27.11.07 The Development of Corporate Credit Risk Management in German Banks 1 Empirical Evidence M. Steiner / A. Friesenegger / C. Miehle / A. Rathgeber (October 2007) Corresponding Author: Dr. Andreas Rathgeber Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg Phone: (0821) 598-4429 Fax: (0821) 598-4223 E-Mail: [email protected]Prof. Dr. Manfred Steiner Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg Phone: (0821) 598-4125 Fax: (0821) 598-4223 E-Mail: [email protected]Dr. Christian Miehle Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg Alexander Friesenegger Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg 1 This work was partly sponsored by the „German Research Foundation” (DFG). Helpful comments from Michael Anklam, Dr. Wolfgang Mader, Dr. Thomas Dittmar, Dr. Jochen Klement, Dr. Nikolaus Starbatty, Dr. Christian Tietze and Dr. Matthias Wagatha are gratefully acknowledged. A special thanks goes to Dr. Christian Willinsky who contributed significantly to the surveys this work is based on.
Transcript
27.11.07
The Development of Corporate Credit Risk Management in German Banks1
Empirical Evidence
M. Steiner / A. Friesenegger / C. Miehle / A. Rathgeber
(October 2007) Corresponding Author: Dr. Andreas Rathgeber Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg Phone: (0821) 598-4429 Fax: (0821) 598-4223 E-Mail: [email protected] Prof. Dr. Manfred Steiner Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg Phone: (0821) 598-4125 Fax: (0821) 598-4223 E-Mail: [email protected] Dr. Christian Miehle Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg Alexander Friesenegger Department of Finance and Banking, School of Business, Augsburg University Universitätsstraße 16, D-86159 Augsburg
1 This work was partly sponsored by the „German Research Foundation” (DFG). Helpful comments from Michael Anklam, Dr. Wolfgang Mader, Dr. Thomas Dittmar, Dr. Jochen Klement, Dr. Nikolaus Starbatty, Dr. Christian Tietze and Dr. Matthias Wagatha are gratefully acknowledged. A special thanks goes to Dr. Christian Willinsky who contributed significantly to the surveys this work is based on.
Credit Risk Management in German Banks 2
The Development of Corporate Credit Risk Management in German Banks
Empirical Evidence
Abstract
On three occasions in the last seven years, standardised questionnaires were sent out to
more than 100 German banks of different sectors interviewing them about their current
credit risk management systems and their possible future developments. The aim of this
survey was firstly to analyse to what extent the modern tools for credit risk are
nowadays being used in the banking sector, and secondly to assess the development of
the usage of these tools over time.
The main result of this survey was that different modern tools are applied in different
ways in German banks. Central influencing factors include the size of the banks, the
bank sector specifics and the regulatory requirements.
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Credit Risk Management in German Banks 3
Executive Summary
For a long time credit risk management was less developed than market risk
management. In science as well as partly in practise in the last years, modern complex
and sophisticated concepts and models were developed to enable adequate measuring
and controlling of credit risks as well as to help close the gap between credit and market
risk management.
On three occasions in the last seven years, standardised questionnaires were sent out to
more than 100 German banks of different sectors interviewing them about their current
credit risk management systems and their possible future developments. The aim of this
survey was firstly to analyse to what extent the modern tools for credit risk are
nowadays being used in the banking sector, and secondly to assess the development of
the usage of these tools over time. Furthermore, we wanted to find out which factors are
influencing this development.
The main result of this survey was that different modern tools are applied in different
ways in German banks. Central influencing factors include the size of the banks, the
bank sector specifics and the regulatory requirements.
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Credit Risk Management in German Banks 4
1 Introduction Credit risk seems to be one of the most important risk types affecting banks (Saunders,
and Cornett 2003, p. 142). Promoted by the use of modern information technology
systems new credit risk models have been developed mostly by researchers, sometimes
by practitioners. Besides, regulating institutions like the Basel Committee, the European
Union and the German Regulation Institution BAFIN aim at a more detailed regulation
of loans. In the new accord of the Basel Committee, referred to as Basel II, new capital
adequacy rules for loans were defined (Basel Committee on Banking Supervision
2006). However, there was the opinion in literature that credit risk measurement
systems in banks are less sophisticated than market risk systems (Shimko 1999, p. XIII).
This survey was meant to find out how the acceptance of credit risk systems in German
has changed in recent years.
Different aspects of risk management in German banks have been the main body of
many surveys about risk management. Some of the surveys concentrate on regulation
(Bigus, and Matzke 2000), some on internal rating systems (Elsas, Ewert, Krahnen,
Rudolph, and Weber 1999 and Norden 2002) and others on the usage of financial
statement analysis (Meyer 2000). Credit risk management instruments like credit
derivatives are often investigated independently of their usage in the credit risk
management process (Brütting, Weber, and Heidenreich 2003a and Burghof, Henke,
and Schirm 2000a). The following Table 1 shows a review of the different surveys.
