1 capital
Total liabilities and capital
3.13
4.80
5.40
4.46
8.22
4.31
65.27
100.00
120.95
100.00
190.70
100.00
aThrough September 1996.
Note: Numbers may not total exactly due to rounding.
Source: National Bank of Poland, Information Bulletins, various issues.
TABLE 5
Polish banks: Selected ratios
(percent)
Category
1992
1993
1994
1996:
4.8
5.5
4.5
3.1
4.3
Nonperforming loans/total loans
30.0
31.2
28.3
21.5
15.1
37.0
Tier 1 capital/asset ratio
Loans/total assets
37.3
36.2
34.3
35.1
Securities/total assets
15.6
19.9
23.9
27.0
30.1
Demand deposits/total deposits
30.2
31.2
34.9
30.3
29.5
Foreign deposits/ total deposits
28.3
35.0
33.9
25.1
22.3
ROA
2.7
-0.2
-0.0
2.0
2.8
ROE
54.0
-4.6
-0.2
44.9
65.7
Loans/GDP
20.3
19.1
17.7
17.9
22.4
aBalance sheet estimates as of September 30, 1996. ROA and ROE through first nine months of 1996.
Source: National Bank of Poland.
24
1995
ECONOMIC PERSPECTIVES
savings deposits rose by 95.3
and 41.2 percent, respectively,
indicating a growing level of
personal savings is finding its
way into the banking system.
Figure 2 illustrates the growth of
inflation-adjusted zloty deposits
and foreign currency deposits
since the end of 1991. Adjusted
for inflation, zloty deposits
showed little change from the
end of 1991 to early 1995, but
have grown significantly since
then. The growth has been ex
clusively in domestic currency
deposits. The re-denomination
of the zloty in January 1995
presumably increased public
confidence in holding domestic
currency deposits, while the
for banks to invest in safe government securi
decline in the value of the dollar in the first
ties until they had adequate capital to bear the
half of 1995 made foreign currency deposits
risks of commercial lending. This behavior is
less attractive to Polish savers. The introduc
similar to that observed among U.S. commer
tion of formal deposit insurance in Poland,
cial banks during the so-called credit crunch
effective February 1995, may also have con
from 1989 to 1992. Indicating an improvement
tributed to the growth in domestic deposits.4
in the Polish banking environment, the share of
Despite this rapid deposit growth, banking
loans to total assets has been increasing since
services are underutilized relative to other
the end of 1994.
countries. For example, OECD (1996) reports
The most striking feature on the liability
that only 10 percent of the population have a
side of the balance sheet is the huge growth in
bank account and cash is by far the most com
domestic deposits in 1995, up 47.6 percent from
mon means of payment in Poland.
the 1994 level. Most of the increase is concen
As these data illustrate, the period from
trated in personal time deposits, which rose by
1992 to the present has been an opportunity for
80.6 percent. However, personal demand and
the banking system to recapitalize and increase
its reserves against nonperform
ing loans. Polish banks have
TABLE 6
held a high proportion of gov
Country comparisons of selected
ernment securities, a policy that
banking ratios for 1995
continues to be very profitable
(percent)
on a retum-on-equity basis. As
Capital/
Securities/
Loans/
shown in table 6, Polish banks
Country
assets
assets
assets
ROA
are still undercapitalized relative
to
the U.S. and the UK. Although
United States
8.1
18.8
60.4
1.13
their capitalization appears com
United Kingdom
4.8
16.1
50.0
0.84
parable to that of German banks,
Germany
3.0
16.4
56.6
0.24
Czech Republic
3.5
16.2
35.0
0.19
German banks have equity hold
Poland
3.1
27.0
35.1
2.00
ings on their balance sheet that
are booked below their market
Notes: Data for the U.S., the Czech Republic, and Poland are for the entire banking
system. Data for Germany are based on a summation of data for Bayerische
value. Until very recently, the
Vereinsbank, Commerzbank, Deutsche Bank, Dresdner Bank, BHF Bank, and
Hypobank. Data for the UK are based on a summation of data for
need to maintain large holdings
Barclays, HSBC, Lloyds, Midland, Nat West, and Standard Chartered Banks.
