Basel III Risk Disclosure Index and Bank Risk

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

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

III

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.

V

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!

VII

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


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


5. Conclusion ........................................................................................................................... 41

6. References ........................................................................................................................... XV

7. Appendices .......................................................................................................................... XVII

Appendix 1 – Bank sample........................................................................................... XVII

Appendix 2 – Set of disclosure items ........................................................................... XIX

IX

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

XI

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

XIII

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

1

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.

3

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.

4

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

5

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

6

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

7

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.).

9

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-

10

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

11

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.

12

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.

13

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

14

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.


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


*** 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


*** p<0.01, ** p<0.05, * p<0.1


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


*** 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.

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

XVIII

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


o Core 1 point

Percentage of a bank’s outstandings in each risk bucket


Average default rates experienced by individual banks on rated credits in each

rating category, together with the bank’s definition of default


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


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


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


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


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


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.


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


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


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


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


Movements of portfolios between the standardised approach and the IMA

2b. Internal Models Approach 9 points 10 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


The level and variability of profits and losses on the “IMA” positions

Description and quantification of important “outliers” in the back test


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


The (potential) application of stress test results

XXV

C. INTEREST RATE RISK (BANKING BOOK) DISCLOSURE

13 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


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.


Specify any sensitivity analysis employed with regard to key assumptions and

their effect on results

The use of other stress test scenarios

Basel III Risk Disclosure Index and Bank Risk

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