Table 1: Review of studies about credit risk management
Author (Year of publication)
Target population (Time of survey)
Target topic Important results
Betsch, Brümmer, Hartmann, and Wittberg (1997)
106 banks with different sizes (1996)
Credit analysis of corporate customers
• Trend to a credit analysis based on systematic data
Poppensieker 7 of the 100 Market risk measurement • Extendable measurement of
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Credit Risk Management in German Banks 5
(1997) biggest German banks (1996)
concepts and banks’ global risk management
market risk, mainly with the concept of VaR
• Wide spread use of internal rating systems, seldom CVaR
• Sometimes market risk management with RAPM
Elsas, Ewert, Krahnen, Rudolph, and Weber (1999)
4 banks (1992-1997)
Credit risk management • Dominance of scoring models • Trend to securitization
Weber, Krahnen, and Vossmann (1999)
4 banks (1992-1997)
Internal rating (part of Elsas, Ewert, Krahnen, Rudolph, and Weber 1999)
• Existence of a rating momentum • Higher migration probability for
internal bank ratings than for external bond ratings
• Loss of importance of financial statement analysis
Bigus, and Matzke (2000)
100 biggest banks (1999)
Regulatory capital • Some usage of internal risk models
• Subordinated importance of market risk systems
Böcker (2000) 136 public banks (1999)
Credit analysis of corporate customers
• No complete usage of all information in credit analysis
• Insufficient future orientation of the data base
Burghof, Henke, and Schirm (2000a)2
61 banks Market for credit derivatives • Usage of credit derivatives at larger banks
• High restrictions combined with high potentials
Günther, and Grüning (2000)
1002 German banks (1996-1997)
Bankruptcy prediction models
• Rising usage of multivariate discriminance analysis
• Nonimportance of neuronal networks
• Long-term experience in running systems
Meyer (2000) 10 leading German banks (1990 and 2000)
Financial statement analysis • Usage of traditional financial statement analysis in different types
• Frequent usage of discriminance analysis in 2000
• Partial use of expert systems in 2000
Kirmße (2002) Over 110 banks, unions and institutions (2000)
Credit derivatives in risk management
• Optimistic view on market development
• Low importance in practise • High restrictions, e.g.
information asymmetries Norden (2002) 12 special banks
in Germany (2001)
Internal Rating for regulatory purposes
• Rare and different fulfilment of regulatory requirements
• Existence of more sophisticated rating systems for consumer credits
2 See also Burghof, Henke, and Schirm (2000b).
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Credit Risk Management in German Banks 6
Brütting, Weber, and Heidenreich (2003a and 2003b)
100 biggest banks (2002)
Market for credit derivatives (comparable to Burghof, Henke, and Schirm 2000a)
• Still increasing usage of credit derivatives at large banks
• Some market restrictions
Kramer (2005) 34 German Banks Rating of special customers like start-ups, hospitals or non-profit organisations
• Heterogeneous methods for classification of special customers
• Need to adjust standard methods • Rare usage of external ratings for
special customers
All these studies either rely on a small sample of banks or analyse a special part of the
credit risk management process in detail with special empirical methods or both.
Therefore there is obviously the need to survey the credit risk management process as a
whole in a broad study at different points of time.
Altogether the following should be pointed out:
1. What is the state of the art of credit risk management in German Banks?
2. Which developments have occurred in the last years?
3. Which factors influenced these developments?
To answer these questions we interviewed the 100 biggest German banks, in terms of
total assets, in a broad study.
The outline of this paper is as follows: In section 2 the structure and design of the study
is described. Thereafter the results are presented in section 3 starting with the
measurement of expected defaults, followed by unexpected defaults and cost accounting
and ending with the managing of credit risks. Section 6 summarises the results.
2 Structure and Design of the Survey
2.1 Credit Risk Management Process as an Object of Survey The process of credit risk management can be divided into two parts: The measurement
of credit risk and the management of credit risk (Saunders, and Cornett 2003, p. 259).
On the one hand the measurement involves the identification and quantification of credit
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Credit Risk Management in German Banks 7
risk including the estimation of expected defaults as a result of exposure at default, loss
given default and default probability (e.g. Ong 1999, p. 94). On the other hand the
measurement involves the estimation of unexpected defaults, especially of loan
portfolios. In the following the unexpected default is defined as the negative deviation
from the expected default.
To measure the expected defaults it is common practise in science to classify the loans
on an ordinal scale with the help of ratings (e.g. Allen, Boudoukh, and Saunders 2004,
p. 124). Footing on the classification in a rating system the rating transition of loans can
be computed with a rating migration analysis (Altman 1998, p. 1232).