Sources: U.S. data are from the FDIC, Statistics on Banking. German, UK,
of government securities to
and Czech banking data are from BankWatch. Polish data are from the
National Bank of Poland.
boost profits, improve capital
FEDERAL RESERVE BANK OF CHICAGO
25
adequacy, and remain above risk-based capital
levels has constrained Polish banks from in
creasing their commercial lending activity.
The undercapitalization of the banking
system and its effect on the balance sheet can
be seen in the performance of the largest stateowned banks (see table 7 for selected ratios by
bank group). The top four banks, PKO BP,
Pekao SA, BGZ, and Bank Handlowy, hold
over 40 percent of banking system assets. This
group has the lowest leverage ratio, the highest
concentration of securities to assets, and the
lowest ratio of loans to assets of any group of
Polish banks in the last two years. The data in
table 7 also indicate that these four banks have
the lowest risk-based ratios of any Polish bank
group from 1993 to 1996. Their combined
capital ratio rose in 1996 due to higher bank
profits and the partial recapitalization of BGZ.
The remaining groups’ capital ratios dropped
in 1996 due to a surge in loans made by banks.
(See appendix on PKO BP and BGZ and sec
tion on consolidation below for more details on
the undercapitalization of these banks. Bank
Handlowy is well-capitalized and profitable.)
26
ECONOMIC PERSPECTIVES
Challenges: The role of foreign banks,
privatization, and consolidation
The role of foreign banks, the timetable
and extent of Polish bank privatization, and the
issue of consolidation are intricately inter
twined. These issues are also closely linked to
the capital adequacy, profitability, and efficiency
of Polish banks.
Foreign banks
In the initial stages of Poland’s economic
transition, the government had encouraged the
entry of foreign banks to the market. In the
liberal licensing environment
of the early 1990s, the govern
TABLE 7
ment tried to promote private
Selected ratios by bank group
banks that would compete with
the
large state-owned banks that
1993
1994
1995
1996 (Oct)
were planned for quick privati
Top four banks
zation. The Ministry of Finance
% of system assets
45.4
45.0
43.8
42.7
sought strategic investors and
Leverage ratio3
5.1
4.4
1.6
3.0
encouraged foreign participation
Risk-based capital ratio
3.5
6.7
5.3
9.0b
through the twinning program
Securities/assets
20.5
27.0
33.2
38.0
(which was implemented with the
Loans/assets
34.5
31.6
30.0
29.8
idea that foreign participating
Remaining statebanks would be allowed to hold
owned banks
a stake in their Polish twin).
% of system assets
22.9
23.3
17.2
15.6
The privatization of most
Leverage ratio
6.2
5.0
5.0
5.1
state-owned banks has been
Risk-based capital ratio
22.5
21.4
20.1
16.9b
Securities/assets
22.1
22.2
23.4
22.4
delayed, however, due to systemic
Loans/assets
39.5
37.0
40.3
47.2
problems related to the lack of
prudential regulation and super
Domestic private banks'
vision, bad loan problems, and
% of system assets
29.3
28.6
35.0
36.6
undercapitalization. Consequently,
Leverage ratio
3.7
2.4
2.7
3.2
the entry of foreign banks into
Risk-based capital ratio
9.8
13.5
14.9
14.5b
Securities/assets
18.0
21.3
22.1
21.2
Poland has also been delayed.
Loans/assets
36.7
36.2
38.3
33.1
Currently, foreign banks are
limited
to purchasing shares in
Foreign banks
existing
Polish banks or to out
% of system assets
2.4
3.1
4.0
5.1
right
purchase
and recapitaliza
Leverage ratio
10.0
10.7
13.4
15.4
tion of failing banks. As of the
Risk-based capital ratio
10.0
12.3
21.7
18.9b
Securities/assets
8.9
14.3
19.3
15.1
end of September 1996, they
Loans/assets
31.3
37.4
41.0
42.5
held 5.1 percent of total bank
assets in Poland and 5.6 percent
’Leverage ratios are computed with Tier 1 plus Tier 2 capital.
bSeptember 1996.
of total loans. The NBP has
'These include banks owned by local governments and state-owned enterprises as
maintained a policy of quickly
well as banks held in receivership by the NBP.
