UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2015 – 2016
Basel III
Risk Disclosure Index and Bank Risk
Masterproef voorgedragen tot het bekomen van de graad van
Master of Science in de
Toegepaste Economische Wetenschappen: Handelsingenieur
Victoria Van Wonterghem
Onder leiding van
Prof. R. Vander Vennet
Elien Meuleman
UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2015 – 2016
Basel III
Risk Disclosure Index and Bank Risk
Masterproef voorgedragen tot het bekomen van de graad van
Master of Science in de
Toegepaste Economische Wetenschappen: Handelsingenieur
Victoria Van Wonterghem
Onder leiding van
Prof. R. Vander Vennet
Elien Meuleman
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I
PERMISSION
The undersigned declares that the content of this master dissertation may be consulted and/or
reproduced, provided the source is acknowledged.
Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of
gereproduceerd worden, mits bronvermelding.
Victoria Van Wonterghem
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Abstract
It has become clear, since the global financial crisis, that idiosyncratic bank shocks can
propagate throughout the entire financial system, resulting in unforeseen, magnified banking
losses all around the world. Disclosure plays an important role in pressuring bank managers to
adopt a prudent risk strategy in order to decrease the chance of experiencing future financial
crises. As such, the goal of this paper is twofold: first, we aim to construct an overall risk
disclosure index that measures to what extent a bank satisfies the disclosure requirements and
recommendations as proposed by the Basel Committee on Banking Supervision in the paper
Consultative Document Pillar 3 (Market Discipline) in 2001. Secondly, the paper continues with
a graphical comparison of these indices with several banking characteristics, by means of scatter
plots and linear regressions. The findings are that disclosure seems to be positively related to
experiencing less capital losses during a stress test scenario, bank size and liquidity and
negatively related to the size of capital buffers (possibly caused by the presence of government
safety nets), non-interest income, interbank exposures and sovereign exposures from the
GIPS/GIIPS countries. The relationship between disclosure and volatility is ambiguous.
De financiële crisis van 2007-08 heeft duidelijk gemaakt dat bankschokken een domino-effect
kunnen teweegbrengen met een ongeziene impact op de globale economie. Het belang van
transparantie over het risicoprofiel van banken mag niet onderschat worden. Deze dissertatie
heeft daarom twee doelen voor ogen: het beoogt enerzijds het opstellen van een index die de
risico disclosure meet voor 30 Europese banken in het jaar 2013. Hiermee wordt gepeild in
hoeverre elke bank tegemoetkomt aan de publicatievereisten die het Basel Committee on
Banking Supervision heeft omschreven in de paper Consultative Document Pillar 3 (Market
Discipline) in 2001. Het opstellen van de risk disclosure index gebeurt middels een check box
approach. Vervolgens worden de indices aan de hand van scatter plots en lineaire regressies
gelinkt worden aan enkele belangrijke bankkarakteristieken. Disclosure blijkt onder meer
positief gerelateerd te zijn aan verminderde kapitaalverliezen gedurende een stress test scenario,
bank grootte en liquiditeit en negatief gerelateerd aan kapitaalbuffers (mogelijks te wijten aan de
aanwezigheid van overheidssteun), non-interest inkomen, interbancair risico en soevereine
schulden van de GIPS/GIIPS landen. De impact van disclosure op volatiliteit is onduidelijk.
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Preface
The realisation of this paper would not have been possible without the support of several people.
First and foremost, I would like to express my gratitude towards prof. dr. Rudi Vander Vennet
for giving me the opportunity to work on this interesting topic. It gave me great insight in the
field of banking transparency and allowed me to grow, not only as an economic graduate, but as
a person as well.
I also owe a lot to Elien Meuleman for her hospitality and constant support during these past two
years. Her door was always open and knowing this motivated me to complete the research.
Furthermore, I could always count on my friends and fellow students, who were there when I
needed a listening ear. Their jokes and laughter provided a welcome distraction from the isolated
nights spent with KBC, Belfius, Argenta and the other banks.
Last, but not least, I would like to thank my parents and brothers for their financial and caring
support throughout these past five years. Thank you so much for everything!
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Table of Contents
Preface ...................................................................................................................................... V
List of abbreviations ................................................................................................................. IX
List of tables ............................................................................................................................. XI
List of figures ........................................................................................................................... XIII
1. Introduction ......................................................................................................................... 1
2. Literature review.................................................................................................................. 3
2.1 Banking Disclosure ...................................................................................................... 3
2.2 Market Discipline ......................................................................................................... 4
2.3 Bank Capital ................................................................................................................. 5
2.4 Financial Leverage ....................................................................................................... 6
2.5 Sovereign Exposures .................................................................................................... 6
3. Methodology ........................................................................................................................ 9
3.1 Construction of the Risk Disclosure Indices ................................................................ 9
3.1.1 Overall RDI ......................................................................................................... 10
3.1.2 Credit RDI ........................................................................................................... 11
3.1.3 Market RDI ......................................................................................................... 11
3.1.4 Interest Rate RDI ................................................................................................ 12
3.2 Ratios ............................................................................................................................ 12
3.2.1 Overall RDI ......................................................................................................... 13
3.2.2 Credit RDI ........................................................................................................... 15
3.2.3 Market RDI ......................................................................................................... 15
3.2.4 Interest Rate RDI ................................................................................................ 16
4. Results ................................................................................................................................. 17
4.1 Risk Disclosure Index................................................................................................... 17
4.2 Ratios ............................................................................................................................ 18
VIII
4.2.1 Overall RDI ......................................................................................................... 19
4.2.2 Credit RDI ........................................................................................................... 30
4.2.3 Market RDI ......................................................................................................... 35
4.2.4 Interest Rate RDI ................................................................................................ 39
5. Conclusion ........................................................................................................................... 41
6. References ........................................................................................................................... XV
7. Appendices .......................................................................................................................... XVII
Appendix 1 – Bank sample........................................................................................... XVII
Appendix 2 – Set of disclosure items ........................................................................... XIX
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List of abbreviations
CAR Capital Adequacy Ratio
CET1 Common Equity Tier 1
CIR Cost-Income Ratio
GIIPS Greece, Italy, Ireland, Portugal and Spain
GIPS Greece, Italy, Portugal and Spain
IMA Internal Models Approach
IRB Internal Rating-Based
IRRBB Interest Rate Risk in the Banking Book
LLRGL Loan Loss Reserves to Gross Loans
LRMES Long-Run Marginal Expected Shortfall
NII Non-Interest Income
NIM Net Interest Margin
RDI Risk Disclosure Index
ROA Return On Assets
ROE Return On Equity
LTD Loan To Deposits
VaR Value at Risk
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List of tables
Table 1: Individual bank RDI’s (in %) ...................................................................................... 17
Table 2: Correlation matrix risk disclosure indices ................................................................... 18
Table 3: Descriptive statistics of the variables related to the Overall RDI ............................... 29
Table 4: Descriptive statistics of the variables related to the Credit RDI ................................. 35
Table 5: Descriptive statistics of the variables related to the Market RDI ................................ 39
Table 5: Descriptive statistics of the variables related to the Interest Rate RDI ....................... 40
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List of figures
Figure 1: Schematic overview risk disclosure index ................................................................. 10
Figure 2: Log Z-score vs. Overall RDI ..................................................................................... 19
Figure 3: CAR vs. Overall RDI ................................................................................................. 21
Figure 4: CET1 ratio vs. Overall RDI ....................................................................................... 22
Figure 5: % change in CET1 ratio vs. Overall RDI .................................................................. 23
Figure 6: CET1 ratio losses vs. Overall RDI ............................................................................. 23
Figure 7: Leverage ratio vs. Overall RDI .................................................................................. 24
Figure 8: Bank size vs. Overall RDI ......................................................................................... 25
Figure 9: ROA vs. Overall RDI ................................................................................................. 26
Figure 10: NIM vs. Overall RDI ............................................................................................... 27
Figure 11: NII vs. Overall RDI ................................................................................................. 28
Figure 12: Credit risk exposure vs. Credit RDI ......................................................................... 30
Figure 13: LLRGL vs. Credit RDI ............................................................................................ 31
Figure 14: Institutions vs. Credit RDI ....................................................................................... 32
Figure 15: Central banks and central governments vs. Credit RDI ........................................... 33
Figure 16: GIPS vs. Credit RDI ................................................................................................ 34
Figure 17: GIIPS vs. Credit RDI ............................................................................................... 34
Figure 18: Total volatility vs. Market RDI ................................................................................ 36
Figure 19: Beta vs. Market RDI ................................................................................................ 36
Figure 20: LRMES vs. Market RDI .......................................................................................... 38
Figure 21: LTD ratio vs. Interest Rate RDI ............................................................................... 40
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1. Introduction
So far, not a lot of research has been done concerning the design of risk disclosure indices
focused on financial institutions. There is a lot more research out there related to the less
complex non-financial institutions. Nevertheless, recently a few studies have tried to construct a
disclosure index that is aimed specifically towards banks. These indices are mostly based upon a
checkbox approach and consider the information provided in the banks’ annual reports. This
paper will also implement a checkbox approach that is based on the consultative document
provided by the Basel Committee on Banking Supervision in 2001 to calculate a risk disclosure
index for a sample of 30 banks, located in Europe. Our focus is on the third pillar of the Basel III
framework, which promotes market discipline and transparency in the banking sector. By
introducing these disclosure requirements and recommendations, the Basel regulations aim to
increase safety and soundness of the market. Furthermore, we will compare several financial
ratios with the risk disclosure index to see if we can identify any variables that are correlated to
the banking disclosure. This will be accomplished by constructing scatter plots and perform
simple linear regressions on the variables. This research attempts to make a valuable contribution
to the existing literature, by simultaneously analysing the three main subcomponents of overall
risk disclosure (credit risk, market risk and interest rate risk in the banking book) and linking
them to several fundamental banking characteristics.