To measure the unexpected defaults the value at risk has been established in the
researching community (Saunders, and Cornett 2003, p. 305). It is referred to as credit
value at risk (CVaR). Moreover the Basel II approach to calculate the risk weight
function is also based on the CVaR methodology (Rose 2002, p. 498). Additionally,
credit portfolio models have been further developed during the last years. Furthermore,
commercial credit portfolio models were brought to market (e.g. Saunders, and Allen
2002, p. 67).
Based on the expected defaults, expected costs can be assigned to loans. This can serve
as the basis for the so called default risk expenses (Koch, and MacDonald 2003, p. 672).
Besides rating based costing, other traditional cost accounting systems like segment
based cost accounting are mentioned in literature (e.g. Saunders, and Cornett 2003, p.
287). The option based cost accounting is one of the latest methods applied in the
banking industry. By the application of this method the default probability is estimated
and the costs are assigned in one step (Merton 1974, p. 452).
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Credit Risk Management in German Banks 8
As regards the management of credit risk, it refers to the buying and selling of credit
risks, whereby the former can be done via lending or selling of a credit derivative. The
decision can be made to incorporate default risk expenses in the lending rates (e.g.
Koch, and MacDonald 2003, p. 682.). The selling of credit risk can be achieved by
settling a credit insurance or buying a credit derivative. Furthermore, diversification
effects can be realized by selling credit derivatives. Especially in such cases it might be
helpful to trade in bundles of credit risk positions or in macro derivatives (Bär 2002).
In the following the whole credit risk process is analysed in general. However, emphasis
is put on areas where the biggest difference between theory and practise could be
expected or where the development in theory was fastest, in order to enable us to answer
our questions.
2.2 Design of Interviews To achieve our goals a complete investigation of the German banking sector was not
practical. Therefore we decided on a target group of 100 banks out of the whole banking
sector. The target group contains the 100 biggest banks as ranked according to their
total assets corrected by sector specifics (see Hoppenstedt 1999 and Hoppenstedt 2002
and Hoppenstedt 2005). The reason for selecting the 100 biggest banks was that these
banks were expected to have the most complex lending business and data structure. For
this matter, our target group most likely applies the new management instruments and
techniques which are our object of investigation (Betsch, Brümmer, Hartmann, and
Wittberg 1997, p. 150).
The ranking of the 100 biggest banks was modified in two ways. Firstly, because we
wanted to analyse credit risk management systems in corporate lending, we considered
only those banks dealing in the business of corporate lending. We concentrated on
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Credit Risk Management in German Banks 9
corporate lending on account that the application of such systems is particularly cost
efficient in this sector. Secondly, in order to avoid redundancies we omitted all banks
which were real subsidies of investigated banks.
Since we wanted to investigate the development as well as the influencing factors, we
conducted investigations during the years 2000, 2003 and 2007. In the second and third
round one purpose was to have the most similar sample possible compared to the first
round. However, in these rounds the sample had to be changed because several banks
had merged and did not exist anymore. Furthermore the size of several banks in the
original sample had changed, such that some of the banks which belonged to the 100
biggest banks in 2000 did not do so anymore. In the light of these changes we extended
the sample to include the 125 biggest banks in round two and three.
A slight change in the sample had to be made, because we planned sectoral analysis of
the data for the private, the public and the cooperative sector. However, within the top
100 and top 125 banks only a few banks belonged to the cooperative sector. To make
sectoral analysis more meaningful, we included the next 12 biggest cooperative banks in
our target group in 2003 and the next 7 biggest cooperative banks in 2007. The sectoral
quotas of the responding banks are depicted in Figure 1.
Figure 1: Sectoral analysis of the responding banks in 2000, 2003 and 2007
Sectoral quotas 2000
29%
17%
54%
23%
54%
21%
23%
51%
28%
Public Banks Private Banks Cooperative Banks
Sectoral quotas 2007Sectoral quotas 2003
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Credit Risk Management in German Banks 10
The questionnaires were sent out in the first round to 103 banks, in the second round to
118 banks and in the third round to 110 banks. In 2000 73 banks responded, which
implies a response rate of 71%. In 2003 there were 62 banks thereby implying a
response rate of 53%. With 54 banks responding, the response rate in 2007 was 49%. In
order to consider differences between banks of different size, the banks have been
divided into four groups according to their total assets. Table 2 gives an overview of the
The questions were mostly answered by members of the departments credit risk
management and controlling.
Prior to the tests we constructed a questionnaire considering possible answers for open-
ended questions. Therefore we interviewed ten banks. Additionally we collected
possible answers of another seven banks (see for the methodology e.g. Dillman 2000) in
a pre-test.