Source: Computed from data obtained from the National Bank of Poland.
resolving troubled banks through
liquidation or merger, regardless of the origin
of the potential buyer. Thus, foreign banks
have been allowed to enter the market if they
could aid in restructuring Polish banks.
Only recently has the Ministry of Finance
agreed to allow foreign banks to control the
majority of equity in a large bank. For exam
ple, the ministry agreed last summer to sell the
remainder of its stake in Bank Slqski to ING
(Netherlands), raising its stake to 51 percent.
This change in policy toward foreign owner
ship of banks may have been influenced by
Poland’s entry to the OECD in the summer of
1996 and the continuing negotiations over its
proposed entry to the European Union. Never
theless, the government has postponed opening
the banking sector to full foreign entry from
1997 to 1999. At that time, foreign banks will
be able to establish a branch merely through
registration.
Since foreign banks are well capitalized,
well managed, and highly profitable, they are
seen as a threat to the inadequately capitalized
and relatively less skilled Polish banks. To
date, foreign banks in Poland have concentrated
in commercial loans, trade finance, cash man
agement, and other fee-based activities, but
their territory is expanding. For example, Citicorp
started accepting deposits from households in
January 1997, while Hypobank (Germany) is
interested in developing consumer and mort
gage lending operations. Several foreign banks
have applied for licenses to operate in Poland.
On the positive side, foreign banks bring in
capital that can help the banking system
expand to meet the needs of Poland’s growing
economy. Furthermore, competition from
foreign banks will induce Polish banks to
become more efficient and offer their customers
better service.
Privatization
Of the original nine banks spun off from
the NBP, four have been successfully priva
tized, WBK of Poznan (1993), Bank Slqski
(1994), BPH of Krakow (1995), and Bank
Gdanski (1995), although Bank Slqski and
BPH subsequently received capital infusions
through foreign participation. Powszechny
Bank Kredytowy of Warsaw is tentatively
scheduled for privatization in 1997 and Bank
Zachodni of Wroclaw in 1998. The remaining
three banks have been consolidated with Pekao
SA into a holding company that is due to be
FEDERAL RESERVE RANK OF CHICAGO
privatized in 1998. In addition, Bank Handlowy
will be privatized later this year.
Originally, the nine regional banks were
scheduled to be privatized by the end of 1996,
but the government decided to delay their
privatization. Thus, seven years into Poland’s
economic transition, 54 percent of total capital
in the banking industry was still held by the
state (OECD, 1996). There are arguments for
and against quick privatization. Proponents of
quick privatization argue that the discipline of
the market will foster more efficient financial
institutions. Private shareholders have a greater
incentive to implement cost reductions and
expansion of profitable financial services than
state-owned institutions. On the other hand, the
presence of de facto government guarantees on
bank liabilities exacerbates the moral hazard
incentive to increase risk taking. Given that
many of the state-owned banks are still inade
quately capitalized, the government can con
tain the potential moral hazard problem associ
ated with undercapitalized banks. In view of
the danger of allowing poorly capitalized banks
to operate with little regulatory oversight, the
Polish government could have justification for
not following the original privatization sched
ule for all banks.
The 1994 EBRP recapitalization and the
improved profitability of the industry as a whole
have allowed banks to increase reserves against
potential losses and to build their capital posi
tions. As a result, the case for privatization
grows stronger every day. Well-capitalized
state-owned banks should be privatized as soon
as possible, because the discipline of private
ownership and management will induce them
to operate more efficiently than they would
under public control. Nevertheless, after seven
years of government protection, many banks,
including some of the largest banks (see appen
dix), remain undercapitalized and unable to
compete effectively with foreign banks. These
banks may need to remain in government
hands for a longer time to contain the moral
hazard problem. However, the policy goal of
eventually privatizing these banks as well keeps
pressure on them to continue modernizing oper
ations, rebuilding capital, and improving cus
tomer service. In the absence of genuine private
ownership, such pressure is needed to improve
their ability to compete.
