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Impact of LGD data on Basel II regulatory capital requirements and securitisations: Leverage of the PECDC data Evert Beeckman
Promotor: prof. ir. Ludo Theunissen
Begeleiders: Sofie Van Volsem, Ruben Olieslagers (BNP Paribas Fortis)
Masterproef ingediend tot het behalen van de academische graad van
Master in de ingenieurswetenschappen: bedrijfskundige systeemtechnieken en
operationeel onderzoek
Vakgroep Accountancy en bedrijfsfinanciering
Voorzitter: prof. dr. Ignace De Beelde
Faculteit Ingenieurswetenschappen
Academiejaar 2008-2009
I even wondered if it had all gone so wrong if there hadn’t been economists but engineers at
the head of the banks. Engineers are schooled to avoid as much risks as possible, economists
to take as much risks as possible.
- Prof. Luc Van Liedekerke
Impact of LGD data on Basel II regulatory capital requirements and securitisations: Leverage of the PECDC data Evert Beeckman
Promotor: prof. ir. Ludo Theunissen
Begeleiders: Sofie Van Volsem, Ruben Olieslagers (BNP Paribas Fortis)
Masterproef ingediend tot het behalen van de academische graad van
Master in de ingenieurswetenschappen: bedrijfskundige systeemtechnieken en
operationeel onderzoek
Vakgroep Accountancy en bedrijfsfinanciering
Voorzitter: prof. dr. Ignace De Beelde
Faculteit Ingenieurswetenschappen
Academiejaar 2008-2009
Preface
I chose to make my thesis, in combination with an internship, at the department of Credit
Portfolio Management within Fortis Merchant Banking. This gave me the opportunity to get to
know the financial world and gain some professional experience. The world I discovered
turned out to be rather dynamic. I started my internship at Fortis, afterwards it was Fortis
Bank, or the Belgian Government, but eventually it became BNP Paribas Fortis.
This master thesis completes five years of new experiences and challenges. All of this has only
been possible thanks to the unremitting support of my family.
I would like to thank Ruben Olieslagers for giving me the possibility to do an internship as part
of this thesis and for his continuous coaching. He encouraged me to always go the extra mile
and introduced me to the associate programme at the bank for a changing world. Finally I
would like to thank Professor Theunissen for guiding me in this master thesis.
Impact of LGD data on Basel II regulatory
capital requirements and securitisations:
Leverage of the PECDC data
by
Evert Beeckman
Master thesis submitted to obtain the academic degree of
Master of Industrial Engineering and Operations Research
Promotor: prof. ir. Ludo Theunissen
Supervisors: Sofie Van Volsem, Ruben Olieslagers (BNP Paribas Fortis)
Ghent University
Faculty of Engineering
Department of Accountancy and Corporate Finance
Chairman: prof. dr. Ignace De Beelde
Academic year 2008-2009
Abstract
This master thesis makes an assessment of the data from the Pan-European Credit Data Consortium
(PECDC). This consortium currently has the largest database of credit default data. The purpose of this
data pooling initiative is to meet both business and regulatory needs. On the one side, the data is used
as a benchmark for securitisations. On the other, it’s used for the backtesting and fine-tuning of credit
risk models that have to be Basel II compliant. This master thesis investigates to what extend the
PECDC data is able to fulfil these needs. A cost-benefit analysis of the participation of BNP Paribas
Fortis to the consortium is performed.
Keywords
Credit modelling, LGD, PECDC, Securitisation, Basel II regulatory capital requirements
Impact of LGD data on Basel II regulatory capital requirements and
securitisations: Leverage of the PECDC data
Evert Beeckman
Promotor: prof. ir. Ludo Theunissen
Supervisors: Sofie Van Volsem, Ruben Olieslagers (BNP Paribas Fortis)
Abstract
This master thesis makes an assessment of the data
from the Pan-European Credit Data Consortium
(PECDC). This consortium currently has the largest
database of credit default data. The purpose of this
data pooling initiative is to meet both business and
regulatory needs. On the one side, the data is used as
a benchmark for securitisations. On the other, it’s
used for the backtesting and fine-tuning of credit risk
models that have to be Basel II compliant. This
master thesis investigates to what extend the PECDC
data is able to fulfil these needs. A cost-benefit
analysis of the participation of BNP Paribas Fortis to
the consortium is performed.
Keywords
Credit modelling, LGD, PECDC, Securitisation, Basel II
regulatory capital requirements
I. Introduction
There is a strong need for qualitative and reliable
credit data. The reason for this is twofold. On the one
hand, the financial institutions want to use this data
for their securitisation transactions. This is the so-
called business need. On the other hand, there is the
regulatory need. Financial institutions are required to
provide more accurate estimates of their credit risks
under the Basel II framework. More advanced internal
models can result in more accurate estimates and
lower regulatory capital requirements. For the
development of these models, a large amount of credit
data is needed. Individual banks do not have enough
credit data, especially not for every sector or every
type of loan. Therefore banks should pool their credit
data. The Pan-European Credit Data Consortium or
PECDC currently has the largest and most detailed
database of credit default data.
II. The financial crisis
This need for qualitative and reliable credit data is
now, during an economic downturn, stronger than
ever. This master thesis started with a discussion of
the current financial crisis. Although some regulatory
gaps clearly existed, the crisis is due to multiple
causes. The de Larosière Group made the following
statement: “The present crisis results from the
complex interaction of market failures, global financial
and monetary imbalances, inappropriate regulation,
weak supervision and poor macro-prudential
oversight. It would be simplistic to believe therefore
that these problems can be “resolved” just by more
regulation.” (de Larosière, 2009).
III. Basel II
This master thesis also provided an overview of the
Basel II regulatory framework. The focus of the
overview was on credit risk, since it is most related to
the subject of this thesis. But the other two risk
components, i.e. operational and market risk, were
also introduced. Fortis Bank chose to implement the
Advanced Internal Rating Based (AIRB) approach for
credit risk modelling, which is the most advanced
approach. The Basel Committee on Banking
Supervision expresses perfectly what is meant by the
regulatory needs: “For all three risk components, the
use of statistical tests for backtesting is severely
limited by data constraints. Therefore, a key issue for
the near future is the building of consistent data sets
in banks. Initiatives to pool data that have been
started by private banking associations may be an
important step forward in this direction, especially for
smaller banks.” (Basel Committee on Banking
Supervision, 2005).
IV. PECDC
PECDC focuses strongly on four points: confidentiality
of the exchanged information, high data quality, one
shared methodology, and the “by banks for banks”
philosophy.
The fact that all participating banks work on the same
methodology supports the standardization of the
collected data and allows the comparability.
Moreover, the PECDC data template and statistics can
evolve in a de facto standard for the industry. In this
way, the transparency of the sector can improve. This
is exactly what investors, national regulators as well as
credit rating agencies demand, now more then ever.
V. The PECDC Database
A first assessment of the PECDC data by Fortis Bank
was rather disappointing. Both the linear and the
logistic regression did not show the expected results,
the explanatory power of the models was low.
However, a large study on the PECDC database
performed by Prof. Dr. Zagst and Stephan Höcht
showed very satisfying results (Zagst and Höcht, 2008).
This study is in fact the first that analyses recovery
rates based on a broad Pan-European dataset. They
showed that the most important component in
workout recovery rates on a facility level is the
presence and quality of collateral. But even more
importantly, this study showed that the database is of
high quality after the application of a few acceptable
filters and restrictions. Another important issue is the
credibility of the PECDC initiative. To this end, a major
step might be the publication of this study of Prof. Dr.
Zagst in an important scientific journal. This would
certainly increase the acceptance of the database by
the regulators, potential investors and the credit rating
agencies. Eventually, this would benefit all the banks
participating in the consortium.
VI. The use of the PECDC data
As mentioned above, the PECDC data is used to fulfil
both business and regulatory needs. Uncertainty about
the credit performance of loans contributes a large
share of securitisation costs to Fortis Bank. This
uncertainty can be reduced by comparing the internal
data with the data obtained from PECDC, i.e.
benchmarking. The more data a financial institute has,
the better the rating it will obtain from a credit rating
agency, and the more willing the regulator will be to
approve the transaction. More data will also make it
easier to convince potential investors of the quality of
the securities. The acceptance of the PECDC data as a
benchmark depends on its data quality and its
credibility.
To remain Basel II compliant, banks are required to
determine a so-called Reference Data Set (RDS).
Ideally, an RDS should cover at least a complete
business cycle, contain all the defaults produced within
the considered time frame, include all the relevant
information to estimate the risk parameters and
include data on the relevant drivers of loss (Basel
Committee on Banking Supervision, 2005). The Dutch
investment bank NIBC investigated if the PECDC data is
eligible for the creation of an RDS (NIBC 2008). It’s
clear that the PECDC data is not perfect and that there
is room for improvement. Nevertheless, if selections
and filters are properly applied to the dataset, the
PECDC data is the most eligible data currently available
for the creation of an RDS.
Besides the Basel Committee on Banking Supervision,
the national regulators also urge financial institutes to
participate in private data pooling initiatives such as
PECDC. The regulator from the UK, the FSA, states this
explicitly in (FSA, 2007). They require all Advanced
Internal Rating Based (AIRB) firms to make use of any
relevant and appropriate external data. The Belgian
regulator, the CBFA, also stimulates banks to make use
of external data.
VII. The leverage of the PECDC data
In the last chapter of this master thesis, the leverage of
the PECDC data is examined. Therefore, a high-level
cost-benefit analysis for the PECDC project within
Fortis Bank is performed.
The cost of the participation in PECDC for Fortis Bank is
assessed by a scenario analysis. In the first scenario,
almost all data collection and uploading is done
manually. Fortis Bank estimated that this scenario
could be completed at a cost of EUR 2.6 million, plus
EUR 0.8 million per year of running costs. In the
second scenario, as much as possible of the data
collection and uploading is done automatically. In the
short term, this scenario would certainly demand a
large investment. A number of paper files have to be
entered in existing database systems and a new
integrated system has to be developed for the
automatic data collection and uploading. In the long
term however, this scenario might be the most cost
effective, especially since PECDC requires a data
delivery every 6 months and a smaller update of the
defaults every 3 months. The third scenario is a mix of
the two previous scenarios: an automatic data
collection system is developed for the databases for
which data collection can most easily be automated;
the rest of the work is still done manually. It’s clear
that this scenario is a compromise, but it can be seen
as an intermediate stage before the implementation of
the second scenario. In the mean time, the benefits of
the participation in PECDC will become clear to Fortis
Bank.
After this analysis of the costs related the PECDC, the
benefits are translated into numbers. As mentioned
above, the PECDC data can result in lower costs
related to securitisation transactions. To get an idea of
the potential benefits, the Park Mountain SME 2007-I
transaction is discussed as an example. In this case, the
uncertainty due to insufficient data quality amounted
5.4% of the total amount of relieved capital. The
potential of the PECDC data is that it can again be used
as a benchmark to create transparency and boost
investor’s confidence. Fortis Bank is currently
considering a synthetic securitisation transaction
without requesting a formal rating from a credit rating
agency. This situation might even multiply the
potential benefit of the PECDC data, because in this
case the value of a benchmark increases.
The PECDC data can also result in lower Basel II
regulatory capital requirements. For the assessment of
the Risk Weighted Assets (RWAs) of Fortis, the CBFA
considers a 5 percentage points add-on to Fortis
Bank’s Loss Given Default (LGD) value. Because of this,
the CBFA’s assessment of Fortis Bank’s RWAs amount
EUR 23 billion more than Fortis Bank’s own
assessment. These EUR 23 billion RWAs entail a
potential annual income of EUR 255 million. The
PECDC data can be used as a benchmark for convincing
the CBFA of the accuracy of the Fortis Bank LGD value.
In this way, the PECDC data could help Fortis Bank to
spare up to EUR 255 million of capital costs on a yearly
basis.
VIII. Conclusion
The final conclusion of this master thesis is that the
leverage of the PECDC data is large. Therefore, Fortis
Bank should continue its participation in the
consortium. The PECDC initiative can increase the
transparency in the sector, which is now more then
ever needed.
IX. References
Basel Committee on Banking Supervision. “Working
Paper No. 14: Studies on the Validation of Internal
Rating Systems.” Bank for International Settlements,
Basel, May, 2005. http://www.bis.org/
Financial Services Authority. “Wholesale LGD models.”
Credit Risk Standing Group Paper, February, 2007.
NIBC Bank N.V. “Reference Data Set (RDS)”, Memo for
the Methodology Committee of PECDC, November,
2008.
The de Larosière Group. “The high-level group on
financial supervision in the EU: Report.” Brussels,
February, 2009.
Zagst, Rudi, Höcht, Stephan. “Modelling Techniques
with LGD Data.” Working paper, HVB-Institute for
Mathematical Finance, 2008.
i
TABLE OF CONTENTS
Table of Contents .......................................................................................................... i
List of Figures ............................................................................................................. iv
List of Tables ................................................................................................................ v
List of Abbrevations ................................................................................................... vi
1 Context .................................................................................................................. 1
1.1 The financial crisis ....................................................................................................................................... 3
1.2 Causes of the current financial crisis ........................................................................................................... 5
1.3 The defendants ........................................................................................................................................... 8
2 Basel II ................................................................................................................. 11
2.1 The Basel II Regulatory Framework ........................................................................................................... 11
2.1.1 Pillar I: Minimum Capital requirements ................................................................................................. 13
2.1.2 Pillar II: Supervisory Review Process ...................................................................................................... 19
2.1.3 Pillar III: Market discipline ...................................................................................................................... 19
2.2 The Implementation of Basel II ................................................................................................................. 20
2.3 Should Basel II have prevented the current credit crisis? .......................................................................... 23
2.4 Revisions to Basel II .................................................................................................................................. 24
3 PECDC ................................................................................................................. 25
3.1 What is PECDC?......................................................................................................................................... 25
3.2 Data Collection ......................................................................................................................................... 30
3.2.1 What is a Default? .................................................................................................................................. 30
3.2.2 The Data Collection Process ................................................................................................................... 31
3.3 FAIL ........................................................................................................................................................... 34
3.4 Algorithmics .............................................................................................................................................. 36
3.5 RMA –AFS: A comparable initiative .......................................................................................................... 38
3.5.1 RMA ........................................................................................................................................................ 38
ii
3.5.2 AFS .......................................................................................................................................................... 38
3.5.3 RAS ......................................................................................................................................................... 39
4 The PECDC Database ......................................................................................... 43
4.1 Linear Regression ...................................................................................................................................... 44
4.2 Logistic Regression .................................................................................................................................... 46
4.3 Conclusions from Fortis Bank’s analysis .................................................................................................... 51
4.4 Study by Prof. Dr. Zagst ............................................................................................................................ 52
4.4.1 Literature Review ................................................................................................................................... 52
4.4.2 The Data ................................................................................................................................................. 53
4.4.3 Univariate Analysis ................................................................................................................................. 55
4.4.4 Multivariate Analysis .............................................................................................................................. 57
4.4.5 Conclusions ............................................................................................................................................. 57
4.5 Summary .................................................................................................................................................. 58
5 Use of the PECDC Data ...................................................................................... 59
5.1 PECDC and Securitisation .......................................................................................................................... 59
5.1.1 Introduction to securitisation ................................................................................................................. 59
5.1.2 Contribution of the PECDC data to securitisations ................................................................................. 64
5.2 PECDC and the Regulatory Aspect ............................................................................................................. 65
5.2.1 Basel Committee on Banking Supervision .............................................................................................. 65
5.2.2 Is the PECDC dataset an RDS? ................................................................................................................ 66
5.2.3 National Regulators ................................................................................................................................ 68
6 Leverage of the PECDC Data ............................................................................. 70
6.1 Cost .......................................................................................................................................................... 70
6.1.1 Scenario 1: Manual data collection and uploading ................................................................................ 70
6.1.2 Scenario 2: Automated data collection and uploading .......................................................................... 73
6.1.3 Scenario 3: Partially automated ............................................................................................................. 74
6.2 Benefits .................................................................................................................................................... 75
6.2.1 Lower securitisation cost ........................................................................................................................ 75
6.2.2 Lower Basel II regulatory capital requirements ..................................................................................... 77
7 Conclusion .......................................................................................................... 79
iii
Appendix 1: Credit Portfolio Risk ............................................................................. 82
Appendix 2: Data fields in FAIL ................................................................................ 83
1. General borrower data ................................................................................................................................... 83
2. Borrower’s Financials ..................................................................................................................................... 83
3. Borrower’s loans ............................................................................................................................................ 84
4. Data of the collateral belonging to the loan ................................................................................................... 85
5. Data of the guarantee belonging to the loan .................................................................................................. 86
Appendix 3: Project Plan data collection ................................................................. 88
Appendix 4: Structured Finance ............................................................................... 90
Appendix 5: Nederlandstalige Samenvatting .......................................................... 92
References ............................................................................................................... 107
iv
LIST OF FIGURES
Figure 1 Financial crisis and real economy feedback loop (World Economic Forum, 2009) ......... 3
Figure 2 Evolution in market value for the largest financial institutes (JP Morgan, 2009) ........... 4
Figure 3 Evolution in market value for the Fortis Holding ............................................................. 4
Figure 4 The VaR approach under Basel II (Van Laere, Baesens and Thibeault, 2007) ............... 16
Figure 5 Division of the RWAs into the 3 main categories of risk ............................................... 20
Figure 6 Effect in solvability for the banks with the IRB approach .............................................. 22
Figure 7 PECDC logo ..................................................................................................................... 25
Figure 8 Loans in default (Fortis Bank Illustration) ...................................................................... 28
Figure 9 Structure of the data collection process (Fortis Bank Illustration) ................................ 32
Figure 10 The Data Collection Process within Fortis (Fortis Bank Illustration) ........................... 33
Figure 11 Printscreen of FAIL (Fortis Bank Illustration) ............................................................... 34
Figure 12 SAS Output for the Linear Regression.......................................................................... 45
Figure 13 SAS Output First Logistic Regression ........................................................................... 47
Figure 14 Confusion matrix of the logistic regression with a cut-off value of 2,085% ................ 48
Figure 15 SAS Output Second Logistic Regression ....................................................................... 49
Figure 16 Confusion matrix for the logistic regression with a cut-off value of 50% ................... 49
Figure 17 Confusion matrix when using no statistical model ...................................................... 49
Figure 18 Histogram for LGD........................................................................................................ 50
Figure 19 Recovery Rates in [-0.5, 1,5] (Zagst and Höcht, 2008) ................................................. 54
Figure 20 Recovery Rates in [0, 1] (Zagst and Höcht, 2008) ........................................................ 54
Figure 21 Traditional Securitisation (Fortis Bank Illustration) ..................................................... 60
Figure 22 Securitisation Transaction Structure (Fortis Bank Illustration) ................................... 61
Figure 23 Securitisation cost breakdown .................................................................................... 64
Figure 24 Scenario 1 ..................................................................................................................... 71
Figure 25 Scenario 2 ..................................................................................................................... 73
Figure 26 Cost of the Park Mountain SME 2007-I securitisation (Fortis Bank Illustration) ......... 75
Figure 27 Cost in RWA (Fortis Bank Illustration) .......................................................................... 77
Figure 28 Short-term action plan for Large Corporates (Fortis Bank Illustration) ...................... 89
v
LIST OF TABLES
Table 1 Changes in RWAs by geographical region and bank’s size (Moody’s, 2008) .................. 21
Table 2 The banks participating in the PECDC (PECDC Illustration) ............................................ 26
Table 3 Banks participating in the RAS consortium ..................................................................... 39
Table 4 Basic statistics of the recovery rates ............................................................................... 53
Table 5 Summary of empirical findings in the literature and from the PECDC data ................... 56
vi
LIST OF ABBREVATIONS
ABS Asset Backed Securities
AFS Automated Financial Systems
AIRB Advanced Internal Rating Based
ARS Auction Rate Securities
ASRF Asymptotic Single Risk Factor
BIS Bank for International Settlements
CBFA Commission Bancaire, Financière et des Assurances
CDO Collateralised Debt Obligation
CDS Credit Default Swap
CLN Credit Linked Note
CLO Collateralised Loan Obligation
CMBS Commercial Mortgage Backed Security
CPM Credit Portfolio Management
CRA Credit Rating Agency
DNB De Nederlandsche Bank
EAD Exposure at Default
EL Expected Loss
FCRM Fortis Central Risk Management
FSA Financial Services Authority
GDP Gross Domestic Product
ICAAP Internal Adequacy Assessment Process
IRB Internal Rating Based
LGD Loss Given Default
MTM Mark-to-market
PCC Percentage Correctly Classified
PD Probability of Default
PECDC Pan-European Credit Data Consortium
PIT Point-In-Time
RAS Risk Analysis Service
vii
RDS Reference Data Set
RMA Risk Management Association
RMBS Residential Mortgage Backed Security
RR Recovery Rate
RWA Risk Weighted Asset
SIV Structured Investment Vehicle
SME Small and Medium Enterprises
SPE Single Purpose Entity
SPV Special Purpose Vehicle
SREP Supervisory Review Process
TTC Through The Cycle
UL Unexpected Loss
VaR Value at Risk
1
1 CONTEXT
At the moment, the financial market is in a deep crisis because nobody is certain of the quality
of the credits. Because of this uncertainty, potential investors distrust the financial market.
This leads to a drying of the market and therefore the banks require extra capital. There is a
strong need for qualitative and reliable credit data. The reason for this is twofold. On the one
hand, the financial institutions want to use this data for their securitisation transactions1. On
the other, there is the regulatory aspect. Financial institutions are required to provide more
accurate estimates of their credit risks under the Basel II framework2. More advanced internal
models can result in more accurate estimates and lower regulatory capital requirements. For
the development of these models, a large amount of credit data is needed. Individual banks do
not have enough credit data, especially not for every sector or every type of loan. Therefore
banks should pool their credit data. The Pan-European Credit Data Consortium or PECDC
currently has the largest and most detailed database of credit default data.
Financial institutes need reliable credit data for their securitisation transactions to be able to
give an accurate estimation of the quality of the underlying assets. Reliable credit data provide
transparency: standardised information of underlying assets to be provided by issuers. It’s
only when transparency exists that risk can become priced correctly. At the moment, there is a
strong need for more transparency. There are five major reasons for this need:
• This transparency would boost investor confidence.
• Issuers would get recognition for the quality of the asset pool they want to
securitise. Transparency would allow a more accurate quality assessment of the
securitisation.
• Basel II focuses more on equity and mezzanine tranches require a more indebt
analysis by investors.
• The Credit Rating Agencies (CRAs) are also under pressure at the moment: they
over-rated a large amount of complex structured credit products which are in fact
the cause of the financial crisis. Because of this, they are now very conservative and
becoming even more conservative. They want more transparency because they
want to prove they are doing a proper job.
• The regulator relies on CRA ratings of structured products in Basel II.
