Informativeness of Value-at-Risk Disclosure in the Banking Industry

by

Xiaohua Fang

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Joseph L. Rotman School of Management

University of Toronto

© Copyright by Xiaohua Fang (2010)

ii

Informativeness of Value-at-Risk Disclosure in the Banking Industry

By Xiaohua Fang (2010)

A thesis submitted in conformity with the requirements for the degree of Doctor of

Philosophy, Joseph L. Rotman School of Management, University of Toronto.

Abstract

Following the Basel Committee’s advocacy of value-at-risk (VaR) disclosure in external

reports of financial institutions, the U.S. Securities and Exchange Commission issued Financial

Reporting Release No. 48 to permit VaR disclosure as one of the most important disclosure

approaches for market-risk quantitative information in 1997. This study is the first to empirically

examine both economic determinants and consequences of VaR disclosure informativeness in the

banking industry. First, this study finds that more informative VaR disclosure is associated with

more effective corporate governance characteristics, including better shareholder protection, a

larger and more independent board, the presence of a separate risk committee under the board of

directors, a more independent risk committee, higher institutional ownership and a better overall

governance environment. These results suggest that corporate governance mechanisms are

important determinants of the informativeness of VaR disclosure. Second, the evidence shows that

the cost of equity capital is negatively associated with the informativeness of VaR disclosure,

consistent with informative VaR disclosure effectively communicating private information to

investors about a bank’s market risk exposure and its risk management system. Additional evidence

during the recent crisis further suggests the importance of VaR disclosure informativeness to the

capital market as a strong signal reflecting the efficacy of risk management practices and the

quality of risk governance mechanisms. However, I still find that a large proportion of the sample

banks choose not to disclose information with respect to some important disclosure items (e.g.,

quantitative stress-test results, and non-trading portfolio VaR). It is necessary for government

regulators to re-consider the current regulation on VaR disclosure in the external reports of the

banking industry.

iii

ACKNOWLEDGEMENTS

I am thankful to my supervisors, Jeffrey Callen and Gordon Richardson, for patiently guiding

me through my doctoral studies; to the other members of my dissertation committee: Varouj

Aivazian, Yue Li and Franco Wong for offering helpful and constructive comments on my thesis;

and to Steve Fortin, the external appraiser, for his detailed assessment of my dissertation.

I would also like to thank the faculties and PhD students whom I have learnt from at the

University of Toronto. These include Francesco Bova, Feng Chen, Feng Chi, Siu Kai Choy, Yiwei

Dou, Gus De Franco, Ole-Kristian Hope, Yu Hou, Stephannie Larocque, Alastair Lawrence,

Sebastian Song Li, Scott Liao, Yanju Liu, Hai Lu, Miguel Angel Minutti Meza, Dushyantkumar

Vyas, Baohua Xin, Song Yong, and Youli Zou.

Finally, I deeply appreciate the love, support, and encouragement of my family.

iv

TABLE OF CONTENTS

ABSTRACT………………………………………………………………………………………….ii

ACKNOWLEDGEMENTS…………………………………………………………………………iii

TABLE OF CONTENTS……………………………………………………………………………iv

LIST OF TABLES…………………………………………………………………………………..vi

LIST OF FIGURES……………………………………………………………………………….. vii

LIST OF APPENDICES…………………………………………………………………………...vii

CHAPTER 1: INTRODUCTION……………………………………………………………………1

CHAPTER 2: VALUE-AT-RISK: CONCEPTUAL AND INSTITUTIONAL BACKGROUND….7

CHAPTER 3: LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT……………….12

3.1 PRIOR LITERATURE…………………………………………………………………………12

3.2 HYPOTHESIS DEVELOPMENT…………………………………………………………….. 15

CHAPTER 4: SAMPLE SELECTION, AND VARIABLE MEASUREMENT…………………..23

4.1 SAMPLE………………………………………………………………………………………. 23

4.2 MEASURE OF THE INFORMATIVENESS OF VAR DISCLOSURE……………………... 24

4.3 CORPORATE GOVERNANCE MEASURES……………………………………………….. 25

4.4 COST OF EQUITY CAPITAL MEASURE …………………………………………………. 29

4.5 CONTROL VARIABLES MEASURE………………………………………………………...31

CHAPTER 5: MULTIPLE REGRESSION ANALYSIS…………………………………………..33

5.1 ECONOMIC DETERMINANTS OF VAR DISCLOSURE INFORMATIVENESS………....33

5.2 IMPACT OF VAR DISCLOSURE INFORMATIVENESS ON COST OF EQUITY

CAPITAL …...…………………………………………………………………………………….. 35

5.3 ADDITIONAL EVIDENCE FROM THE FINANCIAL CRISIS OF 2007-2009……………..37

v

5.4 ROBUSTNESS TESTS………………………………………………………………………..40

5.4.1 USING ANALYSTS’ FORECAST DISPERSION AS AN ALTERNATIVE

MEASURE OF CORPORATE INFORMATION ENVIEONMENT……………………….40

5.4.2 USING VAR AS AN ADDITIONAL MEASURE OF BANK-WIDE RISK……….41

5.4.3 ALTERNATIVE MEASURES OF THE COST OF EQUITY CAPITAL……….42

5.4.4 FIRM FIXED EFFECTS SPECIFICATION....…...…………......………………..…43

5.4.5 PRINCIPAL COMPONENT ANALYSIS………………………………………...…43

5.4.6 U.S. AND NON-U.S. INCORPORATED BANKS…..…………………………...…44

5.4.7 OTHER TESTS…………………………………………………………………...…44

CHAPTER 6: CONCLUSION………………………………………………………………….....46

REFERENCES.. .….…………………………………………………………………... . . .48

APPENDIX A: VAR DISCLOSURE INFORMATIVENESS MEASURE………………….....60

APPENDIX B: ALTERNATIVE MEASURES FOR COST OF EQUITY CAPITAL…….....62

APPENDIX C: VARIABLE DEFINITIONS.…………...……………………………………….65

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LIST OF TABLES

Table 1: The Sample of U.S. Listed Banks (Grouped by Incorporation Regions)

Table 2: VaR Disclosure Informativeness Measure (VaRDIS_Index) from the Years 1997 to 2008

Table 3: Descriptive Statistics

Table 4: Pearson Correlations

Table 5: VaR Disclosure Informativeness and Corporate Governance

Table 6: VaR Disclosure Informativeness and Cost of Equity Capital

Table 7: Additional Evidence from the Financial Crisis of 2007-2009

Table 8: Comparisons of VaRs and VaR Related Disclosure Scores between the Banks with Large

Financial-crisis Losses and the Banks with Small Financial-crisis Losses

Table 9: Using Analysts' Forecast Dispersion as an Alternative Measure of Corporate Information

Environment

Table 10: Using VaR as an Additional Measure of Bank-wide Risk

Table 11: Firm Fixed Effects Specification

Table 12: The First Principal Component of VaR Disclosure Informativeness Measure

Table 13: U.S. and Non-U.S. Incorporated Banks

Table 14: VaR Disclosure Informativeness and Cost of Equity Capital, after Controlling for the Six

Governance Characteristics Simultaneously

Table 15: Orthogonalized VaR Disclosure Informativeness and Cost of Equity Capital

Table 16: Pooled Regressions Controlling for Clustering by Firm and Year

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LIST OF FIGURES

Figure 1: VaR Disclosure Trend Lines from 1997 to 2008

Figure 2: VaR Model Type

LIST OF APPENDICES

Appendix A: VaR Disclosure Informativeness Measure

Appendix B: Alternative Measures for Cost of Equity Capital

Appendix C: Variable Definitions

1

Chapter 1: Introduction

In the 1990s, a number of infamous financial disasters were tied to the use and disclosure of

derivatives (e.g., Daiwa, Barings, Procter & Gamble, Orange County and Long-Term Capital

Management). As Jorion (1997) claimed, such embarrassing debacles could possibly have been

avoided if derivative disclosure had been done properly and promptly. The current global financial

crisis is also linked to a large amount of investments in derivatives related to subprime mortgages

(Gorton [2008]). In Nov 2008, the Group-20 (G20) Summit highlighted the necessity of enhancing

risk disclosure of complex financial instruments by financial institutions to capital markets. During

the past two decades, the Basel Committee, government regulators, and accounting standard setters

around the world have made great efforts to improve risk management practices and risk reporting

systems in the banking industry. This paper contributes to the literature by studying the

informativeness of value-at-risk (VaR) disclosure in the banking industry, one of the most

important initiatives developed through a succession of risk-management regulations and policies.

As a tool for risk measure and disclosure, VaR provides the maximum potential loss on a

portfolio over some holding period from normal market movements. Its most appealing feature is

its ability to aggregate risks across various types of financial instruments and business activities.

Hence, the Basel Committee, government regulators, and accounting standard setters across the

world have established a series of rules and guidelines relating to the disclosure of VaR in external

reports of institutions since the 1993 Group of Thirty report. In the Basel II Accord, the Basel

Committee on Banking Supervision (BCBS) embraces “market discipline” as a primary pillar,

“Pillar III”, complementary to supervisory and regulatory tools for monitoring and limiting risk.

The pillar has greatly increased the disclosures that the bank must make about risk exposures and

has allowed the market to have a better picture of the overall risk position of the bank (BCBS

2

[2001]). As a result, banks’ disclosure of VaR and other forward-looking risk measures (that is,

market risk disclosure) provides market participants “a more meaningful picture of the extent and

nature of the financial risks incurred, and of the efficacy of the risk management practices”

(Multidisciplinary Working Group on Enhanced Disclosure [2001]).