In section 2 of this paper we review the existing literature. Section 3 covers the methodology of
both the risk disclosure indices and the scatter plots. We report the results of our research in
section 4. Finally, section 5 finishes with a conclusion.
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2. Literature review
2.1 Banking Disclosure
There has already been done a lot of research on determining the disclosure behaviour of
companies, this is however not the case for financial institutions. Several authors have stated that
the limited research towards the disclosure of financial institutions is because the composition of
banks is inherently complex and opaque. Morgan (2002) compared banks to black holes, stating
that they remain unfathomable, no matter how much information they disclose. Despite this fact,
in more recent years, several authors have made an effort to construct risk disclosure indices that
measure the disclosure of banks. For example, Baumann and Nier (2004) constructed a measure
of disclosure for a sample of approximately 600 banks to investigate the relationship with the
volatility of a bank’s stock price. Their disclosure index is based upon the Center for
International Financial Analysis Research (CIFAR) index of transparency and considers
information that is disclosed in the annual accounts as represented in the BankScope database,
including information on interest rate risk, credit risk, liquidity risk, market risk and capital.
They used a checkbox approach to measure the level of detail on seventeen dimensions.
Furthermore, Huang (2006) set up a bank disclosure index that also uses a checkbox approach to
quantitatively measure the disclosure practices of about 20.000 commercial banks in roughly 180
countries around the world. He created a composite index for the individual banks and a national
index by taking the weighted-average of the individual index values. The checkbox approach is
based on the measurement framework of Erlend Nier (2004) and includes disclosure items that
are linked to risk categories emphasized by the Basel Committee on Banking Supervision
(hereafter referred to as the Basel Committee) as well as Financial Soundness Indicators (FSI) by
the International Monetary Fund (IMF). He formulated both a core and an encouraged checklist
of disclosure items and created a bank disclosure index that evaluates the level of disclosure on
the seventeen indicators included in the core set. Sowerbutts, Zimmerman and Zer (2013)
introduced quantitative indices that evaluate the progress on the provision of information by 50
major banks from around the world. They evaluated fourteen disclosure indicators in five areas
(funding risk, group structure, valuation methods, intra-annual information and financial
interconnections) and compared the disclosure between banks and over time. They applied a
checkbox approach, assigning a 1 when the required information is clearly presented in the
report and a 0 otherwise, without making value judgements on the quality of the disclosures.
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Nevertheless, we should not mistake more extensive disclosure for greater transparency, as
Greenspan (2003) pointed out. He argued that an improvement in transparency is a bigger
challenge than an improvement in disclosure, because it requires market participants not only to
provide the information, but also to phrase that information in a way that makes it meaningful for
others to understand. Sowerbutts et al. (2013) argued that greater disclosure is not sufficient for
effective market discipline, but the investors must also be able to process the information.
Although more information is beneficial in most cases, an excessive amount of ‘noisy’
information can have an opposing effect and make it more difficult for investors to extract the
key information (Morris & Shin, 2002). This is why Sowerbutts et al. believed that the use of
standardised templates for financial reporting can be beneficial, because it makes it easier for
investors to benchmark financial institutions. Anyway, we believe that even a nominal disclosure
is of significant value, since it would be difficult for banks to cover up inconsistencies year after
year when they are continuously being obliged to disclose information on certain financial
topics.
2.2 Market discipline
The risk disclosure index will facilitate the direct comparability of risk disclosure behaviour
between the financial institutions. It measures the compliance with a set of risk disclosure items
that is recommended by the Basel Committee in 2001. The set of recommendations were
developed to ensure that financial institutions have sufficient capital to pay back creditors and to
withstand sudden capital losses. It is part of the Basel III framework, which consists of three
complementary elements or pillars in total. The first pillar sets minimum capital requirements for
credit risk, market risk and operational risk. The second pillar concerns the supervisory review:
banks do not only have to make sure that they have sufficient capital in place to satisfy these
requirements, they also need to develop an adequate risk management to properly monitor and
manage these risks. Our focus is on the third pillar, which aims to stimulate market discipline
through improved disclosure. Market discipline is the force that restricts bank managers from
taking on too many risks. A greater disclosure enables (potential) investors to better evaluate the
banks’ risk and adapt their portfolio accordingly, discouraging banks from taking on excessive
levels of risk. Market discipline complements the first two pillars by reinforcing capital
regulation and other supervisory efforts to promote safety and soundness in banks and financial
systems (Basel Committee on Banking Supervision, 2001). A great disclosure is of major
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importance. For instance, a lack of adequate disclosure was one of the causes of the financial
crisis in 2007-08. Market discipline reduces this opacity and prevents sudden negative shocks in
investors’ expectations, by making sure that banks act in the interest of their investors. This
reduces the chance of future financial crises. Furthermore, a higher disclosure not only provides
benefits for the investors and the market as a whole, but, as Baumann and Nier (2004) suggest,
banks with a higher disclosure on key items tend to display a lower volatility in their stocks than
banks that disclose less information. A lower stock volatility may result in a lower cost of
capital, which is beneficial for banks.
Since the second part of this paper focuses on the graphical comparison of the risk disclosure
indices with several bank characteristics and financial ratios (z-score, capital ratios, performance
measures…), we will elaborate on the relevance of some of these now, while the methodology of
each ratio will be explained in detail in section 3.
2.3 Bank capital
To begin with, there are several requirements in place that demand building up sufficient capital
buffers to reduce the chance that banks experience difficulties during economic downturns. The
2008 financial crisis has shown that bank capital was not always sufficient to cover the losses
that arose from the risky lending practices banks were involved in. Some banks were known as
‘too big to fail’: governments would bail out these banks if they faced potential failure, because
they knew that if they would let them go bankrupt, it would damage the trust in the entire
financial system. Unfortunately, this support can lead to a sense of blind trust of those banks,
expecting the government to help them out in any case, which in turn can lead to banks taking on
more risk. This is why the Basel Committee has introduced a set of additional loss absorption
capital demands for the global systematically important banks or G-SIBs. Apart from these
specific demands, there are two general capital requirements that apply to all financial
institutions: the Common Equity Tier 1 ratio (CET1) and the Capital Adequacy Ratio (CAR).
The first one requires banks to hold a minimum Common Equity Tier 1 capital ratio of 3.5%,
while the latter needs to be minimally 8%. In addition, the Basel III framework introduced a
countercyclical and a capital conservation buffer that will be phased-in between 2016 and 2018
and will become fully effective in 2019. The underlying idea is to build up additional capital
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buffers during periods of economic growth so that banks are able to better absorb capital losses
in periods of stress (Basel Committee on Banking Supervision, 2015).
2.4 Financial leverage
In addition, the excessive leverage taken on by banks, both on and off-balance sheet, is also
recognised as an underlying cause to the global financial crisis. Financial leverage allows banks
to increase the potential return on an investment, often by buying assets with borrowed funds.
This causes banks to build up an excessive debt, which in economic downturns can lead to
increased losses. In 2011, the Basel Committee introduced a non-risk based leverage ratio to
complement the risk-based minimum capital requirements. Banks are expected to attain a
leverage ratio of minimally 3% during the parallel-run period from 1 January 2013 to 1 January
2017, after which any final adjustments to the ratio will be carried out by 2017 (Basel Committee
on Banking Supervision, 2014).
2.5 Sovereign exposures
European banks tend to have a lot of domestic as well as non-domestic sovereign exposures,
since these are classified as zero-risk weights and exempted by the Capital Requirements
Directive from several limits that are in place to restrict concentration risk (Ciucci & Magnus,
2016). On top of this, sovereign bonds are very attractive because of their high liquidity in the
market; they can be easily bought and sold again. However, this high concentration of sovereign
debt in the banking system bears risks, not only for the banks themselves, but also for the entire
economy. The global financial crisis has shown that government bonds are not as safe as they
were perceived before. The creditworthiness of sovereign exposures can degrade and when it
does, it can jeopardise the credit supply in the banking system in several ways, as stated by
Popov and Van Horen (2013). First of all, since sovereign debt is often used as collateral, the
degradation can harm the quality of the collateral and eventually the funding. Secondly, the
capital losses that arise from the devaluation affect the cost and availability of funding for the
banks. Also, financial institutions are less eager to lend to banks that hold a lot of sovereign
exposures, especially if these include the GIIPS-countries. The acronym GIIPS refers to Greece,
Italy, Ireland, Portugal and Spain, which are the five European Union (EU) member states that
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were not able to refinance their sovereign debt during the European debt crisis in the late 2000s.
Due to the substantial instability of their economies, they were grouped together as GIIPS.