Hereafter questionnaires were sent out in three rounds. The first round took place in
August and September 2000, the second round from May to July 2003 and the third
round in August and September 2007. The respondents had to answer 34 entirely
closed-ended questions in the first two rounds. In the third round some of the questions
had to be omitted because they did not make sense anymore. In most cases this was due
to changes in the regulatory framework. Therefore the third questionnaire comprised 29
questions. Because readers followed a navigational path to avoid order effects, the order
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Credit Risk Management in German Banks 11
of the answers presented in the following does not correspond to the order in the
questionnaire.
3 Measurement of Expected Defaults using Rating Systems
3.1 Completeness and Ability of Classification of Internal Rating Systems
As a prime instrument for measuring expected defaults, rating systems were analysed.
The application of rating systems is one of the key methods, which were suggested by
the Basel Committee (Basel Committee of Banking Supervision 2006).3 91% of the
respondents in the first round reported that in their bank an internal rating system was
already implemented. This figure increased in 2003 to 95% and in 2007 to 96%.
The performance of a rating system depends on to what extend it is applied to customers
and on the ability to classify customers correctly.
In regard to the first aspect 71% of all banks categorised generally every customer in
their rating systems in 2000.4 This value increased in 2003 to 74% and in 2007 to 84%.
Regarding the second aspect an ex post test to assess the ability of a rating system to
categorise customers was not practical (see Weber, Krahnen, and Vossmann 1999, p.
127 for small samples and Meyer 2000, p. 2487). Therefore, ex ante rating principles of
the rating process were defined, which could be used to asses this feature (Krahnen, and
Weber 2001, p. 10). The criteria we used here are the granularity, the up-to-dateness,
3 See Basel Committee on Banking Supervision (2001a, p. 26) or Basel Committee on Banking Supervision (2006, p. 19). Because the consultative document changed with time, different versions of the document at different survey dates could be relevant. However, in many cases the document’s content remained the same, so there is only one version cited. 4 Treacy, and Carey (2000, p. 179) reported in their sample of American corporate and retail banks in 1997 a value of 50% up to 60%.
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Credit Risk Management in German Banks 12
the intersubjectivity as well as the staff who takes part in the rating process (see also
Basel Committee on Banking Supervision 2006).
Since rating systems are assigned by bank staff, human judgement is inevitable in the
estimation of immanent credit risk. The responsibility for the rating can either depend
on the sales department or on other departments, including the possibility of more than
one department being involved in the rating process. In 2000, 83% of the banks
responded that their customers were not or not only rated by their sales department.5
This value rose to 93% in 2003 and 91% in 2007.
Figure 2: Completeness and independence of the rating process in 2000, 2003 and 2007
2000 2003 2007
Completeness of the rating system Rating process independent fromthe sales department
Public Banks Private Banks
Cooperative Banks Total
2000 2003 2007
100%
80%
60%
40%
20%
It should be noted that if corporate clients are rated correctly at one point in time, risk
factors influencing these ratings may change with time. To comply with this change, the
companies' ratings must be updated when new company data is published – minimum
once a year. In 2000 88% of all respondents updated their customers' ratings once in a
year or even more often. In 2003 95% had this practise. This figure changed
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Credit Risk Management in German Banks 13
insignificantly to 98% in 2007. According to more than 80% in the first two rounds and
73% in the third one, the trigger for rating updates was mostly a new company report.
The next criterion was the granularity. The more rating classes there are, the better a
differentiation between customers can be achieved. On the other hand few rating classes
make the rating process easier (see Altman, and Saunders 1998, p. 1723). Furthermore,
in classes with more subjects discrimination might often be more significant. 23% of the
banks distinguished between less than 6 classes in 2000.6 These banks had to change
their rating classes to fulfil the criteria of Basel II (see Basel Committee on Banking
Supervision 2001a, p. 46). In 2003 only 13% had less than 6 classes. This is attributed
to the fact that 81% of the banks reported that they had changed the number of grades in
their systems. For comparison the number of grades at 56% of the top 50 US-banks was
less than 6 in 2000 (Treacy, and Carey 2000, p. 174). When the third round of the
survey started, the legal framework had changed and it was no longer suggested to
differentiate between at least 6 classes but to do so with at least 8 classes.7 Due to this
change in the framework, answers to the question whether a bank differentiated between
at least 6 classes would not have been comparable with former results and therefore this
question was omitted in the questionnaire in 2007.
To guarantee the intersubjectivity of the rating, quantitative information like liquidity or
profitability should be considered in the rating process. In 2000 95% of the responding
banks based their decisions on quantitative information, all of them did so in 2003 and
98% in 2007.
5 For comparison, in 40% of the US-banks the primary responsibility lied with the relationship managers (Treacy, and Carey 2000, p. 179). 6 The most homogeneity can be viewed in the cooperative sector. 7 See §110 Abs.2 Solvabilitätsverordnung (SolvV).