However, a high level of VaR disclosure does not necessarily mean sufficient transparency

and understandability of VaR disclosure to investors and regulators (Woods et al. [2008]).

Academic and anecdotal evidence indicates that the biggest limitations for unaudited VaR

disclosure in the management discussions and analysis (MD&A) section of the annual report are

subjectivity and complexity. Also, a high degree of autonomy granted to banks in estimating their

VaRs and the unaudited nature of VaR disclosure may cause investors to doubt the overall value or

effectiveness of VaR disclosure. While Jorion (2002) and Liu et al. (2004) find that disclosed VaR

figures have predictive powers for trading revenue variability and bank-wide risk measures in the

U.S. banking sector, we know little about the overall quality of VaR disclosure in annual reports

and about its impact on capital markets.

Hence, the main purpose of this paper is to examine the economic determinants and

consequences of the VaR disclosure informativeness in the banking industry: 1) whether better

corporate governance brings about more informative VaR disclosure; and 2) whether more

informative VaR disclosure results in relatively lower costs of equity capital. I am also interested in

the trend of VaR disclosure during the past decade as well as the impact of informative VaR

disclosure on the cost of equity capital during the financial crisis of 2007 – 2009.

This study constructs a sample of 66 commercial banks with VaR disclosure in their annual

reports over the period 1997 to 2008 from the top 150 U.S listed banks, based on total assets in

2007. Compared to studies with small sample sizes (of less than 25 banks) in Jorion (2002), Liu et

3

al. (2004) and Hirtle (2007), this study significantly increases the sample size and reliability of VaR

empirical research. Based on the 2001 Disclosure Survey by the BCBS and prior studies (Hirtle

[2007] and Perignon and Smith [2008]), I constructed a new and comprehensive VaR disclosure

index to measure the informativeness of VaR disclosure in annual reports.

The primary findings are as follows. First, this study finds that more informative VaR

disclosure is associated with more effective corporate governance characteristics, including better

protection of minority shareholders, a larger and more independent board of directors, the presence

of a separate risk committee under the board of directors, a more independent risk committee,

higher institutional ownership and a better overall governance environment. Together, these results

are consistent with the notion that corporate governance mechanisms are important determinants of

the informativeness of VaR disclosure. Second, the evidence shows that the cost of equity capital is

significantly negatively associated with the informativeness of VaR disclosure, implying that more

informative VaR disclosure can effectively reduce the information asymmetry between

management and outside investors about both a bank’s market risk exposure and its risk

management system. This effect is robust to controls for the magnitude of disclosed VaR figures as

well as to alternative proxies for the cost of equity capital.

Additional evidence during the financial crisis of 2007-2009 also shows the significantly

negative association between VaR disclosure informativeness and the cost of equity capital.

Further, a comparison of VaR disclosure informativeness between the two groups of banks with

large and small financial-crisis losses clearly demonstrates much more informativeness of VaR

disclosure in the group with small financial-crisis losses. Thus, the findings during the recent crisis

suggest the importance of VaR disclosure informativeness to the capital market as a strong signal

reflecting the efficacy of risk management practices, and the quality of risk governance

4

mechanisms. On the other hand, I still find that a large proportion of the sample banks choose not

to disclose information with respect to some important disclosure items (e.g., quantitative stress-test

results, and non-trading portfolio VaR). It is necessary for government regulators to re-consider the

current regulation on VaR disclosure in the external reports of the banking industry.

This study makes significant contributions to the literature in several ways. First, this is the

first study to assess both the economic causes and consequences of the informativeness of VaR

disclosure in the banking industry. This study provides the most extensive time series of data on

VaR disclosure informativeness since 1997 when the U.S. Securities and Exchange Commission

(SEC) implemented Financial Reporting Release (FRR) No. 48 requiring that U.S. public firms

report quantitative and qualitative information about market risk in their annual filings. While past

research has investigated the relationship between corporate governance and financial reporting

quality (Dechow et al. [1996], Beasley [1996], Klein [2002] and Beekes and Brown [2006]), no

comprehensive empirical study to date has investigated the effects of governance mechanisms on

the informativeness of risk disclosure or risk reporting, including voluntary and unaudited VaR

disclosure. Similarly, little attention has been paid to the impact of risk disclosure informativeness,

especially VaR disclosure informativeness, on the cost of equity capital. Academic and anecdotal

evidence indicates that the biggest limitations for unaudited VaR disclosure in management

discussion and analysis (MD&A) section are the subjectivity and complexity. These factors might

make it difficult for investors to understand and interpret the information contained in VaR

disclosure. Correspondingly, the investigation of VaR disclosure informativeness enriches research

on the value of risk disclosure to capital market.

In addition, this study introduces a new governance mechanism, the risk committee, to the

literature to study the disclosure informativeness of risk exposure. It is especially important for the

5

research in the banking industry, because numerous activities in the banking business are related to

the taking of a variety of risks, including interest risk, equity risk, credit risk, foreign-exchange risk,

investment risk, and liquidity risk.

Third, this study contributes to the existing VaR literature by providing a new VaR research

direction. Much of the prior VaR literature (e.g., Jorion [2002] and Liu et al. [2004]) studies the

informativeness of disclosed VaR figures in the banking industry. However, the informativeness of

VaR figures cannot be studied fully in isolation from overall VaR disclosure. As Alan Greenspan,

the former Federal Reserve Chairman, remarked in 1996, “Disclosure of quantitative measures of

market risk, such as value-at-risk, is enlightening only when accompanied by a thorough discussion

of how the risk measures were calculated and how they related to actual performance.” Hence, this

study will complement the extant VaR research by investigating the informativeness of overall VaR

disclosure.

Finally, the findings in this study are useful to regulators, standard setters, investors, and

others. Few past studies have directly examined the informativeness of overall VaR disclosure

across banks mandated by SEC FRR No. 48. This study provides direct evidence about the

determinants of and the impact of unaudited VaR disclosure on the capital market during the past

decade, consistent with the purpose of SEC FRR No. 48 and “Pillar III - Market Discipline” in

Basel II to increase publicly available information about firms’ market risk. In addition, from the

viewpoint of risk management and risk governance, this study further sheds light on the role of

VaR disclosure in the financial crisis of 2007-2009.1 Thus, this study helps academics, standard

setters and regulators to re-think current regulation of VaR disclosure practices in the banking

industry, and to analyze how to mitigate or avoid similar global financial crises in the future.

1 Currently, numerous studies have investigated the financial crisis of 2007 from the viewpoints of bank operations, bank governance, regulations, accounting information, and so on (e.g., Gorton [2008], Ryan [2008], Beltratti and Stulz [2009], Erkens et al. [2009], and Adams [2009]).

6

I begin in Chapter 2 by providing the VaR conceptual and institutional background. Chapter 3

offers a discussion of prior literature on VaR and develops the hypotheses. In Chapter 4, I describe

sample selection and variable measurement. I provide the results of multiple regression analysis in

Chapter 5. Chapter 6 concludes the dissertation.

7

Chapter 2: Value-at-Risk: Conceptual and Institutional Background

VaR is a summary statistical measure of financial risk, and is defined as the maximum

potential loss on a portfolio over some holding period from normal market movements. Losses

greater than VaR are experienced only with a low and pre-specified probability. The magnitude of

VaR depends on two arbitrarily chosen parameters – a probability known as the confidence level,

and a holding or horizon period. For example, a bank discloses that VaR on its trading portfolio is

$30 million with a confidence level of 95% and the holding period of the next trading day. This

means that the bank expects that over the next trading day there is a 95% chance that if the bank

incurs a loss, the loss will not be greater than $30M. In other words, there is only a 5% chance that

the bank will incur a loss of more than $30M over the next trading day.

VaR summarizes the effects of leverage, diversification, and probabilities of adverse price

movements in a single dollar amount. Thus, its most attractive feature is the ability to aggregate

risks across different types of market risks (e.g., interest rate and exchange rate risks) and business

activities (e.g., various trading positions) into one number. This feature is especially important

when an entity (e.g., a financial institution) takes positions with offsetting or cross-correlated risks

(Liu et al. [2004]). Banks with trading operations are exposed to a variety of market risks (e.g.,

interest rate, credit, currency, equity, and commodity risk), thus making it necessary for them to

measure risks on an aggregate level by VaR (Jorion [2002] and Jorion [2007]). Woods et al. (2008)

emphasizes that VaR is “the most notable of quantitative risk measures used by financial institution

almost everywhere.”

The Group of Thirty (G30) report in 1993 provided practical guidelines for financial risk

management and claimed that, “Market risk is best measured as value at risk.” Following this, the

Fisher report (Bank for International Settlements, 1994) was the first regulatory document to

8

advocate that financial institutions should disclose various market and credit risk measures,

including VaR. As a critical development for VaR disclosure, the joint 1995 report by the Basel

Committee (of the Bank for International Settlements) and the International Organization of

Securities Commissions (IOSCO) Technical Committee (Basel Committee [1995]) suggested that

financial institutions should provide information about their VaR models to their supervisors. This

information should include summary VaR figures (high/low/average) and breakdowns of earnings

between trading and non-trading books. This framework was important because it set up the basis

for the requirements later laid down in the US, UK and international accounting regulations and

standards (Woods et al. [2008]).