Although the entire EU and the rest of the world suffer from the same symptoms, these five
countries seem to always be at the top of the list when it comes to high debt levels, stagnate
economic growth, unstable and sometimes corrupt governments (Schmidt, s.d.).
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3. Methodology
Our sample consists of 30 European banks (see Appendix 1.1). We evaluated the information
that is disclosed in their Pillar 3 reports of the year 2013. If they did not have a separate Pillar 3
report, we consulted their annual report. Up to the end of 2013, banks are still subject to Basel II,
since the implementation of Basel III was postponed for a year due to a delayed vote in the
European Parliament (European Parliament, 2013). Starting from January 1st, 2014, Basel III is
implemented into the European law by a Capital Requirement Regulation (CRR) and a Capital
Requirement Directive IV (CRD IV) as a response to the 2008 financial crisis. A comparison of
the main principles of Basel III with Basel II by Kubat (2014) reveals three fundamental
innovations: new capital requirements, a non-risk based leverage ratio and the introduction of
liquidity management. We have already discussed the first two additions: Two new capital
buffers are introduced to decrease the direct connection between the risk to which the bank is
exposed and the corresponding capital requirement. The leverage ratio is in place to simplify the
calculation and to avoid the disclosure of deceptive information. Furthermore, liquidity
management is introduced, which was not yet incorporated in the Basel II framework.
The disclosure items are based on the framework that the Basel Committee presents in its
Consultative Document ‘Pillar 3 (Market Discipline)’ of 2001. The Basel agreements were
incorporated into the European law and, as they are held in high regard, the non-European banks
in our sample (VTB Capital and Credit Suisse) also abide by them. In this paper, our focus lies
on credit risk, market risk and structural asset/liability risk or interest rate risk in the banking
book, which are the main contributors to financial risk according to Kuritzkes and Schuermann
(2006).
3.1 Construction of the Risk Disclosure Indices
For the Risk Disclosure Indices (RDI), we performed a checkbox approach on the information
published in the bank reports. Since disclosure is a latent variable and therefore not directly
observable, the number of disclosed items serves as a proxy for the risk disclosure. A 1 is
assigned when the item is disclosed and a 0 otherwise, without verifying that the information
provided in the risk reports is correct. Each type of risk has its own sub-index and these sub-
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indices are converted into a composite total risk disclosure index. First, the Overall RDI is
discussed, after which the paper explains the construction of each sub-index.
The list of the disclosure items for the risk disclosure index can be found in Appendix 2.
3.1.1 Overall RDI
The Overall RDI summarises the three sub-indices: Credit, Market and Interest Rate RDI. The
higher a bank scores on this index, the more transparent its overall risk disclosure and the better
it meets the Pillar 3 guidelines. In short, a bank can score 41 points for its credit risk disclosure,
10 points for its market risk disclosure and 13 points for its interest rate risk disclosure in the
banking book. This adds up to an overall risk disclosure index of 64 points. Relatively, the credit
index contributes for 64% to the total risk index, the market index 16% and the interest rate risk
in the banking book index 20%. These percentages roughly correspond to the composition of the
risk taxonomy that is discussed in the paper of Kuritzkes and Schuermann (2006). They found
that the financial risk accounted for 70% of the total risk, broken down into 46% of credit risk,
6% of market risk and 18% of structural asset/liability risk (which corresponds to our interest
rate risk in the banking book). When we translate this into the relative contributions to the
financial risk, the three risk types account respectively for 66%, 8% and 26%.
See Figure 1 for a schematic diagram of the breakdown of the overall risk disclosure index in
credit risk, market risk and interest rate risk in the banking book.
Figure 1: Schematic overview risk disclosure index
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3.1.2 Credit RDI
The credit risk refers to the default risk or the risk that borrowers will fail to pay their debt.
Consistent with the paper of the Basel Committee of 2001, we divide this risk into 4 sections: a
general section, a section for the standardised approach, a section for the IRB approach and a
section that covers the credit risk mitigation.
Banks are allowed to deviate from the standardised approach that is proposed by the Basel
Committee and make use of an internal rating-based approach (IRB), in which they apply their
own estimated risk parameters to calculate the regulatory capital. In return, these banks need to
meet certain minimum requirements, make additional disclosures and obtain the acceptance from
their national supervisor.
Each section is broken down into a quantitative and a qualitative category. These quantitative
and qualitative categories are then subdivided into a core and a supplementary set, except for the
IRB section, where there is only a core set.
The banks get assigned 1 point per data item that is disclosed in their annual report. For each
section, we calculated the total score by adding up these points. Since the IRB approach includes
more data items than the standardised approach, we converted the maximum score on both
sections to 10 points. This was done to avoid that a bank can achieve a higher risk disclosure
index by using the IRB approach instead of the standardised approach. Furthermore, if banks
apply the standardised approach and the IRB approach simultaneously, we calculated the joint
score by taking the average of both sections. This way, the maximum achievable score on the
general section is 20 points, on the standardised/IRB section 10 points and finally on the credit
risk mitigation section 11 points, which adds up to a total possible credit risk disclosure index of
41 points.
3.1.3 Market RDI
The market risk covers the interest rate risk and equity risk in the trading portfolios, along with
the currency risk and commodity risk. It measures the risk of experiencing losses in positions
that arise from adverse movements of the market prices. The Basel Committee has divided this
category into 2 sections: disclosure items that apply to banks that use the standardised approach
and those who use an internal models approach.
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An Internal Models Approach (IMA) employs the Value at Risk (VaR) as the risk indicator to
determine the capital requirements, while the capital charge serves as the risk indicator in the
standardised approach. Prerequisites for banks using an IMA approach include amongst other
things the approval of the supervisor, back testing and a description of the stress test program.
As with the credit risk data explained above, each section is broken down into a quantitative and
a qualitative category. These quantitative and qualitative categories are then subdivided into a
core and a supplementary set.
Also for this risk category, we assign 1 point for each data item that is included in the banks’
annual report. We convert each section’s maximum score to 10 points: this way, a bank cannot
gain any advantage solely by using one approach instead of the other. Furthermore, since it is
possible that banks apply the standardised and the internal models approach simultaneously (for
example, banks that are transitioning from the standardised to the internal models approach), we
took the average of both scores to make up the total score, which amounts to a possible market
risk disclosure index of 10 points.
3.1.4 Interest Rate RDI
The Interest Rate Risk in the Banking Book (IRRBB) is the risk of experiencing losses in
positions in the banking book that arise from changes in the interest rates.
The BIS paper of 2001 divides this risk in a qualitative and quantitative section, which are
further subdivided into a core and a supplementary set. To obtain the interest rate risk disclosure
index, we calculate the sum of all underlying data items and obtain a possible score of 13 points.
3.2 Ratios
We compared the risk disclosure indices with various financial ratios by means of scatter plots to
assess whether there exists a relation between the disclosure practices and certain bank measures.
The data for the latter are retrieved from the database BankScope and the website of the EBA.
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3.2.1 Overall RDI
First of all, this paper compared the Overall RDI with the risk/return profile of the banks by
means of the following variables: Z-score, capital and leverage ratios, bank size and performance
measures.
The Z-score is an indicator for the bank’s stability and measures how many standard deviations
an observation is distantiated from the mean of a distribution. It indicates the amount the Return
On Assets (ROA) would have to decrease from the mean such that the bank would become
insolvent. It is calculated as the sum of the ROA and the Capital Adequacy Ratio (CAR), divided
by the standard deviation of the ROA. A higher Z-score implies a safer bank. Demirgüç-Kunt,
Detragiache and Tressel (2008) found that compliance with information provision specified in
the Basel Core Principles of 1997 improves bank soundness, measured by the Z-score, in high
income as well as emerging markets, emphasizing the importance of disclosure for bolstering
market discipline. Since the Z-score itself is positively skewed, we use the logarithm of the
variable.
The capital ratios consist of the Capital Adequacy Ratio (CAR), the Common Equity Tier 1
capital ratio (CET1) and the leverage ratio. Banks are required to meet regulatory capital
adequacy rules. The minimum CAR is 8%, as stated in the first pillar of the Basel Accords. CAR
is a measure of the bank’s financial strength and it is calculated as the sum of Tier 1 and Tier 2
capital, divided by the risk-weighted assets. The higher this ratio, the better the bank is able to
absorb losses and the lower the bank’s risk. The CET1 ratio also measures a bank’s financial
strength, but is calculated by dividing the Common Equity Tier 1 capital by the risk-weighted
assets. Banks are required to meet a CET1 ratio of 3.5% in 2013 and 4% in 2014. As of 1
January 2019, the minimum CAR and CET1 ratio are 10.5% and 7% respectively and include the
capital conservation buffer of 2.5%.
In addition to these risk-weighted capital ratios, we included the calculation of the unweighted
leverage ratio. Although it is reasonable to assess the riskiness of banks by incorporating the
risk-weighted assets in the calculation of the capital ratios, the calculation of these assets allows
subjective interpretation. That is, regulators only have strict requirements in place to determine
the risk weights of bank loans. This is not the case for other assets, such as securities and
derivatives, for which banks are allowed to use internal models to determine the risk weights.