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Credit Risk Management in German Banks 14
Figure 3 gives an overview of the rating principles and their fulfilment:
Figure 3: Actuality and intersubjectivity in 2000, 2003 and 2007
Public Banks Private Banks
Cooperative Banks Total
Rating updatesminimum once a year
Usage of financial statementdata in the rating process
2000 2003 2007 200720032000
100%
80%
60%
40%
20%
Without differentiating by sectors the criterion usage of quantitative risk factors is
nearly fulfilled by all banks. The completeness principle is the least fulfilled criteria.
Moreover, the increase of this value during the research period was rather moderate.
To test the significance of the results Chi2-Tests were applied. In some cases the
expected frequencies were too small to meet the generally accepted conditions
postulated by Cochran (1952). In those cases where the Chi2-Test indicated significance
of the results but the pre-conditions for the test were not completely fulfilled, an exact
test according to Freeman and Halton (1951) was applied.
Table 3: Tests for independence of rating criteria and sectors, bank sizes and development (Significance 1%-Level ***, 5%-Level **, 10%-Level *, P-Value of Freeman-Halton-Test)
Chi²-Test for Sectors
(p-value) [p-value FH]
Bank size (p-value)
[p-value FH]
2000 2003 2007 2000 2003 2007
Developments(p-value)
[p-value FH]
Rating in general 1.10 1.70 2.77 3.49 4.34 4.18 1.14
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Credit Risk Management in German Banks 15
Completeness 5.66* (5.89%)
5.25* (7.23%) [7.84%]
0.40 5.55 12.86*** (0.5%) [0.41%]
2.58 4.26
Dependence from sales department 0.93 2.83 2.00 3.25 1.53 2.12 4.55
According to Table 3 the sectoral analysis showed only minor, mostly not significant
differences. Only the principle completeness somehow differed among sectors in 2003.
By that time, private banks graded their customers more consequently (86%) than banks
of the other sectors, especially of the public sector (56%). This is a remarkable result
because regarding all other principles the public banks increased their levels more than
other sectors during the period between the first two rounds. However, the percentage of
public banks grading every customer jumped up to 88% in 2007 and therefore was the
same as that for private banks (89%).
Altogether, between 2000 and 2007 the percentage of fulfilment increased by 20%.
Therefore, in 2007 nearly three quarters of the banks fulfilled all the criteria mentioned
so far. The main reason for this development seems to be the regulatory forces.
3.2 Migration Analysis upon Rating Systems Footing on the rating systems, banks could apply migration analysis. In 2000 every
second bank analysed the migration of rating through time. Additional 31% planned the
introduction of a migration analysis. In 2003 the picture changed. The vast majority of
the banks (61%) used migration analysis. Furthermore, the planners’ rate remained
constant. This development indicated the growing importance of migration analysis for
most banks. The result in 2007, however, was remarkable. 89% of the banks reported
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Credit Risk Management in German Banks 16
that they used migration analysis, another 4% were planning to do so. Obviously, those
banks that had planned to use migration analysis in 2003 had implemented it by 2007.
Figure 4 depicts the sectoral analysis.
Figure 4: Migration analysis in 2000, 2003 and 2007
100%
80%
60%
40%
20%
Implemented2000
Discussed2000
Implemented2003
Discussed2003
Implemented2007
Discussed2007
Public Banks Private Banks
Cooperative Banks Total
Migration analysis was used in the public sector more often in 2003. In this sector there
was also the highest increase with 20 points. This is attributed to the planning situation.
In 2000 more than 30% of all public banks discussed the introduction of the systems in
question. In 2007 all pubic banks had such a system. In the other sectors the percentage
of migration analysis users remained nearly constant.
Table 4: Tests for independence of migration analysis and sectors, bank sizes and development (Significance 1%-Level ***, 5%-Level **, 10%-Level *, P-Value of Freeman-Halton-Test)
Chi²-Test for Sectors
(p-value) [p-value FH]
Bank size (p-value)
[p-value FH]
2000 2003 2007 2000 2003 2007
Developments(p-value)
[p-value FH]
Migration Analysis 0.19 4.90*
(8.61%)
7.48** (2.37%) [0.73%]
7.06* (7.00%) 3.42 4.10 19.98***
(0.005%)
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Credit Risk Management in German Banks 17
All in all rating migration analysis was much more often used in 2007 than in 2000. The
reason can again be found in the Basel Committee's proposals and according to Table 4
sectoral forces (Basel Committee on Banking Supervision 2001a, p. 60).
4 Measurement of Unexpected Defaults using CVaR
4.1 Application of CVaR For the measurement of the unexpected defaults the CVaR can be used. In 2000 only
about one quarter of the banks were using this concept. Further 25% were not using and
not planning to use the concept. The remaining 47% planned to introduce CVaR.