At a global level, the Basel Committee’s 1998 publication, “Enhancing Bank Transparency,”

encouraged banks across the world to disclose timely and reliable information on a range of issues,

including their risk management practices and risk exposures. The Amendment to the Capital

Accord (Basel Committee [1996]) further gave banks the option to have their regulatory capital

requirements determined using VaR figures calculated by their own internal risk models. The

Amendment confirmed VaR as the preferred regulatory measure of market risk. The Basel

Committee and IOSCO reaffirmed their faith in the VaR measure by jointly issuing a report in 1999

calling for banks to publicly disclose summary VaR information (Basel Committee [1999]). The

Basel Committee’s 2001 disclosure survey shows that 89% of banks in its survey were providing

summary quantitative information on market risk exposure using VaR, which further confirms the

popularity of VaR disclosure in the global banking sector (BCBS [2003]). When the final

framework for Pillar III of the Basel II accords was published in 2004, the Basel Committee further

detailed the very specific requirements for risk reporting that became effective in 2008. All banks

seeking to comply with Basel II must report both qualitative and quantitative details with respect to

9

their capital structure, capital adequacy, and types of risk exposure including credit, market,

operational, equities and interest rate risk (Basel Committee [2006]).

Currently, VaR-related disclosure requirements have been incorporated into banking

regulations and accounting standards across the world. In 1997, the SEC issued FRR No. 48

(effective after June 15, 1997) to enhance the public disclosure of US publicly traded corporations

on quantitative and qualitative information about market risk in their annual Form 10-K filings.

FRR No. 48 permits three types of disclosure approaches for quantitative information: 1) Tabular

format; 2) Sensitivity analysis; and 3) VaR.2

Under FRR No. 48, the VaR disclosure alternative permits publicly traded corporations to

express the potential loss in future earnings, fair values, or the cash flows of market risk-sensitive

instruments over a selected period of time, with a selected likelihood of occurrence, from changes

in interest rates, foreign currency exchange rates, commodity prices, and other relevant market rates

or prices. FRR No. 48 states that when preparing VaR disclosures, registrants should select

confidence intervals that reflect reasonably probable near-term changes in market rates and prices.

For each category for which VaR disclosure is presented, FRR No. 48 requires registrants to

provide either (i) the average, high and low amounts, or the distribution of value at risk amounts for

the reporting period; (ii) the average, high and low amounts, or the distribution of actual changes in

fair values, earnings, or cash flows from market risk-sensitive instruments occurring during the

reporting period; or (iii) the percentage or number of times that the actual changes in fair values,

earnings, or cash flows from market risk-sensitive instruments exceeded the value at risk amounts

during the reporting period. And, it also requires registrants to provide a description of the model,

2 Tabular format reports fair values and contractual terms sufficient to determine the amount and timing of cash flows over each of the next five years and beyond five years for derivatives and other financial instruments, grouped based on common characteristics. Sensitivity analysis describes the estimated loss of value, earnings, or cash flow that results from selected, hypothetical market price movements, subject to the constraint that these movements be at least 10% of the beginning market prices.

10

assumptions, and parameters underlying the registrant’s VaR model that are necessary to

understand the registrant’s market risk disclosure. The CFA Institute commented that the SEC FRR

No. 48 disclosure rules were “a significant step toward improving investors’ ability to assess

investment risk.”

Of the three disclosure alternatives, VaR is the one most commonly chosen by large

commercial banks for their trading portfolios, in line with the Basel Committee’s recommendations

on VaR disclosure (Woods et al. [2008]). In this regard, the feature of VaR to aggregate risks

across types of market risk for various trading positions has been widely applied in practice, while

its feature to aggregate risk across trading and non-trading activities generally has not (Liu et al.

[2004]). In 1998, the Financial Accounting Standards Board released the SFAS No. 133,

Accounting for Derivative Instruments and Hedging Activities, effective in June 2000. With

respects to VaR, the disclosure requirements under SFAS 133 reflect those of FRR No. 48.

In the UK the VaR-related accounting standard is FRS 13 (ASB [1998]), which took effect in

1999, and is mandatory only for banks and financial institutions. Similar to FRR No. 48 and SFAS

133, the disclosure of quantitative information can be done in different formats, including VaR. If

the VaR format is used for disclosures, FRS 13 requires public companies to disclose the highest,

lowest and average VaRs during a reporting period, accompanied by actual VaRs on the balance

sheet date. VaR figures must be presented both in an aggregate level for all of the trading positions,

and in a disaggregated level according to risk categories (e.g., interest rate risk, equity risk, etc.)

(Woods et al. [2008]).

In the 1990s, the international accounting standards on risk disclosure were not very explicit.

IAS 32 required public companies to disclose risk management policies and objectives, leaving the

level of disclosure detail to individual discretion. A complementary standard, IAS 30, established

11

more detailed disclosure requirements for banks and financial institutions, but was published too

early to facilitate the inclusion of rules on market risk disclosures for derivatives. Effective Jan.

2001, IAS 39 dealt only with recognition and measurement issues, and banks’ market risk

disclosures remained discretionary under international accounting standards throughout the 1990s.

Like U.S. FRR No. 48 and UK FRS 13, IFRS 7 offers public companies the option to disclose

market risk using a variety of formats, effective Jan. 2007 (Woods et al. [2008] and Woods et al.

[2009]). Hence, one common feature among US, UK, and international regulations on risk

management disclosure is the mandate to disclose market risk measures and the disclosure choice

on the risk measure reported (Woods et al. [2008]).

12

Chapter 3: Literature Review and Hypothesis Development

3.1 Prior Literature

Several studies explicitly examine the usefulness of FRR No. 48 information. Thornton and

Welker (2004) investigate the effect of FRR No. 48 disclosures on market assessments of firms’

equity price sensitivities to oil and gas price changes. Thornton and Welker find that oil and gas

producers disclosing sensitivity analyses or VaR experience greater post-FRR No. 48 commodity

beta shifts at 10-K filing dates than do tabular disclosers and non-disclosers. Linsmeier et al. (2002)

find that after firms disclose FRR No. 48 information about their exposures to interest rates, foreign

exchange rates and energy prices, trading volume sensitivity to changes in these underlying factors

declines, implying reduced investors’ uncertainty and diversity of opinion about the effects of these

changes on the firms. Sribunnak and Wong (2002) find that firms that make market risk disclosures

have lower foreign exchange rate sensitivity than those that do not. These studies all suggest that

FRR No. 48 market risk disclosures provide useful information to investors.

Most of the previous work related to VaR focuses on the information content of disclosed

VaR figures for the risk profiles of public firms or public firms’ trading portfolios. With daily

trading income and VaR data of a sample of six large banks available only to bank regulators,

Berkowitz and O’Brien (2002) evaluate the performance of banks' trading risk models by

examining the statistical accuracy of the VaR forecasts. The results show that although trading

VaRs may be too low in abnormal periods such as the hedge fund crisis in 1998, in most periods

trading VaR figures remain conservatively high (i.e., fewer trading losses in excess of trading VaR

occur than are predicted). While such conservative estimates mean higher levels of capital coverage

for trading risk, the reported VaRs are a less useful measure of actual portfolio risk. Berkowitz and

O’Brien (2002) find that banks’ trading VaR forecasts to predict the level and variability of their

13

trading profits does not outperform forecasts based on the ARMA plus GARCH time-series

models. They attribute these results to VaR models’ complexity, thus reducing the predictive power

of the information generated by VaR models, and substantial computational difficulties in

constructing large-scale structural models of trading risks for large and complex portfolios.3

Jorion (2002) is one of the few papers that focus on banks’ public VaR figures in their trading

portfolios. Jorion (2002) models the theoretical relation between these disclosed VaR figures and

the variability of unexpected trading revenue. Using a small sample of eight large commercial

banks from 1994 to 2000, he finds that banks’ trading VaR figures are associated with the

variability of their subsequent quarter’s unexpected trading revenue, suggesting that disclosed VaR

figures are informative in predicting the future variability of trading revenues. Correspondingly,

analysts and investors can use disclosed VaRs to compare the risk profiles of banks' trading

portfolios.

Liu et al. (2004) extend Jorion’s findings using a larger sample of 17 banks from 1997 to

2002. They find that banks’ trading VaRs have predictive power for trading income variability, and

that their predictive power improves with banks’ technical sophistication and over time. They also

find that banks’ trading VaRs have predictive power for a bank-wide measure of total risk (return

variability) and two bank-wide measures of priced risk (beta and realized returns) for the next

quarter.

Bali and Cakici (2004) find that VaR figures have additional explanatory powers to explain

the cross-sectional variation in expected returns after stock size, book-to-market ratio, liquidity,

3 Similarly, using actual data from the six largest Canadian commercial banks, Perignon et al. (2008) show evidence that banks exhibit a systematic excess of conservatism in their VaR estimates and they attribute VaR overstatement to several factors, including extreme cautiousness and underestimation of diversification effects when aggregating VaRs across business lines and/or risk categories.

14

market beta and total volatility are accounted for.

Lim and Tan (2007) investigate whether the VaR estimates disclosed by 81 non-financial

firms during the period 1997 to 2002 are value relevant using the earnings-returns relation.

Empirical evidence indicates that high VaRs are associated with weaker earnings-returns relation.

Consistent with Jorion (2002) and Liu (2004), further analysis shows that VaR figures are

positively and significantly associated with future stock return volatility. Thus, their findings

suggest that investors perceive the earnings of firms with substantial market risk exposure to be less

persistent, and therefore adjust future abnormal earnings for higher risk exposure.

The only two VaR studies directly related to the informativeness of overall VaR disclosure

are Perignon and Smith (2008) and Hirtle (2007). These two studies use similar methodologies to

develop VaR disclosure indexes to measure the level of VaR disclosure in the banking industry.