Fortis has 4 values and 14 Fortiomas, which are the cultural building
blocks of the organisation and together form the basis of the company
culture. They are guidelines for helping staff and management to
strive for the same goals and to work together effectively. One of
them is to be transparent, since openness is the best policy.
1 Securitisation transactions will be discussed in Chapter 5.
2 The Basel II regulatory framework will be discussed in Chapter 2.
2
If we focus on Fortis Bank, we can distinguish several strong reasons for improving its loan loss
data quality:
One year ago, Fortis’ ambition was “to establish state-of-the-art risk
management systems, commensurate with its ambition to reach the
top league of European banks.”
DNB3 requested Fortis to improve the registration of loan defaults.
• The CBFA4 has put a 2.5 to 5 percentage points add-on to Fortis’
Loss Given Default (LGD) rate due to the lack of evidence to support
the LGD output of the internal model
• The CBFA also defined “terms and conditions” for the use of some
internal models, notably due to insufficient quality or absence of
Fortis historical data. Until the regulator’s requirements are met,
more conservative parameters must be used. As a consequence,
FCRM5 estimates that the Bank’s Basel II Regulatory Capital (RegCap)
could reach 87.5% of Basel I level in 2009, 7.5% above the floor
allowed by the Basel Committee on Banking Supervision6.
Due to the absence of structured historical data to support Fortis’
assessment of default and recovery rates, future loan securitisations
could be relatively expensive
The aim of this first chapter of my thesis is to describe in broad outline the tense situation of
the financial world at the moment. It’s very important to approach this thesis bearing in mind
the current economic reality. The first section of this chapter will provide an overview of the
financial crisis. The causes of the crisis are explored in the second section and the third section
makes an assessment of all the parties involved in the crisis.
3 De Nederlandsche Bank, the Dutch regulator
4 Banking, Finance and Insurance Commission, the Belgian regulator
5 Fortis Central Risk Management
6 The Basel II regulatory framework will be discussed in the next chapter of this text.
Strategic
ambition
Regulatory
compliance
Capital
requirements
Securitisation
costs
3
1.1 THE FINANCIAL CRISIS
The current global economic downturn started as a financial crisis around July 2007 in the
United States. The financial sector is affected worst. So far, the total amount of write-offs by
banks and insurance companies exceeds 1 trillion euros, the full scale of the losses is unknown.
If we consider the global stock markets since August 2007, we can see a decrease in the value
of the listed companies of more than 16 trillion euros, which is equivalent to about 1.5 times
the GDP7 of the European Union. Martin Wolf from the Financial Times describes the problems
in the financial sector as follows:
“We are painfully learning that the world's mega-banks are too complex to
manage, too big to fail and too hard to restructure.”
We can in fact distinguish three different stages in the economic downturn. The first stage was
the financial crisis as stated above, mainly caused by the problems resulting from the
subprime credits. There was a sharp drop in asset values and many financial institutions faced
severe liquidity problems. This has led to a reduced credit supply, which affected the real
economy. This brings us to the second stage: an economic crisis. Companies of all industrial
sectors are now facing difficulties. This results in closures of companies and retrenchments in
staff. The consequence is off course a lower consumption, which leads to a further
deterioration of the economy. This means an increasing number of credit defaults, and so the
financial sector is hit again…
Figure 1 below illustrates this feedback loop between the financial sector and the real
economy. The illustration was presented in March 2009 in Genève on the World Economic
Forum.
Figure 1 Financial crisis and real economy feedback loop (World Economic Forum, 2009)
7 Gross Domestic Product
4
An intriguing discussion of the decline of Fortis can be found in the book (freely translated)
‘Bankruptcy, How Fortis threw away all its credit’ (Michielsen and Sephiha, 2009).
The American bank JP Morgan studied the evolution of the market capitalization of a few of
the biggest financial institutes in the world. They compared the market values as of the start of
the crisis and the market value as of the date the study was performed, January 20th 2009. For
the market values at the start of the crisis, they used the second quarter reports of 2007. The
following figure clearly illustrates the dramatic evolution in market capitalization. All numbers
are expressed in billion US dollar.
Figure 2 Evolution in market value for the largest financial institutes (JP Morgan, 2009)
We can immediately notice that some financial institutes are affected much more than others.
For the financial institutes studied by JP Morgan, the Royal Bank of Scotland (RBS) is affected
most seriously, with a decline in market capitalization of 96%. Santander, on the other hand,
‘only’ has a decline of 45%.
Over the same period, the market capitalization for the Fortis
Holding declined from 41,1 to 3,1 billion Euro, which is a
decline of 93%. This decline is visualised in Figure 3.
41.1
3.1 Figure 3 Evolution in market
value for the Fortis Holding
5
1.2 CAUSES OF THE CURRENT FINANCIAL CRISIS
To understand what is happening in the financial sector, it’s a sine qua non to know the real
causes of the crisis. A lot of studies have already been written on this subject, and we can be
sure that much more studies on this subject will be published in the future. I will summarize
the findings of the report from the High Level Group on Financial Supervision in the EU. This
group was responsible for giving an advice to the European Commission, on the future of
European financial regulation and supervision. The group was chaired by Jacques de Larosière,
the former president of the International Monetary Fund, and is therefore also called the de
Larosière Group.
(de Larosière, 2009) says:
“The present crisis results from the complex interaction of market failures,
global financial and monetary imbalances, inappropriate regulation, weak
supervision and poor macro-prudential oversight. It would be simplistic to
believe therefore that these problems can be “resolved” just by more
regulation.”
The de Larosière Group started with an analysis of the causes of the crisis, making a distinction
between 5 groups of causes. According to them, these are the major causes:
I. Macroeconomic causes
• Ample liquidity and low interest rates have been the major underlying factor behind the
present crisis, but financial innovation amplified and accelerated the consequences of
excess liquidity and rapid credit expansion.
• Very low US interest rates helped create a widespread housing bubble. Within Europe
there are different housing finance models.
• In the US, personal saving fell from 7% as a percentage of disposable income in 1990, to
below zero in 2005 and 2006. Consumer credit and mortgages expanded rapidly. In
particular, subprime mortgage lending in the US rose significantly from $180 billion in 2001
to $625 billion in 2005.
• This was accompanied by the accumulation of huge global imbalances. The credit
expansion in the USA was financed by massive capital inflows from the major emerging
countries with external surpluses, notably China.
• In this environment of plentiful liquidity and low returns, investors actively sought higher
yields and went searching for opportunities. Risk became mis-priced.
• This led to increases in leverage and even more risky financial products. Financial
institutions engaged in very high leverage (on and off balance sheet) - with many financial
institutions having a leverage ratio of beyond 30 - sometimes as high as 60 - making them
exceedingly vulnerable to even a modest fall in asset values.
6
II. Risk Management
• An overestimation of the ability of financial firms as a whole to manage their risks, and a
corresponding underestimation of the capital they should hold.
• The extreme complexity of structured financial products, sometimes involving several
layers of CDOs8, made proper risk assessment challenging for even the most sophisticated
in the market.
• This was aggravated further by a lack of transparency in important segments of financial
markets – even within financial institutions – and the buildup of a "shadow" banking
system.
• The Basel 1 framework did not cater adequately for, and in fact encouraged, pushing risk
taking off balance-sheets. This has been partly corrected by the Basel 2 framework.
• The explosive growth of the Over-The-Counter credit derivatives markets, which were
supposed to mitigate risk, but in fact added to it.
• The originate-to-distribute model as it developed, created perverse incentives. A mortgage
lender knowing beforehand that he would transfer (sell) his entire default risks through
MBS9 or CDOs had no incentive to ensure high lending standards.
• This was compounded by financial institutions and supervisors substantially
underestimating liquidity risk.
III. Role of Credit Rating Agencies
• Credit Rating Agencies (CRAs) lowered the perception of credit risk by giving AAA ratings to
the senior tranches of structured financial products like CDOs, the same rating they gave to
standard government and corporate bonds.
• The major underestimation by CRAs resulted largely from flaws in their rating
methodologies.
• The conflicts of interests in CRAs made matters worse. Issuers shopped around to ensure
they could get an AAA rating for their products.
• The fact that regulators required certain regulated investors to only invest in AAA-rated
products also increased demand for such financial assets.
8 Collateralized Debt Obligations (CDOs) are a type of structured asset-backed security (ABS) whose value and
payments are derived from a portfolio of fixed-income underlying assets (Wikipedia definition). Structured
finance products will be discussed in great detail in Chapter 5.
9 A Mortgage-Backed Security (MBS) is an asset-backed security whose cash flows are backed by the principal
and interest payments of a set of mortgage loans. (cfr. Wikipedia)
7
IV. Corporate governance failures
• The checks and balances of corporate governance also failed. Many boards and senior
managements of financial firms were seriously underestimating the risks they were
running.
• Remuneration and incentive schemes within financial institutions contributed to excessive
risk-taking.
V. Regulatory, supervisory and crisis management failures
• These pressures were not contained by regulatory or supervisory policy or practice. There
was too much reliance on both the risk management capabilities of the banks themselves
and on the adequacy of ratings.
• Insufficient attention was given to the liquidity of markets.
• Derivatives markets rapidly expanded (especially credit derivatives markets) and off-
balance sheet vehicles were allowed to proliferate– with credit derivatives playing a
significant role triggering the crisis. These developments led over time to opacity and a lack
of transparency.
• This points to serious limitations in the existing supervisory framework globally, both in a
national and cross-border context.
• Regulators and supervisors focused on the micro-prudential supervision of individual
financial institutions and not sufficiently on the macro-systemic risks of a contagion of
correlated horizontal shocks
• There was little consensus among policy makers or regulators at the highest level on the
seriousness of the problem, or on the measures to be taken.
• Multilateral surveillance (IMF) did not function efficiently.
As mentioned, the aim of the de Larosière Group was to formulate some recommendations
for the strengthening of the financial regulation and supervision of the financial sector in
Europe. They made 31 recommendations, which can be reduced to the following four
categories:
1. A gradual increase of the minimal capital requirements
2. The introduction of capital buffers
3. More stringent rules for liquidity management
4. More rigorous rules for internal audits and risk management
8
1.3 THE DEFENDANTS
As the crisis broke out, different parties have been accused in the media as being responsible
for everything that went wrong… Objectivity was often hard to find. Michielsen10 (2008) gives
an overview of all the parties involved in the financial crisis. He lists their mistakes, while also
providing some arguments why they could not have prevented the crisis. I will give a summary
of his article, with the mistakes made by the parties involved (indicated with a - ) and the
arguments why they could not have prevented the crisis (indicated with a + ).
1. Bankers
- They bought and sold products they did not understand, they neglected the risks.
- They evaded the rules by constructing sort of a parallel banking system outside of
control.
- They have behaved unethical, thereby jeopardizing the global financial system.
+ Studies encouraged them to participate is the financial innovation to improve market
efficiency and the spread of risks
+ They were under great pressure to increase profitability
+ They estimated the risks of certain products using external ratings, which afterwards
turn out to be seriously wrong.
+ The fact that the credit crisis resulted in a global crisis of the financial system, is due to
an unforeseen and unfortunate confluence of events to which the bankers themselves
have no debt.
2. Policymakers
- They liberalized the financial market too much.
- They often gave the wrong incentives that moved people and banks to take more risk.
- They took no action when it came clear that a soap bubble was developing, because of
the positive spillovers to the real economy.
- They only saw the severity of the crisis too late. In the beginning of the crisis, their
approach was amateurish.
+ For national policymakers, it’s impossible to get a grip on a financial system that is
global.
+ They believed the free market could combat abuses.
+ It’s an illusion that the government can control the whole economy.
10 Michielsen is also co-author of the book (freely translated) ‘Bankruptcy, How Fortis threw away all its credit).
9
3. Rating agencies
- They have given excellent ratings to financial products they did not know well enough,
and which later turned out to be junk.
- They are not independent, because they helped designing financial products which
they afterwards provided with a rating.
+ Banks and investors themselves should also have assessed the risks of certain products.
+ Currently, nobody wants toxic credit products anymore, and therefore the prizes have
dropped sharply. But so far, the level of defaults is not as high, which means the ratings
were maybe not as bad.
4. Analysts
- They continuously run behind the facts. They did not see the crisis coming at all.
- A number of analysts caused panic among shareholders and investors by writing about
speculative scenarios and false rumours.
- They have to analyse, not predict.
+ Some analysts reported already in an early stage on the potential risks some financial
institutes were facing.
5. Speculators
- Speculative investors have contributed to the falling of some bank stocks by
speculating à la baisse. Sometimes they even spread false rumours on the market.
+ Short selling is a legitimate market activity, which often serves to hedge portfolios or to
optimise a market strategy.
+ Short selling creates market liquidity.
+ The accusation of deliberately spreading false rumours has never been proofed.
6. Media
- Exaggerated sensationalism, sometimes an appalling lack of economic knowledge and
the absence of any feeling of responsibility made that the reporting in the media only
fuelled the distrust of the savers and investors.
+ Don’t shoot the messenger.
10
7. Regulators
- They failed in their mission. They took no resolute action when banks entered more
risky paths.
- They only took action too late, when the credit crisis took alarming proportions.
+ Their power is limited by law. Financial institutes operate global, while regulators are
national. From competitive considerations, it’s impossible for one regulator to be much
stricter than the regulators from other countries.
+ They firstly focus on solvency; nobody expected the liquidity of the financial system to
fall out.
11
2 BASEL II
For this overview on Basel II, the focus will be on those elements that are important for the
PECDC project. It’s not in the aim of my thesis to give a deep understanding of all the
components of the Basel II framework, but through the course of my thesis it will be shown
useful to have a background on some aspects of the framework.
The first section of this chapter provides an introduction to the Basel II framework. All the
information in this overview comes from (Basel Committee on Banking Supervision, 2006a)
and internal Fortis Bank documentation, except when stated otherwise. The second section
discusses the results of the implementation of Basel II so far. In the third section, I pose a
hazardous question: Should Basel II have prevented the current crisis? This chapter is
concluded by an overview of the revisions to the framework that have already been proposed.
2.1 THE BASEL II REGULATORY FRAMEWORK
Basel II is a worldwide framework with guidelines regarding the capital adequacy requirements
for banks. It aims at creating an international standard that banking regulators can use when
developing regulations about how much capital banks need to put aside to preserve
themselves from the risks they are facing. “At some level the capital is adequate, implying that
the deposits are safe enough” (Sharpe, 1978). Basel II is not an accord or legislation, but a kind
of “gentlemen’s agreement”, contrary to its predecessor: Basel I.
In 1988, the Basel Committee on Banking Supervision developed the set of rules that is now
known as the 1988 Basel Capital Accord, or “Basel I”. This set of rules mainly targeted credit
risk, which is not an illogical choice since the default history of financial institutions shows that
credit risk is the most important threat to bank solvency (Van Laere, Baesens and Thibeault,
2008). Under Basel I, banks were required to hold capital equal to at least 8% of the risk-
weighted assets (RWAs). RWAs are the total of all assets held by the bank, which are weighted
for credit risk according to a rule determined by the regulator. In 1996, supplementary rules
related to trading risk were added in an amendment to Basel I.
Under the Basel I Accord the amount of capital being put aside by a bank as a type of ‘buffer’
for the risk taken was very simple and standardized: “one-size fits all”. For example, for every
100 euros of money lent to a company, the bank had to put aside 8 euros as capital.
Driven by the need for a more risk-sensitive approach to capital requirements and to
incorporate more advanced modelling and risk management in the regulatory banking system,
the Basel Committee of Banking Supervision designed a new worldwide framework called
“Basel II” in 2004 to replace the existing Basel I legislation. In 2005 and 2006 the framework
was translated into a Directive at European Level and into national legislation.
12
Basel II and the Capital Requirements Directive (CRD) should allow banks to compete on a
level playing field. A metaphorical playing field is said to be level if no external interferences
such as government regulations affects the ability of the players to compete fairly.
One of its unique aspects is its comprehensive approach to deal with risk management in the
banking industry by adopting three complementary and mutually reinforcing pillars:
Pillar I Minimum capital requirements
Pillar II Supervisory review process
Pillar III Market discipline
This three-pillar structure reaches far beyond Basel I and seeks to align regulatory
requirements with economic principles of risk management. It places the responsibility for risk
management and for maintaining adequate levels of regulatory capital within the bank’s
management. It’s important to make the distinction between regulatory and economic capital.
Regulatory capital is the mandatory amount of capital required by the regulators. Economic
capital, however, is the best internal estimate of the amount of capital needed to secure
survival of the institute in a worst case scenario (Smithson, 2003). This amount is often
calculated as “value at risk”, because it’s the amount of capital needed to stay solvent over a
certain time period with a pre-specified probability.
Three of the most important risks every financial institute bears are credit risk, operational risk
and market risk:
• Credit risk is the risk that a borrower can not pay back the loan to the bank.
• Operational risk encompasses all non-financial risks that a financial institute faces.
• Market or trading risk is the risk that the value of an investment decreases over time.
If we compare Basel I to Basel II, we can see changes in minimum capital requirements for
credit risk. Next to this, additional capital is required for the first time to cover operational
risks. Capital held against market risk will not change significantly under Basel II.
For the calculation of the minimum capital requirements, Basel II allows banks to choose
between different approaches on how to compute regulatory capital for the three risk classes
mentioned above. The more advanced approaches allow financial institutions to use their
internal models in determining the risk and capital requirement. The various approaches
enable financial institutions to choose the approach they consider to be best suited to their
specific features. Moreover, incentives are in place for banks to adopt the more sophisticated
The purpose of Basel II is to improve the stability and soundness of the financial system by
more closely linking capital requirements to risks and by promoting a more forward-looking
approach to capital management. Furthermore, the objectives of Basel II are broadly to
maintain the aggregate level of minimum capital requirements, while also providing
incentives to adopt more risk-sensitive approaches.
13
approaches and thus improve their risk management over time. Fortis Bank has opted to use
the most advanced approaches of Basel II as of 2008 after approval by the supervisor.
Basel II should drive the development of enhanced risk and capital management processes and
have a potentially major impact on banks’ capital management and strategy.
I will now give an overview of the three pillars of Basel II. The focus will be on pillar I, since this
pillar is the most relevant for the subject of my thesis and of the three risks, Credit Risk is the
most important in this discussion.
2.1.1 Pillar I: Minimum Capital requirements
Pillar I is a set of rules to calculate the minimum regulatory capital a bank must have to protect
itself from credit risk, market risk and operational risk.
As mentioned before, different approaches exist on how to compute regulatory capital for the
three risks pillar I consists of. For the credit risk, three approaches exist within the Basel II
framework: the standardised approach, the foundation IRB (Internal Rating Based) approach
and the advanced IRB approach. For the operational risk, there also exist three different
approaches: the basic indicator approach, the standardised approach and the advanced
measurement approach. For the market risk, there are two possibilities: the standardised
approach and the internal Value at Risk (VaR) approach.
Fortis has chosen to go for the most advanced approaches: the advanced IRB approach for
credit risk, the advanced measurement approach for operational risk, and the internal VaR
approach for market risk. They gave three major reasons for this decision: best fit with their
internal risk management, lowest capital requirement and alignment with most of their peer
banks.
Pillar I gives us the minimal capital ratio, with RWA still standing for Risk Weighted Asset:
%8,
≥RiskMarketandlOperationaCreditforRWAs
CapitalTotal
“The denominator of the capital ratio should reflect the bank’s risk exposure. Practice shows
that it’s not that straightforward to develop a measure of risk exposure that is both accurate
and easy to apply across different financial institutions” (Van Laere, Baesens and Thibeault,
2008). This formula means that the ratio of a bank’s core and supplementary capital to its risk
weighted assets must be equal to at least 8%. Remember that in the Basel I legislation this 8%
was always applied. With the Basel II framework, the amount of money a bank has to put aside
depends on several factors, so it can be less or more than 8%. On the average, however, the
ratio will still be the same 8%. In practice, this means that the financial institutes who opt to
use the most advanced approaches will benefit from the introduction of the Basel II
framework, because they will have less regulatory capital.
14
A. Credit risk
Credit risk is the risk that a borrower can not pay back the loan to the bank or other type of
credit line, or its interest. Some basic credit risk components are used to calculate the capital
requirement: the Probability of Default (PD), the Exposure at Default (EAD), the Loss Given
Default (LGD), the maturity (M) and correlations. Using PD, EAD and LGD, the Expected and
Unexpected Loss can be calculated. The calculating of these credit risk components is one of
the uses of the credit default data from PECDC; therefore I will discuss these credit risk
components in more detail.
• The Probability of Default (PD) is the assessment of the likelihood of default within 1 year
of the borrower (pool for the Retail) over one year. This component is usually calculated
through the use of an internal or external rating system. The PD is expressed as a
percentage and is counterparty specific.
E.g. PD = 1%
• Exposure at default (EAD) is the amount of debt outstanding at the time of default. There
are two measures of exposure: credit line and current utilisation. Credit line is the
maximum credit that a client may draw down whilst current utilisation is the amount
currently drawn down.
A conservative approach would use total credit line as the EAD, since there is no reason
why a defaulting client won’t draw down all their available credit. It therefore over-
estimates the risk the bank faces. The current utilisation is a less conservative approach,
since it assumes that a client draws down no additional credit as they approach default. It
therefore underestimates the credit risk. In practice we estimate exposure at default as a
value between these two measures (Olieslagers, 2006).
E.g. EAD = EUR 10 m
• The Loss Given Default (LGD) is the assessment of the loss incurred on a facility at default
of a counterparty. The LGD is expressed as a percentage of EAD and is transaction specific.
When a customer defaults, a bank does not necessarily loose the full amount of debt
outstanding as it’s possible that the borrower might recover and resume payments, or that
a recovery will be made against the security held against the loan. The impact of this is
captured in the calculation of LGD, which quantifies the total economic value of a loss as a
percent of Exposure at Default. LGD consists of the following three components:
1. Principle loss: the amount of the original loan which is neither repaid or recovered;
2. Interest: the amount of interest which is due but not received;
3. Expenses: workout and legal costs incurred in the bank’s attempt to recover loss.
The total economic value of a loss is typically greater than the traditional accounting loss
(charge-offs) due to the impact of the time value of money and administrative costs
(Olieslagers, 2006).
15
Therefore, the Loss Given Default can be calculated using the following formula:
EAD
LossEconomicLGD =
E.g. LGD = 100% (= loss of total amount)
• The effective maturity (M) of each credit facility. E.g. M = 1 year
• The Credit Default Correlations are not fully considered within Basel II, only a quite simple
approach where correlations are function of the type of asset class is used. Maybe this
basic risk component will be considered in the next framework, Basel III.
The risk components, especially the probability of default, can be modelled in a Through The
Cycle (TTC) way or being more Point-In-Time (PIT). Through the cycle means that the risk
components should stay relatively stable over the business cycle, whereas point-in-time
components have a shorter-term assessment horizon (Gonzalez et al., 2004). They show that
market based models are more point-in-time, whereas rating agencies try to offer through the
cycle ratings.
• The Expected Loss (EL) is the expected annual level of credit losses over an economic cycle.