In 2003 the situation changed. Nearly every second bank (48%) was already using
CVaR and further 34% planned to do the same. This shows a rapid growth in use of this
methodology, especially when we include former studies like Poppensieker (1997). The
author found out that the use of CVaR in 1996 was 2 over 7. In 2007 the percentage of
the banks using CVaR had further increased to 76% and another 11% were planning to
use it. The total of banks using CVaR or planning to do so remained more or less on the
same level (82% versus 87%), allowing for the conclusion that the majority of those
banks planning to use the concept in 2003 did use it in 2007.
Table 5 depicts the use of CVaR in banks of different sizes:
In 2000 the bigger banks applied the CVaR-concept more often. The growth rate of the
use of the concept was about 20% in all classes, which indicates a growing percentage
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Credit Risk Management in German Banks 18
of use in all classes. Although especially the bigger banks used the CVaR-concept, the
difference in general decreased.
4.2 Factors for Calculating CVaR To calculate the CVaR of a credit portfolio it is possible either to calculate the
deviations of the expected default probability or after having assigned a market value to
each loan, the CVaR can be computed by measuring the deviation of the value of the
credit portfolio (Saunders, and Cornett 2003, p. 305). When using the first approach the
so called default frequency based CVaR is defined as the maximum loss of defaults
which is not exceeded with a certain probability within a fixed time interval. The so
called loan value based CVaR refers to the maximum loss based on changes of market
value.
The applied CVaR concept determines the complexity of the measurement concept and
the potential use of so called commercial credit models (see Bluhm, Overbeck, and
Wagner 2003, p. 66). For instance in the case of loan value based CVaR data on credit
spread movements in relation to credit quality change is inevitable.
In 2000 90% of the banks, which already used the CVaR concept, reported that they
applied a default frequency based concept. Furthermore, among the banks which
planned to introduce the CVaR, a majority of 62% wanted to use a default oriented
CVaR. This implies that most banks understand the CVaR as default frequency based
CVaR. Therefore, it is no wonder that in 2003 79% of all CVaR users and planners had
a default frequency based concept. Four years later in 2007, this proportion decreased to
75%. Thus the conclusion can be drawn that there is a light tendency to the more
sophisticated loan value based CVaR.
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Credit Risk Management in German Banks 19
In this light it pays to take a closer look at the sectoral analysis of the CVaR-concept. If
one concentrates on the banks which are using or planning to use the CVaR, there are
big differences between the banking sectors as depicted in Figure 5:
Figure 5: Default oriented CVaR in 2000, 2003 and 2007
2003 2000 2007
PublicBanks
PrivateBanks
CooperativeBanks
Total
100%
80%
60%
40%
20%
From 2000 to 2003, the number of banks with default frequency based CVaR was
increasing in the private and cooperative sector while in the public sector fewer banks
used this concept. There was a significant difference between sectors in 2003 regarding
the use of the default frequency based CVaR as depicted in Table 6. As the number of
banks using this concept slightly increased until 2007 in the public sector and decreased
in the private and cooperative sector, the difference between sectors was still noticeable
but no longer significant in 2007. The overall use of the default frequency based CVaR
was rather constant in the period under consideration, though slightly decreasing.
Table 6: Tests for independence of CVaR and sectors, bank sizes and development (Significance 1%-Level ***, 5%-Level **, 10%-Level *, P-Value of Freeman-Halton-Test)
Chi²-Test for Sectors
(p-value) [p-value FH]
Bank size (p-value)
[p-value FH]
2000 2003 2007 2000 2003 2007
Developments(p-value)
[p-value FH]
Use of CVaR 3.18 4.03 1.20 6.81*
(7.84%) [8.78%]
4.66 1.99 30.01***
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Credit Risk Management in German Banks 20
CVaR-concept 3.01 7.18**
(2.75%) [1.79%]
3.36 3.19 5.74 3.29 0.94
Especially public banks (34%) applied a loan value based concept in 2007, which is a
remarkable increase in relation to 2000. Compared with the result in 2003, private and
cooperative banks used this concept a bit more often in 2007 but still considerably less
than public banks. The reason for these big differences can be found in the bank policy.
As it seems, commercial products are often bought for a whole sector.
5 Default Risk Expenses
5.1 Use of Cost Accounting Systems After the quantification of the expected defaults banks compute default risk expenses
(see also Basel Committee on Banking Supervision 2001b, p. 48 which defines it as one
of its goals). One third of the banks which responded to our questionnaire computed
their expenses in 2000. Additional 51% planned to do the same. In 2003 the situation
changed dramatically with the number of banks calculating risk expenses rising to 82%.
Further 11% planned to do so until 2007. That means nearly all banks which planned to
introduce an accounting system fulfilled their plans in the period from 2000 to 2003.