Banks can thus characterize the assets they hold as ‘low risk’, allowing them to raise their risk-
based capital ratios without holding any extra capital (Bair, 2013). The leverage ratio was
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introduced by the Basel Committee in 2011. It is calculated by dividing a capital measure by an
exposure measure. The capital measure equals the Tier 1 capital, while the exposure equals the
sum of the on-balance sheet exposures, derivative exposures, securities financing transaction
exposures and off-balance sheet items. During the parallel run period from 2013 to 2017, banks
are required to maintain a leverage ratio higher than 3% (Basel Committee on Banking
Supervision, 2014).
Furthermore, we included two figures resulting from the stress test that was carried out by the
European Banking Authority (EBA) in 2014: the % change in CET1 ratio and the CET1 ratio
losses. The EBA has a range of supervisory tools to maintain financial stability in the European
Union and to guard the correct performance of the banking sector. The stress test aims to assess
the resilience of individual banks during adverse scenarios. The % change in CET1 indicates
the fraction of Common Equity Tier 1 capital that is lost due to the adverse scenario the bank is
put through from 2013 to 2014. If the change is only minor, it means that the bank is fairly
resilient. Next, the variable CET1 ratio losses equals the sum of all drivers that impact the %
change in CET1 ratio aside from the operating profit. These include impairments on financial as
well as non-financial assets, transitional adjustments and variations in risk exposure.
The following variable is the bank size, which is measured as the logarithm of total assets.
Authors have documented a positive relationship between firm size and disclosure (e.g. Linsley
& Shrives, 2006). Also, Laeven, Ratnovski and Tong (2014) found strong evidence that
individual and systemic risk increases with bank size. This is consistent with the presence of
agency conflicts in large organisations as well as with the hypothesis that regulators are reluctant
to unwind banks that are too big to fail, leading them to take on excessive risks.
The performance measures include Return On Assets (ROA), Net Interest Margin (NIM) and
Non-Interest Income ratio (NII). ROA measures the bank’s profitability and adjusts for its size.
It is measured as the net income divided by the total assets. NIM calculates the difference
between the interest income and the interest expenses as a percentage of the assets. This spread is
called the interest margin, and the larger the interest margin, the higher the likelihood that the
bank will be profitable. The NII determines the contribution of non-interest revenues, such as
service charges to deposits and trading income, to the operating income. We divide the non-
interest operating income by the sum of net interest income and non-interest income. In the past
decades, banks have increased their exposure to non-interest income. Although this shift towards
non-interest income can yield diversification benefits for banks, several studies have shown that
15
non-interest income has a higher volatility, but is not necessarily more profitable than traditional
lending activities (Li & Zhang, 2013; Williams & Prather, 2010; Stiroh & Rumble, 2006). Banks
need to be aware that the increase in exposure to non-interest income can come to a point where
the higher volatility offsets the marginal benefit of diversification.
3.2.2 Credit RDI
The first sub-index is the Credit RDI and is compared to several credit risk exposures.
Firstly, we retrieved the credit risk exposures of the year 2013 that is presented in the EU-wide
transparency exercise conducted by the EBA in 2014. To be able to compare it with the Credit
RDI, the exposure is scaled on the total risk exposure. Another proxy for credit risk is the Loan
Loss Reserves to Gross Loans ratio (LLRGL). Banks build up loan losses reserves to cover
estimated losses on loans, due to default. The LLRGL depends on the quality of the bank loans
and is higher if the loans are of poorer quality. Furthermore, the stress test of the EBA provides
us with credit risk exposures broken down into several exposure classes. The focus is on the
classes institutions and central banks and central governments. For both classes, we scale the
exposures on the total exposure, allowing us to observe how these components of credit risk are
correlated to the Credit RDI. Finally, the stress test also gives us an overview of the sovereign
exposures. The Credit RDI is compared to the share of sovereign exposures that a banks holds
towards GIPS and GIIPS nations. This allows us to investigate whether being exposed to these
less stable economies affects or is affected by the credit risk disclosure.
3.2.3 Market RDI
There are 25 banks in our sample that are listed on a stock exchange. For these banks, we have
computed the following three risk measures: total volatility, beta and Long-Run Marginal
Expected Shortfall (LRMES).
The total volatility is the annualised standard deviation of the daily stock returns over the time
period 2011-2013. To annualise the standard deviation, we multiplied it with the square root of
the number of trading days, which is 261 days. Furthermore, in order to measure the systematic
risk, we calculated beta. It is an indicator of the volatility of the individual stocks relative to the
market. We matched the daily stock prices of the financial institutions with the daily stock prices
of the MSCI Europe Index over the time period 2011-2013. Daves, Ehrhardt and Kunkel (2000)
recommend using a daily return interval and estimation period of three years when estimating
16
beta via regression analysis, because this results in a minimal standard error. Finally, the
LRMES represents the expected loss of equity value of a financial institution in a crisis scenario.
This variable is determined by the systemic importance of a bank. According to the paper of
Acharya, Engle and Richardson (2012), a bank is considered systemically important if its failure
is likely to have a major adverse impact on the financial system and economy. This means that a
large bank or a bank that is heavily interconnected with other banks is characterised by a higher
LRMES. We approximate the LRMES by the formula
LRMES = 1-exp(-k*MES/100)
with k being equal to 18.
3.2.4 Interest Rate RDI
We analysed the impact of interest rate risk disclosure on the Loan To Deposits ratio (LTD). It
divides a bank’s total loans by its total deposits and is a commonly used statistic to assess a
bank’s liquidity. Since loans can be seen as an investment over a longer period of time, it reduces
liquidity. If the LTD ratio is higher than 1, it means that the bank has borrowed money to fund its
lending. If it is lower than 1, the bank has relied entirely on its own deposits for lending to its
customers. On the one hand, if the LTD ratio is high, this imposes the threat that the bank does
not have sufficient liquidity to cover unanticipated funding requirements, for example in case of
an economic crisis. On the other hand, if the LTD ratio is low, this can indicate that the bank’s
earnings are too low and that the return is not optimal.
17
4. Results
4.1 Risk Disclosure Indices
In Table 1, the banks are ranked according to their total risk disclosure index and further
subdivided into the credit, market and interest rate risk (banking book) disclosure index. In each
column, the highest score for that specific index was marked by underlining it.
Table 1: Individual bank RDI’s (in %)
BANKS OVERALL
RDI
CREDIT
RDI
MARKET
RDI
INTEREST
RATE RDI
1. UniCredit 65.97 69.41 47.62 69.23
2. Royal Bank of Scotland Group 64.75 71.04 53.17 53.85
3. KBC Group 64.14 73.17 40.48 53.85
4. HSBC Holdings 63.85 74.09 34.92 53.85
5. Intesa Sanpaolo 63.40 75.54 46.03 38.46
6. Lloyds Banking Group 62.08 66.87 53.17 53.85
7. ING Group 61.79 69.51 40.48 53.85
8. Nordea Bank 61.63 73.75 42.06 38.46
9. Barclays 60.70 63.62 47.62 61.54
10. Deutsche Bank 58.50 68.60 53.17 30.77
11. Belfius Bank 56.53 66.87 47.62 30.77
12. BNP Paribas 56.45 63.62 40.48 46.15
13. Caixabank 56.17 62.09 34.92 53.85
14. Alpha Bank 55.74 67.68 49.21 23.08
15. Argenta Savings Bank 55.38 54.23 - 58.97
16. Société Générale 54.96 56.40 40.48 61.54
17. Erste Group Bank 53.88 59.99 38.89 46.15
18. Banco Comercial Português 53.62 58.84 61.90 30.77
19. BPCE Group 52.57 58.64 46.03 38.46
20. Commerzbank 51.56 58.03 42.06 38.46
21. Swedbank 50.33 59.25 49.21 23.08
22. Crédit Agricole 50.03 61.18 29.37 30.77
23. Banco Bilbao Vizcaya Argentaria 49.75 55.59 40.48 38.46
24. ABN Amro Group 49.58 60.67 28.57 30.77
25. Credit Suisse Group 47.96 51.02 27.78 53.85
26. Cooperatieve Rabobank 47.61 51.02 55.56 30.77
27. National Bank of Greece 47.39 53.96 42.06 30.77
28. Bank of Ireland 45.36 65.65 11.11 7.69
29. Danske Bank 39.59 39.74 40.48 38.46
30. VTB Capital 39.24 51.66 29.37 7.69
18
The overall risk disclosure index ranges from 39.24% to 65.97%. UniCredit is leading with a
RDI of 66.97%, followed closely by the Royal Bank of Scotland and KBC. On the other hand,
Danske Bank and VTB Capital are at the bottom with an Overall RDI of approximately 39%.
Regarding the Credit RDI we can see that, although UniCredit attains the highest Overall RDI,
Intesa Sanpaolo is dominating the Credit RDI with 75.54%. Again, Danske Bank achieves only a
minor score and is at the very bottom with 39.74%.
Furthermore, Banco Comercial Português is leading in the market risk disclosure section: it
discloses approximately 61.90% of the required and recommended market risk items. The Bank
of Ireland is lagging behind, acquiring a Market RDI of only 11.11%. We note that Argenta
Savings Bank doesn’t have a trading book, nor holds any foreign currency instruments and is
therefore excluded from this evaluation.
Lastly, UniCredit ranks first with its disclosure on the interest rate risk in the banking book of
69.23%. The Bank of Ireland and VTB Capital lag behind, both scoring a modest 7.69%. The
latter had also attained the lowest score on the Overall RDI.