EL is the result of the multiplication of the Probability of Default, the Loss Given Default
and the Exposure at Default:
EADLGDPDEL **¨=
Actual losses for any given year will vary from the EL, but EL is the amount that a bank
expects to lose on average over an economic cycle. EL should be seen as a cost of doing
business rather than as a risk in itself. If losses always equal their expected levels, there
would be no uncertainty (Olieslagers, 2006).
• The Unexpected Loss (UL) is defined as the volatility (or one standard deviation) of annual
losses. The UL for a transaction can be measured either on a stand-alone basis or, if the
transaction is pooled with other assets, on a contributory basis to the portfolio
(Olieslagers, 2006). The stand-alone UL11 (ULSA) can be calculated as follows:
EADPDPDLGDLGDVarPDULSA *))1(**²(* −+=
Olieslagers (2006) makes the following remark on PD and LGD:
11 The stand-alone UL is the UL for an individual asset, in contrast to the UL of a portfolio (ULP).
16
“Implicit in the credit risk methodology is the assumption that PD and LGD are
statistically independent. There is rarely enough data to construct a joint PD-LGD
distribution. However, one should expect some correlation between LGD and PD.”
So far, I discussed the credit risk components for individual assets. Combining multiple assets
creates a portfolio, and in this case we are working with credit portfolio risk. Modern portfolio
theory arises from the work of Harry Markowitz (1952). The formulas for the expected and
unexpected loss of a portfolio (ELP and ULP) are reviewed in Appendix 1.
The philosophy of the advanced IRB approach is based on the frequency of bank insolvencies
supervisors are willing to accept. In order to prevent moral hazard considerations for banks to
take too much risk, it’s not advisable to completely eliminate the credit risk. By means of a
stochastic credit portfolio model, capital is set to assure that there is only a very small
predefined probability for the amount of unexpected loss to exceed the amount of capital
(Van Laere, Baesens and Thibeault, 2007). This VAR approach is illustrated in Figure 4 below.
Figure 4 The VaR approach under Basel II (Van Laere, Baesens and Thibeault, 2007)
The risk weight function under Basel II is based on an Asymptotic Single Risk Factor (ASRF),
where all systematic risk that affects borrowers is captured in one single risk measure (Gordy,
2003). The model was further specified taking into account Merton's (1973) and Vasicek's
(2002) ground work (Van Laere, Baesens and Thibeault, 2007).
)(*5.11
)(*)5.2(1**)999.0(*)
1(
)1(
)(*
5.0
5.0 PDb
PDbMLGDPDG
R
R
R
PDGNLGDK
−
−+
−
−+
−=
17
This is the formula for capital requirement (K) for the IRB approach, which calculates the
conditional expected loss based on conditional PDs and downturn LGDs. The factors PD, LGD
and M have been discussed above. The following functions and factors from the formula have
not yet been discussed:
• N(x) denotes the cumulative distribution function for a standard normal random
variable.
• G(z) denotes the inverse cumulative distribution function for a standard normal
variable (i.e. the value of x such that N(x) = z).
• R is the asset correlation factor, which is determined by the asset class. Under the IRB
approach, the different asset classes are:
1. Corporate
2. Banks
3. Sovereign
4. Retail
5. Equity
6. Securitisation exposures
• The maturity adjustment b, which is a function of PD:
[ ] 2)ln(*05478.011852.0)( PDPDb −=
Besides this, an additional scaling factor of 1.06 has to be applied as a consequence of the fifth
Quantitative Impact study (QIS 5) on calibration performed by the Basel Committee on
Banking Supervision (BCBS, 2006). We can now calculate the following formulas, taking this
scaling factor into account (example with a minimal capital ratio of 8%):
KtrequiremenCapital *06.1(€) =
EADKEADKRWAsAssetsWeightedRisk **5.12*06.1**%8
1*06.1)( ==−
KULEcapCaptialEconomic P *)( =
B. Operational risk
The operational risk encompasses all non financial risks that a financial institute faces. We can
make a distinction between operational event risk and business risk, the latter is not taken
into account in the Basel II regulatory framework. Operational event risk comprises losses
resulting from inadequate or failed internal processes, people or systems, or from external
events, including legal risk. Examples are fire, system failure, fraud, litigation, processing error,
breach of regulation, etc. An example of an external event were the 9/11 attacks in the United
18
States. Business risk is defined as the risk of loss due to changes in the competitive
environment. Examples of this risk are changes in volumes, margins, costs, competitive
environment, and contagion effect. A major problem concerning business risk is that it’s very
difficult to quantify this type of risk. Within Fortis Bank, scenario analysis is used to calculate
this business risk for specific portfolios.
C. Market risk
Market risk is the risk that the value of an investment decreases over time, due to moves in
market factors. Within the Basel II framework, four different types of market risk are
considered: interest rate risk, currency risk, equity risk and commodities risk. Within Fortis
Bank, the internal Value at Risk (VaR) approach is taken towards the measuring of market
risk.
The introduction of the previous chapter shortly explained that PECDC pools credit default
data. Now, the link between PECDC and the Basel Committee on Banking Supervision can be
made. In its Working Paper No. 14, which is titled “Studies on the Validation of Internal Rating
Systems”, the Basel Committee on Banking Supervision underlines the importance of this kind
of initiatives:
However, some other risks than credit, operational or market risk exist and those are not
considered in Pillar I. For instance, interest rate risk in the banking book, liquidity risks as well
as other risks such as pension fund deficits, private equity, industrial stakes and cross-holdings
need to be assessed as part of the main challenges posed to banks. Another issue is that since
it’s extremely unlikely that all risk events will take place at the same time, an allowance could
be made for diversification when combining the individual risks instead of simply summing up
all the risks.
For all three risk components, the use of statistical tests for backtesting is
severely limited by data constraints. Therefore, a key issue for the near future is
the building of consistent data sets in banks. Initiatives to pool data that have
been started by private banking associations may be an important step forward
in this direction, especially for smaller banks (Basel Committee on Banking
Supervision, 2005).
19
2.1.2 Pillar II: Supervisory Review Process
In order to overcome some of the shortcomings of the first pillar, the Basel II Committee
designed the second pillar. It promotes more advanced risk management practices in the
financial world, which allow banks to dialogue with the supervisor about their more advanced
approaches. The Supervisory Review Process (SREP) aims to ensure that a bank’s overall
capital level is sufficient to cover all its risks. Banks are required to establish an Internal Capital
Adequacy Assessment Process (ICAAP) to capture all the material risks, including those that
are only partially or not covered under Pillar I.
2.1.3 Pillar III: Market discipline
This third pillar of Basel II aims to promote greater market discipline by enhancing
transparency in information disclosure. More information will be published concerning a
bank’s risks, capital adequacy and risk management practices to the external world. The Basel
Committee on Banking Supervision describes this pillar as follows:
The purpose of Pillar 3 - market discipline is to complement the minimum capital
requirements (Pillar 1) and the supervisory review process (Pillar 2). The Committee
aims to encourage market discipline by developing a set of disclosure requirements
which will allow market participants to assess key pieces of information on the
scope of application, capital, risk exposures, risk assessment processes, and hence
the capital adequacy of the institution. The Committee believes that such disclosures
have particular relevance under the New Accord, where reliance on internal
methodologies gives banks more discretion in assessing capital requirements (Basel
Committee on Banking Supervision, 2003).
20
2.2 THE IMPLEMENTATION OF BASEL II
To study the results of the implementation of Basel II, Moody’s investigated 158 banks in 32
countries, including 19 EU-member countries and 13 developed and developing countries from
outside the EU. The research shows that, at the end of 2007, risks in market portfolios
accounted for less than 15% of all RWAs for most of the banks examined, even those with
large businesses. This is a very low percentage, and in some cases, it was only 6% or 7%.
For most banks, the bulk of the losses suffered during the crisis resulted from positions in the
trading book, not credit defaults in the banking book. This has led the regulators to propose
the introduction of an Incremental Risk Charge (IRC) that will oblige banks to hold much more
capital against the risk in their trading books that are not captured by traditional market risk
models (Moody’s Global Credit Research, 2008). “Typically, market risk accounts for no more
than 12% of total RWAs. Bank risk models are not capturing all the market risks in the trading
book, and that results in a capital charge that is too low for some banks”, says Alessandra
Mongiardino, who is a senior credit officer at Moody’s in London. The standard Value-at-Risk
(VaR) models for measuring market risk have two important limitations:
1. VaR models are designed for liquid products, but many complex, structured products
are not liquid.
2. VaR models are not designed to capture the risks in the tail of the distribution curve.
Moody’s research focuses on the effect the move from Basel I to Basel II has on banks’ RWAs
and its Tier I capital. The RWAs can be divided into the three main categories of risk, for which
the research found the following distribution:
1. Credit risk accounts for 80% or more of all RWAs for 93% of banks in the sample.
2. Market risk accounts for less than 15% of RWAs for 96% of all banks, including those
with large trading businesses.
3. Operational risk has figures comparable to market risk, but has led to fewer losses for
most banks
Figure 5 Division of the RWAs into the 3 main categories of risk
Credit Risk
Market Risk
Operational Risk
21
Two examples: for the Swiss bank UBS, market risk accounts for around 6% of total RWAs and
for Deutsche Bank, market risk accounts for 7%.
There are floors to prevent any bank’s minimum capital level dropping too sharply during the
move from Basel I to Basel II:
• In 2008, minimum required capital cannot drop below 90% of its Basel I level
• In 2009, minimum required capital cannot drop below 80% of its Basel I level
One declared objective of the Basel Committee is that the shift to the new standard should not
lead to an overall fall in the minimum level of required capital in the banking system. Risk
weights attached to different types of borrowers were chosen to achieve that objective. In
theory, the more risk-sensitive Basel II will mean that the regulatory capital at some banks may
fall, while for others it will rise, roughly balancing out (Global Risk Regulator, 2009).
Moody’s research also shows that almost two-thirds of the banks in the sample have some
improvement in their Tier I capital ratios because of the move to Basel II. The changes in RWAs
are summarized by geographical region and bank’s size (without floors) in the following table.
We can see from the following table that the largest reductions in RWAs are made with the
IRB approach.
< € 15 billion € 15 – 155 billion > € 115 billion
IRB Standardised IRB Standardised IRB Standardised
Developing NA 11% (18) -7% (1) 7% (5) NA NA
EU -28% (7) -2% (29) -23 (16) 2% (19) -9% (25) -6% (3)
NON-EU
developed -18% (3) -2% (5) -11% (12) -16% (3) -11% (12) NA
Table 1 Changes in RWAs by geographical region and bank’s size (Moody’s, 2008)
22
“It’s practically impossible to accurately measure the appropriate level of risk-
based capital. Trying to set bank capital based on risk estimates gives the
wrong numbers and provides banks with enormous scope for manipulation.
The more sophisticated you are, the better you are able to manipulate the
capital structure and save big sums.”
The next graph shows the effect in solvability for the banks with the IRB approach. We can see
that most of these banks encountered a positive effect in solvability.
7,2
14
32
23
8,4
1,1 0,30
5
10
15
20
25
30
35
< -1 % -1 tot 0 % 0 tot 1% 1 tot 2% 2 tot 4% 4 tot 6% > 6%
Solvability effect in %-points
% o
t th
e b
an
ks
Figure 6 Effect in solvability for the banks with the IRB approach
Another lesson from the research is the importance of Basel II’s second and third pillars in
reinforcing the capital framework.
Critics contend that Basel II, and particularly its IRB approach, will result in inadequate
regulatory capital being held by banks. Jon Danielson from the London School of Economics
says:
Did Basel II result in inadequate regulatory capital buffers in the banks? What was the role of
Basel II in the financial crisis? – The next section will give an overview of different opinions
regarding these issues…
23
2.3 SHOULD BASEL II HAVE PREVENTED THE CURRENT CREDIT CRISIS?
This is indeed a complicated question, which has no straightforward answer. I will give a short
overview of arguments of both opinions, i.e. those in favour of Basel II as well as its
opponents.
A first remark we have to make is that Basel II focuses mainly on solvability. Most banks,
including Fortis Bank, initially suffered from liquidity problems, but for a number of
institutions, they turned into solvency problems. For Fortis Bank, the amount of credit risk was
too big in the Pillar I ratio.
A second remark we can make is that Basel II was only introduced after the bad credits were
purchased. (de Larosiere, 2009) says: “It’s wrong to blame the Basel 2 rules per se for being
one of the major causes of the crisis. These rules entered into force only on 1 January 2008 in
the EU and will only be applicable in the US on 1 April 2010. Furthermore, the Basel 2
framework contains several improvements which would have helped mitigate to some extent
the emergence of the crisis had they been fully applied in the preceding years. For example,
had the capital treatment for liquidity lines given to special purpose vehicles been in
application then they might have mitigated some of the difficulties. In this regard Basel 2 is an
improvement relative to the previous "leverage ratios" that failed to deal effectively with off-
balance sheet operations.”
However, most parties involved in this discussion agree that an update of the Basel II Capital
Framework is desirable. (de Larosiere, 2009) says: “The Basel 2 framework needs fundamental
review. It underestimated some important risks and over-estimated banks' ability to handle
them. The perceived wisdom that distribution of risks through securitisation took risk away
from the banks turned out, on a global basis, also to be incorrect.” Further in this thesis, in
Chapter 5, I will discuss the process of a securitisation transaction in detail. It will then become
clear why securitisations did not always achieve their goal of distributing risks.
Another opinion is that the Basel II Capital Framework is also an assessment of the financial
health of an institute. Therefore, the implementation of it should have indicated the enormous
risks financial institutes were facing.
We can say with confidence that some regulatory gaps existed in the Basel II framework, and
these were harshly exposed by the global financial crisis. The last section of this chapter will
summarize the most important revisions that have yet been announced, and which aim at
eliminating these regulatory gaps.
24
2.4 REVISIONS TO BASEL II
This section is based on an article in the Global Risk Regulator, which presents the revisions to
Basel II that have been proposed in a package of consultation papers (Global Risk Regulator,
2009).
The Basel Committee issued a package of rule revisions concerning three major risks inherent
in banks’ portfolio’s:
1. Trading activities
2. Securitisations
3. Exposures to off-balance sheet vehicles
These revisions will result in Basel 2.5, but their will be no wholesale changes compared with
Basel II. The goal of the revisions is to strengthen capital adequacy, risk management and
supervision. The result will be that most banks will have to hold a substantially higher level of
capital.
The politics, the financial institutes and the Basel Committee are three major stakeholders in
this discussion. Off course, they have different desires and opinions:
Politics: Require credible new rules from the regulators to reduce chances of
future crises.
Financial institutes Feel compelled to build up their capital base at the cost of lending to
individuals and companies.
Basel 2.5 Capital buffers to absorb losses and support continued lending to the
economy.
The Financial Stability Forum (FSF) made recommendations for 67 weaknesses they discovered
in the Basel II Capital Framework. The most important is now to turn these recommendations
into concrete actions. The most important proposals can be summarized as follows:
Pillar I Higher capital charges for re-securitisations, e.g. for CDOs of ABS
Pillar II Firm-wide governance and risk management
Pillar III Allow market participants to assess a bank’s capital adequacy through
info on capital, risk exposure and risk assessment.
Trading Book The trading book proposal is called Incremental Risk Charge (IRC):
• Supplement to current VaR trading book framework
• Include default and migration risk for unsecuritised credit products
• Will reduce incentive for regulatory arbitrage between banking and
trading books.
25
3 PECDC
After the two introductory chapters on the current financial downturn and the Basel II
framework, we are now ready to deal with the actual subject of this thesis: PECDC. In this
chapter, I will clarify what hides behind this enigmatic acronym. The focus of the next chapter
is then specifically on the PECDC database. Next, chapter 5 will state the use of the data.
This chapter will start with an overview of PECDC; its purpose, its history and the next steps.
Then the data collection process will be clarified, followed by an explanation of the FAIL
application that was developed for this data collection within Fortis Bank. Next, section 4
contains a brief discussion of an example of the statistics that are calculated on the PECDC
data by an independent third party, Algorithmics Inc. This chapter is then concluded by the
discussion of a comparable alternative.
3.1 WHAT IS PECDC?
PECDC stands for Pan – European Credit Data Consortium and was
formed in 2004. It’s the first multi asset class, cross-border industry
data pooling initiative for credit risk. The purpose of this data pooling
initiative is to meet both business and regulatory needs. The focus is
to collect data to assist with the measurement of Loss Given Default
(LGD) and Exposure at Default (EAD). Since the data delivery of March
2009, also Probability of Default (PD) data is collected. The anonymous
data is collected on the basis of confidentiality, flexibility,
comparability and reciprocity.
Data is collected and analysed on the following 8 distinct asset classes:
Aircraft finance (global), Shipping finance (global),
Commodities finance (global), Large corporate borrowers (global),
SME12 (Europe or South Africa), Project finance (global),
Real estate finance (Europe), Banks (global)
The databank includes credit default events since 1998. At the moment, 32 of the largest
banks in the world are involved in the initiative (see Table 2). The European countries with
participating banks are Belgium, The Netherlands, France, the UK, Germany, Ireland, Portugal,
Switzerland and the Nordics. Countries outside Europe with participating banks are South-
Africa, Japan, Australia and the US. Furthermore, the PECDC data template is used by US banks
and Indian banks. At Fortis Bank the collection of data for PECDC has been started in 2007
within Fortis Bank Nederland.
12 Small and Medium Enterprises
Figure 7 PECDC logo
26
ABN AMRO LLOYDS TSB FIRSTRAND BANK LTD
ANZ NEDBANK LTD JPMORGAN CHASE
BANK OF TOKYO-MITSUBISHI UFJ CAIXA GERAL DE DEPOSITOS HVB
BNP PARIBAS SNS PROP. FINANCE KfW
CREDIT SUISSE SUMITOMO-MITSUI BKG CORP NATIXIS
DANSKE BANK A/S ABSA BANK LIMITED NIBC BANK
DRESDNER BANK BANK OF IRELAND ROYAL BANK OF SCOTLAND
FORTIS BANK BARCLAYS BANK SCANDIN ENSKILDA BANK
HBOS CALYON SOCIETE GENERALE
KBC COMMERZBANK STANDARD BANK
DnB NOR WESTPAC
Table 2 The banks participating in the PECDC (PECDC Illustration)
Most of the banks participating in the PECDC use the information for benchmarking, i.e.
verifying that internal credit statistics are in line with the market. In the case of Fortis Bank,
the business needs for the participation are the necessity for benchmarking for securitisation
transactions. This will be further discussed in chapter 5. The regulatory needs result from the
fact that Fortis Bank is required to provide more accurate estimates of its credit risks, since
they apply the internal ratings-based approach (IRB) under Basel II (as discussed in the
previous chapter).
PECDC chose Algorithmics to provide a platform and services for the collection, processing and
delivery of the data. This company serves as an independent party, which is of primordial
concern for the working of the consortium. They compute aggregate statistics by country,
industry sector, type of borrower, time and size of Exposure at Default and collateral recovery
rates. Algorithmics and an example of the aggregate statistics will be further discussed in
section 3.4.
In a case study (Algorithmics Inc., 2006), Algorithmics lists the most important points for the
PECDC data pooling initiative:
1. Confidentiality of the information exchanged
Borrower identity is protected by not including borrower names, and bank identities
are protected by aggregating the data, and not assigning a country code to data unless
there are at least three banks for a particular asset class for a particular country
(Algorithmics Inc., 2006).
2. Data quality
PECDC also focuses heavy on data quality: the data has to be reliable, representative
and significant (Algorithmics Inc., 2006).
27
3. All banks work on the same methodology
All participating banks work on the same methodology supervised by the Methodology
Committee. This way, PECDC supports the standardization of the collected data and
allows the comparability.
The participating banks and Algorithmics created a single template for all asset classes,
greatly increasing the efficiency of data extraction and delivery to the pool. If the
PECDC data template and statistics evolve into a de facto standard for the industry, it
will be helpful for both banks and those who supervise them (Algorithmics Inc., 2006).
4. “By banks for banks”
This means that the ownership of the data remains with the banks and that the data
are, in principle, open to every bank. It also means that the banks control the project,
instead of a governance approach. They say that the banks are in the driver’s seat. A
last result of the “by banks for banks” organization of the consortium is that the best
experts are working jointly together (Algorithmics Inc., 2006).
How did PECDC originate? - In the same article (Algorithmics Inc., 2006), Algorithmics
describes the creation of the consortium. Based on their article, I will give a brief description of
“The birth of PECDC”.
It was the Dutch independent private merchant bank NIBC who initiated the data consortium
project. In 2002, NIBC completed the first securitisation of a shipping loan portfolio. But
despite the fact that the portfolio was one of the best on the bank’s loan portfolio, NIBC
discovered that its value was not properly recognized in the market price of the Collateralized
Loan Obligation (CLO) it’ssued.
“Banks like to keep their cards close to their chests when it comes to their loan statistics, and
the statistics of one bank is not enough to convince the rating agencies of the value of a
portfolio,” says Jeroen Batema, Head of Portfolio Management at NIBC at that time and now
working at Credit Portfolio Management13 within Fortis Bank. One alternative was to provide
information on the risk in the form of credit statistics, because banks and investors are more
familiar with this type of information. But NIBC’s data alone would not be enough to offer
sufficient credibility. “Investors want a broader base than that provided by a single institution,
so we needed to combine our data with the loss statistics of a large group of banks,” says
Batema.
13 Credit Portfolio Management (CPM) is indeed also the department where I did my internship as part of this
thesis.
28
Initially, the large Dutch banks were not interested to join the initiative. “By the end of 2003,
we thought we should look abroad, as many of the asset classes in which we are active are in
fact international. If we could find interest from banks in other countries, then we thought the
large Dutch banks would probably follow,” says Batema.
A number of large banks expressed their interest in the project, including Barclays, Calyon,
JPMorgan and Royal Bank of Scotland. In June 2004 a meeting took place with the interested
banks, some providers of credit data statistics, including Algorithmics, and some observers,
including the European Banking Federation and the European Investment Bank. The meeting
took place two days after the central banks governors of the G10 countries endorsed the Basel
II framework. With a clear objective of creating an inter-bank data pool of credit loss data,
and agreement on the best way forward, the meeting formally established the Pan-European
Credit Data Consortium, with Barclays, BNP Paribas, Dresdner Bank, NIBC and Royal Bank of
Scotland forming the management committee, with Batema as chairman.
In December 2004, a contract between the banks was signed. A template for the data and the
pooling process were ready in June 2005. The first pilot pooling occurred in November 2005.
“Although a huge amount of data was collected, the PECDC was not able to publish very
granular statistics, because there were fewer than three banks contributing from each
country. Nevertheless, the exercise was an important achievement, proving the concept and
the process”, says Batema. The pilot was followed by the first production pooling of data in
April 2006. This was a great success, with 18 banks delivering data representing more than 50
times more default observations on an annual basis than that available from the bond
markets.