Perignon and Smith (2008) use a VaR disclosure index to capture many different facets of market

risk disclosure for a sample of USA and international commercial banks. They find large

differences in the level of disclosure among the sample banks, and an overall upward trend in the

quantity of information released to the public from 1996 to 2005. In a sample of five banks with the

highest VaR disclosure scores, they provide further evidence that daily VaR computed using

historical simulation contains very little information about the future volatility of trading revenues,

and that a simple GARCH model often dominates bank proprietary VaR models, consistent with

Berkowitz and O’Brien (2002). However, Perignon and Smith (2008) and Berkowitz and O’Brien

(2002) suffer from the issue of small sample size, thus compromising the power of the tests and the

reliability of the findings.

Hirtle (2007) investigates the relation between the amount of market risk information

disclosed by bank holding companies (BHCs) with large trading operations and their subsequent

15

risk profile and performance. Using a self-constructed index of publicly disclosed information

about BHCs’ forward-looking estimates of market risk exposure in their trading and market-making

activities, Hirtle finds that more disclosure is associated with lower risk, especially idiosyncratic

risk, and with better performance as measured by higher risk-adjusted returns. These findings

suggest that greater disclosure is associated with more efficient risk taking and thus improved risk-

return trade-offs.

In sum, while the existing research empirically shows the information content of disclosed

VaR figures in terms of both trading revenue variability and bank-wide (or corporate-wide) market

risk exposures, the research on the informativeness of overall VaR disclosure is still at an early

stage, and there is little evidence of the impact of the informativeness of overall VaR disclosure on

the capital market.

3.2 Hypothesis Development

A natural extension of prior research and of VaR’s conceptual and institutional background is

an examination of the economic determinants and consequences of informativeness of overall VaR

disclosure in the banking industry. That is, does a higher quality of corporate governance lead to

more informative VaR disclosure, and does more informative VaR disclosure result in lower costs–

of-equity capital in the banking industry?

Agency theory emphasizes that management does not always act in the best interests of

shareholders. Jensen and Meckling (1976) claim that because managers do not always act in the

best interest of shareholders, corporate governance mechanisms can reduce agency costs through

effective monitoring. Fama (1980) emphasizes that the board of directors is the critical internal

control mechanism to monitor managers. The board of directors monitors and reviews the firm’s

16

financial reporting process, the audit process, and internal controls. The board of directors uses

accounting information and stock prices in incentive compensation contracts and hiring and firing

decisions. In addition, boards serve a decision ratification role and provide input into the strategic

planning processes of firms (Bushman and Smith [2001]). Block-holder ownership can also control

agency problems (Kaplan and Minton [1994]). Recent empirical work has demonstrated

overwhelming evidence that a well-designed governance structure is effective in monitoring the

corporate financial accounting process and in improving financial reporting quality (e.g. Dechow et

al. [1996], Beasley [1996], Klein [2002], Krishnan [2005], Karamanou and Vafeas [2005], and

Agrawal and Chadha [2005]).

Especially in the banking industry, a good corporate governance mechanism is critical to an

effective risk management process and risk disclosure. A long-standing regulatory assumption is

that good corporate governance implies a sound risk management process (Bies [2006], and Basel

Committee [2005]). “Guidance for the Directors of Banks” (2003) published by the Global

Corporate Governance Forum of the International Finance Corporation also highlights the

importance of banks’ corporate governance practices to their risk management processes. Risk

management cannot be effective without the effective monitoring of corporate governance (Bhat

[2008], and Basel Committee [2005]).

In February 2006, the Basel Committee on Banking Supervision issued a specific guidance,

“Enhancing Corporate Governance for Banking Organizations”, to help promote the adoption of

sound corporate governance practices by banking organizations worldwide. Complementary to the

“Enhancing Bank Transparency” set out by The Basel Committee (BCBS [1998]), the guidance

further emphasizes that a sound corporate governance mechanism enhances the effectiveness of the

risk management process, and provides a timely and accurate public disclosure about risk

17

management practices and risk exposure to achieve a satisfactory level of bank transparency. Jaime

Caruana, General Manager of the Bank for International Settlements and Former Chairman of the

BCBS, noted: "Sound corporate governance is an important element of bank safety and soundness

and the stability of the financial system. The Basel Committee believes that this paper will help to

foster more effective risk management and greater transparency on the part of banking

organizations."

In the context of VaR disclosure, the private information bank managers possess about the

VaR model inputs and the underlying true market risk of financial instruments may give rise to the

problem of moral hazard (Lucas [2001]). Under the Market Risk Amendment to the Basel Accord

(BCBS [1996]), the capital charge for market risk is based on the output of a bank’s internal VaR

model, not on an externally imposed supervisory measure (Hirtle [2003]). As a result, a high

degree of autonomy granted to banks in calculating VaRs and in setting risk capital charges might

have some unexpected harmful effects (Perignon and Smith [2008]). In particular, banks might be

induced to underestimate their VaRs, underreport their true market risk, or misreport important

information regarding VaR in order to reduce their market risk charge. Consequently, this

compromises the quality of its risk management system (e.g., Lucas [2001] and Daníelsson,

Jorgensen and de Vries [2002]).

Moreover, the unaudited nature of VaR information disclosure may cause investors to doubt

the value or effectiveness of VaR disclosure. Sound corporate governance mechanisms must be in

place in order to prevent the concealment of a bank’s market risk exposure by the manager and

ensure the informativeness or quality of overall VaR disclosure. Hull (2008) emphasizes that in the

current financial crisis, severe agency problems reflected by excessive short-term bonus

compensation on Wall Street contribute to excessive risk-taking actions and a poor risk

18

management process in the banking industry, causing bank managers to be less forthcoming about

their risk exposures.4 Hence, as “Enhancing Corporate Governance for Banking Organizations”

(BCBS [2006]) suggests, good corporate governance reduces the managers' willingness and ability

to conceal important information of banks’ market risk exposure, by effectively both constraining

managers’ improper or excessive risk-taking practices and monitoring their risk reporting process.

It is expected that higher quality corporate governance brings about more informative VaR

disclosure that better reflects the banks’ market risk exposure and risk management system.

H1: Corporate governance quality is positively associated with informativeness of VaR disclosure

in the banking industry.

Theoretical research demonstrates the importance of the quality of firm-specific information

on the cost of capital. The liquidity model presented by Amihud and Mendelson (1986) and

Diamond and Verrecchia (1991) suggests that greater disclosure reduces the amount of information

revealed by a large trade, thereby reducing the adverse price impact associated with such trades.

Correspondingly, greater disclosure increases the demand by investors for the firm's securities and

raises the current price of the firm's stock, thus reducing the cost of equity capital. Leuz and

Verrecchia (2005) examine the link between information quality, capital investment decisions, and

the cost of capital. They show that high information quality improves the coordination between

firms and their investors with respect to the firm’s capital investment decisions, thereby lowering

the information risk and the risk premium required by investors. Essentially, investors price the risk

4 John et al. (2008) and Laeven and Levine (2008) find a positive relation between corporate governance and risk taking. The result also suggests that better corporate governance demands more informative VaR disclosure from management.

19

arising from poor-quality reporting in higher costs of capital. Easley and O’Hara (2004) develop a

multi-asset, multi-period rational expectations model in which the private versus public

composition of information affects required returns and thus the cost of capital. In this model, the

required return is affected by information risk, which includes both the amount of and the precision

of public and private information. They clearly show that more precise information lowers the cost

of capital by reducing the (information-based) risk premium to uninformed investors. Moreover,

numerous empirical studies demonstrate or suggest such a relation between information quality and

cost of capital (e.g. Lang and Lundholm [1996], Healy et al. [1999], Botosan [1997], Bushee and

Noe [2000], Botosan and Plumlee [2002], Easley et al. [2002], and Francis et al. [2005]).

Baumann and Nier (2004) further extend prior studies to the banking industry by constructing

a bank-wide disclosure index based on the number of balance sheet and income statement items

reported by a cross-country sample of banks. They find that a higher quality of information

disclosure is associated with lower stock price volatility, implying a lower cost of capital. By

constructing a bank-wide disclosure index based on a Disclosure Survey by the BCBS in 2001,

Bhat (2008) finds that the disclosure level of the bank positively moderates the return and Fair-

Value-Gain-Loss (FVGL) association. She also provides additional evidence suggesting that

disclosures related to market risk modeling, credit risk modeling, and derivatives mitigate

information reliability concerns of fair values. In other words, these disclosures enable market

participants to evaluate fair values in context with underlying risk, and increase the relevance of

FVGL in estimating bank value.

Uniquely, this study is more focused on the informativeness of VaR disclosure, an important

disclosure about a bank’s market risk exposure and risk management system. Based on Leuz and

Verrecchia (2005) and Easley and O’Hara (2004), more informative VaR disclosure should

20

improve the coordination between banks and their investors with respect to the management’s

decisions of market risk exposure and risk management practices, and reduce the information-based

risk premium to uninformed investors, thereby lowering the cost of equity capital. Jorgensen and

Kirschenheiter (2003) analytically show that a firm with risk disclosure has a lower risk premium

than a non-disclosing firm.

The BCBS has embraced “market discipline” as a primary pillar, “Pillar III.” 5 The pillar has

greatly increased the disclosures that the bank must make about risk exposures and has allowed the

market to have a better picture of the overall risk position of the bank (BCBS [2001]). As a result,

banks’ disclosure of VaR and other forward-looking risk measures (that is, market risk disclosure)

is very important to market participants as a means to provide “a clearer and more meaningful

picture both of the extent and nature of the financial risks incurred, and of the efficacy of the risk

management practices” (Multidisciplinary Working Group on Enhanced Disclosure [2001]).