Secondly, all banks which rated their corporate customers also computed their expenses.
However, in 2007 the situation remained more or less unchanged with 85% calculating
risk expenses and another 8% with such plans. It is not sure whether all the banks that
had planned to calculate risk expenses by 2007 actually realized their plans but the
proportion of the banks that calculate risk expenses is still growing.
There is an interesting result regarding the dependence of bank size and cost accounting
especially when this relation is regarded over time, which is demonstrated in Table 7.
Table 7: Accounting of default risk expenses depending on bank size
In 2000 there was a remarkable correlation between bank size and cost accounting
system (see also Table 8). Especially among the group with total assets of 50 bn € or
more, 68% had a system in place. Contrarily, in the group with total assets of less than 5
bn € no bank used such a system. In 2003 the correlation fell. However, there was still a
tendency: In the group of the biggest banks all had a cost accounting system, as in the
group of the smallest banks 64%. The situation in 2007 was almost the same as in 2003.
Among the banks of large or medium size the number of those calculating risk expenses
is still growing. It is particularly noticeable that after a remarkable increase from 2000
to 2003 the proportion of small banks that calculate risk expenses remained nearly
unchanged in 2007.
5.2 Methods for Calculating Expenses - Option Pricing Theory There are different methodologies for cost accounting, which are systematised
depending on their source and the use of data for calculating defaults. Beside the direct
use of market data like credit spreads, methodologies basing on historical default data
are often found in literature (e.g. Saunders, and Cornett 2003, p. 281). In 2003 market
data based methodologies were hardly applied. 71% of all banks computed their default
risk expenses basing on historical default frequencies. This did not change in 2007 with
a large majority of 79% using this methodology.
Nowadays option based systems are more and more under review. In 2000 only 6% of
all banks were using option based pricing models. Additional 12% were planning to
introduce such models. This figure rose to 11% in 2003. Further 9% planned the
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Credit Risk Management in German Banks 22
introduction of such models. Hereby the so called Merton-model was mostly used.
Further developments like the Longstaff/Schwartz-model are only applied in rare cases
(see e.g. Cossin 1997). However, in 2007 only 8% of the banks were using option based
pricing models and another 6% were planning to do so. These complex models were
hardly applied in the period under consideration and there seems to be no significant
trend that this will change in the near future.
Table 8: Independence of cost accounting / option pricing and sectors, bank sizes and development (Significance 1%-Level ***, 5%-Level **, 10%-Level *, P-Value of Freeman-Halton-Test)
Chi²-Test for Sectors
(p-value) [p-value FH]
Bank size (p-value)
[p-value FH]
2000 2003 2007 2000 2003 2007
Developments(p-value)
[p-value FH]
Cost accounting systems 2.63 4.39
7.34** (2.55%) [1.78%]
14.33*** (0.25%) [0.19%]
4.09 4.94 7.34** (2.55%)
Option Pricing Models 4.16 4.06 1.48
12.03*** (0.73%) [1.32%]
3.96 3.97 1.21
It is noticeable that in 2000 these models were significantly more often applied in banks
with total assets of more than 50 bn €. In the meantime 8% of the banks with total assets
of more than 10 bn € but less than 50 bn € were using these models. However, in the
group of the biggest banks 33% were using option based pricing models, which is
according to Table 8 significantly more. Although the total of banks using option based
pricing models decreased from 2003 to 2007, it can still be observed that these models
are used noticeably more often by those banks with total assets of more than 50 bn €
(25%) while smaller banks hardly do so (5%). This can probably be attributed to the
high complexity of these models (Saunders, and Cornett 2003, p. 297).
All in all, cost accounting systems in general have become important for most banks
over the last few years while option pricing models are hardly used, nor is there a
significant trend that this will change in the near future.
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Credit Risk Management in German Banks 23
6 Control of Credit Risks
6.1 Lending Rates Incorporating Default Risk Expenses Having discussed the measurement of risk, the next step is to consider the control of
risk. In order to control risk, the first move when issuing credits is to risk adjust the
lending rates. This implies that riskier borrowers pay more interest and vice versa. In
fact, in 2000 83% of all banks which computed default risk expenses set their conditions
risk adjusted. In a pre-test in 1999 the result was completely different. At that time
banks responded that the high competition prevented them from charging directly for
default risks. In 2000 this figure fell to 4%.
In 2003 slightly more, namely 86% of the banks which computed credit risk expenses,
set their lending rates risk adjusted. The remaining 14% planned to do the same.
Including the increase in banks which used cost accounting systems, the unconditional
value of such banks rose enormously.
Regarding risk adjusted pricing, the situation in 2007 did not change dramatically
compared to 2003. Again, 86% of the banks computing risk expenses set their rates risk
adjusted. A minority of 4% was of the opinion that this was not feasible for competitive
reasons.