And how is PECDC doing at the moment? The consortium is certainly up and running. There
are now semi-annual data deliveries and every three months there is an update of the
defaults. Figure 8 compares the credit default data currently used by Fortis (995 loans in
default) to the total credit default data available from PECDC (50,202 loans in default).
Figure 8 Loans in default (Fortis Bank Illustration)
Aircraft Finance
Shipping Finance
Commodities Finance
29
To conclude, I will list some interesting facts regarding the current state of PECDC and the
participation of Fortis Bank.
• “The economic downturn might affect PECDC; the risk exists that a number of banks
cancel their participation to the consortium due to cost cutting measures. Therefore
PECDC will focus on an increase of participating banks. This increase is anyway necessary,
since more participating banks means more data, and the more data PECDC has, the
higher it’s benchmarking value,” says Batema.
• The consortium has now evolved to an association according to the Dutch law.
• Since the data delivery of March 2009, also Probability of Default (PD) data is collected,
which is more high-level data in comparison with LGD and EAD data. PECDC first
concentrated on LGD and EAD data because this data was most needed at that time.
• After the separation of Fortis Bank and Fortis Bank Nederland, the further participation of
Fortis Bank to the consortium has, for a while, been questioned. But the aim of Fortis Bank
is to continue participating to the consortium. The data delivery of March 2009 will not be
fulfilled, but Fortis Bank will deliver data in October 2009. Fortis Bank Nederland will also
continue participating in the consortium; they completed the data delivery in March 2009.
• PECDC can also contribute regarding the potential acquisition of Fortis Bank by BNP
Paribas. Both banks participate in the consortium, so the data they collected for PECDC
can be used as a benchmark since this data will be easy to compare.
30
3.2 DATA COLLECTION
Within Fortis, all the credit default data required by PECDC is collected with the FAIL
application. Every borrower who goes into default has to be entered in the FAIL application.
Before discussing this application into detail, I will explain the general process of the data
collection. But first of all, which loans have to be collected?
3.2.1 What is a Default?
Hereby it’s important to pay attention to the difference between the default state and
bankruptcy. To make a clear distinction between these two states, I present the Fortis
impairment definition, which is the same as the IAS impairment definition, or the Directive
2006/48/EC (the Basel II definition):
Triggers for reclassification
During the “life” of an obligor, some events can result in the reclassification of the obligor in
another Credit Reporting Class. Such events are called triggers. There are two kinds of triggers:
1. Obligatory triggers: triggers that imperatively lead to a reclassification of the obligor to
impaired. Exceptionally, a classification to one of the impaired classes based upon
obligatory triggers can be reversed by a judgmental decision and must always be
accounted for.
• Bankruptcy
• Chapter 11 (& alike)
• 90 days past due, this trigger is considered as a backstop warning.
• Other banks calling their lines
• Distressed debt restructuring
• Material fraud.
A default (or impairment) is considered to have occurred with regard to a particular
obligor when either or both of the following two events have taken place:
• The bank considers that the obligor is unlikely to pay its credit obligations to
the banking group in full, without recourse by the bank to actions such as
realizing security (if held).
• The obligor is past due more than 90 days on any material credit obligation to
the banking group. Overdrafts will be considered as being past due once the
customer has breached an advised limit or been advised of a limit smaller
than current outstanding.
• Remark: the defaulted or impaired character of an obligor is thus fully
independent of the existence of collateral!
31
2. Judgmental triggers: triggers that possibly (but not imperatively) lead to a
reclassification of the obligor to ‘impaired’. The decision whether to reclassify the
obligor or not is left to the competent authority. The list below is not exhaustive.
According to local practices and rules other judgmental triggers can be added.
• Unpaid social premiums, VAT, taxes
• Excess drawing or unpaid interest / principal
• Deterioration to an orange rating
• Existence of a red rating
• Negative equity
• Regular payment problems
• Improper use of credit lines
• Legal action by other creditors
• Other banks requesting collateral
• Non-respect of important commitments
• Auditor’s qualification
• Request for consolidation or re-negotiation of credits
• Loss or death of a key manager.
Regarding the default date, the PECDC Methodology Committee formulated the following
directive:
3.2.2 The Data Collection Process
As mentioned above, credit data is collected on 8 distinct categories which are also called
asset classes. The banks decided to collect data from 1998. The observation data is collected at
five key moments in the lifecycle of each loan: at the date of origination, one year prior to
default (1 YPTD), at default, post default and at resolution. The data from one year before
default is in fact the data at 31 December of the year prior to default. Other information
gathered includes the rating of the counterparty, the nature of the collateral and guarantees,
the exposure at default (EAD) and value of the collateral and the details of each recovery cash
flow following default.
“Some banks have a policy of transferring their defaulted loans to a specialised
department, which record recovery cash flows only from the date of transfer.
However, this date of transfer is generally not the date of default, as per the Basel II
definition. These banks are invited to take extra care that they enter the actual date
of default and the cash flows (and more generally other data) from that date on.
Otherwise, their data set is at risk of biasing the time series.”
32
The following figure illustrates the structure of this data collection process.
Figure 9 Structure of the data collection process (Fortis Bank Illustration)
When a borrower goes into default, the process of intensive care (IC) starts. This has the aim
of bringing the loan back from defaulted to performing. However, if the borrower does not
succeed to get back in the normal state, the bank will try to recover as much of the loan as
possible, by selling the collateral if possible. Because these stages of intensive care and
recovery can take multiple years, there can be multiple post default observations, as
illustrated in Figure 9.
In Figure 9, we can also notice that every rating matches with a MS, which is a number of the
Fortis Masterscale. This Masterscale varies from 0 to 20, whereby a rating of 18, 19 and 20
represents a default. Notice that at resolution, e.g., the loan can have a MS of 8 (performing)
or 20 (defaulted).
“Information on the cash flows between the moment of default and the moment of resolution
is very important so that you can get a complete view of what kind of payments were made
between the obligor and the bank concerning a loan, and the source of the payments, for
example from the sale of collateral,” says Batema. Indeed, this enables a bank to look at
different collateral types, and see what kind of cash flows were received given a certain value
at one year prior to default, and so on. The following figure gives an overview of the data
collection process with Fortis Bank.
33
Figure 10 The Data Collection Process within Fortis (Fortis Bank Illustration)
When Fortis started with the collection of the credit default data, the purpose was to develop
a tool for automated data collection. Unfortunately, this turned out to be far more difficult
then first anticipated. Because the deadline for the first data upload to PECDC was already too
close, the automation of the data collection process was postponed and the collection was
performed manually. Off course, this was a very work intensive task. The aim still is to
automate the process, especially since PECDC requires an update of the defaults every three
months.
The next section discusses the FAIL application, which collects all the data required by PECDC.
This is however not an automated data collection tool: the data has to be entered manually.
Automated data collection would mean that the application pulls the required data out of the
correct database systems.
34
3.3 FAIL
The system FAIL (Fortis Application for Impaired Loans) is Fortis Merchant Banks registration
system for borrowers in default. Next to this the data is used by FCRM/Credit Modelling for
credit risk modelling purposes. The application was developed by Fortis Bank Nederland,
which caused some problems after the sudden partition of Fortis Bank Belgium and Fortis
Bank Nederland. Meanwhile, however, an agreement on the use of the tool by both banks has
been reached. The figure below is a printscreen of the application.
Figure 11 Printscreen of FAIL (Fortis Bank Illustration)
The FAIL application supports different business processes:
3. Borrower goes into default: data concerning this borrower must be entered in FAIL
4. During default period: Periodical update of borrower information
5. Borrower’s default status ends: enter a final update of borrower information
6. Export of data for credit risk modelling purposes
7. Import of data for credit risk modelling purposes
8. The uploading of the data to the PECDC database
35
When a borrower goes into default (to be decided by the credit committee), the borrower has
to be entered in FAIL. The application is structured in such a way that it requires 2 categories
of information:
A. General information: characteristics of a facility, a collateral, a borrower or a guarantor
B. Event related information: status of a facility or collateral at Origination, 1 YPD, Default
or/and Resolve dates
Eventually, all of the following information has to be entered:
1. General borrower data
2. Borrower’s financials
3. Borrower’s loans
4. Data of the collateral belonging to the loan
5. Data of the guarantee belonging to the loan
A detailed description of all the data fields that need to be filled in for each of these 5 parts
can be found in Appendix 2.
36
3.4 ALGORITHMICS
Algorithmics was founded in 1989 in Toronto, Canada. It offers enterprise risk management
solutions and services to financial institutions. The company employs over 700 people in 18
global offices and works with clients from all over the world. In January 2005, Algorithmics was
acquired by Fitch Group, which is also the parent company of Fitch Ratings. In 2007, the firm
was honoured as the leader in enterprise risk management in Risk magazine's Technology
Rankings. As mentioned before, Algorithmics provides the PECDC with a platform and services
for the collection, processing and delivery of the data. They also compute statistics on type of
borrower, time and size of Exposure at Default and collateral recovery rates.
In September 2004 PECDC selected Algorithmics as third party with the necessary data pooling
expertise. Algorithmics already had over seven years experience of collecting loss data in the
US through its North American Loan Loss Database, which included both loss given default and
probability of default data -PD data was the next priority. Algorithmics had a good reputation,
as well as a profit incentive. The latter being important to make sure they would invest in
good quality data service. For the participating banks it was very important that they could
control the project, instead of the regulator for instance. “Algorithmics understood best of all
the potential partners that an industry-led initiative had the best chance of success,” says
Batema. Algorithmics agreed to abandon its own initiative that it already had underway to
collect credit data in Europe, and adopted the PECDC business model for a bank-controlled
data pool (Algorithmics Inc., 2006).
To illustrate the function of Algorithmics, I will summarize the most important conclusions that
can be drawn from the qualitative statistics and quantitative analysis they delivered based
on the June 2008 PECDC Database.
• 42969 entities were collected of which 30898 were borrowers and 12071 acted as
guarantors.
• The entities are distributed across more than 25 European and over 95 Non-European
Countries.
• In total, 52278 loan level LGD observations have been collected across all Basel II
defined asset classes. SME is the most observed asset class (82 %), followed by Large
Corporate (13 %) and Banks (1 %). This implies that there are only a little amount of
observations for the other asset classes.
• 93 % of the collateral observations have been assigned a Collateral Value, with the
majority of these valuations being appraisals carried out by the actual lender.
• LGD statistics are presented on both a nominal and an economic basis. The economic
LGD is defined as one minus the present value of all post-default cash flows paid to, or
funded by the bank, as a percentage of the borrower or loan default amount.
37
• The PECDC LGD Database contains defaulted debt of more than EUR 72,135 million.
• The SME asset class exhibited the highest LGD (41.3 %) and Private Banking the second
highest (36.3 %). Aircraft Finance has the lowest LGD at 7.8 %.
• Two countries are, by far, the most frequent country of residence: the UK and
Germany. It’s also remarkable that the amount of entities that have The Netherlands as
country of residence is almost 10 times as big as the amount for Belgium.
• The entities can be public (stock listed) or private. Unfortunately, for most of the
entities (78 %), this characteristic is not given.
• The entities are distributed over 20 industry groups. However, 25 % of the entities are
classified as other or unknown. Moreover, confusion of the appropriate industry to
select for an entity is probable, especially for large companies.
• Most of the loans have no collateral (57 %)
• Most of the loans have no guarantor (72 %). Algorithmics believes this big amount may
be exaggerated due to the info being unavailable and also because many institutions
view guarantees at the collateral level.
• The reason for default is in most cases 90 days past due (45 %), whereas bankruptcy is
only in 14 % of the number of loans the reason.
• The loan status at resolution is mainly distributed over 4 cases. In decreasing order:
paid in full post default, partial write-off, complete write-off and return to performing.
38
3.5 RMA –AFS: A COMPARABLE INITIATIVE
3.5.1 RMA
The Risk Management Association (RMA), founded in 1914, is a not-for profit, member-driven
professional association whose sole purpose is to advance the use of sound risk principles in
the financial services industry. The association promotes an enterprise approach to risk
management that focuses on credit risk, market risk, and operational risk.
RMA is headquarted in Philadelphia, Pennsylvania, and has 3,000 institutional members that
include banks of all sizes as well as nonbank financial institutions. Over 20,000 risk
management professionals represent these institutions in the association. RMA is present in
North America, Europe, and Asia/Pacific. RMA tries to maintain a strong relationship with
members and regulators to help them develop new risk management techniques and
innovative products. Over 70% of RMA's revenue is derived from providing products and
services to members. RMA has also provided input to the regulators to reform the Basel
Capital Accord, Basel I, into the Basel II Directives.
Fortis has already had positive experiences with RMA in the context of Operational risk with
the ORX database. The Operational Riskdata eXchange Association (ORX) is the world's leading
operational risk loss data consortium for the financial services industry.
3.5.2 AFS
Automated Financial Systems, Inc. (AFS) is an information technology and software
development company providing products and professional services exclusively to the
financial services industry. They have almost 40 years of experience. AFS is headquartered in
Exton, PA, a suburb of Philadelphia. Its European subsidiary, AFS GmbH, is located in Vienna,
Austria. They collaborate with banks from all over the world to build lending processes based
on a straight-through model and on-demand technology and services.
AFS delivers a fully integrated lending system designed to process any type of loan (consumer,
business banking, commercial, commercial real estate and other specialty lines of business and
capital markets). It uses straight-through processing from origination through decisioning,
closing, booking, servicing, recovery, reporting, and securitisation.
39
Their mission is to provide solutions that assist banks to:
• Streamline the Credit Process
• Support Basel II and Regulatory Compliance
• Improve the Ability to Service Customers
• Maximize Revenue Potential
• Maximize Profitability
• Provide Effective Portfolio Management
• Improve Data Quality
• Enable Consolidation of Operations and Technology
• Preservation and More Efficient Use of Capital
AFS is successful, especially in North America:
• More commercial loans in North America are processed on AFS than any other system.
• AFS processes $ 2 trillion commercial loans every day by 20 of the top 30 commercial
banks in the US.
• Industry leader in investment lending process and risk provider.
• More than 10.000 banks’ staff have real-time access to AFS MIS via the internet.
3.5.3 RAS
In 2003, RMA and AFS made a partnership for the Risk Analysis Service (RAS). RAS is an
industry-led consortium focused on key credit risk metrics, including risk ratings, past
due/delinquencies, non-accruals, charge-offs, etc. The purpose is the benchmarking of a banks
performance against peer group statistics. An in-depth analysis is performed by factors such as
industry, location, deal size, collateral, time period, vintage. RAS members perform actionable
comparisons of their own data with that of peers banks and the industry as a whole across
multiple asset types and segmentations. Its benchmarking data— normalized through
standard data definitions for meaningful comparability across the industry—empowers
business strategy while satisfying regulators, boards of directors and investors as they seek to
understand whether your institution's levels of risk are in relation to the industry. The
following table gives an overview of the banks participating in this initiative.
Banco
Santander Citizens Bank PNC Bank TD Banknorth
BancorpSouth First Hawaiian Regions Bank UniCredit
Group
Bank of Amercia First Horizon Sovereign U.S. Bancorp
Bank of the
West
First Republic
Bank SunTrust Wachovia
BB&t Huntington Synovous Financial
Corp.
Table 3 Banks participating in the RAS consortium
40
In short, we use RAS to benchmark our data against data from other institutions
within the same markets. We need those enlarged samples to build and calibrate our
internal credit risk models for low-default portfolios.
- Rui Barrento, Head of Risk Infrastructure and Methodology, Santander Group
RAS is extremely important because a good benchmark helps institutions improve
their systems and asset quality.
- Henning Giesecke, CRO, UniCredit Group
In September and October 2006, RMA and AFS visited Fortis Bank and other European banks
to present their proposal to form a European consortium: RAS Europe. At that moment, RAS
was already successfully implemented in the US. From the participating banks, only Banco
Santander and UniCredit are also represented in Europe. Other European banks interested in
the RAS Europe initiative were KBC, Dexia, ING, Deutsche Bank, Dresdner Bank, UBS and HSBC.
ABN Amro and BNP Paribas were especially interested in the roll-out in Europe because they
had been working with RAS in the US. The consortium had a steering committee in place
where definition standards and confidentiality could be discussed. Fortis Bank had been asked
to participate in the steering committee.
For Fortis Bank, the benefits of this initiative were:
• Powerful analytical tool for active credit portfolio management
• Provides internal information to refine current models and to benchmark with their
peer banks
• Entering into a consortium at this point would allow them to be in the driver’s seat
(steering committee).
• Experience in the US had shown that data pooling could provide an internal incentive
to improve data quality and availability (they could learn from their peers).
• User-friendly web-based analytical tool (slice and dice).
However, Fortis Bank had also noticed some issues:
• Coverage European banks
Intentions need to be followed by actual delivery
• Confidentiality
Participating banks are the owners of the data
No single bank’s information may represent 25% of any reported dimension
A minimum of five banks in any reporting dimension
Client names cannot be traced
• Comparability
Standard definitions specified in advance, by the steering committee
Basle Definitions can be used
41
• Disclosure
No information will be released without consent of the consortium, i.e. the banks own
the data
Disclosure restrictions should be stipulated in the contract
• Control
RMA & AFS will perform a set of quality checks off and on site.
Banks with insufficient data quality are not be accepted into the consortium.
Partial data availability will be analysed case by case.
• Resources
Basle II parameters should be the starting point as this will require limited additional
resources.
• Overlap with Pan European Credit Data Consortium (PECDC)?
This was the most important issue: Fortis was already involved in the PECDC. The
PECDC included 17 banks at that time, mainly in the UK, Benelux and the Scandinavian
area. At the time, Fortis had delivered data for the commodities, aviation and shipping
portfolios and they were thinking about extending to large corporates and SME.
Indeed, we can immediately notice the similarity between the RAS Europe and the PECDC. In
the RAS Europe initiative, AFS takes care of the IT side of the consortium, whereas this is done
by Algorithmics for the PECDC. Off course, the partnership between AFS and RMA is not fully
equal to the relation between PECDC and Algorithmics, because in the latter case it was PECDC
that specifically asked Algorithmics to take care of the data analysis part. Another difference is
that RAS is about collecting commercial loan information to benchmark portfolio behaviour of
good portfolios, not only defaults. PECDC focuses on default data for modelling purposes and
securitisation benchmarking.
In March 2007, Fortis Bank decided not to join the RAS Europe initiative. The official reason
was that Fortis Bank was experiencing some difficulties to upstream the information in the
format needed as input for the benchmarking. At that time, all available resources were fully
dedicated to the Basel II priorities. However, the fact that Fortis Bank was already participating
in the PECDC was also important in this decision. And, last but not least, some people at Fortis
Bank were not in favour of the RAS approach.
To conclude this comparison, I will give a short overview of the RAS approach. To start with,
RAS proposed the following program structure:
• Bank Steering Committee directed
• Three year initial term
• 90-day data initialization and validation period
• 60-day production cycle from receipt of bank data
• Web-based Risk Analysis Workstation delivery
• Quarterly updates
• Quarterly Participant Bank Webcasts
• Semi-Annual Participating Bank Meetings
42
It was proposed that product segmentation would be provided by a combination of Facility
Type and Loan Tenor as follows:
Facility Type Loan Tenor
Revolver/line <= 1 year
Stand-alone loan 1 year
Bridge No stated maturity
Overdraft
Demand facility
Asset-based: finance lease
Asset-based: operating lease
Receivable financing
Covered bonds
Guarantees
Construction facility
Other
Unknown
For the standard delivery, deal size would be stratified as follows:
< €49,999
€50,000 - €99,999
€100,000 - €499,999
€500,000 - €999,999
€1 - €4.99 million
€5 - €9.99 million
€10 - €24.99 million
€25 - €99.99 million
€100 - €499.99 million
€500 - €999.99 million
> €1 billion
The pricing model of the RAS Europe
1. Initialization Fee for the Core European Service:
This is a one-time fee of €100,000 plus VAT and tax at applicable rate to cover, among other
items, data and process discovery, data set-up, formatting, validation, and balancing of data
feed(s).
2. Annual Operating Fee for the Core European Service:
This is an annual fee of €60,000 plus VAT and tax at applicable rate associated with on-going
data processing report production, as well as quarterly Webcasts and annual user’s meeting.
3. Customization:
Participants may choose to receive reports that are tailored to their specific needs. The
participant and AFS must mutually agree upon the scope and cost before any work will be
performed.
43
4 THE PECDC DATABASE
The previous chapter explained the purpose and methodology of PECDC and compared it with
an alternative. This chapter contains an assessment of the actual database from the
consortium. After receiving the first database from PECDC in June 2008, Fortis Bank wanted to
investigate the determinants and the behaviour of LGD values. First, a linear regression was
performed and next a logistic regression. The results and conclusions of both regressions will
be discussed. Afterwards I will discuss the findings of Prof. Dr. Zagst, who made an extended
study on the PECDC LGD database, version June 2007.
But before we start, it might be useful to know a little more about PECDC related terminology:
• Cured versus uncured loans
If the corresponding LGD value of a loan equals zero, then we consider it as a cured
default. The comparison of the cured defaults versus the total amount of loans gives us
the cure rate
• Secured versus unsecured loans
Secured loans are loans for which collateral is available, and unsecured loans are those
for which no collateral is available. In a normal scenario, average LGD values should be
higher for unsecured loans. In general, subordinated loans cannot be secured, although
this is sometimes the case in the PECDC database. The average LGD value for
subordinated loans is normally higher than that for secured and unsecured loans.
• Loss adjustments for accrued interest
The loss adjustment for accrued interest can be calculated as the difference between
the Cap LGD and the Nominal LGD. It represents the losses due to the time difference
between the moment of default and the moment of receiving (parts of) the payment.
It’s sometimes referred to as recovery costs, but those can be broader including
funding costs, legal costs …
44
4.1 LINEAR REGRESSION
A regression analysis on the PECDC Large Corporate data wanted to investigate if collateral
influences the LGD value. The hypothesis is that the higher the collateral value, the lower
the LGD. The confidence level is set at a 95% level. The correlation is considered as important
if it’s significant and the correlation coefficient is higher than 20%.
To start with, the relationship between collateral and LGD was investigated for all loans from
Large Corporate available in the database. The different collateral types were grouped
together in Cash, Mortgage, Pledge, Intangibles and Others. A regression analysis was run with
LGD as dependent variable and the following independent or explanatory variables related to
each collateral type:
• The value of the collateral type divided by the exposure at default (EAD) (%)
• An indicator to point out if the collateral type is available for a certain loan or not
• Country: Belgium, The Netherlands, France, Germany, Portugal, Spain, UK
• Guarantor available yes / no
• Seniority codes: PariPassu, senior, subordinated, equity, supersenior
Only the variables Percentage Pledge, Pledge Indicator and Seniority code “subordinated”
were significant at a significance level of 0,05%; p-value < 0,05; t-value > |2|. However, the
explaining power of the model, the R², is rather low: R² = 0,3024. This indicates that these
three variables are not able to explain the LGD value to a satisfying level. In the academic
world, a R² value of 70% or more is required to conclude that the selected independent
variables are able to explain the dependent variable. The people from Credit Modelling within
Fortis consider a R² value of 60% as good.