Table 2 presents the correlations between the Overall RDI, Credit RDI, Market RDI and Interest
Rate RDI.
Table 2: Correlation matrix risk disclosure indices
OVERALL RDI CREDIT RDI MARKET RDI INTEREST RATE
RDI
OVERALL RDI 1.0000
CREDIT RDI 0.8480*** 1.0000
MARKET RDI 0.4107** 0.1256 1.0000
INTEREST RATE
RDI 0.6448*** 0.2021 0.2352 1.0000
*** p<0.01, ** p<0.05, * p<0.1
4.2 Ratios
In this section, we present a number of scatter plots that visualise the correlation between the
disclosure indices and the different ratios. The risk disclosure indices are plotted on the vertical
or Y-axis, while the financial ratios are plotted on the horizontal or X-axis. We analyse the data
19
by adding a linear regression line to each scatter plot: this model describes the relationship
between the variables numerically. However, since our data sample is rather small to start from
and since not all ratios are available for the 30 banks, the majority of the scatter plots is
characterised by low R2-values and high p-values.
4.2.1 Overall RDI
Figure 2 shows the association between the Overall RDI and the logarithm of the Z-score. The
relatively constant regression lines suggest that there is no correlation between the Overall RDI
and insolvency risk. This is in contrast with the paper of Demirgüç-Kunt, Detragiache and
Tressel (2008) that found a positive and statistically significant relationship between the
compliance of banks with information provision and the Z-score, although the investigated banks
are also situated in advanced countries. This can be due to the small size of our banking sample.
Figure 2: Log Z-score vs. Overall RDI
2013: Y = 0.1303X + 1.8115; R2 = 0.0003
2014: Y = 0.1226X + 1.8366; R2 = 0.0002
20
Figure 3 shows the correlation between CAR and the Overall RDI. All financial institutions in
the sample are already conform with the 10,5% minimum CAR of Basel III, although this
minimum requirement will be phased-in between 2016 and 2018 and will be fully effective from
2019. Furthermore, the scatter plots show a rather negative relationship between the Overall RDI
and the CAR, which indicates that a bank with a lower transparency behaviour is associated with
a higher capital buffer.
This outcome seems to contradict Nier and Baumann (2006), who found that banks with a low
disclosure are likely to have low capital buffers due to a reduced market discipline. However,
they also stated that the presence of a government safety net can reduce the effect of disclosure,
causing capital buffers to be lower than expected. We therefore calculated the average capital
adequacy ratio for banks that received government support from 2007 until 2013 and for those
that haven’t. We found that, on average, the former have indeed a lower capital adequacy ratio (-
1,84% in 2013 and -0,15% in 2014), although they are characterised by a slightly higher Overall
RDI (+0,59%). Hence, government support could be a plausible reason for the scatter plot
showing a negative relationship between the Overall RDI and the CAR. These averages however
do not differ on a 5% significance level.
21
Figure 3: CAR vs. Overall RDI
2013: Y = -0.0772X + 0.2047; R2 = 0.0380
2014: Y = -0.1077X + 0.2311; R2 = 0.0656
As for the CET1 ratio, Figure 4 shows that all banks in our sample have a ratio greater than
4,5% in both years, although this minimum Basel III requirement is not effective until 1 January
2015. During the transition period, banks are required to hold a CET1 ratio of only 3,5% in 2013
and 4% in 2014. The banks satisfy this requirement amply. There seem to be a slightly negative
correlation in 2014. We calculated the difference in averages between banks that received
government support from 2007 until 2013 and those that didn’t, to see whether the negative
correlation could be related to the presence of a government safety net. In contrast with the
capital adequacy ratio, the presence of government support does not seem to decrease the effect
of disclosure on the CET1 ratio: the group that received government support attains on average a
higher (+0,25%) instead of a lower CET1 ratio in 2014. These averages do not differ at a 5%
significance level. Moreover, a lot of data regarding the CET1 ratio is missing in 2014, reducing
22
the number of observations of the second group to only 9 observations. This can result in a
distorted image of reality.
Figure 4: CET1 ratio vs. Overall RDI
2013: Y = -0.0519X + 0.1461; R2 = 0.0121
2014: Y = -0.0656X + 0.1634; R2 = 0.0687
Figure 5 represents the correlation of the Overall RDI with the % change in CET1 ratio during
the year 2013 – 2014. A small negative % change in the CET1 ratio corresponds to a higher
score on the risk disclosure index, which indicates that more transparent banks correlate to being
more resilient and better able to endure a crisis scenario without suffering substantial capital
losses.
23
Figure 5: % change in CET1 ratio vs. Overall RDI
2013 – 2014: Y = 0.0171X – 0.0253; R2 = 0.0141
A similar scenario is applicable to the scatter plot of the CET1 ratio losses, for which there is an
upward trendline (see Figure 6). It indicates that a higher risk disclosure corresponds to
experiencing less capital losses that are due to impairments, transitional adjustments and
variations in risk exposure amount during a stress scenario.
Figure 6: CET1 ratio losses vs. Overall RDI
2013 - 2014: Y = 0.0294X – 0.0436; R2 = 0.0315
Figure 7 shows that there is a slightly positive correlation between the Overall RDI and the
leverage ratio. Since a higher leverage ratio indicates that the bank holds more Tier 1 capital in
comparison to its exposures, a higher banking disclosure is associated with a more enhanced
balance between capital and risk exposures. Furthermore, all banks meet the minimum leverage
24
ratio of 3%. We also observe that the unweighted leverage ratios are considerably lower than the
risk-based capital ratios in Figure 4. This can be attributed to the fact that banks are able to
influence the calculation of the risk-weighted assets, thereby bolstering their Basel capital ratios,
as we have discussed in the methodology.
Figure 7: Leverage ratio vs. Overall RDI
2014: Y = 0.0180X + 0.0371; R2 = 0.0187
The scatter plot in Figure 8 shows a significant positive relationship between the overall risk
disclosure index and the bank size, as measured by the logarithm of total assets. This is
significant at the 1% significance level in 2013 and at the 5% significance level in 2014. It is
consistent with the findings of Linsley and Shrives (2006), as we mentioned earlier. It’s possible
that larger financial institutions disclose more risk information because of economies of scale.
Also, since they bear a higher systemic risk (Laeven et al., 2014), they can be pressured to
publish more information because of stricter disclosure regulations. In addition, G-SIBs are also
required to meet more stringent capital ratios than other banks (e.g. the minimum Total Loss
Absorbing Capacity (TLAC) requirement starting from January 1st, 2019). These types of
regulations are in place to avoid a too big to fail interdependence between the G-SIBs and the
sovereign countries. Furthermore, the fact that larger banks are associated with a higher
disclosure behaviour is beneficial for market participants. The failure of these banks can have an
enormous impact on the market. We will go into more detail on the systemic risk when we
review the scatter plot of the LRMES, see further.
25
Figure 8: Bank size vs. Overall RDI
2013: Y = 3.8230X + 6.5202; R2 = 0.2244
2014: Y = 3.6329X + 6.6489; R2 = 0.2108
As to the first performance measures, ROA, Figures 9 displays a negative correlation between
the Overall RDI and the ROA in 2013 and a positive one in 2014. A possible explanation for this
change in correlation is put forward by Elbannan and Elbannan (2015), who conducted a
research on a sample of Egyptian financial institutions. They suggest that a higher risk disclosure
signals the lower risk of the bank and attracts more deposits. This in turn eventually leads to a
better performance and a higher profitability. Another explanation could be that the low returns
in 2013 stimulate the banks to build up a sound risk management in order to be able to make
more calculated decisions. This can lead to them not only achieving a better performance in the
following year, but at the same time can result in a higher RDI. Such an argument is introduced
by Hirtle (2007), who investigated the effect of market risk disclosure on banking performance.
She suggested that there are many potential channels for the exercise of market discipline: “The
26
same risk management systems that produce better risk-adjusted performance may also generate
the information needed to make more detailed risk disclosures, which may be used by the bank
as a public signal of their superior risk management abilities.” (Hirtle, 2007). It is therefore
possible that disclosure is a by-product of better performance rather than the cause of it.
Although this is not the traditional view of market discipline, it is consistent with the idea that
disclosure stimulates bank managers to optimize overall performance.
Figure 9: ROA vs. Overall RDI
2013: Y = -0.0221X + 0.0131; R2 = 0.09763
2014: Y = 0.0093X – 0.0034; R2 = 0.03451
Finally, the Overall RDI is compared with two other performance measures: NIM (Figure 10)
and NII (Figure 11). The regression slope between the Overall RDI and the NIM explains very
little of the total variance, with R2-values of no more than 2%. The NII however does show a
clear negative relationship with the RDI in both years. In fact, this relationship is significant at
the 5% significance level in 2014. As we stated before, studies have shown that non-interest
27
income can be more volatile than traditional interest income: Stiroh (2004) states that greater
reliance on non-interest income is associated with higher risk and lower-risk adjusted profits.
Thus, when banks would have an excessive amount of non-interest income, the higher risk could
offset the diversification benefit, resulting in a higher bank risk. Overexposure to non-interest
income sources can occur when bank managers are focused on pursuing absolute returns rather
than considering the risk-return trade-off (Williams & Prather, 2010). A low RDI can be a sign
of such inadequate risk management, providing a possible explanation for the negative
correlation between the RDI and the non-interest income ratio.