Consistent with signaling theory, a bank with a high-quality risk management system and risk

governance mechanism can communicate the existence of such a mechanism to investors by more

informative VaR disclosure, conveying greater organizational legitimacy. 6 Hirtle (2007) also

suggests that market risk disclosure by a bank can signal its superior risk management ability.

Therefore, it is expected that more informative VaR disclosure, an important disclosure about the

banks’ market risk exposure and risk management system, results in a lower risk premium required

by investors, and thus a lower cost of equity capital in the banking industry.

H2: Informativeness of VaR disclosure is negatively associated with the cost of equity capital in

5 “Pillar III” is complementary to supervisory and regulatory tools for monitoring and limiting risk. 6 “It is thus incumbent on modern organizations to display the application of viable risk management practices, not simply because of a conviction that they are fundamental to enhancing the quality of their management controls, but because communicating the existence of such mechanisms conveys legitimacy (DiMaggio and Powell, 1983; Meyer and Rowan, 1977; Scott, 2001).” Editorial of Management Accounting Research 20 (2009).

21

the banking industry.

However, some studies question whether VaR disclosure is useful or informative. Logan and

Montgomery (1997) testify that the SEC rules “are unlikely to enable investors to better understand

a public company's use of these (derivatives) instruments and its aggregate risk exposure.” They

argue that managers who choose VaR disclosures might base their estimates on subjective and

questionable assumptions about future events and actions, thereby creating the opportunity to

misrepresent a firms’ net market risk exposure. Similarly, Woods et al. [2009] claim that,

“Disclosures based upon the ‘eyes of management’ may not align with, and inform the eyes of the

market, and to the extent that they do not, then we have opacity not transparency.”

In addition, there are different methods for computing VaR with different levels of accuracy

and efficiency (Huang and Lin [2004], Angelidis et al. [2007] and Ryan [2007]). Beder (1995)

shows that, dependent on parameters, data, assumptions, and methodology, VaR calculations differ

significantly for the same portfolio. FRR No. 48 provides a variety of options for calculating and

reporting VaR so that comparability of VaR disclosures across banks and over time might be low

(Liu et al. [2004]). Investors may not be able to understand the implications of each method on

VaR, and may have difficulty in processing the quantitative, probabilistic VaR information (Hodder

et al. [2001]). Hence, such disclosures might not necessarily improve investors’ assessments of

firms’ market risk exposure. Further, Woods et al. (2008) claim that “Even if VaR information

were put in the audited section of institutions’ annual reports, the information itself is almost

impossible for conventionally-trained audit teams to audit.” In other words, investors may face the

same, and even bigger, problems in understanding and interpreting the information contained in

VaR disclosure, especially because VaR disclosure is unaudited. Obviously, these issues could

affect the value of VaR disclosure to investors, and work against finding a relation between

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informativeness of VaR disclosure and the cost of equity capital.

Furthermore, all the VaR methods are based on historical data that rely on the idea that the

future will be like the past. Therefore, they are subject to estimation risks associated with a specific

historical period and might not be a good projection of future randomness (Linsmeier and Pearson

[2000] and Jorion [2007]). In addition, VaR only estimates the maximum loss if a tail event does

not occur, for example, the most a bank can lose during 95% of the time, but tells us nothing about

losses if a tail event does occur. In other words, VaR does not provide an estimate of the absolute

worst loss.7 VaR also does not indicate the direction of the market price movement giving rise to

losses, which renders VaR disclosures less useful for assessing the sensitivity of the firm to a

potential or actual market price movement (Liu et al. [2009] and Ryan [2007]). Hence, these

drawbacks of VaR may have limited the efficacy of VaR disclosure to mitigate the

contemporaneous hike of the cost of equity capital during the financial crisis of 2007-2009, “the

largest financial shock since the Great Depression” (IMF [2008]).

7“VaR must be complemented by stress-testing. This involves looking at the effect of extreme scenarios on the portfolio. Stress-testing is much more subjective than VaR because it poorly accounts for correlations and depends heavily on the choice of scenarios. Nevertheless, I would advocate the use of both methods.” (Jorion [1997]).

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Chapter 4: Sample Selection, and Variable Measurement

4.1 Sample

To include financial institutions in the sample, I require publicly disclosed VaR information.

Similar to Liu et al. (2004), I collected annual reports or annual SEC filings for the top 150 U.S

listed banks from the years 1997 to 2008, based on 2007 total assets. I eliminated financial

institutions that do not disclose VaR data in their annual reports or 10-K filings. The sample

consists of 66 deposit-taking commercial banks.8 9 These large banks have relatively large trading

operations and are exposed to a variety of risks, including equity, interest rate, credit, commodity

and currency risks. Thus, they are more likely to have an established VaR reporting system. The

sample includes 31 U.S.-incorporated banks, and 35 international-incorporated banks from 14 non-

U.S. countries.

Compared with small sample sizes (of less than 25 banks) in Jorion (2002), Liu et al. (2004)

and Hirtle (2007), this study significantly increases the sample size and enhances the reliability of

VaR empirical research. Table 1 presents the list of banks in my sample. We see that the sample

banks are widely distributed across different regions in the world. For these banks, I collected the

required data from the following sources: 1) stock information from CRSP and Google or Yahoo

Finance; 2) accounting data from annual SEC filings, Bureau van Dijk’s BankScope and Osiris

databases, and Compustat; 3) corporate governance information from annual SEC filings, including

8 I collected annual reports or annual SEC filings for the largest 400 U.S listed financial institutions from the years 1997 to 2008, based on 2007 total assets. I eliminated the financial institutions that never disclosed VaR data in their annual reports or 10-K filings. The sample is left with 101 financial institutions, including commercial banks, investment banks, insurance companies, brokers, and others. It includes 66 commercial banks, 7 investment banks, 15 insurance companies, and 13 other types of financial companies (e.g., brokers). To avoid the heterogeneity of different sectors within the financial industry, this study excludes investment banks, insurance companies, brokers, and other non-bank financial institutions from the sample. 9 During the sample period, several banks were acquired in a merger, generally by other banks in the sample. For example, Bank One Corp. merged with J.P. Morgan Chase & Co in 2004. Fleet Boston Financial merged with Bank of America in 2004. AllFirst Financial merged with M&T Bank Corporation in 2004, and Allied Irish Banks hold a 23% stake in the M&T Bank Corporation. I treat the acquired and acquiring banks as separate entities.

24

proxy statements, 10-K and 20-F; 4) institutional ownership data from 13-F filings via Thomson

Reuters; and 5) analyst data from IBES.

4.2 Measure of the Informativeness of VaR Disclosure

Based on a 2001 Disclosure Survey by the BCBS as well as Perignon and Smith (2008) and

Hirtle (2007), I constructed a new and comprehensive VaR disclosure index to measure the

informativeness of VaR disclosure in annual reports (refer to Appendix A). The BCBS survey was

completed by each respective supervisory authority. The BCBS categorizes market risk internal

modeling with sixteen disclosure items, which are similar to fifteen VaR disclosure items of

Perignon and Smith (2008) and eighteen market risk disclosure items of Hirtle (2007). Therefore,

based on the BCBS survey and prior studies, I constructed a new and comprehensive VaR

disclosure index (VaRDIS_Index) from the “Market Risk” section in the bank’s annual report or

annual SEC filings from the years 1997 to 2008. Each item in the disclosure index is weighted

equally, such that a maximum of 32 points are obtained if all the items are publicly released by the

bank.

Table 2 provides mean scores for the VaRDIS_Index, each subcategory, and the VaR

disclosure items of the 66 sample banks from 1997 to 2008. The table shows that both the

VaRDIS_Index and each of the 11 categories under the index increased significantly from 1997 to

2008. The mean VaRDIS_Index is 16.1 in 2008, compared to 6.6 in 1997. Also, most of the 30

disclosure items showed a significant increase over time. The trend lines of the mean and median

VaRDIS_Index in Figure 1 further demonstrate increasing VaR disclosure informativeness over

time. Figure 2 displays the relative frequency of each VaR model type used by the sample banks

that disclose the model types for the years of 1997 to 2008. Consistent with Perignon and Smith

25

(2008), historical simulation is the most popular VaR method, due to the ease of implementing the

VaR system, and the delta-normal method is close behind. Overall, the evidence indicates that more

and more banks are forthcoming about their VaR information over time. However, unreported

results illustrate a wide variations in VaRDIS_Index across the banks for each year and increasing

variations in the VaRDIS_Index over time. Compared with the possible maximum of 32 points, the

current VaRDIS_Index level in the banking industry is less than satisfactory with a wide range from

1 to 29.

Of course, equal weighting may be subject to criticism for not reflecting the actual weights

assigned by the users of financial statements. However, the main strength of the VaR disclosure

index is that it is designed specifically for banks. To alleviate the effects of extreme observations

and to mitigate measurement errors, I use the percentile rank of the VaR disclosure index within

each year (VaRDIS), rather than its raw score (VaRDIS_Index), in the regression analyses.10

4.3 Corporate Governance Measures

This study captures the strength of each sample bank’s governance environment by using a

country-level governance index, five bank-level measures of the corporate governance attributes

and a summary measure that combines the six governance characteristics into a single measure. The

purpose of combining the six governance characteristics into the summary measure is to create a

comprehensive measure that better captures the bank’s overall governance environment.11 If strong

governance in multiple dimensions represents a relatively stronger governance mechanism, the

10 The regression results are robust if we use the raw VaR disclosure score (VaRDIS_Index). Using the percentile rank of the VaR disclosure index also allows me to interpret the resulting coefficient estimates in the cost of equity capital test as the incremental risk premium between banks with the most informative and with the least informative VaR disclosures. 11 Song et al. (2009), Bushman et al. (2004), DeFond et al. (2005) also create a summary corporate governance measure, based on several measures of individual governance mechanisms.