Furthermore, the reasons for these customer policy changes are interesting.
Differentiating the results in bank groups of different size, the following results are
obtained:
Table 9: Risk adjusted lending rates for different bank sizes
As depicted in Table 9, in 2000 risk adjusted lending rates were only found in the group
of the big banks, which is a significant dependence between bank size and lending rates.
Table 10: Tests for risk adjusted lending rates in banks of different size and sectors (Significance 1%-Level ***, 5%-Level **, 10%-Level *, P-Value of Freeman-Halton-Test)
Among the banks with total assets of more than 50 bn € the use of credit derivatives
rose from 32% to 67% and in the group lower than 50 bn € from 13% to 41%. Among
the smaller banks, controlling credit risk via credit derivatives was less popular in 2000
than it was in 2007. This relation is in line with Burghof, Henke, and Schirm (2000a, p.
536) and Brütting, Weber, and Heidenreich (2003a, p. 758), who analysed trading in
credit derivatives in general. Nevertheless, it is surprising that among the banks with
total assets of less than 5 bn € none used credit derivatives in 2003. However, since
there were only a few responses to this question, the result has to be taken with a pinch
of salt.
Table 12: Tests for independence of credit derivatives and sectors, bank sizes and development (Significance 1%-Level ***, 5%-Level **, 10%-Level *, P-Value of Freeman-Halton-Test)
Chi²-Test for Sectors
(p-value) [p-value FH]
Bank size (p-value)
[p-value FH]
2000 2003 2007 2000 2003 2007
Developments (p-value)
[p-value FH]
8 An analysis of groups showed smaller significances.
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Credit Risk Management in German Banks 26
Credit derivatives 2.35 9.56***
(0.84%) 0.24 2.96 11.02** (1.16%) [0.85%]
4.87 5.72
The difference in the use of credit derivatives was significantly dependent on the bank
size in 2003 (see Table 12). It seems that bigger banks used credit derivatives earlier
and more commonly than smaller ones. In 2007, this dependency was still noticeable
but insignificant as shown in Table 11. Accordingly, among the group of the biggest
banks 67% applied credit derivatives and another 11% planned to do so by 2010. If
these plans are realised, about 80% of the banks with total assets of more than 50 bn €
will be controlling their credit risk using derivatives; quite a high figure compared to
smaller banks with 30% to 40% of users and planners.
The reason probably is the high volumes in which derivatives are traded (see Rose
2002, p. 295). Another point may be that up to now there are no standard models which
could be applied to price and hedge credit derivatives. Looking at the more
sophisticated market oriented CVaR, it can be observed that in 2003 95% of all
applicants controlled their credit risk using credit derivatives (for the application see
Bluhm, Overbeck, and Wagner 2003, p. 211). In 2007 this figure decreased to 73%,
however, this is still remarkably more than the average.
Altogether the use of credit derivatives seems to depend on the size of the bank as well
as its know how in measuring credit risk.
7 Summary and Outlook In a study conducted in 2000, 2003 and 2007, more than 100 German banks from
different sectors were sent questionnaires with standardised questions concerning their
credit risk management. The goal of this study was to evaluate the use, the historical
27.11.07
Credit Risk Management in German Banks 27
developments as well as the major drivers of innovations in the credit risk management
systems.
In this work different elements of the credit risk management process were analysed
with a focus on the measurement and the management of credit risks. As regards in the
measurement of expected defaults only a relatively slow growth was observed starting
at a high level. This coincides with relatively low dynamic in science in account to the
analysis of the classification and discrimination methods in the 1970ties. Contrarily the
application frequency of CVaR has sharply increased. In this field, rapid growth of new
theoretical models can be constated. Looking at the cost accounting systems, a huge
difference between the currently used methods and the option pricing methods favoured
by the science community can be observed.
In controlling credit risk, risk adjusted lending rates are increasingly applied, especially
by banks of medium size. A remarkable growth can be observed in the use of credit
derivatives. This trend is generally attributed to the application in banks with total assets
higher than 10 bn €.
All in all, we observed a distinguished picture of the new credit risk systems applied in
banks. Depending on the instrument in question, either a sharp increase or a stagnation
can be observed. The size of the banks and specifics of the banking sector are factors
that determine these growth rates. Besides, changes in the regulatory framework are
apparently a driving force of the recent developments. However, it seems that legal
obligation is not the only driver. Market forces appear to play an important role as well.
This becomes obvious when considering the use of CVaR or risk adjusted lending rates.
These concepts have become very popular over the last years although there is currently
no legal obligation to apply CVaR or risk adjusted lending rates.
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Credit Risk Management in German Banks 28
27.11.07
Credit Risk Management in German Banks 29
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