In order to improve the model and to raise the R², different techniques were used. First,
interaction effects between the three most important collateral types (Cash, Pledge and
Mortgage) were included. The interaction effects did not change the R² value of the model.
Next, the quadrates of the percentages were added into the model to capture non-linear
behaviour better. Using quadratic terms makes it possible to capture U – shaped relationships
in a linear regression equation. This lead to a small increase in the R² value to 0,3448, but this
increase did not compensate for the increase in complexity of the model. Hereby it should be
mentioned that the R² value will always increase slightly by adding more variables in the
model. Therefore it was concluded that including the quadratic terms in the model does not
improve the overall results.
Running the analysis country by country did also not improve the results. The R² value for
Belgium was even lower than the original R². Results for other countries were in line with
these results.
45
The final step in this investigation was to adjust the scope of the regression models. Instead of
performing the analysis for all loans to Large Corporates, the analysis was now only performed
for the uncured loans. The results of this analysis can be found in the following figure.
Figure 12 SAS Output for the Linear Regression
The analysis showed that percentage pledge, pledge indicator, France, Germany and equity
were significant and the R² value increased to 0,4435. However, the R² is still too low to
consider the results as high quality results. Moreover, a regression analysis with the
percentage of pledge as the only independent variable led to almost the same R² value:
0,4394. This shows that the entire R² can be allocated to the percentage of pledge.
The conclusion from this analysis is that the explaining power, the R² value, of the
model is too low to be able to predict the LGD value based on the available collateral
pledge on business. Other factors will determine the final LGD value too.
46
4.2 LOGISTIC REGRESSION
Based on the distribution of the observed LGD, we can expect that logistic regression can
capture the LGD distribution better. Other credit default databases have already been
analyzed using logistic regression. Previous studies from Fortis already showed that LGD can
be calculated by means of the following formula:
Xb
Xb
e
e+
+
+1
with b = -2.93
X = 4.03*Iclass1,var1 + 0.546*Iclass2,var1 + 0*Iclass3,var1 + 1.936var2
+ 0.174*Iclass1,var3+0*Iclass2,var3 + 1.02*Iclass1,var4 + 0*Iclass2,var4
where Class1 = Loan secured by first lien mortgage
Class2 = Loan secured, first lien
Class3 = Secured loan but not first lien
Iclassi = 1 if customer falls into this class i
= 0 otherwise
var1 = type of first collateral taken with rank=1
var2 = total collateral value when pledged / EAD
var3 = number of collaterals
var4 = length of relationship
This Logistic Regression model was rebuilt on the PECDC database in order to determine
whether LGD is dependent on type of collateral and collateral value. Hereby, it’s important to
mention that only collateral / securities that belonged to 1 loan were considered. This way, the
bias was corrected that might be caused by assigning the total collateral value to 1 loan where
the collateral should be divided over the different loans it belongs to.
The independent variables used in the model were:
• Country: Belgium, Netherlands, France, Germany, US, UK, Europe, Hongkong,
Switzerland, Singapore
• Guarantor available yes / no and Guarantor percentage
• Seniority codes: Senior/Paripassu, Subordinated, Other, Equity
• Security Rank: 1 = Secured by first and non-shared lien on assets
2 = Secured by first and pari-passu lien on assets
3 = Secured by second lien on assets
• Variables to indicate whether a loan is secured by cash, mortgages, pledge on business,
intangibles or other securities
• Interactions between the previous two types of variables, i.e. variable to indicate
whether a loan is secured by cash with security rank 1
47
• Number of securities associated with one loan
• Total collateral value / Exposure at Default
• Information about the Length of Relationship was not available in PECDC and thus not
used as a variable
The dependent variable in a Logistic Regression model is binary. 50% of all LGD values were
classified as a ‘0’ and 50% as a ‘1’. By doing this, the LGD values below 2,085% were classified
as ‘0’ and LGD values over 2,085% as ‘1’. Consequently, the low LGD values are classified as
zeroes and the high(er) LGD values as ones. The 2,085% is called the cut-off value.
In order to simplify the results of the regression analysis, it was decided to run a stepwise
procedure. This procedure only keeps the significant variables in the model. The SAS output in
Figure 13 below shows the retained variables and their estimates. The remaining significant
variables are:
• Guarantor percentage
• Indicator for unknown seniority
• Country indicators: United States, Great-Britain, Singapore and Hongkong
• Indicator for mortgages with security rank 1
Figure 13 SAS Output First Logistic Regression
We can assess the effect of a variable by looking at the following equation.
The Probability that LGD equals 1 = Xb
Xb
e
e+
+
+1
with b = Intercept = - 0.1125
X = -0.00598 * Guarantee_Percentage + 1.52152 * Sen_unknown
+ 1.2788 * US + 1.2284 * GB + 1.4837 * Singapore
– 2.3194 * Hongkong - 1.0297 * Mortgage_rank1
48
In general the probability that the LGD equals 1
• decreases if - there is a guarantor
- it’s a loan from Hongkong
- there is a mortgage with rank 1 associated with the loan
• increases if - it’s a loan from US, GB or Singapore
To assess whether this model is satisfying, we need to determine how many loans are
classified correctly if we use the model. For example, how many loans with a low LGD are
classified as a loan with a low LGD? Therefore, we use the model on a test sample: a random
selection of 40% of the entire data sample. Due to sampling, the proportion of zeroes and
ones is no longer exactly 50/50. The results of this process can be summarized in a confusion
matrix:
ACTUAL VALUES
PREDICTED VALUES
1 0
1 238 71
0 668 829
Figure 14 Confusion matrix of the logistic regression with a cut-off value of 2,085%
Based on this confusion matrix we can see that 59% ((238 + 829) / 1800) is assigned the
correct LGD value, this is called the Percentage Correctly Classified (PCC). Unfortunately, we
do not know the performance of the model that was built before on other databases.
Consequently, other studies cannot be used as a benchmark for the performance of this
model.
So far, the goal of the analyses was to investigate whether a relationship between collateral
and LGD could be revealed by using a logistic regression analysis instead of a linear regression.
The results of the analyses show that LGD is lower if the loan is secured by a first and non-
shared lien on mortgages. Other collateral related variables were insignificant.
As a last analysis, we use a cut-off value of 50%. This means that the LGD values below 50%
were classified as ‘0’ and LGD values over 50% as ‘1’. In this case; 80,0% of all LGD values was
classified as a ‘0’ and 12,0% as a ‘1’. The results of this logistic regression are presented in
Figure 15 below.
49
Figure 15 SAS Output Second Logistic Regression
In comparison with the first logistic regression, we notice that the factor mortgage doesn’t
have a significant effect anymore. This factor has been thrown out of the model by the
stepwise selection procedure.
To evaluate the model, we can again take a look at the confusion matrix:
ACTUAL VALUES
PREDICTED VALUES
1 0
1 1 5
0 215 1578
Figure 16 Confusion matrix for the logistic regression with a cut-off value of 50%
We can compute that the PCC is now 87,7% (1579/1799). Unfortunately, this measure gives us
a very distorted picture.
If we use no statistical model and classify every loan as a low LGD, we get the following
confusion matrix:
ACTUAL VALUES
PREDICTED VALUES
1 0
1 0 0
0 216 1583
Figure 17 Confusion matrix when using no statistical model
In this case we have a PCC of 87,9% and this without any statistical model. This PCC is even
better than the previous results. Therefore it’s particularly important to know which cut-off
value we should use.
50
When we look at the distribution of the LGD values, we notice that the biggest group of them
has a low LGD value. This is the reason why the first cut-off value was 2,085%, which is very
low.
Figure 18 below shows the histogram of the LGD values after replacing the outliers by the
value of the first and 99th percentile. According to the Credit Modelling department at Fortis14,
this distribution of the PECDC database differs strongly from other databases. For credit
default databases, a typical U-shape is expected. However, in the PECDC database there are
more low LGD values than in other, comparable databases. As we can see, approximately 45%
of the contracts have an LGD value around zero. This peak should be around an LGD value of
20, according to the Credit Modelling department at Fortis. Therefore the PECDC database
should first be analyzed into more detail. As we will see in the following section, it’s
necessary to ‘clean’ the database before running a regression analysis.
Figure 18 Histogram for LGD
14 Bernard Heylens and Jean Paulus
51
4.3 CONCLUSIONS FROM FORTIS BANK’S ANALYSIS
According to Fortis Bank, these are the main advantages (+) and disadvantages (-) of the
PECDC database (Van Geel, 2008):
++++ The PECDC database is the first multi-asset class and cross-border database
containing LGD, PD and EAD values. Consequently, the information in PECDC can be
used for benchmarking, and fulfill both regulatory as business needs.
- If the data available for Fortis Bank needs to be analyzed by country and year,
the number of observations per country and year gets very small sometimes.
Due to this small number of observations, the results at this level cannot be
generalized. If the PECDC database enlarges in the future, this problem will be
solved.
++++ In the overall PECDC database 37% of the contracts are secured by collateral and 28%
are secured by a guarantor. Consequently, it’s possible to analyze the average
performance of secured and unsecured contracts detailed.
- The number of contracts with securities is relatively low. If possible, it should
be investigated if there is indeed a small number of contracts secured or if this
information is just not entered into the PECDC database all the time.
- In the PECDC database available at Fortis, the number of observations by
collateral type (i.e. mortgage, cash…) is too small for advanced modelling
techniques. Moreover, the number of contracts with missing values for
collateral value, rank of security or percentage guaranteed is higher than 20%.
The significance of variables with 20% (or more) missing values can be damaged
in modelling processes. These variables are “desired” instead of “required”.
- The Credit Modelling department at Fortis 15 believes that the high percentage of
LGD’s equal to zero is unrealistic.
Three possible causes for this problem were suggested:
1. The data in the PECDC database is incorrect (worst case scenario)
2. The data in the PECDC database is biased by the fact that banks mostly enter
cured defaults in the database
3. The definitions for defaults, the way of dealing with defaults and the definition
of cured contracts might be different for the PECDC participating countries or
member banks.
15 Bernard Heylens and Jean Paulus
52
4.4 STUDY BY PROF. DR. ZAGST
During three years, Prof. Dr. Zagst and Stephan Höcht performed a study on the PECDC LGD
database: “Modelling Techniques with LGD Data.” Prof. Dr. Zagst and Stephan Höcht work for
the HVB-Institute for Mathematical Finance, which is part of the University of Munich
(Technische Universität München).
Prof. Dr. Zagst has given a presentation of his study on the PECDC Analytics Meeting, which
took place at the 11th December 2008 at the headoffice of Fortis in Brussels. I had the
opportunity to attend this day, where several studies regarding credit modelling were
presented. Algorithmics also presented the statistics from the PECDC database of December
2008. Furthermore, there was a discussion on the methodology of PECDC.
In this section, I will give an overview of their study, based on their presentation (Zagst, R.,
Höcht, S., 2008).
As most of the literature about this topic, this study works with the Recovery Rate (RR). There
is a simple relation between RR and LGD:
4.4.1 Literature Review
Most of the studies on recovery rates are based on data from the US bond market rather than
on loan recoveries (e.g. Altman and Kishore 1996). There are also some studies that
concentrate on recoveries from bank loans, but again most of them with focus on the US (e.g.
Asarnow and Edwards (1995)). Recently, a number of studies on bank loan recovery rates on
the European market emerged (Grunert and Weber (2005), Dermine and Nete de Carvalho
(2006) and Davydenko and Franks (2008)). Yet there is no study that analyses recovery rates
based on a broad Pan-European dataset.
The study of Prof. Dr. Zagst has three main contributions:
1. A detailed overview on factors that might influence recoveries and their appearance in
literature.
2. Introduction of further explanatory variables which have not been considered yet in
the literature (e.g. different asset classes as proposed in §215ff of the Basel Committee
on Banking Supervision (2004)).
LGDRR −= 1
53
3. Description of determinants and behaviour of loan recovery rates on a facility level (i.e.
for each defaulted instrument) for the first time based on such a large Pan-European
database. This has two big advantages:
A. Consistent definition of default and recovery rate over different jurisdictions.
B. Empirical comparison over different countries, industry sectors and asset classes.
4.4.2 The Data
Prof. Dr. Zagst has performed his study based on the PECDC database version June 2007. The
regressions in the previous sections of this chapter were based on the database version June
2008, which is a little more extensive.
Prof. Dr. Zagst works with the economic recovery rate, which is the present value of all post-
default cash flows as a percentage of the default amount. The cash flows are discounted by
the Euro Libor Risk Free Rates as at the loan default date.
However, the most important point regarding the data is the “cleaning” of the database.
Prof. Dr. Zagst has removed the following facilities from the database:
1. Facilities with default amount zero, as those do not represent a real physical loss
2. Facilities that are not yet fully resolved or exhibit cash flows that are not reasonable,
i.e. total sum of all reported cash flows divided by outstanding amount at default < 90%
or >110%
3. Facilities with abnormally high or low recoveries, i.e. < -50% or > 150%
In his study, Prof. Dr. Zagst works with two datasets; the first contains the facilities with
recoveries in [-0.5, 1.5], and the second with recoveries in [0, 1].
Table 4 below shows some basic statistics of the recovery rates from both datasets.
RR in [-0.5, 1.5] RR in [0, 1]
Simple Weighted16 Simple Weighted16
Mean 55.6% 71.2% 60.7% 69.3%
St.dev. 44.3% 32.3% 40.1% 30.8
Median 75.6% 85.8% 80.3 82.2%
25%-quantile 0.0% 48.7% 14.2% 48.1%
75%-quantile 98.2% 97.5% 97.6% 96.1%
Number 31865 25232
Table 4 Basic statistics of the recovery rates
16 Weighted: Recovery rates weighted with the size of the issue at the date of default.
54
The numbers in Table 4 are completely in line with the expectations. This is illustrated in the
distributions of the recovery rates below, in Figure 19 and 20. The overall distribution of the
recovery rates is bimodal or U-shaped. This applies also for almost all subcategories, e.g.
recovery rates for different industries, facility types, asset classes … Similar results can be
found in many other studies, e.g. Asarnow and Edwards (1995), Araten et al. (2004) or
Scheurmann (2004).
Figure 19 Recovery Rates in [-0.5, 1,5] (Zagst and Höcht, 2008)
Figure 20 Recovery Rates in [0, 1] (Zagst and Höcht, 2008)
55
4.4.3 Univariate Analysis
The independent variable for the Univariate analysis is the facility-level economic recovery
rate. Prof. Dr. Zagst works with five categories of explanatory variables:
1. Default process: factors describing the time from initiation of the contract to
resolution as well as the reason that caused the default event and the exposure at
default (EAD), e.g. time to default or workout period.
2. Facility level: factors describing the defaulted instrument, e.g. facility type or size of
the issue.
3. Entity level: factors describing the borrowing entity as a whole, e.g. industry or
geographical jurisdiction.
4. Collateralisation: factors describing the impact of collateral, e.g. quota of collateral or
rank of security.
5. Macroeconomic: factors describing the macroeconomic environment, e.g. GDP or
Euribor17.
17 The Euro Interbank Offered Rate (or Euribor) is a daily reference rate based on the averaged interest rates at
which banks offer to lend unsecured funds to other banks in the euro wholesale money market or interbank
market (Wikipedia definition).
56
Table 5 below gives a summary of empirical findings in the literature and from PECDC data.
Influence factor + (positive) o (not sign.) - (negative) * (significant) PECDC
Seniority X O
Presence of Collateral X +
Liquidity of Collateral X +
Quota of Collateral X +
Presence of Quarantee X +
Industry of Borrower X X O
Size of Issue X X X +
Number of Loans X +
Facility Type X X *
Default Type X X *
Time to Default X +
Time to Resolution X X +/-
Geography X X *
Rating/Creditworthiness X X +
Aggregated Def. Rates X X o/-
Macroeconomics X X +/o/-
Rank of Security +
Facility Asset Class *
Table 5 Summary of empirical findings in the literature and from the PECDC data
57
4.4.4 Multivariate Analysis
For the multivariate analysis, Prof. Dr. Zagst examined two sets of explanatory variables:
1. All variables which are available for all defaulted facilities.
2. All facilities for which spread observations are available.
Prof. Dr. Zagst chose to use Mallow’s Cp-statistic as a variable selection criterion in a
backward-forward selection procedure.
The multivariate analysis showed that the spread has a negative impact on recovery rates.
Besides the spread, the most important factors in the model with spreads refer to the degree
and quality of collateralisation and the type of default. The quality of collateral, rank of
security, and EAD all have a positive impact on recovery rates.
In the model without spreads the facility asset class, the facility type, the industry group, and
the size of the issue are significant besides the factors describing collateralisation and type of
default.
4.4.5 Conclusions
The study investigated workout recoveries of bank loans with regard to their determinants and
their behaviour.
It was shown that the discount rates chosen for the calculation of workout recoveries can have
a great impact on the recovery value for facilities with a long workout period. For facilities with
a moderate workout time the influence of the chosen discount rate is rather small.
The application of three cleaning rules resulted in a bimodal or U-shaped distribution of
recovery rates.
The most important component in workout recovery rates on a facility level is the presence
and quality of collateral. In addition to that, the creditworthiness measured by the spread at
default and the reason for default play a significant role in determining loan recoveries. The
size of issue and the issuer have a positive impact on recoveries.
Macroeconomic variables play only in a minor role on the facility level in the dataset.
58
4.5 SUMMARY
In my opinion, PECDC has two big issues it should focus on:
1. Data quality
2. Credibility
As the study from Prof. Dr. Zagst proofs, the data quality of PECDC is very good in
comparison with other loan loss collections. After the application of a few acceptable filters
and restrictions, the database is of high quality. Regarding the second issue, the credibility
of the initiative, there is still a lot of work to do. However, a major step might be the
publication of the study of Prof. Dr. Zagst in an important scientific journal. This would
certainly increase the acceptance of the database by the regulators, potential investors and
the credit rating agencies, which would eventually benefit all the participants of the
consortium.
59
5 USE OF THE PECDC DATA
Why do financial institutes participate in the Pan-European Credit Data Consortium? We are
now ready to answer this important question because we know what PECDC is (Chapter 3), we
know how the data collection process works (Chapter 3), and we have a good understanding
of the database itself (Chapter 4). The aim of this chapter is to explore the use of the PECDC
data by the financial institutes participating in the consortium. As mentioned in the
introduction of the first chapter, the use of the PECDC data is twofold. On the one hand, the
financial institutions want to use this data as a benchmark for their securitisation
transactions. On the other hand, there is the regulatory aspect; financial institutions are
required to provide more accurate estimates of their credit risks under the Basel II regulatory
framework. Moreover, to remain Basel II compliant banks are required to determine a so-
called Reference Data Set (RDS). A general background on the Basel II regulatory framework
has been provided in Chapter 2.
For these two uses of the PECDC data, it’s important to analyse them bearing in mind the
current market conditions, which have been discussed in Chapter 1.
The first section of this chapter starts with an introduction to structured finance and
securitisations, followed by the contribution of the PECDC data to securitisations. In the
second section, the contribution of the data to internal modelling is discussed.
5.1 PECDC AND SECURITISATION
5.1.1 Introduction to securitisation
Securitisation is a financial technique whereby financial assets are pooled and sold in the form
of securities. These assets can be mortgages, auto loans, student loans, credit card receivables,
lease payments, accounts receivable, corporate debts, etc. Below are two definitions of the
concept “securitisation”; together they will make the concept better understandable.
1. Securitisation is a structured process whereby interests in financial assets, such as
mortgages, loans, or other receivables, are packaged, underwritten, and sold in the
form of securities (Fortis Bank definition).
2. Securitisation is a structured finance process, which involves pooling and repackaging
of cash-flow producing financial assets into securities that are then sold to investors
(Wikipedia definition).
In general, two types of securitisations can be distinguished: traditional and synthetic
securitisations. But before this difference is explained, a more urgent question is answered:
“What is a securitisation used for?” There are in fact two important motives for a
securitisation transaction:
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1. Improve liquidity: Securitisation makes future cash flows available for immediate
spending or investment. This is usually achieved through traditional securitisation.
2. Lower capital requirements: Domestic regulators insist that banks keep certain levels
of capital on their balance sheet to offset particular risks. By means of securitisation,
banks will be able to lessen the regulatory capital because they transfer the risk
associated with the securitized assets. This is usually achieved through synthetic
securitisation.
Initially, the originator owns the assets engaged in the
deal. In a traditional securitisation, a suitably large
portfolio of assets is selected, pooled and sold to a
Special Purpose Vehicle (SPV) or Single Purpose Entity
(SPE) also called the issuer. The SPV, in turn, issues
securities to investors. These securities are usually
notes, commercial paper, bills, bonds or preferred
stock. The means received from the issue are on to the
originator as payment for the assets.
In a synthetic securitisation however, the pool of assets is not transferred itself, but only the
credit risk associated with it. More specifically, the owner of the assets transfers the credit risk
of a portfolio of assets to another entity or directly to the capital markets. Although the credit
risk of the portfolio is transferred, the actual ownership remains with the original owner. The
credit risk in the portfolio of assets could be managed through the use of credit derivatives,
such as Credit Linked Notes (CLN) and Credit Default Swaps (CDS), which can themselves be
packaged into a 'synthetic' asset pool, and securities can then be issued based on the risk
characteristics of the pool. This process may either be funded or unfunded. In a funded
process the sponsoring bank receives cash upfront from the risk purchaser and can look to
that cash to absorb losses on the specified assets. In the unfunded process the risk purchaser
only provides a promise to make payments in the future to absorb losses when they would
occur.
The source of repayment to the investors is cash generated from the assets which back the
transaction. These assets can be any type of asset with a reasonable stream of future cash
flows.
The SPV is established specifically to facilitate the securitisation. It’s designed to be
bankruptcy-remote, meaning that if the originator goes into bankruptcy, this should not affect
the viability of the SPV. In other words, the assets of the issuer will not be distributed to the
creditors of the originator. In order to achieve this, the governing documents of the issuer
restrict its activities to only those necessary to complete the issuance of securities.
The next step in the process is then the construction of the securities that lie behind the
securitisation. It can be required for some classes of the underlying securities to obtain a
formal credit rating. In case there are different classes of securities, it has to be examined how
these classes differ in terms of timing of payments, creditworthiness, …
Figure 21 Traditional Securitisation
(Fortis Bank Illustration)
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Different parties are usually involved in structuring and marketing the securities. The regulator
has to approve the transaction. To facilitate investor demand, credit rating agencies (such as
Fitch, Moody’s and S&P) assess the likelihood that the SPV will default on its obligations and
assign an appropriate rating. The recent criticism regarding these ratings has already been
discussed in Chapter 1. The SPV wants to ensure a rating as high as possible for the securities,
therefore it usually obtains liquidity support and credit enhancement.