26

summary measure will better capture the strength of a firm’s overall governance environment than

individual governance measures.

I use the anti-director index (Antidir) of La Porta et al. (1998) as revised in Djankov et al.

(2008) to measure the country-level governance environment. As a measure of the effect of the

legal environment on investor protection, the anti-director index from La Porta et al. (1998) reflects

such aspects of minority rights as (i) the ease of voting for directors, (ii) the freedom of trading

shares during a shareholders meeting, (iii) the possibility of electing directors through a cumulative

voting mechanism or a proportional representation of minorities on the board, (iv) the existence of

a grievance mechanism for oppressed minority shareholders, such as a class-action lawsuit or

appraisal rights for major corporate decisions, (v) the existence of a preemptive right to new

security issues by the firm, and (vi) the percentage of votes needed to call an extraordinary

shareholder meeting. Considerable evidence shows that country-level governance variables are

important determinants of firm policies and valuations as well as of financial development. La

Porta et al. (1998) find evidence of higher valuation of firms in countries with better protection of

minority shareholders. La Porta et al. (1998) also provide evidence that higher accounting

disclosure levels are expected in countries with a higher value of anti-director index due to a

stronger demand for transparency. John et al. (2008) show that better investor protection, measured

by higher anti-director index, mitigates the taking of private benefits leading to excess risk-

avoidance. Here, this study measures shareholder protection using the Antidir of La Porta et al.

(1998) as revised in Djankov et al. (2008). The Antidir is directly aimed at the regulation of self-

dealing in different countries, taking values from 0 to 5. A higher value means better protection of

minority shareholder rights against management and large shareholders.

The five bank-level governance attributes are:

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(1) Board size (BoardSize) - A larger board has the advantage of more advisors, more

expertise and resources available to the organization and better monitoring of management (e.g.,

Dalton et al. [1999]), but it requires higher coordination costs (e.g,. increased decision-making

time) and, possibly, more free riding among board members (Jensen [1993], Yermack [1996] and

Eisenberg et al. [1998]). However, Agrawal and Knoeber (1999) argue for larger boards in firms

where information is otherwise difficult to obtain, Bushman et al. (2004) argue that a complex

organizational structure would require more board directors as a response to a demand for specific

information. Adams and Mehran (2005, 2008) find that organizational structure is significantly

related to bank board size, and that banking firms with larger boards perform better, suggesting that

constraints on board size in the banking industry may be counter-productive. Therefore, I use the

number of board members for each bank-year observation to measure BoardSize. A high (low)

value of BoardSize means a strong (weak) governance mechanism in the sample banks.

(2) Board Independence (BoardIndep) - Prior research has provided plenty of evidence that a

higher proportion of outside or independent directors is associated with stronger corporate

governance, i.e. more effectiveness in monitoring the management (e.g.,Weisbach [1988], Dechow,

Sloan, and Sweeney [1996], Core, Holthausen, and Larcker [1999], Klein [2002], Anderson et al

[2004], and Karamanou and Vafeas [2005]). Bhat (2008) further shows similar, strong evidence in

the U.S. commercial banking industry. Thus, following prior studies, I use the percentage of

independent directors on the board of directors to measure BoardIndep.

(3) Risk Committee (RiskCom) - A risk committee fulfills, on behalf of the board, oversight

responsibility relating to the policy standards and guidelines for the firm’s risk assessment, risk

management, and risk reporting process. Although the SEC and stock exchanges have no listing

requirement related to a risk committee under the board of directors, it is expected that the

28

existence of a risk committee as one of several standing committees enhances the firm’s risk-

governance capacity and board-monitoring effectiveness in the banking industry. To my

knowledge, this study is the first in the literature to utilize the information of the risk committee of

the board of directors as one dimension of a corporate governance mechanism. A risk committee is

especially important for the banking industry, because numerous activities in the banking business

are related to the taking of a variety of risks, including interest risk, equity risk, credit risk, foreign-

exchange risk, investment risk, and liquidity risk. Hence, I code 1 to RiskCom if the board of

directors of a bank has a risk committee as one of the standing committees responsible to the board,

and 0 otherwise.

(4) Risk Committee Independence (RiskComIndep) – Based on the discussion above, it is

expected that a higher proportion of outside or independent directors on the risk committee is

associated with more effective monitoring of management. Therefore, I use the percentage of

independent directors on the risk committee to measure RiskComIndep.12

(5) Institutional Ownership (InstiOwner) - Shleifer and Vishny (1986, 1997) argue that large

shareholders have incentives to monitor managers since they have greater benefits through this

monitoring. Consistent with this ”active monitoring hypothesis”, Ajinkya et al. (2005) provides

evidence that firms with greater institutional ownership are more likely to issue earnings forecasts,

and are inclined to forecast more frequently, more accurately and less optimistically. Jarrell and

Poulsen (1987) and Brickley et al. (1988) document that institutional shareholders are more likely

to vote against harmful amendments. McConnell and Servaes (1990) find a significant positive

relationship between Tobin’ Q and the proportion of shares held by institutional investors,

consistent with institutional investors improving corporate governance through external monitoring.

12 I code 0 to RiskComIndep if the board of directors of the sample bank does not have a risk committee as one of the standing committees responsible to the board.

29

Therefore, this study defines InstiOwner as the percentage of the firm’s equity held by institutional

investors.

To reduce the random measurement error of individual governance variables, I also create a

summary governance measure (CompCG) as the average of the percentiles of all the six governance

attribute measures for each sample observation. For every year, each of the six governance metrics

is sorted in ascending order before the percentile values are computed. Hence, high values of the

summary measure represent an overall strong governance environment.

Table 3 provides descriptive statistics of the main variables for the full sample of 617 firm-

years from 1997 to 2008. The median Antidir is 3 in the sample. The median BoardSize is 15 and

ranges from 5 to 33 (untabulated). The mean BoardIndep is 76.4% and the mean InstiOwner is

52.4%, both of which are close to the mean board independence and institutional ownership ratios

in Erkens et al. (2009). Meanwhile, about half of the bank-year observations have a RiskCom. The

RiskComIndep in the sample is, on average, 45.7%, lower than BoardIndep.

Pooled Pearson correlations among the main variables are reported in Table 4. Each

governance attribute is significantly correlated with at least two other governance attributes. The

overall governance measure (CompCG) is strongly correlated with the six components of

governance measures, which suggests that CompCG captures the overall governance environment

in the sample. Consistent with the findings in La Porta et al. (1998), the significantly negative

correlation between Antidir and InstiOwner suggests that small, diversified shareholders are

unlikely to be important in countries that fail to protect their rights.

4.4 Cost of Equity Capital Measure

The prior literature suggests several approaches to estimate the ex-ante firm-level cost of

30

equity capital (e.g., Gebhardt et al. [2001], Easton [2004] and Claus and Thomas [2001]). These

estimation approaches use price and analysts’ forecasts of earnings to derive an internal rate of

return as the estimate of the cost of equity capital.13 Botosan and Plumlee [2005] compare the

validity of four proxies for the expected cost of equity, and conclude that the Value Line Cost of

Equity estimate and Easton (2004)’s PEG estimate outperform the other estimates, based on their

consistent and predictable associations with known risk attributes. Botosan et al. [2009] reconfirm

that the Value Line Cost of Equity estimate and PEG estimate represent the most reliable proxies

for the cost of equity capital by showing both the link between different cost of equity estimates

and realized returns and the link between those estimates and firm-specific risk characteristics.

Hence, I use the PEG estimate (Easton [2004]) as the main measure of the cost of equity capital

(CofC):

CofC = 2 1 0( ) /feps feps p−,

where 0p = current price per share;

2feps = a two-period-ahead median forecast of accounting earnings per share;

1feps = a one-period-ahead median forecast of accounting earnings per share.

The PEG estimate has the additional advantage of having less onerous data requirements

because it only requires price and earnings growth to estimate the cost of equity capital. To estimate

the cost of equity capital, I require available data for the one-year ahead and two-year ahead

consensus (median) analysts’ earnings forecasts, and the stock price as of the end of 3 months after

each fiscal year to ensure that the VaR information released through annual reports or annual SEC

13 Accounting-earnings based approaches to estimate the cost of equity capital are derived from the dividend discount model. Although the accounting literature regarding cost of equity capital typically removes the banking industry from their sample because of comparability issues, these models are not oriented towards specific industries. Hence, the PEG approach in Easton (2004) and other estimation approaches used in this study remain valid for extracting the cost of equity capital in the banking industry.

31

filings is known to the market. In table 3, the median CofC in the sample is 9.2%, similar to

estimates documented in the extant literature. Hail and Leuz (2006) show that estimates of the ex-

ante cost of equity capital obtained from different approaches are fairly similar and strongly

positively correlated. As a robustness test, I evaluate in Section 5.4.3 the sensitivities of the main

results to alternative measures of the cost of equity capital.