Figure 10: NIM vs. Overall RDI
2013: Y = -0.0120X + 0.0210; R2 = 0.0193
2014: Y = -0.0004X + 0.0153; R2 = 0.0000
28
Figure 11: NII vs. RDI
2013: Y = -0.7741X + 0.7879; R2 = 0.0794
2014: Y = -1.0587X + 0.9258: R2 = 0.1621
See Table 3 for a summary of the descriptive statistics of these variables.
29
Table 3: Descriptive statistics of the variables related to the Overall RDI
VARIABLES OVERALL RDI MEAN STD MIN MAX
Log Z-score
2013 0.1303
(1.5211) 1.8827 0.5792 0.3216 2.6912
2014 0.1226
(1.5696) 1.9036 0.5972 0.3815 2.7219
CAR
2013 -0.0772
(0.0735) 0.1625 0.0285 0.1120 0.2167
2014 -0.1077
(0.0782) 0.1722 0.0308 0.1260 0.2550
CET1 ratio
2013 -0.0519
(0.0901) 0.1174 0.0315 0.0750 0.2410
2014 -0.0656
(0.0527) 0.1267 0.0177 0.1018 0.1568
% change in CET1
ratio
2013 - 2014 0.0171
(0.0280) -0.0158 0.0096 -0.0419 -0.0035
CET1 ratio losses
2013 - 2014 0.0294
(0.0320) -0.0273 0.0111 -0.0580 -0.0107
Leverage ratio
2014 0.0180
(0.0278) 0.0471 0.0092 0.0354 0.0702
Bank size
2013 3.8230***
(1.3433) 8.6107 0.5807 6.7693 9.2871
2014 3.6329**
(1.3285) 8.6355 0.5694 6.9791 9.3364
ROA
2013 -0.0221*
(0.0129) 0.0010 0.0052 -0.0161 0.0073
2014 0.0093
(0.0093) 0.0017 0.0036 -0.0080 0.0078
NIM
2013 -0.0120
(0.0162) 0.0144 0.0062 0.0087 0.0336
2014 -0.0004
(0.0170) 0.0151 0.0065 0.0045 0.0322
NII
2013 -0.7741
(0.5074) 0.3664 0.1978 -0.1458 0.7181
2014 -1.0587**
(0.4548) 0.3469 0.1892 -0.1764 0.7660
Standard errors between parentheses.
*** p<0.01, ** p<0.05, * p<0.1
30
4.2.2 Credit RDI
Figures 12 and 13 assess the relationship between the Credit RDI and the credit risk exposure
and LLRGL ratio. First of all, the data points on the scatter plot of the credit risk exposure are
rather negative correlated with the credit RDI. This result supports the theory of market
discipline: a higher credit risk disclosure is associated with a reduced exposure to credit risk. It is
likely that the publication of information on their credit risk to the public provides bank
managers with an incentive to prevent them from taking excessive credit risks.
Figure 12: Credit risk exposure vs Credit RDI
2013: Y = -0.1763X + 0.9464; R2 = 0.0663
The LLRGL ratio seems to be rather positively related to the credit risk disclosure index (see
Figure 13). However, this positive association does not necessarily contradict the previous
conclusion that stated that a higher disclosure is associated with a lower risk. Loan loss reserves
are not as highly associated with credit risk as credit risk exposures are. Regulators even prefer
that banks maintain these reserves as high as possible, since they function as capital buffers
against losses arising from excessive risk-taking. For instance, the International Accounting
Standards Board (IASB) has set out to implement a new accounting standard on 1 January 2018,
the IFRS 9, which improves the current IAS 39 package. It includes the transitioning from the
incurred loss model to the expected loss model: banks will not only have to recognise credit
losses that already occurred, but also losses that are expected to occur in the future (Agnew,
2014). As Rapoport (2014) stated, many observers believe that the incurred loss model has led
banks to respond too slow in taking losses during the financial crisis of 2007-08. The expected
loss model will require banks to hold greater loan loss reserves.
31
Figure 13: LLRGL vs. Credit RDI
2013: Y = 0.0453X + 0.0103; R2 = 0.0165
2014: Y = 0.0422X + 0.0100; R2 = 0.0125
We can derive from the scatter plot displaying the correlation between the different types of
exposure classes for 2013 and 2014 in Figure 14 that there is a significant negative relationship
between the Credit RDI and the institutions exposure class in 2014 at the 5% significance level.
This class covers exposures towards financial institutions, i.e. interbank exposures. Although the
interbank market allows participants to transfer their risks, it makes it also possible for one
bank’s crisis to propagate through the system (Iori, Jafarey & Padilla, 2006). Local idiosyncratic
shocks can intensify and spread across multiple banks, up to the point where there is a risk of a
systemic crisis occurring (Langfield & Soramäki, 2016). Thus, while borrowing from or lending
to other financial institutions is an important source of liquidity for banks, it comes at a price.
The negative relationship between the institutions exposure class and the credit risk disclosure
index can indicate that banks that have built up an adequate risk management acknowledge this
32
systemic risk and make efforts to keep their exposure towards other financial institutions under
control.
Figure 14: Institutions vs. Credit RDI
2013: Y = 0.0151X + 0.0641; R2 = 0.0004
2014: Y = -0.3049X + 0.2636; R2 = 0.2119
Next, Figure 15 class of central banks and central governments is positively related to the credit
risk disclosure index for both years. First of all, exposures towards central banks could be
regarded as more secure than exposures towards other financial intermediaries, because unlike
the latter, central banks are able to obtain funds during times of crisis by issuing short-term
riskless government debt. This is part of their responsibility to maintain financial stability in the
banking sector. However, central banks cannot create new liabilities at zero cost endlessly,
because it hinders the price stability in the market. On the contrary, exposures towards central
governments entail a greater risk, due to the likelihood of spillovers between bank risk and
sovereign risk. De Bruyckere, Gerhardt, Schepens and Vander Vennet (2013) found empirical
33
evidence on bank/sovereign contagion during the European financial and sovereign debt crisis.
It’s possible that banks that hold a high amount of exposures towards central banks carry out a
more prudent risk management strategy which leads them to hold more exposures towards
‘safer’ sovereign countries. A more detailed analysis of sovereign exposures is provided below.
Hence, in brief, if banks with a higher credit RDI hold, on average, a higher amount of exposures
towards central banks and central governments and if a higher credit RDI corresponds to
competent risk managing, this could imply that greater credit risk disclosure is associated with
more prudential risk-taking.
Figure 15: Central banks and central governments vs. Credit RDI
2013: Y = 0.1667X + 0.0587; R2 = 0.0426
2014: Y = 0.2699X – 0.0230; R2 = 0.1016
Finally, the scatter plots in Figures 16 and 17 indicate that a higher Credit RDI is associated with
a lower share of sovereign exposures towards GIPS/GIIPS countries. If the borrowing country
has a strong and stable economy, sovereign debt is classified as low risk. However, if the country
34
has a rather unstable economy, which is the case for GIPS/GIIPS nations, the sovereign exposure
entails high risks. Hence, banks with a higher disclosure seem to diversify the country
composition of their sovereign portfolio, making them less exposed to GIPS/GIIPS sovereigns.
This is in support of the notion we made earlier regarding the exposure class central banks and
central governments. We stated that it is possible that more transparent banks are likely to hold a
higher amount of exposures towards central governments, located in stable economies.
Figure 16: GIPS vs. Credit RDI
2013: Y = -0.0828X + 0.0713; R2 = 0.1801
Figure 17: GIIPS vs. Credit RDI
-0.1485X + 0.1625; R2 = 0.0377
The descriptive statistics for the variables are included in Table 4.
35
Table 4: Descriptive statistics of the variables related to the Credit RDI
Standard errors between parentheses.
*** p<0.01, ** p<0.05, * p<0.1
4.2.3 Market RDI
Figures 18 to 20 show the Market RDI plotted against the total volatility, beta and LRMES.
First of all, there is only a slightly positive relationship between the total volatility and the
Market RDI (Figure 18). Baumann and Nier (2004) considered it possible that disclosure injects
more volatility into the market instead of less. This can be due to an increase in price
movements, induced by the increase in information disclosure. There is also a greater chance of
analysts misinterpreting the data.
VARIABLES CREDIT RDI MEAN STD MIN MAX
Institutions
2013 0.0151
(0.1526) 0.0736 0.0631 0.0076 0.2297
2014 -0.3049**
(0.1315) 0.0692 0.0545 0.0037 0.2046
Central banks and
central governments
2013 0.1669
(0.1551) 0.1635 0.0655 0.0546 0.3224
2014 0.2699
(0.1795) 0.1492 0.0697 0.0434 0.2989
Credit risk exposure
2013 -0.1763
(0.1298) 0.8356 0.0555 0.7006 0.9165
LLRGL
2013 0.0453
(0.0673) 0.0383 0.0297 0.0018 0.1163
2014 0.0422
(0.0721) 0.0361 0.0318 0.0018 0.1344
GIPS
2013 -0.0829**
(0.0395) 0.0188 0.0170 0.0000 0.0548
GIIPS
2013 -0.1485
(0.1768) 0.0696 0.0653 0.0000 0.2308
36
Figure 18: Total volatility vs. Market RDI
2011-2013: Y = 0.0298X + 0.4809; R2 = 0.0003
Next, Figure 19 shows the scatter plot of the RDI and beta. Almost all financial institutions have
a beta larger than 1, except for HSBC (0.9892). A beta larger than 1 indicates that the stock
prices move in the same direction as the market, but with a higher magnitude. It indicates that the
stocks are very sensitive to systematic risk. There is a very light, negative correlation between
the beta and the Market RDI, suggesting that banks that disclose information on their market risk
tend to be less volatile than the market. Disclosure can mitigate the impact of news about the
banking performance (Baumann & Nier, 2004), resulting in a less extreme response in stock
trading in comparison with the market.