Liquidity support is provided to a SPV to assist meeting payments to investors in case there
would be insufficient cash flow from the receivables.
To make a specific tranche or an entire transaction stronger, and thusly more appealing to
investors, different methods of credit enhancement can be applied:
• Excess spread: In this case, the average interest rates of the underlying loans are
higher than the interest rate of the sold security, allowing a buffer in case some of the
underlying loans are late or non-performing.
• Overcollateralization: This method is similar to the excess spread, but here the total
value of the underlying loans is larger than the security being sold. This forms again a
buffer against bad underlying loans.
• Wrapping: The performance of some or all tranches of a transaction is guaranteed up
to a certain amount by a third party with an excellent credit rating, usually by monoline
insurance companies. So these companies insure investors against insolvency of bond
issuers. However, the crisis has taught us that these monocline insurance companies
did not deserve their excellent credit rating…
Moreover, individual securities are often split into tranches, or categorized into varying
degrees of subordination to make the end product more appealing. The process of a
securitisation transaction and the tranching is shown in Figure 22 below.
Figure 22 Securitisation Transaction Structure (Fortis Bank Illustration)
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Each tranche has a different level of credit protection or risk exposure. In general, there is a
senior (“A”) class of securities and one or more junior subordinated (“B,” “C,” etc.) classes that
function as protective layers for the “A” class. The senior classes have first claim on the cash
that the SPV receives, and the more junior classes only start receiving repayment after the
more senior classes have been repaid. Because of the cascading effect between classes, this
arrangement is often referred to as a cash flow waterfall. The most junior class (often called
the equity class) is the most exposed to payment risk, but also gets the best return for bearing
this risk. Just as for every investment, the investor in a securitisation faces the risk – return
trade-off. Bodie, Kane and Marcus (2008) describe this fundamental investment trade-off as
follows:
“Investors face a trade-off between risk and expected return. Historical data
confirm our intuition that assets with low degrees of risk provide lower returns on
average than do those of higher risk. “
The construction of the security is now completed. The general term for this structured
finance product is Asset-Backed Security (ABS). After constructing the securities, they have to
be sold to investors to gather funds for the asset transfer. This can be regarded as the last step
in the process of a securitisation transaction. Mostly, standard capital market distribution
channels will be employed. The note sale is underwritten and distributed by a single
investment bank or a syndicate of banks.
In Chapter 1, the causes of the financial crisis have been discussed. It was indicated that too
complex structured finance products are one of the causes. The problem is indeed that there
are much more exotic products than the straight forward ABS products as described above…
The best known structured finance products are the Collateralized debt obligations (CDOs).
These are derivative products that are usually based on other ABS products. A CDO will take
another ABS, either alone or in conjunction with other ABS, will restructure or repackage this
to form a product that is not directly linked to the underlying loans anymore. CDOs come in
many different varieties, such as Arbitrage or balance-sheet motivated. Arbitrage CDOs take
advantage of the fact that they can buy high yielding assets and restructure them into lower
payments to investors, while balance-sheet CDOs are used to move assets off their balance
sheets to remove some of its credit risk.
The de Larosière report clearly stated that too complex structured finance products with
inappropriate ratings are one of the causes.
• The extreme complexity of structured financial products, sometimes involving several
layers of CDOs, made proper risk assessment challenging for even the most sophisticated
in the market (de Larosière, 2009).
• Credit Rating Agencies (CRAs) lowered the perception of credit risk by giving AAA ratings to
the senior tranches of structured financial products like CDOs, the same rating they gave to
standard government and corporate bonds (de Larosière, 2009).
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The problem is indeed that some structured finance products are far too complex. The story
doesn’t end with ABS products… There exists also something as CDO² (CDO squared). These
are the next iteration of a CDO. It’s a derivative of a derivative. It takes CDO (one or many) and
restructures them so that the investors can purchase whichever tranche that suits their needs.
This illustrates the complexity of some structured finance products. More information on
these products and an explication of some other acronyms concerning structured finance are
explained in Appendix 4.
Concerning securitisation transactions, (de Larosière, 2009) says:
“In this environment of plentiful liquidity and low returns, investors actively sought higher
yields and went searching for opportunities. Risk became mis-priced. Those originating
investment products responded to this by developing more and more innovative and complex
instruments designed to offer improved yields, often combined with increased leverage. In
particular, financial institutions converted their loans into mortgage or asset backed securities
(ABS), subsequently turned into collateralised debt obligations (CDOs) often via off-balance
special purpose vehicles (SPVs) and structured investment vehicles (SIVs), generating a
dramatic expansion of leverage within the financial system as a whole. The issuance of US ABS,
for example, quadrupled from $337 billion in 2000 to over $1,250 billion in 2006 and non-
agency US mortgage-backed securities (MBS) rose from roughly $100 billion in 2000 to $773
billion in 2006. Although securitisation is in principle a desirable economic model, it was
accompanied by opacity which camouflaged the poor quality of the underlying assets. This
contributed to credit expansion and the belief that risks were spread.”
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5.1.2 Contribution of the PECDC data to securitisations
Financial institutes need reliable credit data for their securitisation transactions to be able to
give an accurate estimation of the quality of the underlying assets. Reliable credit data provide
transparency: standardised information of underlying assets to be provided by issuers. It’s
only when transparency exists that risk can become priced correctly.
Uncertainty about the credit performance of loans contributes a large share of securitisation
costs to Fortis Bank. This uncertainty can be reduced by comparing the internal data with the
data obtained from PECDC. The more data a financial institute has, the better the rating will be
it can obtain from a credit rating agency and the more willing the regulator will be to approve
the transaction. More data will also make it easier to convince potential investors of the
quality of the securities. Moreover, this data has to be of good quality. The previous chapter
showed that, after the proper application of a few acceptable filters, the data quality obtained
by PECDC is better than that of other datasets. Another important factor is the credibility: the
regulator, the credit rating agencies and the potential investors have to be convinced of the
data quality offered by the consortium.
Figure 23 below gives a cost breakdown for the securitisation of a loan portfolio:
Uncertainty
Lack of perfect information
regarding the product
characteristics, caused by
• insufficient data
• limited market
transparency
• imperfect models
• illiquid markets
Portfolio management
• Collection of cash flows
• Monitoring of the loans in the portfolio
Variability (unexpected loss)
Deviation of the actual loss from the
expected loss in adverse scenarios
Expected loss
Predictable loss on the
portfolio, given the
probability of default and
loss given default ratios
of the loans
Figure 23 Securitisation cost breakdown
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5.2 PECDC AND THE REGULATORY ASPECT
Financial institutions are required to provide more accurate estimates of their credit risks
under the Basel II framework. More advanced internal models can result in more accurate
estimates and lower regulatory capital requirements. For the development of these models, a
large amount of credit data is needed. Individual banks do not have enough credit data,
especially not for every sector or every type of loan. The Pan-European Credit Data
Consortium offers banks the possibility to pool their credit default data.
In Chapter 2, it was already shortly indicated that the Basel Committee on Banking
Supervision urges banks to participate in this kind of initiatives. This will be further explained
now, with the introduction of the concept of a Reference Data Set.
Besides the Basel Committee on Banking Supervision, the national regulators also support the
PECDC initiative. To prove this, the opinions of both the Belgian regulator and the regulator
from the United Kingdom will be discussed.
5.2.1 Basel Committee on Banking Supervision
The participating banks have received many economic LGD realisations from the PECDC. Not
all these realisations are comparable to their portfolios or are useful for analysis. Selections of
the data have to be made to create data subsets that are useful for analysis and comparable to
their portfolios. Such a data subset is called a Reference Data Set (RDS). To remain Basel II
compliant banks are required to determine an RDS when they estimate risk parameters. In
Working Paper No. 14 from the Basel Committee on Banking Supervision (2005), an RDS is
defined as follows:
For a certain portfolio, an internal or external reference data set (RDS) is required to
estimate the risk parameters (PDs, LGDs and EADs) that are needed for internal uses
and the computation of capital requirements in the IRB approach. Ideally, these RDSs
should:
• Cover at least a complete business cycle,
• Contain all the defaults produced within the considered time frame,
• Include all the relevant information to estimate the risk parameters, and
• Include data on the relevant drivers of loss.
In practice, banks use RDSs that include internal and/or external data that may cover
different time frames, use different definitions of default and, in some cases, contain a
biased sample of all the defaults produced within the timeframe. Thus, it’s necessary to
check for consistency within the RDSs. Otherwise, the final estimates of LGD will be
inaccurate or biased.
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5.2.2 Is the PECDC dataset an RDS?
The PECDC database contains data as far back as 1981, and up to 2009. Because the average
work out period of a default is around 2 years, the years 2006, 2007 and 2008 are far from
complete. These years are therefore not eligible for inclusion in the RDS.
The Dutch investment bank NIBC, which is also a member of PECDC, examined how well the
PECDC dataset complies with the above Basel II criteria for an ideal RDS. Their main findings
for each of the four criteria are summarized below.
1. Do the PECDC data cover a complete business cycle?
Although business cycles are irregular and different for each asset class, most last between 3
and 5 years. The 8 years (1998-2005) of data present in the PECDC dataset probably cover a
full business cycle for at least the asset classes Large Corporate and SME. Note that this period
does not yet include a severe downturn year like the Asia Crisis (1997) or the Credit Crisis
(2007-2008). Downturn events like 9/11 and the Internet bubble are included in this data
range (NIBC, 2008).
2. Do the PECDC data contain all the defaults produced within the timeframe?
In other words, did all banks send all relevant defaults during the years 1998-2008? It’s clear
from the data and the contracts that not all relevant defaults are included in each year:
• Some banks only participate in 1 or a couple of asset classes
• Not all banks have sent data from 1998 onwards
• Some defaults are not resolved and therefore not (yet) submitted; this applies
primarily to the later years (2006-2008).
• Due to data issues it was not possible for banks to deliver all relevant defaults during a
year, for example, if the systems in a foreign office were not eligible for PECDC delivery.
At first glance, the PECDC data clearly do not meet the second criterion. However, considering
the fact that the database does contain many observations per year, one could argue that
these defaults are a representative subset of all relevant defaults and therefore sufficient to
create an RDS. There is no reason to assume a bias in the submitted defaults, especially since
the data are made completely anonymous (NIBC, 2008).
3. Do the PECDC data contain all the relevant information to estimate the risk parameters?
As mentioned in the first section of this chapter, it was only very recently that PECDC started
with the collection of PD data. This means that, since the data collection of March 2009, all
three risk parameters (PD, LGD and EAD) are included in the database.
For the estimation of economic and accounting LGD the outstanding at default and resolution
plus the cash flows and charge-offs between default and resolution are required. All this
information is present in the PECDC database (NIBC, 2008).
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For the estimation of EAD the outstanding and limit at default and the outstanding and limit
prior to default are required. The outstanding and limit are available for all facilities at default
and for almost 67% of the facilities 1-year prior to default (NIBC, 2008).
We can conclude that all the relevant information is present to estimate LGD, sufficient data is
present to estimate EAD and in the near future, the database can also be used for the
estimation of PD.
4. Do the PECDC data include all the data on the relevant drivers of loss?
Within the economic science there is no definite consensus about which drivers are the most
important for the estimation of LGD. This is one of the main reasons why the PECDC data
pooling is taking place. Most banks do not have sufficient historical data to calibrate an LGD
model. Up until now the PECDC data were not of such quality to allow calibration of an LGD
model. The question whether all data on the relevant drivers are available can therefore not
be completely answered yet. There is consensus about some drivers of LGD: for instance
collateral, guarantees, seniority and country of jurisdiction. Information on these drivers is
available in the PECDC database. The question whether all data on the relevant drivers is
present will remain for the time being. Currently the focus is on improving the data quality. If
an accepted data quality level is met, the focus will probably turn to the drivers of LGD and
whether the data template is sufficient to provide the data for all relevant drivers.
Conclusion: Are the PECDC data eligible for the creation of an RDS for LGD and EAD analysis?
The PECDC database is the largest database available worldwide containing data for LGD and
EAD analysis on bank loans. From the previous chapter it’s clear that the PECDC data is not
perfect and that there is room for improvement. Nevertheless, if selections and filters are
properly applied to the dataset the PECDC data are the most eligible data currently available
for the creation of an RDS.
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5.2.3 National Regulators
This section gives an overview of the viewpoint and opinion of the regulator from the United
Kingdom, the Financial Services Authority (FSA), and the Belgian regulator, the CBFA,
regarding PECDC.
A number of financial institutes from the UK are participating in PECDC, e.g. Barclays, Royal
Bank of Scotland and Bank of Ireland. The FSA has clearly communicated its viewpoint on
PECDC.
In its paper “Wholesale LGD models”, published in the beginning of 2007, the FSA says:
A number of firms have chosen to participate in industry-wide data gathering exercises to
improve available data in the near future, notably the Pan-European Credit Data Consortium.
As a result of the scarcity of useful internal loss data, most firms have taken the approach of
using a combination of some or all of the following to produce LGD estimates:
• Available internal data
• Expert opinions from credit departments, recoveries departments and front line
lending units
• Benchmarks provided by consultants
• Available external data
As a minimum, by the end of 2007, all AIRB18 firms must have done the following:
• Where external benchmarks have been used, these benchmarks must be sufficiently
understood by the firm and the extent of their relevance and suitability sufficiently
identified for it to satisfy itself that they are fit for purpose
• Be able to make use of any relevant and appropriate external data
Although we acknowledge that these further measures will have a longer term horizon, if not
already doing so then all AIRB firms must continue to seek and utilise relevant and appropriate
external data.
18 AIRB firms are firms that use the Advanced Internal Rating Based approach for credit risk modelling in the first
pillar of the Basel II regulatory framework.
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On the PECDC Analytics Meeting, which took place at December 11 2008 at the headoffice of
Fortis in Brussels, the FSA presented its viewpoint on PECDC and its purpose. These were the
essentials of their presentation:
• Supervisors expect firms to make use of greater loss data, not to cut costs.
• Are firms serious about LGD modelling or not?
This is the time, do not take your eye off the ball!
• Understanding drivers of current losses helps avoid losses in the future.
• Previous pooling exercises were undermined by a low cost, easy to do approach.
• The FSA has committed to PECDC.
It’s not part of the culture of the CBFA to take strong positions on initiatives as PECDC. The
viewpoint of the CBFA regarding PECDC has to be concluded from verbal agreements and
discussions between them and the Credit Modelling department at Fortis Bank.
“In the discussion of the internal modelling, the CBFA has questioned the PECDC data because
it suited them well. The fact is that the average LGD values from the PECDC database are
smaller than the conservative LGD estimates that Fortis Bank uses for their internal modelling.
Therefore, the CBFA wants Fortis Bank to use the conservative LGD estimates instead of the
PECDC averages. But contradictory, the CBFA has always strongly encouraged banks to search
for external data,“ says Jean-Marc Montens from the Credit Modelling department at Fortis
Bank.
“Basel II explicitly mentions ‘data pooling’ as a possibility for low default portfolios. The CBFA
also attaches importance to the fact that all available information is used in the development
and review of the model. In that way, the participation of Fortis Bank in PECDC is important for
the CBFA. On the other hand, the collection of data by itself is not enough. A great deal will
depend on the use of it in the modelling and benchmarking. The CBFA studies the whole of the
modelling process, and is not inclined to speak out on specific parts of the process,” says Uwe
Kleineidam from the Credit Modelling department at Fortis Bank.
The conclusion of this section is that both the Basel Committee on Banking
Supervision and the national regulators strongly support the PECDC initiative, and
even urge financial institutes to participate in this kind of initiatives.
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6 LEVERAGE OF THE PECDC DATA
The previous chapter comprehensively explained that the use of the PECDC data is twofold. On
the one side, the data is used as a benchmark for securitisation transactions, and on the other
side, there is the regulatory aspect. The aim of this concluding chapter is to present a high-
level cost-benefit analysis for the PECDC project within Fortis Bank. I tackled this question as
a management exercise: should Fortis Bank continue to participate in the consortium?
The first section of this chapter analyses the cost for Fortis Bank of its participation in the
consortium. Thereafter, the benefits of the participation are assessed. Based on the discussion
of the previous chapter, the second section tries to translate both the business needs and the
regulatory needs into figures.
6.1 COST
A scenario analysis will be made to assess the cost for Fortis Bank of its participation in
PECDC.
The aim of Fortis Bank was originally to develop a system that realized an automatic data
collection. But unfortunately, it turned out that it was not easy to develop and implement such
a system. Therefore, the collection of the required data and the entering (or uploading) of the
data in the FAIL application were mostly done manually. This allows us already to distinguish
between two possible scenarios: one in which all data collection and uploading is done
manually, and one in which this is all done fully automated.
There is however a third scenario: a mix of the first two scenarios. In this case some data
would be collected and uploaded manually, and some automatically. This means that the
database systems that are most easy to integrate would be subject to the automated system,
and the others would still be treated manually. Next, these three scenarios will be discussed in
more detail.
6.1.1 Scenario 1: Manual data collection and uploading
Within Fortis Bank, it was found that collecting all the required data for PECDC was a very
difficult and laborious exercise. This was due to several factors and some of them were not
anticipated upfront. There are several database systems that had to be consulted to find all
the required information. For the older files, it was sometimes even necessary to consult
paper files, which is a very time consuming activity. Since there was a tight deadline for the
data delivery of April 2008, there was not enough time to develop an integrated system for an
automatic data collection. Therefore it was decided to do the work manually.
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Once the required data for a file was collected, it had to be entered in the FAIL application,
which was developed especially for this project. This application was discussed in Chapter 3.
But also uploading the files in FAIL was not as easy as anticipated. Every person that had to
work with the application needed a half day of training, which was indeed expensive for Fortis
Bank.
Figure 24 below illustrates this first scenario. The blue arrows show the automated data flow,
while the red arrows refer to the manual collection of data. The figure shows that both people
from the central offices as people from the local offices collect data from multiple database
systems. As mentioned, some data can only be found in paper files. The data from these paper
files is mostly uploaded in the existing database systems. The collected data is then manually
entered in the FAIL application. The FAIL application stores all the data in one central
database, in one standardised format. The data is than automatically uploaded to the PECDC
database. The dotted line makes the distinction between Fortis Bank (to the left) and PECDC
(to the right). Fortis Bank retrieves then data from PECDC, depending on their own data
delivery. This data is also stored in the central database. The teams who need the data for
business or regulatory needs can now access the database.
Figure 24 Scenario 1
Paper Files
FAIL Central
DB
Central and
Local People
PECDC
DB
systems
1. Business needs
2. Regulatory needs
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Within Fortis Bank, an assessment of the total cost of this scenario has been made. The aim
was to take into account all factors contributing to the cost of the project. This is the
conclusion of that assessment, which has been presented to the top of risk management:
Building on existing systems and the collaboration of several teams within
MB19 and FCRM20, the project could be completed at a cost of EUR 2.6 m, plus
EUR 0.8 m per year of running costs (as of the first year).
The calculation of the project cost of EUR 2.6 m is composed of 2 parts:
1. Unit cost hypothesis
This first part of the costs results from the necessity of collecting all the required data
and entering it in the FAIL application. The assumption is that 1 FTE21 can handle 150
observations and costs EUR 0.10 m. The forecast was that 1200 observations had to be
processed; this would result in a total cost of EUR 0.8 m.
2. One-off project costs
The workload of all the other activities related to the PECDC project is estimated on 18
FTE’s, which results in a cost of EUR 1.8 m.
The calculation of the running costs is also composed of 2 parts:
1. Data maintenance
Based on experience with other databases of a comparable size, the total amount of
running costs for data maintenance is estimated on EUR 0.5 m.
2. Data analysis
The necessary data analysis of the database will require 2 FTE’s. The cost of 1 FTE for
data analysis amounts EUR 0.15 m, a data analyst is more expensive than a worker who
has to enter the data in the FAIL application. This means that the cost for data analysis
amounts EUR 0.3 m.
19 Merchant Banking: Fortis Bank divides its banking activities into 4 categories: Retail Banking, Commercial
Banking, Private Banking and Merchant Banking.
20 Fortis Central Risk Management
21 Full Time Equivalent: This measure indicates how much workers are required to fulfill a task, for example 0,5
FTE means that 1 half-time worker is needed, 2 FTE’s means that 2 full-time workers are needed.
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6.1.2 Scenario 2: Automated data collection and uploading
Figure 25 below illustrates the second scenario, in which as much as possible of the data
collection and uploading is done automatically. Again, the blue arrows show the automatic
data flow, while the red arrows show the manual data flow. There is still some manual data
flow in this scenario, because there is some data that is, for the moment, only available on
paper files.
In this scenario, the people from the central and local offices first update the database
systems with the data from the paper files. All data can then be automatically collected by an
integrated system that enters the data in the FAIL format. From here, this scenario is the same
as the first scenario: the data is stored in one database from which the data is uploaded to the
PECDC database, and then data is retrieved from the PECDC database.
There are no figures available for the total cost of this scenario. In the short term, this scenario
will certainly be more expensive than the first scenario. In the long term however, this
scenario might be the most cost effective, especially since PECDC requires a data delivery
every 6 months and a smaller update of the defaults every 3 months.
Figure 25 Scenario 2
Paper Files
Central and
Local People
FAIL Central
DB PECDC
DB
systems 1. Business needs
2. Regulatory needs
Paper Files
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6.1.3 Scenario 3: Partially automated
This scenario is a mix of the two previous scenarios: an automatic data collection system is
developed for the databases for which data collection can most easily be automated; the rest
of the work is still done manually. It’s clear that this scenario is a compromise. On the one
hand it’s already a better situation than scenario 1 and on the other hand, this strategy is not
as expensive as scenario 2.
The current economic reality (see Chapter 1) has forced many organisations, including Fortis
Bank, to take cost cutting measures. Bearing this in mind, this scenario is probably the most
realistic in the short term since the required investment is lower than that in scenario 2.
For the long term, it’s clear that a fully automatic system is required. Therefore, this scenario
can be chosen as an intermediate stage, during which the benefits of the participation in
PECDC will become clear to Fortis Bank.
This brings us to the following section of this final chapter, in which the benefits of the PECDC
data are analysed.
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6.2 BENEFITS
The PECDC data could result in lower costs related to securitisation transactions and lower
Basel II regulatory capital requirements. An internal Fortis Bank document, also presented to
the top of risk management, states it as follows:
6.2.1 Lower securitisation cost
As discussed in the first section of Chapter 5, uncertainty can significantly contribute to the
cost of a securitisation transaction. This will be illustrated more clearly with an example: the
Park Mountain SME 2007-I securitisation from Fortis Bank. The total nominal value of the
portfolio for this transaction was EUR 3 billion. Figure 26 below gives an overview of the
different parts of the actual cost of this securitisation transaction, as a percentage of the
relieved capital.
Figure 26 Cost of the Park Mountain SME 2007-I securitisation (Fortis Bank Illustration)
1. The combination of better internal data with external data obtained from the
Pan-European Credit Data Consortium should help to obtain lower
securitisation costs and support expansion in credit markets where Fortis
Bank has a limited presence.