27
Consolidation
The privatization of the remaining stateowned banks has been put into limbo by the
government’s bank consolidation plans. In late
1994, the Ministry of Finance announced plans
to consolidate two groups of banks, one around
Pekao SA and the other around Bank Handlowy. The Bank Handlowy consolidation was
opposed by BPH (Krakow), one of the banks to
be included in the merger, and by the Fund for
the Privatization of Polish Banks, which con
trols foreign donations that have been used to
recapitalize the banking system. As a result,
the Bank Handlowy consolidation was aban
doned and BH will be privatized by itself later
this year. However, in September 1996, an
agreement was signed officially joining Pekao
SA with three of the original nine NBP banks:
BDK (Lublin), PBG (Lodz), and PBKS (Szcze
cin). A full merger of their operations will be
worked out over the next year.
The case for consolidation is based partly
on the belief that Polish banks lack the size and
capital to compete with foreign banks in the
loan market and that foreign banks will be able
to offer more favorable lending terms to credit
worthy Polish firms. Thus, the quality of the
loan portfolios at Polish banks will gradually
deteriorate, potentially increasing the risk of
failure. There are two main arguments in favor
of consolidation: 1) the ability of a large bank
to make larger loans, given the constraint on
lending no more than 15 percent of a bank’s
capital to any one borrower; and 2) the cost
savings of consolidation before privatization.
Some argue that loans made by a consolidated
bank would have substantially lower transac
tions costs than loans made through a lending
consortium (Wyczanski and Golajewska,
1996). Moreover, some research suggests that
the cost per zloty of assets of privatizing a
consolidated bank would be lower than the cost
of privatizing a group of banks and then allow
ing them to merge (Bonin and Leven, 1996).
Proponents of privatizing banks before
consolidating them argue that banks’ manage
ment can choose how best to consolidate. Private
banks deciding to consolidate would have an
incentive to improve efficiency to increase
their value at the time of the merger. Proponents
also contend that with private firms, the deci
sion to consolidate is more likely to be made
on strict economic grounds, allowing for anti
trust considerations, than if these firms were
consolidated first by the government.
In the banking literature, the main argu
ments for mergers are efficiency gains (through
economies of scale and scope) and risk reduc
tion (through a diversified asset portfolio).
However, research evidence shows that most
mergers do not, on average, decrease cost ra
tios (total expenses to assets and noninterest
expenses to assets). Studies that take into account
the cost effects of changes in output mix gener
ally come to the same conclusion. (Laderman,
1995). We only have indirect evidence on
whether, in general, bank mergers reduce risk.
Boyd and Graham (1991) found that between
1971 and 1988, U.S. banks with $1 billion or
more in assets failed at roughly twice the rate
of banks with less than $1 billion. This failure
rate, however, could have been due to lower
capital ratios rather than operating risk.
It is not clear that the banks which are
consolidated to form Group Pekao will enjoy
economies of scale or scope. Although the
decision to merge Pekao SA and three other
banks has been finalized, the details of any
projected cost savings have not been worked
out. It could be argued that this merger will
produce a more extensive branch network and
a more geographically and industrially diversi
fied loan portfolio. Currently, there are big
disparities in regional growth and some banks
are heavily concentrated in certain types of
loans, so there may be some advantages to such
diversification.
On the other hand, since this consolidation
is being arranged by the Ministry of Finance
and not by direct negotiation among the banks,
the merger of the Pekao SA group has produced
tensions among the banks involved. These
tensions, which may reflect different organiza
tional structures, goals, and corporate cultures,
may produce problems that could raise the cost
of consolidation. For example, the smaller banks
have expressed concern about losing autonomy.
Before the merger, Pekao SA planned to cen
tralize operations like distribution, credit and
debit card management, the ATM network,
brokerage offices, and international payments.