4.5 Control Variables Measure

The main control variables are as follows. Beta is market beta from a regression of prior 60

monthly returns on the corresponding value-weighted CRSP market return. The data used to

compute the beta is obtained from the CRSP database. Prior literature indicates that a higher value

of beta implies higher firm-wide risk taking and a higher cost of equity capital (e.g., Fama and

French [1993] and Hirtle [2007]). Capital Ratio is the ratio of Tier 1 and Tier 2 capital to total risk-

weighted assets multiplied by 100, which is used to measure bank-wide risk taking (Beltratti and

Stulz [2009]). The higher the ratio, the lower the risk a bank takes, and the stronger its financial

ability to sustain future losses.14 Size is the natural log of a firm’s market value at the beginning of

the fiscal year. A large body of empirical research indicates a negative association between firm

size and ex ante cost of equity capital (e.g. Botosan [1997] and Botosan and Plumlee [2002]). BM is

the ratio of the firm’s book value of equity divided by its market value of equity at the beginning of

the fiscal year. Bamber and Cheon [1998] use BM to proxy for proprietary costs. Numest is the

natural log of the number of analysts that issue earnings forecasts for the firm during the fiscal year.

Numerous studies use Numest to measure the overall corporate information environment. Issue is

equal to 1 if the firm has an equity or debt issuance in the current or following year, and 0

14 I also use a Tier 1 capital ratio in the sensitivity test, and the results (untabulated) are similar.

32

otherwise. This variable captures disclosure behavior motivated by capital market transactions.

Frankel et al. (1995) document a positive association between a firm's tendencies to access capital

markets and to disclose earnings forecasts. News is equal to 1 if the firm’s current EPS is greater

than the previous-year’s EPS, and 0 otherwise. Skinner [1994] shows that, consistent with

litigation-risk and reputation-effect arguments, managers have incentives to preempt large negative

(but not large positive) earnings surprises by voluntarily disclosing that information early. Trading

Assets Ratio is the ratio of Trading/Dealing Account Securities (total) from the Compustat Bank,

divided by total assets at the end of the fiscal year. As a Bankscope measure of the banking

liquidity ratio, Liquidity is liquid assets, which include cash, interbank lending, and government

bonds and, where appropriate, the trading portfolio, scaled by total deposits and borrowings. Loans

Ratio is defined as the ratio of loans, net of total allowance for loan losses, to total assets. Tangible

Equity Ratio is defined as the ratio of tangible equity to total liabilities as a balance sheet measure

of a bank’s risk taking (Beltratti and Stulz [2009] and Blankespoor [2009]). When we do not have

data for intangible assets, we use total equity in the numerator. Depository Funding is a ratio of

depository funding over total assets, provided by the Bankscope, to proxy for a bank’s stability

(Ratnovski and Huang [2009]). I also control for a sensitivity analysis format (Sensitivity) and

tabular format (Tabular) in the regressions. Sensitivity is equal to 1 if a bank uses sensitivity

analysis in a year, and 0 otherwise. Tabular is equal to 1 if a bank uses tabular format in a year, and

0 otherwise.

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Chapter 5: Multiple Regression Analysis

5.1 Economic Determinants of VaR Disclosure Informativeness

To test the first hypothesis, I estimate the following regression equation, based on Lang and

Lundholm (1993), Ajinkya et al. (2005), Hirtle (2007), and Bhat (2008):

VaRDISi, t = α0 + α1Governance Attributei,t + α2Betai,t + α3BM i,t + α4Sizei,t (1)

+ α5 Trading Assets Ratioi,t + α6Capital Ratioi,t + α7Liquidityi,t

+ α8Loans Ratioi,t + α9Tangible Equity Ratioi,t + α10Depository Fundingi,t

+ α11Issuei,t + α12Newsi,,t + α13Sensitivityi,t + α14Tabulari,t + εi,t

H1 predicts that α1 is positive. To mitigate the concerns about cross-sectional dependencies in the

data, I estimate equation (1) for each year from 1997 to 2008. I report the mean of the annual

coefficient estimates, and assess statistical significance using the time-series standard errors of

these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for

autocorrelation following Newey and West (1987).15

Table 5 presents the test results of estimating equation (1) using each of seven different

measures of corporate governance. The evidence indicates that VaR disclosure informativeness

(VaRDIS) is strongly associated with each of the governance measures, including Antidir,

BoardSize, BoardIndepe, RiskCom, RiskComIndep, InstiOwner, and CompCG, in the predicted

direction. Specially, the coefficient on CompCG (.158) indicates that the difference of VaR

disclosure informativeness between banks with the best overall corporate governance environment

and those with the worst one is approximately 4.42 (.158 * (29-1)) points, about one third of the

sample mean of VaRDIS_Index. Thus, the overall evidence suggests that corporate governance

mechanisms are important economic determinants of VaR disclosure informativeness, and that

15 To assess the sensitivity of our results to this procedure, I also estimate pooled regressions controlling for unspecified heteroscedasticity and autocorrelation effects. Results (not reported) yield similar inferences as the annual regressions.

34

better corporate governance enhances the informativeness of VaR disclosure in the banking

industry.

VaRDIS is significantly increasing in Size and Trading Assets Ratio across all the regressions

of different measures of corporate governance. It suggests that larger banks and banks with more

trading assets are more forthcoming about their market risk exposures. The significant positive

relation between VaRDIS and BM suggests that proprietary cost is an important element to make

VaR public disclosure less informative. Across all the regressions, the consistently positive relation

between VaRDIS and Beta and the consistently negative relation between VaRDIS and Capital

Ratio suggest that the risk-taking level by banks is an important determinant of overall VaR

disclosure informativeness. Similarly, the negative relation between VaRDIS and Tangible Equity

Ratio across almost all the regressions means that the high risk-taking level increases the

informativeness of overall VaR disclosure. The consistently negative relation between VaRDIS and

Loans Ratio implies that banks with higher proportion of loan assets over total assets are less likely

to have informative VaR disclosure. Probably these banks are more inclined to use other disclosure

formats, e.g. the sensitivity analysis format, because the main risk to which they are exposed might

be interest rate risk. The significant negative relation between VaRDIS and Issue across almost all

the regressions is not consistent with the general disclosure literature based on non-financial

samples. It suggests that banks decrease the informativeness of VaR disclosure during debt or

equity issues. Consistent with good news hypothesis in the extant disclosure literature, the positive

relation between VaRDIS and News implies that banks with good earnings news are more

forthcoming about their market risk exposures. The negative coefficient on Sensitivity and the

positive coefficient on Tabular mean that the VaR disclosure approach is an important substitute

35

for the sensitivity analysis format and a complement of the tabular format.16

5.2 Impact of VaR Disclosure Informativeness on Cost of Equity Capital

To test the second hypotheses established above, I estimate the following regression equation,

based on Francis et al. (2004), Francis et al. (2005), and Hirtle (2007):

CofCi,t = β0 + β1VaRDISi,t + β2Governance Attributei,t + β3Betai,t + β4BMi,t + β5Sizei,t (2)

+ β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Numesti,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + εi,t .

H2 predicts that β1 is negative. Moral Hazard is an indicator of the generosity of the deposit

insurance regime in a country (Demirguc-Kunt and Detragiache [2002]).17 18 To mitigate concerns

about cross-sectional dependencies in the data, I estimate equation (2) for each year from 1997 to

2006.19 I report the mean of the annual coefficient estimates, and assess statistical significance

using the time-series standard errors of these estimates (Fama and MacBeth 1973). I further adjust

the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987).20

Table 6 reports the results of estimating equation (2) controlling for seven different measures

of corporate governance mechanism separately. Consistent with the prior asset pricing literature,

16 In 1997, about 69% of the banks used the sensitivity analysis approach, and about 90% of the banks used the tabular approach. Since 2003, over 90% of the banks have used the former approach and over 95% of the banks used the latter approaches. 17

Demirguc-Kunt and Detragiache (2002) find that explicit deposit insurance tends to increase the likelihood of

banking crises due to the moral hazard issue. Caprio et al. (2007) use this indicator as a control variable when examining the relationship between governance and bank valuation. 18 I also control for Moral hazard in regression equation (1), and the main results (untabulated) remain strong. The coefficient on Antidir becomes weaker, probably due to a strong correlation of -66.16% between Antidir and Moral

Hazard at a <.0001 significance level. It is expected that regimes with better legal shareholder protection bring about fewer moral hazard problems in the banking industry, consistent with Demirguc-kunt and Detragiache (2002). 19 Since the second half of 2007, the global banking industry has suffered from a severe credit crisis. Hence, the CofC regression here does not cover the fiscal years 2007 and 2008. 20 To assess the sensitivity of our results to this procedure, I also estimate pooled regressions controlling for unspecified heteroscedasticity and autocorrelation effects. Results (not reported) yield similar inferences as the annul regressions.

36

CofC across all regressions of different governance measures is significantly related to Beta and

BM. The positive relation between CofC and Size in the banking industry suggests that investors

perceived large banks to be more risk-taking than small ones. The insignificant relations between

CofC and seven different measures of corporate governance mechanism imply no direct impact of

corporate governance on the cost of equity capital. The strong negative relation between CofC and

TradingAssets across almost all regressions suggests a diversification effect of trading assets on the

whole bank portfolio. In addition, the consistently negative relation between CofC and Capital

Ratio implies that the fewer risks the banks take, the lower the cost of equity capital. The

consistently negative relation between CofC and Loans Ratio means that a large proportion of

performing loans (that is loans, net of total allowance for loan losses) reduces a bank’s risk level

and benefits from a low cost of equity capital. The positive sign of the coefficients on Tangible

Equity Ratio and Depository Funding is inconsistent with the prediction that a bank with less risk-

taking level or more stability has lower cost of equity capital. The consistently negative relation

between CofC and Numest implies that the better the overall information environment of banks, the

lower is the cost of equity capital. The significant positive relation between CofC and Moral

Hazard across almost all the regression is consistent with the notion that a bank with a more

generous deposit insurance regime is inclined to take a higher level of risk and thus has a higher

required return of equity. The consistently negative relation between CofC and Sensitivity implies

that a bank providing the sensitivity analysis format experiences a lower cost of equity capital.