2. The combined dataset will improve Basel II risk weighted assets modelling,
especially of Loss Given Default (LGD), and should result in lower additional
capital requirements by the regulators.
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The Fortis Bank cost of capital, which amounts 18.5%, is its so-called hurdle rate. This hurdle
rate or the required rate of return is the minimum rate of return on an investment a company
is willing to accept. It’s the cost of capital a company uses to assess a potential investment.
As we can see, the uncertainty due to insufficient data quality amounts 5.4% of the total
amount of relieved capital. This is more than one fourth of the total actual cost of the
securitisation transaction. Off course, this 5.4% means a huge nominal amount. It’s mainly in
this part where we find the potential of the PECDC data: it can again be used as a benchmark.
This would create transparency and boost investor’s confidence. According to the people from
the department of Credit Portfolio Management at Fortis Bank, the cost due to uncertainty
amounts nowadays even one third of the total securitisation cost.
Because of the dubious practises of the credit rating agencies, as discussed in Chapter 1, Fortis
Bank is now considering the possibility to execute a synthetic securitisation transaction
without requesting a formal rating from a credit rating agency. This situation might even
multiply the potential benefit of the PECDC data, because in this case the value of a
benchmark increases. The PECDC database would offer the best benchmark in the market.
To illustrate the costs of acquiring a formal rating from a credit rating agency for a synthetic
transaction, the example of the Park Mountain SME 2007-I transaction is now further
discussed.
The costs for obtaining a formal rating from a credit rating agency are as follows22:
1. Initial fee EUR 400,000
2. Additional initial fee: combination notes EUR 10,000
3. Annual Monitoring fees EUR 20,000
4. Annual Monitoring fees: combination notes EUR 5,000
The annual fees have to be paid 5 times, since the standard maturity used within Fortis Bank is
5 years. This results in a total cost of 535,000 Euros.
However, for the Park Mountain SME 2007-I transaction, Fortis Bank acquired a formal rating
from each of the three credit rating agencies: Moody’s, Fitch and Standard&Poor’s. This
resulted in a total upfront cost of EUR 1,310,000. At that time, this cost had to be made to
convince the potential investors of the quality of the securities offered by Fortis Bank. Today,
the situation has changed… However, this figure provides an indication of the cost Fortis Bank
has to make to convince the potential investors of the quality of the securitisation transaction.
22 These numbers were found on the contract with Moody’s for the securitisation transaction in the example. The
people from the department of Credit Portfolio Management at Fortis Bank confirmed that the charges of the
two other CRAs are completely in line with these numbers.
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6.2.2 Lower Basel II regulatory capital requirements
The following figure shows the total amount of Risk Weighted Assets (RWAs) resulting from
Fortis Bank’s own assessment using an LGD value of 32.5% (on the left) and the CBFA’s
assessment based on an LGD value of 37.5% (on the right).
Figure 27 Cost in RWA (Fortis Bank Illustration)
As we can see from Figure 27, Fortis Bank’s assessment leads to a total of EUR 149 billion
RWAs, while the CBFA’s assessment results in EUR 172 billion RWAs. The difference between
these two assessments of the RWAs of Fortis Bank amounts no less than EUR 23 billion. This is
due the fact that the CBFA considers a 5 percentage points add-on to Fortis Bank’s LGD value.
The key question is now why the CBFA uses this add-on, since Fortis Bank has taken a
conservative approach to credit RWA computation.
The CBFA argues that Fortis Merchant Banking has a lack of data on its loan portfolio. The
DNB has also requested Fortis Bank to improve the registration of loan defaults, when Fortis
Bank Nederland was still part of Fortis Bank.
On the positive side, this means that there is a huge opportunity. If Fortis Bank is able to
convince the CBFA that its LGD value is indeed a correct representation of the quality of its
loan portfolio, this would result in lower additional capital requirements. Off course, this is
where the PECDC project plays its role. The PECDC data can be used as a benchmark for
convincing the CBFA of the accuracy of the Fortis Bank LGD value.
78
These EUR 23 billion RWAs entail a potential annual income of EUR 255 million:
mEURratehurdleratiotiercorebnEUR 255%5.18*1%6*23 =
The hurdle rate was explained in the previous section. The core tier 1 ratio is the core capital
of a bank, which includes shareholders' equity, the Fund for General Banking Risks and hybrid
financing, expressed as a percentage of the risk-weighted balance sheet total.
The conclusion is thus that the PECDC data could help Fortis Bank to spare up to EUR 255
million of capital costs on a yearly basis. This is of course only the case if Fortis Bank could
convince the CBFA, using the PECDC data, to drop the 5 percentage points add-on and allow
Fortis Bank the work with an LGD value of 32.5%. In case the CBFA decides to apply a 2.5
percentage points add-on, the benefit would still be huge: a yearly saving of EUR 127.5 million.
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7 CONCLUSION
There is a strong need for qualitative and reliable credit data. The reason for this is twofold.
On the one hand, the financial institutions want to use this data for their securitisation
transactions. This is the so-called business need. On the other hand, there is the regulatory
need. Financial institutions are required to provide more accurate estimates of their credit
risks under the Basel II framework. More advanced internal models can result in more
accurate estimates and lower regulatory capital requirements. For the development of these
models, a large amount of credit data is needed. Individual banks do not have enough credit
data, especially not for every sector or every type of loan. Therefore banks should pool their
credit data. The Pan-European Credit Data Consortium or PECDC currently has the largest and
most detailed database of credit default data.
This need for qualitative and reliable credit data is now, during an economic downturn,
stronger than ever. This master thesis started with a discussion of the current financial crisis.
Although some regulatory gaps clearly existed, the crisis is due to multiple causes. The de
Larosière Group made the following statement: “The present crisis results from the complex
interaction of market failures, global financial and monetary imbalances, inappropriate
regulation, weak supervision and poor macro-prudential oversight. It would be simplistic to
believe therefore that these problems can be “resolved” just by more regulation.” (de
Larosière, 2009).
This master thesis also provided an overview of the Basel II regulatory framework. The focus
of the overview was on credit risk, since it is most related to the subject of this thesis. But the
other two risk components, i.e. operational and market risk, were also introduced. Fortis Bank
chose to implement the Advanced Internal Rating Based (AIRB) approach for credit risk
modelling, which is the most advanced approach. The Basel Committee on Banking
Supervision expresses perfectly what is meant by the regulatory needs: “For all three risk
components, the use of statistical tests for backtesting is severely limited by data constraints.
Therefore, a key issue for the near future is the building of consistent data sets in banks.
Initiatives to pool data that have been started by private banking associations may be an
important step forward in this direction, especially for smaller banks.” (Basel Committee on
Banking Supervision, 2005).
PECDC focuses strongly on four points: confidentiality of the exchanged information, high data
quality, one shared methodology, and the “by banks for banks” philosophy.
The fact that all participating banks work on the same methodology supports the
standardization of the collected data and allows the comparability. Moreover, the PECDC data
template and statistics can evolve in a de facto standard for the industry. In this way, the
transparency of the sector can improve. This is exactly what investors, national regulators as
well as credit rating agencies demand, now more then ever.
80
A first assessment of the PECDC data by Fortis Bank was rather disappointing. Both the linear
and the logistic regression did not show the expected results, the explanatory power of the
models was low. However, a large study on the PECDC database performed by Prof. Dr. Zagst
and Stephan Höcht showed very satisfying results (Zagst and Höcht, 2008). This study is in fact
the first that analyses recovery rates based on a broad Pan-European dataset. They showed
that the most important component in workout recovery rates on a facility level is the
presence and quality of collateral. But even more importantly, this study showed that the
database is of high quality after the application of a few acceptable filters and restrictions.
Another important issue is the credibility of the PECDC initiative. To this end, a major step
might be the publication of this study of Prof. Dr. Zagst in an important scientific journal. This
would certainly increase the acceptance of the database by the regulators, potential investors
and the credit rating agencies. Eventually, this would benefit all the banks participating in the
consortium.
As mentioned above, the PECDC data is used to fulfil both business and regulatory needs.
Uncertainty about the credit performance of loans contributes a large share of securitisation
costs to Fortis Bank. This uncertainty can be reduced by comparing the internal data with the
data obtained from PECDC, i.e. benchmarking. The more data a financial institute has, the
better the rating it will obtain from a credit rating agency, and the more willing the regulator
will be to approve the transaction. More data will also make it easier to convince potential
investors of the quality of the securities. The acceptance of the PECDC data as a benchmark
depends on its data quality and its credibility.
To remain Basel II compliant, banks are required to determine a so-called Reference Data Set
(RDS) (Basel Committee on Banking Supervision, 2005). Ideally, an RDS should cover at least a
complete business cycle, contain all the defaults produced within the considered time frame,
include all the relevant information to estimate the risk parameters and include data on the
relevant drivers of loss. The Dutch investment bank NIBC investigated if the PECDC data is
eligible for the creation of an RDS (NIBC 2008). It’s clear that the PECDC data is not perfect and
that there is room for improvement. Nevertheless, if selections and filters are properly applied
to the dataset, the PECDC data is the most eligible data currently available for the creation of
an RDS.
Besides the Basel Committee on Banking Supervision, the national regulators also urge
financial institutes to participate in private data pooling initiatives such as PECDC. The
regulator from the UK, the FSA, states this explicitly in (FSA, 2007). They require all Advanced
Internal Rating Based (AIRB) firms to make use of any relevant and appropriate external data.
The Belgian regulator, the CBFA, also stimulates banks to make use of external data.
81
In the last chapter of this master thesis, the leverage of the PECDC data is examined.
Therefore, a high-level cost-benefit analysis for the PECDC project within Fortis Bank is
performed.
The cost of the participation in PECDC for Fortis Bank is assessed by a scenario analysis. In the
first scenario, almost all data collection and uploading is done manually. Fortis Bank estimated
that this scenario could be completed at a cost of EUR 2.6 million, plus EUR 0.8 million per year
of running costs. In the second scenario, as much as possible of the data collection and
uploading is done automatically. In the short term, this scenario would certainly demand a
large investment. A number of paper files have to be entered in existing database systems and
a new integrated system has to be developed for the automatic data collection and uploading.
In the long term however, this scenario might be the most cost effective, especially since
PECDC requires a data delivery every 6 months and a smaller update of the defaults every 3
months. The third scenario is a mix of the two previous scenarios: an automatic data collection
system is developed for the databases for which data collection can most easily be automated;
the rest of the work is still done manually. It’s clear that this scenario is a compromise, but it
can be seen as an intermediate stage before the implementation of the second scenario. In
the mean time, the benefits of the participation in PECDC will become clear to Fortis Bank.
After this analysis of the costs related the PECDC, the benefits are translated into numbers. As
mentioned above, the PECDC data can result in lower costs related to securitisation
transactions. To get an idea of the potential benefits, the Park Mountain SME 2007-I
transaction is discussed as an example. In this case, the uncertainty due to insufficient data
quality amounted 5.4% of the total amount of relieved capital. The potential of the PECDC
data is that it can again be used as a benchmark to create transparency and boost investor’s
confidence. Fortis Bank is currently considering a synthetic securitisation transaction without
requesting a formal rating from a credit rating agency. This situation might even multiply the
potential benefit of the PECDC data, because in this case the value of a benchmark increases.
The PECDC data can also result in lower Basel II regulatory capital requirements. For the
assessment of the Risk Weighted Assets (RWAs) of Fortis, the CBFA considers a 5 percentage
points add-on to Fortis Bank’s Loss Given Default (LGD) value. Because of this, the CBFA’s
assessment of Fortis Bank’s RWAs amount EUR 23 billion more than Fortis Bank’s own
assessment. These EUR 23 billion RWAs entail a potential annual income of EUR 255 million.
The PECDC data can be used as a benchmark for convincing the CBFA of the accuracy of the
Fortis Bank LGD value. In this way, the PECDC data could help Fortis Bank to spare up to EUR
255 million of capital costs on a yearly basis.
The final conclusion of this master thesis is that the leverage of the PECDC data is large.
Therefore, Fortis Bank should continue its participation in the consortium. The PECDC initiative
can increase the transparency in the sector, which is now more then ever needed.
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APPENDIX 1: CREDIT PORTFOLIO RISK
In section 2.1.1 A, credit risk is discussed for an individual asset. Now, we want to calculate the
Expected Loss and Unexpected Loss for multiple assets, a portfolio of assets. The keyword in
portfolio theory is diversification.
Combining assets in a portfolio (ELP) will lead to less risk for a given return through
diversification. The Expected Loss of a portfolio is the sum of the expected losses of the
individual assets in the portfolio (Smithson, 2003).
∑=i
iP ELEL
The Unexpected Loss of a portfolio (ULP) is not the sum of the stand-alone UL, but it will take
into account the correlations between the individual UL. Correlations can for example be
applied through a variance-covariance matrix. Due to the fact that the losses of most
individual exposures are not perfectly correlated, the riskiness of a portfolio will be smaller
than the weighted sum of the individual exposures (Smithson, 2003).
∑∑∑ ==i
ci
i j
ijjiP ULrULULUL **
With: rij = loss correlations
ULci = the individual contributions of UL to the portfolio UL
The problem of using modern portfolio theory for credit portfolio risk models is the fact that
the theory is based on the assumption of a normal distribution. As a credit loss distribution has
a very fat tail, the assumption should be corrected. Not withstanding that the distribution of
losses is different for all portfolios, because of, for example, exposures towards a different
type of borrowers (Vanden Abeele, 2008)
83
APPENDIX 2: DATA FIELDS IN FAIL
1. GENERAL BORROWER DATA
In this first part, some general information of the borrower is collected.
• Name*
The legal name of the client
• Group
If the borrower belongs to a group you have to fill in the legal name of the company and it’s Client
ID from the source system
• Scope*
Public listed or private company
• Sub type*
• Operating Company*
operating company or not
• Asset class*
• Residence* The country of residence of the company
• Business*
The country of business where the borrower generates most of his business
• Primary industry*
The industry where the borrower generates most of his revenues.
• Secondary industry
The industry where the borrower generates the second largest part of his revenues.
• First recognition date
The date the default was first recognized by Fortis Bank. This date may differ from the actual
default date.
• Obligatory Reason for default*
This field is filled in if the borrower went into default by obligatory reason.
• Judgmental Reason for default* (nr. 1, 2 and 3)
Three judgmental reasons are filled in if the borrower went into default because of judgmental
reasons.
2. BORROWER’S FINANCIALS
In this part the annual financials (f.i. from an annual report) up to two years prior to default must be
entered. The following data must be entered:
• Date*
The date of the annuals financials.
• Currency*
The currency for the amount ‘entity sales’.
• Entity Sales*
The annual sales amount taken from the financials (must be fiscal year-end).
For General Corporate Lending facilities there are three more data fields:
• Total assets
The annual total amount of assets taken from the financials (must be fiscal year-end).
• Total liabilities
The annual total of liabilities taken from the financials (must be fiscal year-end).
• Equity
The annual total equity taken from the financials (must be fiscal year-end).
84
3. BORROWER’S LOANS
This part is divided into 2 subparts, a part to enter the loan details and a part to enter the loan details
of the three mandatory snapshots.
Subpart 1: The loan details
• Facility type*
The type of the loan.
• Sub facility*
Indicates whether the loan is a sub facility or not.
• Facility asset class*
The Basel II asset class category.
• Syndicated*
Indicates whether the loan is part of a syndication.
• Lead Syndicate
If the loan is part of a syndication, this field indicates whether Fortis Bank acts as the lead
syndicate/agent bank.
• Syndicated currency
The currency.
• Total syndicated amount
The total syndicated amount.
• Balloon Total Amount %
The percentage of the total loan amount that will be paid as a balloon payment.
This is only applicable for loans with a balloon payment.
• Maturity Date
The contractual end date. The field may only left empty if the loan has no end date.
• Seniority Code*
The seniority code
• Country of legal jurisdiction*
The country of legal jurisdiction of the loan
Subpart 2: The three mandatory snapshots: details for origination, one-year-prior-to-default
and at default
• Loan Currency*
The currency of the loan.
• Commitment*
The maximum amount of the loan, according to the contractual agreement.
• Outstanding*
The amount outstanding on the date of the snapshot. This should include past due interest.
• Debt Senior
The percentage of debt that is senior to the obligation in question (Total interest bearing debt + Off
balance obligation).
• Debt Subordinated
The percentage of debt that is subordinated to the obligation in question (Total interest bearing
debt + Off balance obligation)
• Borrower Rating
The debtor rating of the main client according to the Fortis Masterscale.
• Off Bal Obligation
The percentage of off balance sheet obligations as percentage of the total debt (Total interest
bearing debt + Off balance obligation).
• Base Rate
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The base rate interest for the loan.
• Spread
The spread in base points
• Total interest rate
The total interest rate of the loan at the moment the borrower goes in default.
• Expected Facility LGD
The LGD rating for this facility at event date.
4. DATA OF THE COLLATERAL BELONGING TO THE LOAN
This part consists of three subparts: Collateral Details, Collateral part of the three mandatory snapshots
and Identification of the link between the collateral and the loan.
Subpart 1: The Collateral Details
• Type
The type of the collateral from the list.
• Min. Cover Ratio
The coverage ratio guaranteed by the collateral (as agreed with the borrower at time of origination
or renewal of the loan).
• Legal Jurisdiction
The country of legal jurisdiction of the collateral.
• Rank of security
The rank of security from the list.
• Priority Claim Percentage
The priority claim percentage.
Next to this, there are some specific collateral fields depending on the type. There are five types:
1. Ship
2. Aircraft
3. Commodity
4. Project, applies to project finance
5. Charter/Lease/Offtaker contract
For a ship enter the following information:
• Use
Select the use from the list. The list contains the Clarkson classification
• Type
Enter the ship type
• Size
Enter the size of the ship in actual units
• Units
Select the measurement unit from the list
For an aircraft enter the following information:
• Type
Enter the type of aircraft
• Engines manufacturer
Select the engines manufacturer from the list
• Engine type
Enter the engine type
• # Engines
Enter the number of engines
For commodity enter the following information:
86
• Type
Select the commodity type from the list
• Hedged
Indicate whether the commodity is hedged
For project enter the following information:
• Type
Select the type of project finance from the list
• Year of Construction
Enter the year of construction of the primary collateral item
• State of Completion
Select if the project is under construction or delivered
For Charter/Lease/Offtaker contract enter the following information:
• Contract > 2 yrs
Indicate whether there is a contract with at least two years remaining at the date of default
• Nature of contract
Select the type of legal entity indicated in the contract
• Debt service covered by contract
Enter the percentage of annual debt service that was covered by the contract
Subpart 2: Collateral part of the three mandatory snapshots
The following details are required for origination, one-year-prior-to-default and at default.
• Book Value
The book value of the collateral on the date mentioned in ‘Book Value Date’ date, including
currency.
• Book value date
The date of the book value. The date must be different from the snap shot date
• Market Value
The market value of the collateral on the date mentioned in ‘Book Value Date’, including currency
• Market Value date
Enter the date of the market value. The date must be different from the snap shot date
• Total Asset Value
Enter the Total Nominal Value of the Asset irrespective of the banks claim.
Subpart 3: The link between the collateral and the loan.
The last part of the collateral information is linking the collateral to the loan. There is a box which
presents the links of the collateral to the possible loans. Cross collateralization is possible.
5. DATA OF THE GUARANTEE BELONGING TO THE LOAN
This part also consists of three subparts: guarantor details, guarantor part of the three
mandatory snapshots and identification of the link between the guarantor and the loan(s).
Subpart 1: guarantor details
• Fortis ID
• Name
• Sector
• Type*
• Residence*
The country of residence of the guarantor
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• Primary Industry*
The industry where the guarantor generates most of his revenues.
• Coverage
The type of coverage of the export insurance.
Mandatory if export insurance is provided by the guarantor.
Subpart 2: guarantor part of the three mandatory snapshots
The following details are required for origination, one-year-prior-to-default and at default.
• Internal Rating
The debtor rating of the guarantor according to the Fortis Masterscale on the event date
• Rating Fitch
The debtor rating of the guarantor according to Fitch on the event date
• Rating Moody’s
The debtor rating of the guarantor according to Moody’s on the event date
• Rating S&P
The debtor rating of the guarantor according to S&P on the event date
• Guarantee %
The percentage of the borrower’s committed amount that is guaranteed by the guarantor
• Guarantee amount
The amount that is guaranteed by the guarantor
Subpart 3: link between the guarantor and the loan(s)
The last part of the guarantor information is linking the guarantor to the loan(s). There is a box which
presents the links of the guarantor to the possible loans (multiple loans are possible).
88
APPENDIX 3: PROJECT PLAN DATA COLLECTION
Commitment
• Obtain management
approval and budget
• Assign internal
resources
Project setup
• Build detailed project
plan
• Prioritise data
collection
• Identify stakeholders
and contributors,
including project
manager
• Define project
organisation
• Define project
governance
Commitment
• Assign external
resources
Data collection
• Identify all default
events as defined in
Basel II
• Collect paper files
• Identify the feeding
systems for cash flow
information
• Define the data
granularity
Organisation design
• Define running mode
governance
Phase IV Phase III Phase II Phase I
Data input
• Transfer electronic
data to FAIL
• Establish cash flow
information link with
FAIL
• Manually input
additional data in
FAIL
• Perform quality
control
Data exchange with
PECDC
• Send formatted data
to PECDC
• Collect data from
PECDC
• Integrate in Fortis
datasets
Analysis and use
• Analyse the overall
dataset
• Incorporate insights
into
– Basel II RegCap
modelling
– Own securitisation
structuring
– MB business
development
89
Figure 28 below gives an overview of the short-term action plan for the data delivery for Large
Corporates.
Figure 28 Short-term action plan for Large Corporates (Fortis Bank Illustration)
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APPENDIX 4: STRUCTURED FINANCE
In this enclosure, some general terms regarding structured finance are discussed. The aim is to
briefly explain how these seemingly confusing and complex financial products work. I found
most of the information used in this section in internal Fortis Bank documentation.
ABS: Asset-Backed Security is the general term covering debt securities (which usually issue a
regular payment to investors) that securitise cash-flow generating assets (by pooling things
such as individual mortgages or auto loans), tranching them into different risk and interest
rates, then fortifying them through various credit enhancements, making them more
interesting to investors, and finally asking for a credit rating for each of the tranches so that
investors have a relative idea of the expected credit risk. ABS products are not standardized,
but may have similar characteristics. The descriptions below are loose definitions, and may not
be applicable in all cases.
RMBS: (Residential Mortgage Backed Securities) This is one of the most common forms of ABS.
It pools together residential mortgages (as opposed to commercial mortgages), and using the
mortgage payments that are made by borrowers to make regular payments to the investors.