This would relegate the other banks in the group
to gathering deposits and making limited loans.
The mayor of Lodz has argued that the merger
would inhibit regional development by limiting
PBG’s ability to make local loan and deposit
decisions. The smaller banks have proposed a
more decentralized organizational structure,
akin to a multibank holding company in the
28
ECONOMIC PERSPECTIVES
U.S., which would reduce the potential econo
mies of scale from the merger. Even if this
issue can be resolved in a manner that leads to
a more efficient bank, the four banks together
do not have enough capital to build a strong
banking organization. To be viable in the long
run, the consolidated bank will need an infusion
of new capital. At the present time, it is diffi
cult to see where such capital will be found.
The issue of whether to privatize first or
consolidate first is not clear. What is clear is
that if there is insufficient capital, consolida
tion only yields a larger institution with insuf
ficient capital. This may exacerbate the moral
hazard incentive because the government
would be more likely to fully guarantee the
liabilities of a large bank than a small bank in
the event of a failure. A large bank therefore
has a greater incentive to take risks. In our
view, until a solution is found that increases
the overall capital of the combined group of
banks, the proposed consolidation of Pekao SA
should not go forward.
Conclusion
Since 1989, Poland has been engaged in a
process of economic transformation on several
fronts. With respect to bank reform, the overall
performance has been mixed. On the positive
side, Polish banks have used the latest technology
to modernize their operations and have enhanced
the knowledge base of their staff through train
ing. They have improved their ability to evalu
ate creditworthiness and, out of necessity, have
developed departments to resolve problem loans
to financially distressed borrowers. In general,
Polish banks appear to be profitable, and capital
adequacy is gradually improving. On the nega
tive side, they have not yet proven successful
in effecting changes in corporate governance
that would successfully restructure firms with
nonperforming loans. In addition, the large
state-owned banks, PKO BP and BGZ, contin
ue to pose problems for the government and
the banking system. Their size, undercapitaliza
tion, state protection, and slow institutional
change have impeded the overall development
of the industry.
As Poland looks toward the year 2000, it
aims to become more integrated into the West
by joining both NATO and the European
Union. Reform of the banking system is neces
sary for this integration to occur. However,
reform is inhibited both by a deep distrust of
foreigners, partly because several foreign powers
have at one time or another controlled its territory,
and by a reluctance of entrenched management
at state-owned banks to make necessary chang
es or concede power. What is happening in the
banking industry reflects these conflicting atti
tudes. The government knows that the country
would benefit from western experience and
capital, but it also wants its banking system to
be dominated by Polish-owned and -operated
banks. Consolidation of banks has been pro
posed as a way of building institutions large
enough to compete with multinational banking
organizations such as Citicorp or Deutsche
Bank. In the final analysis, however, capital
adequacy is more important than size. With
adequate capital, a bank can pursue profit
opportunities, take intelligent risks, or expand
operations. Without adequate capital, a bank’s
growth is constrained, it is limited to holding
less risky securities instead of potentially more
profitable loans, and it has a hard time making
needed investments that can enhance its effi
ciency. The main problem facing Polish banks
today is not that they are too small but that
they do not have enough capital. Solving the
capital problem will enable Poland to build a
strong banking industry with or without for
eign participation. In order to recapitalize the
banks using domestic funds only, the govern
ment would have to divert resources from other
areas of need. Given current budgetary difficul
ties, this does not seem feasible. Thus, utilizing
foreign sources of capital seems to be a neces
sary ingredient in achieving the goal of an effi
cient and sound banking system.
APPENDIX: Big banks, big problems: PKO BP and BGZ
As of September 1996, PKO BP (State Savings
Bank) and BGZ (Bank for Food Economy) together
held 28.6 percent of the total assets of the Polish
banking system. Their combined capital was less
than 2 percent of assets. Clearly, PKO BP and BGZ
are not the only problem banks in Poland. However,
FEDERAL RESERVE RANK OF CHICAGO
due to their slow restructuring, large capital needs,
and sizable nonperforming loan portfolios relative
to capitalization, these banks are likely to remain a
drain on the government budget and an impediment
to the development of a sound banking system for
years to come.