Figure 19: Beta vs. Market RDI
2011-2013: Y = -0.3494X + 1.8388; R2 = 0.0086
37
The opposing scatter plots indicate that the impact of disclosure on volatility statistics is rather
ambiguous.
Finally, when looking at the scatter plots of LRMES, we can see that there is a slightly
decreasing trend for both years 2013 and 2014. Since the LRMES is an indicator of systemic
importance, systemically important banks appear to be less transparent on its market risk. This is
an unhealthy finding: systemically important banks are capable of intensifying the downward
spiral of capital losses in the financial sector in a crisis scenario. If they would be transparent on
their risks, regulators could promptly intervene when necessary and market participants could
properly comprehend the riskiness of the bank. Considering the fact that the main characteristics
of systemic risk are captured by bank size, leverage and the degree of interconnectedness
(Acharya et al., 2012) and the finding that the overall risk disclosure significantly increases with
bank size, can lead us to propose the following: the degree of leverage and interconnectedness
determine and/or are determined by the extent to which a bank discloses information on its
market risk. A higher degree of interconnectedness and leverage could contribute to a lower risk
disclosure, which corresponds to the previous findings that lower risk disclosure is associated
with a higher level of leverage and a significantly higher exposure towards other financial
institutions.
38
Figure 20: LRMES vs. Market RDI
2013: -0.0297X + 0.5176; R2 = 0.0027
2014: -0.0374X + 0.4749; R2 = 0.0054
See Table 5 for a summary of the descriptive statistics of the total volatility, beta and LRMES.
39
Table 5: Descriptive statistics of the variables related to the Market RDI
VARIABLES MARKET RDI MEAN STD MIN MAX
Total volatility
2011 – 2013 0.0298
(0.3962) 0.4934 0.1874 0.2208 1.0331
Beta
2011 – 2013 -0.3494
(0.8187) 1.6911 0.3888 0.9892 2.3993
LRMES
2013 -0.0297
(0.1210) 0.5051 0.0580 0.3582 0.5797
2014 -0.0374
(0.1087) 0.4592 0.0521 0.3137 0.5266
Standard errors between parentheses.
*** p<0.01, ** p<0.05, * p<0.1
4.2.4 Interest Rate RDI
The Interest Rate RDI was plotted against the LTD ratio, see Figure 21. The majority of the
banks in the sample have a LTD ratio smaller than 1, indicating that they rely entirely on their
own capital to fund the loans to their customers. There is a negative correlation between the LTD
ratio and the Interest Rate RDI in both years: a higher disclosure is associated with maintaining
more adequate funding to cover unforeseen liquidity needs. Banks that prefer to be able to access
funding in the short term rather than to obtain higher earnings appear to disclose more
information about their interest rate risk. The published information on interest rate risk can
pressure bank managers to maintain enough liquidity for unforeseen requirements.
40
Figure 21: LTD ratio vs. Interest Rate RDI
2013: Y = -0.3559X + 0.9709; R2 = 0.0681
2014: Y = -0.2725X + 0.9212; R2 = 0.0459
Table 6 gives a summary overview of the statistics of the LTD ratio.
Table 6: Descriptive statistics of the variables related to the Interest Rate RDI
VARIABLE INTEREST
RATE RDI MEAN STD MIN MAX
LTD ratio
2013 -0.3560
(0.2488) 0.8252 0.2089 0.3818 1.2875
2014 -0.2725
(0.2349) 0.8096 0.1949 0.4036 1.1668
Standard errors between parentheses.
*** p<0.01, ** p<0.05, * p<0.1
41
5. Conclusion
This paper has aimed to construct a Risk Disclosure Index (RDI) for a sample of 30 European
banks, based on a check box approach that compared the compliance of the banks’ risk report
with the set of disclosure requirements and recommendations, provided by the Basel Committee
in 2001. The disclosure of financial banking information is part of the 3rd pillar of the Basel III
framework that is in place to support the capital requirements and the supervisory review, which
constitute the first two pillars. This mechanism is called market discipline and aims to decrease
the banking opacity that is considered to have been an underlying cause of the global financial
crisis. We contribute to existing literature by simultaneously analysing the three main
subcomponents of risk disclosure (credit risk, market risk and interest rate risk in the banking
book) and linking them to fundamental banking characteristics.
The Overall RDI is compared to the risk/return profile of the banks by plotting it against several
financial ratios. First of all, there appears to be no clear relationship with the Z-score, but this
could be attributed to the small data sample. Secondly, the Overall RDI is negatively related to
the risk-weighted capital ratios CAR and CET1. This does not necessarily interferes with the
goal of disclosure as a tool for establishing market discipline, since Nier and Baumann (2006)
stated that the effect of disclosure on capital buffers can be reduced by the presence of
government safety nets. In addition, the Overall RDI is positively correlated with (negative) %
changes in CET1 ratio and CET1 ratio losses during the stress test period from 2013 to 2014. If a
high overall disclosure is the outcome of an adequate risk management, the same risk
management can lead to a better understanding and withstanding of crisis scenarios, leading to
lower capital losses. Furthermore, the unweighted leverage ratios are considerably lower than the
risk-weighted capital ratios and display a small but positive correlation with the Overall RDI:
disclosure seems to contribute to a better balance between capital and risk exposure. As to the
bank size: larger banks tend to significantly publish more information, which can be attributed to
economies of scale and pressure from regulators. Finally, the Overall RDI was compared to
several performance measures: disclosure seems to have a positive effect on the evolution of the
ROA throughout both years. Although there is no clear relationship with NIM, the Overall RDI
significantly decreases NII. Non-interest revenues not only offer a diversification benefit over
interest income, but their higher volatility can eventually offset the diversification benefit,
leading to a higher risk.
42
The Credit RDI shows a declining relationship with the fraction of credit risk exposures a bank
holds in its portfolio, indicating that more disclosure can encourage banks to manage their credit
risk portfolio in a more prudent manner. Increased disclosure on credit risk corresponds to a
higher LLRGL ratio. A high LLRGL ratio is mainly associated with poor loan quality, but
regulators solicit for these capital buffers to be as high as possible and are planning the transition
from an incurred to an expected loss model, which will boost the loan loss reserves.
Furthermore, credit risk disclosure significantly reduces the amount of exposures towards the
interbank market, as measured by the exposure class of institutions. Although interbank lending
allows banks to mitigate their risks, the past has shown that such a high level of
interconnectedness can magnify banking failure throughout the entire financial system. On the
contrary to the previous, credit risk disclosure is positively related to the exposure towards
central banks and central governments. Central banks are perceived as a reliable debtor,
primarily thanks to their main goal of promoting financial stability in the market. This is not the
case for exposures towards central governments, due to the possibility of bank/sovereign risk
spillovers. The scatter plot of GIPS provides more clarity: credit risk disclosure significantly
decreases the relative amount of sovereign exposures a bank holds towards the economically less
stable GIPS countries.
The effect of market risk disclosure on volatility measures is ambiguous: the Market RDI is
positively correlated to the total volatility, but negatively to the beta (both correlations are very
minor). As suggested by Baumann and Nier (2004), an increase in disclosure can, on the one
hand, inject more volatility into the market by generating additional stock price movements,
while on the other hand reduce information asymmetries. Furthermore, the systemic risk measure
LRMES appears to have an adverse relationship with the Market RDI: systemically important
banks tend to disclose less information than others. Since systemic risk is determined by size,
leverage as well as degree of interconnectedness and since we found that overall risk disclosure
increases significantly with bank size, we could suggest that highly leveraged and highly
interconnected banks are most likely to publish the least information. This corresponds with the
positive relationship between the leverage ratio and overall risk disclosure and with the negative
relationship that was found between exposures towards institutions and credit risk disclosure.
Finally, interest rate risk disclosure is negatively related to the LTD ratio, implying that more
disclosure encourages bank managers to hold a higher amount of funding in short term
investments. This way, they are able to easily access these funds in times of crises.
43
As to directions for future research: including government support indicators (such as the Fitch’s
support ratings) could provide a conclusive answer on whether the negative correlation between
disclosure and capital buffers can be attributed to the presence of government safety nets. A
larger sample of banks could also lead to unambiguous insights into the impact of market risk
disclosure on total volatility. Further, another valuable addition to this research would be
analysing to what extent the amount of disclosure in the annual and risk reports contributes to the
degree of transparency in the banking sector.