These residential mortgages can have different risk profiles related to the creditworthiness of
the debtors, going from sub-prime (most risky ones) over Alt-A (risk between subprime and
prime) to prime (best risk profile). Most RMBS are tranched and like most ABS have a structure
where the highest or senior level debt having the lowest returns, but also the safest. The lower
or junior tranches are the opposite: while they have higher returns, they are also riskier.
Sub-prime Mortgages: Known in the US as Sub-prime mortgages and in the UK as Non-
conforming mortgages, these are residential mortgages (used to buy or refinance a home)
given to borrowers with a potentially higher risk of default, such as borrowers with a history of
loan delinquency, default, or bankruptcy. They are blamed for starting the current credit
crunch. As sub-prime mortgages had higher delinquencies and defaults due to borrowers not
paying their mortgages, this negatively affected RMBS and CDO performance and ability to
repay its investors, which snowballed into larger problems in global markets.
CMBS: (Commercial Mortgage Backed Securities) Similar to RMBS with the major difference
being the underlying loans consisting of commercial mortgages (mortgages on commercial real
estate, such as an office building, warehouse or shopping mall)
CDO: (Collateralized debt obligations) These are a derivative product that are usually based on
other ABS. A CDO will take another ABS, either alone or in conjunction with other ABS, will
restructure or repackage (through tranching and credit enhancements) to form a product that
is not directly linked to the underlying loans anymore. CDO comes in many different varieties,
such as a Cash CDO (as described above), Synthetic CDO (described below), Arbitrage or
balance-sheet motivated. Arbitrage CDO take advantage of the fact that they can buy high
yielding assets and restructure them into lower payments to investors, while balance-sheet
CDO is used to move assets off their balance sheets to remove some of its credit risk.
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CDO²: (CDO squared) These are the next iteration of a CDO. It’s a derivative of a derivative. It
takes CDO (one or many) and restructures/ repackages them so that the investors can
purchase whichever tranche that suits their needs. CDO have also been in the news recently
because they were sold widely. When the underlying loans began to fail, it became very
difficult to determine which CDO would be affected, as it was difficult if not impossible to track
the underlying loans, thereby undermining the confidence of the entire securitisation market.
CDS: (Credit default swaps) These are contracts made between two parties that are similar to
how an insurance company makes a contract with its customers/policy holders. The
.protection buyer. will purchase protection from the .protection seller.. The buyer will make
regular payments (premiums) to the seller. By taking in these payments, the seller is insuring
the buyer against the loss of a specific bond or loan. In terms of a CDO, CDS is used primarily
by synthetic CDO. This kind of CDO becomes a credit seller and uses the cash from the
premiums paid to pay its investors (usually, the amount paid and risk to the investor is
organized by tranching).
ARS: (Auction Rate Securities) These are unrelated to the ABS world, but since they have been
repeated frequently in the news, a little clarification may help. ARS are bonds that can be
bought and purchased, but the key difference is that the interest rate is reset regularly
through an auction. They appeared in the news from the controversy surrounding the fact that
these products were sold as .safe as cash. investments, when in actuality they were not, and
also because the banks that had sold these securities dropped out of the auction, refusing the
to be bidders of last resort, as they had in the past, thereby causing the values of these
securities to drop.
Toxic Assets: This term has been used very frequently by the media, but it does not have a
standard definition. In the general bond market, it refers to high-yield debt, below investment
grade, which pays a very high return, but is also very risky. In the ABS world, it refers similarly
to the lower level tranches (below the senior level) who.s ability to repay investors their return
is the first to be negatively affected if the underlying assets do not perform well enough. For
example, In the case of the US sub-prime RMBS, when the underlying subprime mortgages
began to fail, the tranches will also start to fail in sequence, starting with the lowest and
riskiest tranche. This in turn, makes the security a .distressed asset.. Therefore, as prices start
to decline, assets can become toxic in potentially every segment. So, what bank A describes as
its .toxic. assets can differ from what bank B considers as .toxic. because of the lack of a clear
definition of what .toxic. really means.
92
APPENDIX 5: NEDERLANDSTALIGE SAMENVATTING
Deze bijlage omvat een Nederlandstalige samenvatting van het eindwerk. De bedoeling is om
het volledige eindwerk bondig te bespreken. Deze samenvatting is een verplicht onderdeel van
het eindwerk, aangezien dit in het Engels opgesteld is. Het is echter zo dat veel van de
gebruikte Engelstalige termen vakjargon zijn, die zich niet zomaar laten vertalen.
Dit eindwerk onderzoekt de impact van LGD gegevens op enerzijds de Basel II wettelijke
kapitaalvereisten en anderzijds effectisering. Het doel van dit eindwerk is om de
hefboomwerking van de gegevens van het Pan-European Credit Data Consortium (PECDC) te
bepalen in dit opzicht.
Deze samenvatting volgt dezelfde structuur als het eindwerk, dat 6 hoofdstukken telt. Het
eerste hoofdstuk omvat een uiteenzetting van de huidige economische situatie in het
algemeen en de financiële crisis in het bijzonder. Het tweede hoofdstuk geeft de lezer een
inleiding over Basel II, een internationaal gestandaardiseerd stelsel van regels voor het
bepalen van de kapitaalsvereisten van banken. De twee daarop volgende hoofdstukken
hebben dan betrekking op PECDC zelf. Hoofdstuk drie geeft een omschrijving van het Pan-
European Credit Data Consortium en zijn werking en maakt ook de vergelijking met een
alternatief, RAS genaamd. In hoofdstuk vier worden de gegevens van PECDC geanalyseerd en
wordt nagegaan of het mogelijk is om een model op te stellen op basis van deze gegevens. De
twee laatste hoofdstukken van dit eindwerk kijken vervolgens vanuit het perspectief van de
bank die deelneemt in het consortium. Hoofdstuk vijf beschrijft waarvoor de gegevens van
PECDC kunnen gebruikt worden. Het laatste en zesde hoofdstuk tenslotte bepaalt de
hefboomwerking van de gegevens van PECDC.
93
1. Context
We kunnen drie fasen onderscheiden in de economische toestand van de laatste twee jaar. De
huidige economische daling begon als een financiële crisis rond juli 2007 in de Verenigde
Staten van Amerika. Deze crisis is hoofdzakelijk te wijten aan de zogenaamde sub-prime
kredieten. In een tweede fase is deze financiële crisis uitgedeind naar de reële economie. Dit
resulteert dan in een toenemend aantal consumenten en bedrijven dat niet langer zijn
kredietverplichtingen bij de financiële instellingen kan voldoen. Dit resulteert dan in de derde
fase van de crisis die zich opnieuw bij de financiële instellingen situeert. De onderstaande
figuur geeft een overzicht van deze situatie en is voorgesteld in maart 2009 te Genève op het
Wereld Economisch Forum.
Figuur 29 Financiële crisis end reële economie terugloop lus (World Economic Forum, 2009)
Er zijn reeds heel wat artikels geschreven over de huidige financiële crisis, en we kunnen er
zeker van zijn dat er nog veel meer zullen geschreven worden in de toekomst. Eén van de
interessantste analyses van de financiële crisis tot nu toe is te vinden in het verslag van de
Hoge Niveau Groep over Financieel Toezicht die werd voorgezeten door Jacques de Larosière,
de vorige president van het Internationaal Monetair Fonds. Om deze reden werd deze groep
meestal omschreven als de de Larosière Groep. In hun analyse zoeken ze ook naar de
dieperliggende oorzaken van de huidige financiële crisis. Ze ordenenden die oorzaken in vijf
categorieën:
1. Macro economisch
2. Risicobeheer
3. Rol van de kredietbeoordelaars
4. Mislukkingen in Deugdelijk Bestuur
5. Wettelijke tekorten, tekorten van de toezichthouders en het falen van crisisbeheer
94
2. Basel II
Basel II is een internationaal gestandaardiseerd stelsel van regels voor het bepalen van de
kapitaalsvereisten van banken. Dit stelsel werd ontwikkeld door het zogenaamde Basel
Committee on Banking Supervision en is voorgesteld in 2004. Het is de opvolger van Basel I dat
in werking is van 1988.
Essentieel bij Basel II is dat het stelsel van regels bestaat uit drie complementaire en elkaar
versterkende pilaren:
Pilaar I Minimale kapitaalvereisten
Pilaar II Controle herzieningsproces
Pilaar III Marktdiscipline
Onder pilaar I vallen de drie belangrijkste risico’s voor financiële instellingen:
Kredietrisico is het risico dat een ontlener de lening niet kan terug betalen
Operationeel risico omvat alle niet financiële risico’s van een financiële instelling
Marktrisico is het risico dat de waarde van een investering afneemt in de tijd
Voor het onderwerp van dit eindwerk is het belangrijk om een basiskennis te hebben van
kredietrisico, daarom wordt dit type risico uitgebreid besproken. Kredietrisico heeft de
volgende componenten:
• Probability of Default (PD): de kans op falen
• Exposure At Default (EAD): de hoeveelheid openstaande schuld bij faling
• Loss Given Default (LGD): de schatting van het geleden verlies op een faciliteit bij faling
van een tegenpartij.
• Maturity (M): de looptijd
• Expected Loss (EL): het verwachte verlies
• Unexpected Loss (UL): de volatiliteit van de jaarlijkse verliezen.
Deze componenten laten ons nu toe om de kapitaalvereiste (K) te berekenen:
)(*5.11
)(*)5.2(1**)999.0(*)
1(
)1(
)(*
5.0
5.0 PDb
PDbMLGDPDG
R
R
R
PDGNLGDK
−
−+
−
−+
−=
Hierbij staat N(x) voor de cumulatieve distributie van de standaard normale verdeling. G(z)
staat voor de inverse cumulatieve functie van de standaard normale verdeling. R is de activa
correlatiefactor. De functie b(PD) maakt een correctie voor de maturiteit.
95
3. PECDC
3.1 Wat is PECDC?
PECDC staat voor Pan-European Credit Data Consortium. Het consortium is opgericht in 2004
en heeft als doel om zowel business als wetgevende noden te vervullen. Er wordt data
verzameld in 8 verschillende activa klassen. De databank bevat krediet wanbetaling
gebeurtenissen sinds 1998. Momenteel zijn 32 van de grootste banken van de hele wereld
betrokken in het initiatief. De onderstaande tabel geeft een overzicht van de deelnemende
banken.
ABN AMRO LLOYDS TSB FIRSTRAND BANK LTD
ANZ NEDBANK LTD JPMORGAN CHASE
BANK OF TOKYO-MITSUBISHI UFJ CAIXA GERAL DE DEPOSITOS HVB
BNP PARIBAS SNS PROP. FINANCE KfW
CREDIT SUISSE SUMITOMO-MITSUI BKG CORP NATIXIS
DANSKE BANK A/S ABSA BANK LIMITED NIBC BANK
DRESDNER BANK BANK OF IRELAND ROYAL BANK OF SCOTLAND
FORTIS BANK BARCLAYS BANK SCANDIN ENSKILDA BANK
HBOS CALYON SOCIETE GENERALE
KBC COMMERZBANK STANDARD BANK
DnB NOR WESTPAC
Tabel 1 Deelnemende banken in PECDC (PECDC afbeelding)
De vier basisprincipes voor het PECDC initiatief zijn:
1. Confidentialiteit van de uitgewisselde informatie
2. Data kwaliteit
3. Alle banken gebruiken dezelfde methodologie
4. “Door banken voor banken”
96
3.2 De data collectie
De gegevens worden op verschillende ogenblikken verzameld in de levenscyclus van een
krediet. De onderstaande figuur geeft deze ogenblikken aan:
Figuur 2 Structuur van het data collectie proces (Fortis Bank Afbeelding)
3.3 FAIL
De applicatie FAIL, wat staat voor Fortis Application for Impaired loans, is de applicatie die
Fortis Merchant Banking gebruikt voor de registratie van ontleners in default. De
onderstaande figuur is een printscreen van deze applicatie.
Figuur 3 Printscreen van FAIL (Fortis Bank Afbeelding)
97
3.4 Algorithmics
Het consortium, PECDC, werkt samen met een onafhankelijke derde partij voor het maken van
kwantitatieve data analyses en kwalitatieve statistieken.
3.5 RMA – AFS: Een vergelijkbaar initiatief
De Risk Management Association (RMA) en Automated Financial Systems (AFS) hebben in
2003 een initiatief voorgesteld dat vergelijkbaar is met het huidige PECDC. RMA en AFS
hadden een partnerschap opgericht voor Risk Analysis Service (RAS). Fortis Bank heeft toen
overwogen om deel te nemen aan dit initiatief. Er waren echter meerdere bezwaren tegen dit
initiatief, o.a. overlap met het PECDC initiatief, waardoor Fortis Bank uiteindelijk niet
deelgenomen heeft aan RAS.
98
4. De PECDC databank
In dit hoofdstuk worden gekeken naar de gegevens van PECDC. Vooreerst worden enkele
modellen besproken opgesteld door Fortis Bank op basis van de PECDC data. Vervolgens wordt
een studie besproken van Prof. Dr. Zagst, waarin op basis van de PECDC data een kredietrisico
model opgesteld wordt.
4.1 Lineaire regressie
Deze eerste regressie op de PECDC data uitgevoerd door Fortis Bank wou onderzoeken of
collateral de LGD waarde beïnvloedt. Hierbij is de hypothese dat hoe hoger de collateral
waarde is, de lager de LGD waarde.
Onderstaande figuur geeft de SAS uitvoer weer.
Figuur 4 SAS Uitvoer voor de Lineaire Regressie
Zoals in bovenstaande uitvoer te zien is, is de R² waarde van dit model te laag om de LGD
waarde te kunnen voorspellen op basis van de data.
99
4.2 Logistische Regressie
In een tweede analyse wou Fortis Bank onderzoeken of een logistische regressie de LGD
verdeling beter kan vastleggen. Tegen de verwachtingen in bleken ook hier de resultaten
tegen te vallen; de verschillende Logistische modellen bleken niet in staat om de LGD waarden
goed te kunnen voorspellen.
De mensen van de afdeling Krediet Modellering van Fortis Bank wijten dit aan feit dat de
verdeling van de LGD waarden van PECDC niet conform is met interne LGD databanken.
Onderstaande figuur geeft de verdeling van de LGD waarden van de PECDC databank weer.
Figuur 5 Verdeling van de LGD waarden van PECDC
Normaal moet een dergelijke verdeling een U-vorm hebben, wat dus uiterst geschikt is
regressie met een bimodale verdeling. In bovenstaande figuur ontbreekt dus de piek rond de
100%.
4.3 Conclusies van Fortis Bank’s Analyse
Het besluit is dat er eerst filters moeten toegepast worden op de PECDC databank vooraleer
deze data gebruikt wordt voor modellering.
100
4.4 Studie door Prof. Dr. Zagst
Prof. Dr. Zagst en Stephan Höcht van de Universiteit van Munchen hebben gedurende 2 jaar
de PECDC databank onderzocht. Hun conclusie is dat de databank, na het toepassen van
enkele beperkingen en filters, uiterst geschikt is voor modellering. Onderstaande figuur geeft
de distributie van de Recovery Rates (RR, RR=1-LGD) na het toepassen van de beperkingen en
filters.
Figuur 6 Verdeling van de Recovery Rates in [0, 1]
Figuur 6 toont aan dat de RR waarden na het toepassen van de filters en beperkingen wel
degelijk een U-vormige verdeling hebben.
De conclusie van de multivariabele analyse van Prof. Dr. Zagst is dat de aanwezigheid en
kwaliteit van collateral de belangrijkste component is voor recovery rates. Hiernaast speelt
ook de kredietwaardigheid een significatne rol in het bepalen van de recuperatie bij kredieten.
Macro-economische variabelen daarentegen spelen slechts een kleine rol hieromtrent.
Het besluit van dit hoofdstuk is dat de kwaliteit van PECDC databank zeer goed is, mits het
toepassen van enkele aanvaardbare beperkingen en filters. Het consortium moet echter
blijven aandacht besteden aan de data kwaliteit. Verder is de geloofwaardigheid van de
databank erg belangrijk. In dit opzet kan het publiceren van de studie uitgevoerd door Prof. Dr.
Zagst een grote stap in de goede richting zijn.
101
5. Het gebruik van de PECDC gegevens
De PECDC data wordt gebruikt voor twee doeleinden. Enerzijds wordt de data gebruik als
referentie bij effectisering. Anderzijds kan de data gebruikt worden om nauwkeurigere
schattingen te produceren van de kredietrisico’s onder het Basel II stelsel. Onder Basel II zijn
de banken die gaan voor de geavanceerde aanpak verplicht om te werken met externe data,
een zogenaamde referentie dataverzameling (RDS).
5.1 PECDC en effectisering
Onderstaande figuur geeft een kostenverdeling weer van een effectisering. De rechterhelft
van de figuur geeft aan dat 1/6e van de kosten voortkomt uit het beheer van het portfolio,
1/6e is te wijten aan het Expected Loss (EL) en 1/6e is te wijten aan het Unexpected Loss (UL).
Daarnaast is de helft van de kosten te wijten aan onzekerheid. Het precies is in dit deel dat het
potentieel van de PECDC data zich situeert: deze data kan als referentie gebruikt worden om
potentiële investeerders te overtuigen van de kwaliteit van de onderliggende activa. Op deze
manier wordt de kwaliteit aangetoond van de effectisering.
Figuur 7 Kostenverdeling van een effectisering
5.2 PECDC en het wettelijke aspect
De banken die gaan voor de geavanceerde aanpak onder Basel II zijn verplicht om gebruik te
maken van externe data. Ze moeten een zogenaamde referentie dataverzameling (RDS)
bezitten. Dit wordt formeel aangegeven in “Working Paper nr. 14” van het Basel Committee
on Banking Supervision (2005).
102
Een studie van de Nederlandse investeringsbank NIBC heeft aangetoond dat de PECDC
dataverzameling voldoet aan de voorwaarden voor een referentie dataverzameling.
Naast het Basel Committee on Banking Supervision laten de nationale toezichthouders ook
steeds luider hun stem horen in dit debat. Ook zij leggen aan de banken op dat deze zoveel
mogelijk moeten gebruik maken van externe gegevens.
De nationale toezichthouder van het Verenigd Koninkrijk, de FSA, specifieert dit in de paper
‘Wholesale LGD models’ die dateert van begin 2007.
De Belgische toezichthouder, de CBFA, heeft dit nog niet formeel neergeschreven, maar de
mensen van de afdeling Krediet Modellering van Fortis Bank geven aan dat de CBFA hen
mondeling meegedeeld heeft om zoveel mogelijk gebruik te maken van externe gegevens.
Het besluit van dit onderdeel is dus dat zowel de Basel Committee on Banking Supervision als
de nationale toezichthouders de financiële instellingen ertoe aanzetten om deel te nemen in
initiatieven zoals PECDC.
103
6. Hefboomwerking van de PECDC gegevens
Dit hoofdstuk maakt een kosten-baten analyse van de deelname van Fortis Bank aan PECDC.
Bij de analyse van de kosten wordt gewerkt met een scenario analyse. Daarna worden zowel
de baten van de PECDC data als referentie bij effectisering, als de baten van de PECDC data
voor het wettelijke aspect opgemaakt.
6.1 Kosten
6.1.1 Scenario 1: Manuele data verzameling
De kosten gerelateerd aan het PECDC project voor Fortis Bank hebben voornamelijk te maken
met het opleiden van de mensen die de dossiers moeten ingeven in de FAIL applicatie, en het
ontwerpen, ontwikkelen en onderhouden van de FAIL applicatie.
Onderstaande figuur geeft aan hoe de data verzameling in dit eerste scenario verloopt. De
rode peilen geven de manuele data verwerking aan, terwijl de blauwe pijlen geautomatiseerde
data stromen weergeven. In dit eerste scenario moeten de mensen uit de centrale en lokale
diensten de nodige gegevens zelf opzoeken in de verschillende databanken en zelfs in
papieren dossiers.
Figuur 8 Scenario 1
Paper Files
FAIL Central
DB
Central and
Local People
PECDC
DB
systems
3. Business needs
4. Regulatory
104
Fortis Bank heeft een analyse gemaakt van alle kosten in dit scenario. De totale eenmalige kost
bedraagt EUR 2.6 miljoen, plus een jaarlijkse kost van EUR 0.8 miljoen.
Momenteel gebeurt de data verzameling binnen Fortis Bank volgens dit scenario.
6.1.2 Scenario 2: geautomatiseerde data verzameling
In dit tweede scenario wordt de FAIL applicatie uitgebreid met een geïntegreerd en
geautomatiseerd systeem dat zelf alle benodigde gegevens uit de betreffende databanken kan
ophalen. Onderstaande figuur 9 geeft de werking van dit scenario weer.
Figuur 9 Scenario 2
De kosten van dit scenario zijn momenteel niet bekend, maar liggen zeker hoger dan de kosten
van scenario 1.
Central and
Local People
FAIL Central
DB PECDC
DB
systems 3. Business needs
4. Regulatory needs
Paper Files
105
6.1.3 Scenario 3: gedeeltelijke automatisering
Dit scenario is een compromis van de vorige twee scenario’s. Dit betekent concreet dat enkel
de databanken die eenvoudig kunnen geïntegreerd worden met de FAIL applicatie ook
daadwerkelijk betrokken worden in het geautomatiseerde systeem.
Op korte termijn is dit wellicht het meest realistische scenario: enerzijds is het reeds een
duidelijke verbetering t.o.v. de huidige situatie, scenario 1, en anderzijds is het goedkoper dan
de ideale situatie, die van scenario 2.
6.2 Voordelen
6.2.1 Lagere kosten voor effectisering
Onderstaande figuur geeft een kostenanalyse weer van een effectisering uitgevoerd door
Fortis Bank, de Park Mountain SME 2007-I effectisering. 5.4% van de kosten is te wijten aan
een gebrek aan data kwaliteit, het hier dat het potentieel van de PECDC data zich bevindt.
Figuur 10 Kosten van de Park Mountain SME 2007-I effectisering
106
6.2.2 Lagere Basel II wettelijke kapitaalsvereisten
Voor het bepalen van het totale bedrag aan RWAs dat de bank bezit, werkte Fortis Bank zelf
met een LGD waarde van 32.5%. Dit resulteerde in totaal bedrag van EUR 149 miljard RWAs.
Echter, de CBFA opteerde voor een LGD waarde van 37.5%, i.e. 5 procent punten hoger dan de
Fortis Bank waarde. Deze analyse resulteerde in een totaal bedrag aan RWAs van EUR 172
miljard. Het verschil tussen deze twee analyses van EUR 23 miljard brengt een maandelijkse
extra kost van EUR 255 miljoen met zich mee voor Fortis Bank. Dit wordt voorgesteld op
onderstaande figuur.
Figuur 11 Kosten in RWA (Fortis Bank Afbeelding)
Fortis Bank kan de CBFA overtuigen van de correctheid van hun LGD waarde van 32.5% met
behulp van de PECDC data als referentie. Dit brengt dan een potentieel jaarlijks inkomen van
EUR 255 miljoen met zich mee.
107
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