29
PKO BP
BGZ
PKO BP was established in 1919 as the Post
Office Savings Bank, taking its present name in
1950. It was consolidated with the NBP in the mid1970s and was separated from the NBP in 1988.
PKO BP is the largest bank in Poland, with over
1,000 branches and outlets controlling approximate
ly 21 percent of total banking system assets and 26
percent of total deposits as of September 1996.
Before 1990, PKO BP’s assets consisted mainly of
state residential construction loans. Currently, the
bank has a high concentration of securities (39 per
cent of total assets) and a low concentration of loans
(28.6 percent of total assets) with new lending primarily
going to finance private residential construction. Net
earnings for 1996 were 985 million doty, compared
with 345 million zloty in 1995.
The bank is burdened with a large proportion
of nonperforming housing loans inherited from the
Communist era. At the end of 1993, 84 percent of
its loans were housing related and 93 percent of
these were made before 1990. By the end of 1994,
16 percent of PKO BP’s loans were classified as
nonperforming and provisions against these losses
were only 37.2 percent of what was required. These
loans were mainly given to the 500 largest building
cooperatives that constructed and managed low-cost
housing. Over the last six years, these cooperatives
have been repaying only 20 percent to 30 percent of
the interest due to PKO BP. The remaining interest
has been capitalized and added to the loan amounts.
Due to the forced capitalization of interest, the
Ministry of Finance purchased 2.9 billion doty of
capitalized interest from 1990 to 1992 to maintain
PKO BP’s liquidity (OECD, 1996). In 1993, the
bank received 573.4 million doty ($272 million) in
restructuring bonds as a result of the EBRP. The bad
loan problem persists. Although the housing cooper
atives are still legally liable for their debts, the bank
has not pursued collection. In addition, PKO BP
operates inefficiently. For example, the bank’s em
ployee-asset ratio of 5.1 per million doty of assets
as of the end of 1994 is more than twice as high as
that of any other state-owned bank in Poland.
Although PKO BP’s recent earnings perfor
mance has been very good, its future remains un
certain. The government will conduct a diagnostic
study later this year and it already has plans to con
vert PKO BP into a joint stock company owned by
the Ministry of Finance in 1998. The joint stock
company could then be restructured for privatiza
tion, but the process is likely to take several years.
The government has decided that PKO BP should
be controlled by Polish capital, but the source of
this capital is unknown. Several options are being
discussed, including issuing shares to deposit hold
ers, selling some shares to state pension funds that
have yet to be created, and/or selling shares to local
governments. Delaying privatization should not
affect Poland’s entry into the EU, since PKO BP
(and BGZ) will be exempt from EU regulations.
BGZ was started in 1919 as the State Agricul
tural Bank and took its current form in 1975 when
the Agricultural Bank merged with the Central Asso
ciation of Credit and Savings Cooperatives. The
bank performed financial and coordinating services
for nearly 1,600 farm cooperative banks. At the end
of 1994, these cooperatives held only 8 percent of
the loans in the banking system but served about
one-third of the rural population. The bank’s internal
decisionmaking structure is influenced by the Peas
ant Party (PSL), which is heavily supported by
Polish farmers and agricultural interests.
The cooperative banks were originally re
quired to affiliate with BGZ, which, in turn, provid
ed the coops with refinancing credit, clearing facilities,
and a depository for surplus liquidity. BGZ func
tioned as a conduit for direct and subsidized funds
from the state to farmers, either directly or through
the cooperatives. In an effort to reduce BGZ’s
central control and give more authority to the coop
erative banks, the government passed the Coopera
tive Law of 1990. BGZ was legally separated from
the cooperatives. Although BGZ then formed agree
ments with 1,270 of the cooperatives to continue
their previous relationship, approximately 400
cooperatives became independent of BGZ. Neither
BGZ nor any other supervisory authority was given
responsibility for overseeing these cooperatives.