44
XV
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7. Appendices
Appendix 1 – Bank sample
BANKS COUNTRY BVD ID
ABN Amro Group N.V. Netherlands NL34370515
Alpha Bank AE Greece GR094014249
Argenta Savings Bank Belgium BE0404453574
Banco Bilbao Vizcaya
Argentaria SA Spain ESA48265169
Banco Comercial Português,
SA-Millennium bcp Portugal PT501525882
Bank of Ireland-Governor and
Company of the Bank of
Ireland
Ireland IERC000206
Barclays Plc United Kingdom GB00048839
Belfius Banque SA/NV-Belfius
Bank SA/NV Belgium BE0403201185
BNP Paribas France FR662042449
BPCE Group France FR10708
Caixabank, S.A. Spain ESA08663619
Commerzbank AG Germany DE13190
Cooperatieve Rabobank U.A. Netherlands NL30046259
Crédit Agricole S.A. France FR784608416
Credit Suisse Group AG Switzerland CHCHE105884494
Danske Bank A/S Denmark DK61126228
Deutsche Bank AG Germany DE13216
Erste Group Bank AG Austria AT46146
HSBC Holdings Plc United Kingdom GB00617987
ING Groep NV Netherlands NL33231073
Intesa Sanpaolo Italy ITTO0947156
KBC Groep NV/ KBC Groupe
SA-KBC Group Belgium BE0403227515
Lloyds Banking Group Plc United Kingdom GBSC095000
National Bank of Greece SA Greece GR094014201
Nordea Bank AB (publ) Sweden SE5164060120
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BANKS COUNTRY BVD ID
Royal Bank of Scotland Group
Plc United Kingdom GBSC045551
Société Générale SA France FR552120222
Swedbank AB Sweden SE5020177753
UniCredit SpA Italy ITRM1179152
VTB Capital Plc Russia GB00159752
XIX
Appendix 2 – Set of disclosure items
A. CREDIT RISK DISCLOSURE 41 POINTS
1. General 20 points
- Quantitative 14 points
o Core 6 points
Total unweighted credit exposures, broken down by loans, securities and OTC
derivatives
Geographic distribution of exposures
Industry/counterparty type distribution of exposures
Maturity breakdown
Volumes of past due/impaired loans, broken down by industry/counterparty type
Allowance for credit losses, including information on provisions, recoveries and
charge offs
o Supplementary 8 points
Average of exposure over period
More detailed breakdown of type of exposures
More detailed breakdowns of geographic and industry/counterparty type
distribution
More information about lumpiness or significant concentrations of credit risk
Information about results from credit scoring models
Maturity breakdown for particular types of portfolio
More detail on number of days overdue
Volumes of credit risk transferred into securitisation vehicles/by credit derivatives
- Qualitative 6 points
o Core 5 points
Structure, management and organisation of credit risk management function
Strategies, objectives and practices of credit risk management
Information on techniques and methods for managing past due and impaired
assets
Definitions of non performing, past due, impaired and default
Definitions of specific and general provisions: triggering events, statistical
methods…
o Supplementary 1 point
Description of credit scoring, or portfolio credit risk models
XX
2a. Standardised Approach 8 points 10 points
- Quantitative 2 points
o Core 1 point
Percentage of a bank’s outstandings in each risk bucket
o Supplementary 1 point
Average default rates experienced by individual banks on rated credits in each
rating category, together with the bank’s definition of default
- Qualitative 6 points
o Core 4 points
Names of all ECAI used
More than one rating agency is used
Types of exposure for which the standardised approach is used
Alignment of different alphanumerical scales with risk buckets
o Supplementary 2 points
Changes in the list of rating agencies used by the bank and reasons for such
changes or no changes
The bank’s policy for translating public ratings on particular bond issues into
borrower ratings on its loans
2b. Internal Rating-Based Approach 15 points 10 points
- Quantitative 8 points
o Core 8 points
Percentage of nominal exposure covered by IRB approach
For each portfolio: assumptions related to each PD-LGD grade, for each PD-LGD
bucket the nominal exposure amount before and after CRMT and extra EAD
assumptions and information for credits with a variable exposure
For each portfolio: the weighted average maturity or maturity breakdowns and
appropriate granularity adjustment
For each portfolio and PD-LGD grade: the number of defaults
For each portfolio and PD-LGD grade: the nominal and (un)drawn amount at
default
In advanced approach for each portfolio the summary statistics of EAD-
distribution, also weighted with exposure
XXI
For each portfolio: number of borrowers, distribution of borrowers across rating
grades for the last 1 – 3 years, distribution of rating migrations for the last 1 – 3
years and in the advanced approach the distribution of ratings migrations
weighted with nominal exposure and EAD, both after 1 – 3 years
Where banks use their own LGD estimates: a comparison between economic
capital, actual capital held and minimum capital requirements as well as summary
indicators of economic capital attributed to major lines of business
- Qualitative 7 points
o Core 7 points
Supervisor’s acceptance of approach
For each portfolio indicate whether an own estimation or a supervisory vector for
LGD and/or EAD are used
For each portfolio describe methods for estimation and validation of PD, LGD
and EAD
Required data for estimation of the model, internal use of estimates besides for
IRB capital purposes, responsibility for and independence of rating process
Explanation of structure of internal rating system and relation between internal
and external ratings
The process for managing and recognising credit risk mitigation
For each portfolio: employed definitions of PD, LGD,EAD and mapping of
internal and reference definitions of default
3. Credit Risk Mitigation 11 points
- Quantitative 9 points
o Core 4 points
Total exposure, amount of exposure secured by collateral and on-balance sheet
netting contracts, and RWA excluding and including the effects for collateral/on-
balance sheet netting.
Exposure covered by guarantees/credit derivatives, and RWA excluding and
including the effects for guarantees/credit derivatives. These must be disclosed by
type of guarantee/derivative.
The exposures above disclosed by risk weight bucket/internal risk grade.
Type of selected regulatory calculation methodologies.
XXII
o Supplementary 5 points
Net exposure amounts used for internal risk management purposes, disclosed by
risk weight bucket/internal risk grade and total annual recovery amounts from
collateralised transactions.
Exposure amounts by types of eligible collateral, published for each geographical
grouping used by the bank for internal management purposes.
Information on on-balance sheet netting covering loans and deposits, disclosed
separately along risk weight buckets/internal risk grades, published for each type
of counterparty.
Total exposures covered by guarantees/credit derivatives, RWA excluding and
including the effects for guarantees/credit derivatives by geographical and
industrial sector.
Main guarantors/protection providers.
- Qualitative 2 points
o Core 2 points
The institution’s overall strategy and process for managing collateral, including
the monitoring of collateral value over time and key internal policies for the
recognition of collateral such as the LTV-ratio and maturity mismatches.
Strategy and process for monitoring the continuing credit worthiness of protection
providers and administering the guarantees and credit derivatives.
XXIII
B. MARKET RISK DISCLOSURE 10 POINTS
1a. Standardised Approach 7 points 10 points
- Quantitative 4 points
o Core 2 points
Levels of market risks in terms of capital requirements for interest rate risk, equity
position risk, foreign exchange risk and commodity risk
Capital charge for positions in options
o Supplementary 2 points
The level and variability of profits and losses on positions covered by the
disclosures
The capital charge for positions in options specified for different risk categories
and portfolios
- Qualitative 3 points
o Core 2 points
Specify which portfolios are covered by the standardised approach
Specify for which portfolios which of the methods from the standardised approach
is used
o Supplementary 1 point
Movements of portfolios between the standardised approach and the IMA
2b. Internal Models Approach 9 points 10 points
- Quantitative 4 points
o Core 2 points
The level and variability of market risks in terms of value at risk specified in IMA
Back test results on an aggregated level
o Supplementary 2 points
The level and variability of profits and losses on the “IMA” positions
Description and quantification of important “outliers” in the back test
- Qualitative 5 points
o Core 4 points
(Partial) acceptance of the IMA by the supervisor
Specify which portfolios are covered by the IMA
XXIV
General overview of (changes in) the characteristics of the internal models used
Description of the stress test program
o Supplementary 1 points
The (potential) application of stress test results
XXV
C. INTEREST RATE RISK (BANKING BOOK) DISCLOSURE
13 POINTS
- Quantitative 7 points
o Core 7 points
The size of the standardised interest rate shock by currency
The absolute increase (decrease) in economic value for the upward and downward
rate shocks
The absolute increase (decrease) in earnings for the upward and downward rate
shocks
The increase (decrease) in economic value as a percent of both economic and
actual regulatory capital
The increase (decrease) in earnings as a percent of earnings
The bank’s internal limits on IRR exposure in terms of both economic value and
earnings
Notional value of derivatives used for hedging banking book assets or liabilities
- Qualitative 6 points
o Core 4 points
Description of the risk management structure for overseeing IRR
Identify the nature of IRR in the banking book and key assumptions employed in
its measurement, amongst other things: identify portfolios with embedded
optionality and assumptions employed to model them and identify the use of
hedging programs
General overview of the characteristics of the internal measurement systems used
Description of methodology chosen to incorporate the supervisory rate scenario
(standardised parallel rate shock or actual rate moves) and identification of the
number of separate rate scenarios that were incorporated to account for material
currency exposures.
o Supplementary 2 points
Specify any sensitivity analysis employed with regard to key assumptions and
their effect on results
The use of other stress test scenarios