Not surprisingly, many of these unsupervised coop
eratives increased their risk exposure by making
irresponsible loans and guaranteeing loans. The
number of troubled or insolvent cooperatives grew
rapidly in 1993 and 1994. By 1994, the activities of
43 cooperative banks were suspended. More than
200 cooperatives were insolvent at the end of 1995.
The cooperatives were not the only source of
BGZ’s problems. Although it inherited many of its
problem loans from the 1980s, difficult conditions
for European agriculture in general and the drought
of 1992 in particular have also contributed to its
bad loan problem. BGZ’s loan portfolio, compris
ing about one-half of its assets, is mainly concen
trated in food processing, agriculture, and food
cooperatives. In 1992, about 24 percent of problem
loans were from the food processing sector, which
accounts for 40 percent of the bank’s loan portfolio.
By the end of 1995, BGZ’s share of nonperforming
loans stood at 59 percent of total loans. The separa
tion of BGZ from some of its cooperatives, the lack
of supervision, and the absence of a restructuring
plan led to irresponsible practices. To correct these
problems, the government required BGZ to convert
into a state-owned corporation by the end of 1992
as a precondition for recapitalization in 1993. BGZ
did not comply, in part because compliance would
have limited the PSL’s control over the bank’s
board of directors. With the PSL representing the
swing vote in Parliament, BGZ was able to secure
a capital infusion via recapitalization bonds of 4.3
billion zloty. As a condition for BGZ receiving
30
ECONOMIC PERSPECTIVES
1.6 billion zloty in recapitalization bonds in 1994,
the Restructuring Act of June 24, 1994 was passed.
The law creates 11 regional banks to improve
supervision of the cooperatives. The majority of
BGZ’s branches will be transferred to the regional
banks and all state shares will eventually be sold to
these banks. BGZ will function as a holding company,
controlling and coordinating the activities of the
regional banks and performing all parent company
functions for the cooperative units, including inter
national business. State influence will continue
under the new structure.
To date, the restructuring has gone slowly.
Only three of the 11 regional banks have been
created and BGZ continues to have problems. In
1996, the bank was given an additional capital
infusion of 700 million zloty ($260 million). A
true assessment of BGZ’s problems is hampered
by politics and a lack of financial transparency.
BGZ continues to receive direct subsidies from
the government and to offer below market rate
loans. Likewise, BGZ controls the loan and deposit
interest rates of the cooperatives. In addition, BGZ’s
deposits are frilly covered by the Bank Guarantee
Fund and the Ministry of Finance.
The restructuring of BGZ will continue through
the year 2000 and there are no plans to privatize the
bank or remove its state guarantees before 2002.
Without additional capital or huge earnings, it is
difficult to see how BGZ will reach its target 6 percent
solvency ratio by 1999 (or the 8 percent needed for
privatization). Recently, Credit Agricole (France’s
largest cooperative bank) entered into a twinning
agreement with BGZ. BGZ is also trying to form
joint ventures with RUS (the pension fund for farm
ers) and Allianz AG of Germany (the largest insur
ance company in Europe). The European Bank for
Reconstruction and Development is considering
taking a 10 percent to 20 percent stake in BGZ and
making the bank a large loan. Foreign participation
in BGZ may be its only hope for restructuring, recapi
talization, and privatization.
NOTES
'See Ugolini (1996) for an excellent discussion of the
reorganization of the National Bank of Poland.
"The zloty was re-denominated at the beginning of 1995
with one new zloty equal to 10,000 old zloty. To avoid
confusion, all figures in the article have been recalculated
using new zloty.
3The Balcerowicz Plan was named for Leszek Balcerowicz, Deputy Prime Minister and Minister of Finance in
the first non-Communist Polish government. For a
detailed discussion of the plan and its economic effects,
see Slay (1994).
4Prior to the introduction of formal deposit insurance, the
Polish government did offer deposit guarantees. Deposits
at banks in existence at the beginning of 1989 were
always guaranteed. Later, the NBP declared that house
hold deposits up to 2,000 ECU would be fully guaranteed
and deposits between 2,000 ECU and 3,000 ECU would
be partially guaranteed.
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