This study is primarily concerned with the coefficient on VaRDIS. After controlling for

Antidir, BoardIndep, RiskCom, RiskComIndep, InstiOwner and CompCG separately, VaRDIS is

strongly negatively associated with CofC. In addition, VaRDIS is negatively associated with CofC,

after controlling for BoardSize. Thus, the evidence suggests that informative VaR disclosure, which

37

reflects the banks’ market risk exposure in their business activities and their risk management

system, reduces the cost of equity capital in the banking industry. Furthermore, the impact of VaR

disclosure informativeness on the cost of equity capital is about the same across all seven

regressions. In economic terms, the effect of VaR disclosure informativeness on the cost of equity

capital is about 200 basis points between a bank with the most informative VaR disclosure and a

bank with the least informative VaR disclosure. This sizable magnitude suggests that capital market

participants may perceive informativeness of a bank’s overall VaR disclosure as a strong signal of

the efficacy of its risk management system. Overall, these results imply that VaR disclosure

informativeness is one of the most important economic determinants of the cost of equity capital in

the banking industry.

5.3 Additional Evidence from the Financial Crisis of 2007-2009

In this section, I provide evidence of the impact of VaR disclosure informative on the cost of

equity capital during the 2007-2009 financial crisis by estimating the following regression

equation:

CofCi,t = β0 + β1VaRDISi,t * Crisis + β2 VaRDISi,t * Non_Crisis + β3Crisis (3)

+ β4 CompCG + β5 Betai,t + β6BMi,t + β7Sizei,t + β8Trading Assets Ratioi,t

+ β9 Capital Ratioi,t + β10Liquidityi,t + β11Loans Ratioi,t + β12Tangible Equity Ratioi,t

+ β13 Depository Fundingi,t + β14Issuei,t + β15Newsi,t + β16Numesti,,t

+ β17Moral Hazardi,t + β18Sensitivityi,t + β19Tabulari,t + εi,t .

Crisis is a dummy variable equal to 1 for the financial crisis period of the years 2007 and 2008, and

0 otherwise. Non_Crisis is a dummy variable equal to 1 for the non-crisis period from the years

38

1997 to 2006, and 0 otherwise.21 Differing from other research in the literature on the financial

crisis (e.g., Gorton [2008], Ryan [2008], Beltratti and Stulz [2009], Erkens et al. [2009], and Adams

[2009]), the test here is focused on whether VaR disclosure informativeness had an impact on the

cost of equity capital during the crisis period.22

Table 7 presents the regression result. Consistent with the results in Table 6, the significantly

negative coefficient on VaRDISi,t * Non_Crisis suggests a strong effect of VaR disclosure

informativeness on the cost of equity capital during the non-crisis period from the years 1997 to

2006. The significantly positive β3 means an overall increase of about 400 basis points in the cost of

equity capital during the financial crisis of 2007-2009. Here, we are interested in the coefficient of

β1. The significantly negative coefficient on β1 implies that as a signal reflecting the efficacy of risk

management practices and the quality of the risk governance mechanism, VaR disclosure

informativeness had a strong impact on the cost of equity capital during the financial crisis of 2007-

2009. In economic terms, the effect of VaR disclosure informativeness on the cost of equity capital

during the recent financial crisis is 580 basis points between a bank with the most informative VaR

disclosure and a bank with the least informative VaR disclosure. While the average cost of equity

capital rose in the banking industry during the crisis, banks with a high-quality risk management

system might have communicated the quality of such a system to investors through more

informative VaR disclosure, and thus benefited from a considerable reduction in the cost of equity

capital.

21 Gorton (2008) indicates that most events of the Subprime Crisis started after March, 2007. Only two events occurred before the end of March 2007:1) On Dec. 2006 Ownit Mortgage Solutions files for bankruptcy; 2) On March 13 the Mortgage Banker Association data for the last three months of 2006 shows that late or missed payments on mortgages rose to 4.95%, rising to 13.3% in the subprime market. Subprime lender Accredited Home Lenders loses 65% of its value, having lost 28% a day earlier. 22 Untabulated results shows that the average estimates of the cost of equity capital of the sample banks are 12.4% as of 3 months after the fiscal year 2008, and 22.92% as of 3 month after the fiscal year 2009. They are higher than the average estimate as of the 3 months after the fiscal year 2007 (9.25%) and as of 3 months after the fiscal year 2006 (8.92%).

39

In addition, I partitioned the sample banks in the year 2007 into two groups with large and

small financial-crisis losses, based on the median percentage change in the provision of credit loss

from the fiscal years 2006 to 2007. I use a t-test and a Wilcoxon rank sum test to assess whether the

means and medians of VaRs (that is, the self-reported adjusted ending trading VaR amount of each

bank, scaled by the market value of equity) and VaR-related disclosure scores are different between

the two groups in the year 2007. The VaR-related disclosure scores include VaRDIS_Index, Stress

Testing (Category VIII under VaRDIS_Index) score, and Limitations of VaR (Category IX under

VaRDIS_Index) scores. Panel A of Table 8 reports the means of the VaRs and the VaR-related

disclosure scores across the two groups, and two-sided p-values for differences in the means and

medians across the two groups. Similarly, Panel B presents the information for the sample banks in

the year 2008, partitioned into the two groups based on the median percentage change in the

provision of credit loss from the fiscal years 2007 to 2008. In both panels, the evidence consistently

indicates that, though the mean and median VaRs are small and not significantly different across the

two groups, the group with small financial-crisis losses has a significantly higher mean

VaRDIS_Index, Stress Testing score, and Limitations of VaR score than does the group with large

financial-crisis losses. It is consistent with the notion that VaR disclosure informativeness signals

the efficacy of risk management practices and the quality of risk governance mechanisms.

However, as Table 2 shows, a large proportion of bank-year observations did not have any

information regarding several important disclosure items from 1997 to 2008. Especially, from the

years 2005 to 2008, less than 20% of the bank-year observations provided information about the

quantitative results of stress tests (i.e., an important complement to VaR), and less than 30% of the

bank-year observations provided information about non-trading VaR. This may have limited the

usefulness of VaR information disclosure to capital markets during the financial crisis of 2007-

40

2009 when the information on both the quantitative results of stress tests and the market risk of

non-trading portfolios were important for capital market participants.23

The current VaR measure used in the banking industry is based on historical data and does

not capture the effect of tail events. As “A Special Report on International Banking” from The

Economist indicated in May 2009, a new refined VaR measure, such as COVaR, and the disclosure

of stress-testing results and the non-trading portfolio VaR may be demanded in the near future to

prevent or mitigate a future systemic financial crisis. 24 Thus, it is necessary for government

regulators to re-consider the current regulations on VaR disclosure in the external reports of the

banking industry.

5.4 Robustness Tests

5.4.1 Using Analysts’ Forecast Dispersion as an Alternative Measure of Corporate

Information Environment

In the Section 5.2, I use the natural log of the number of analysts following (Numest) to

measure the overall corporate information environment. Previous literature also uses analysts’

forecast dispersion to proxy for the overall corporate information environment. When there is a

greater uncertainty about a firm’s environment, analysts’ forecasts tend to vary widely. On the

other hand, when the environment is quite more certain, analysts’ forecasts become less dispersed.

Lang and Lundholm (1996) document a negative relation between corporate disclosure policy and

23 Some banks excluded the subprime-related assets in VaR disclosures in their 2007 annual reports. For example, in the 4th quarter of 2007, Citigroup’s VaR measure did not include the market risk from exposure to ABS CDOs and associated direct subprime exposures, including hedges, in the Securities and Banking business. Similarly, at the end of 2007, Merrill Lynch’s VaR disclosure in their annual report also excludes U.S. sub-prime residential ABS CDO net exposures. 24 Tobias Adrian of the Federal Reserve Bank of New York and Markus Brunnermeier of Princeton University have proposed a measure called CoVaR, or “conditional value at risk,” which tries to capture the risk of loss in a portfolio due to other institutions being in trouble. Taking account of such spillover effects greatly increases some banks’ value at risk.

41

analysts’ forecast dispersion. Hence, I use the inter-analyst standard deviation of the most recent

forecasts for each bank-year, deflated by the stock price at the end of fiscal year (Forecast

Dispersion) as an alternative measure of corporate information environment.

Table 9 provides the regression results for equation (2), using the Forecast Dispersion an

alternative measure of corporate information environment. The significantly positive coefficient on

Forecast Dispersion suggests a positive relation between the corporate information risk, proxied by

Forecast Dispersion, and the cost of equity capital. Meanwhile, I find that VaRDIS is still

significantly negatively associated with CofC.

5.4.2 Using VaR as an Additional Measure of Bank-wide Risk

Jorion (2002) and Liu et al. (2004) suggest that a bank’s trading VaRs reflect bank-wide risk

levels. Thus, I use the self-reported adjusted ending trading VaR amount of each bank, scaled by

the market value of equity (VaR) to proxy for bank-wide risk as an additional control for banks’

risk-taking in the regression equations. Because some banks choose not to disclose VaR figures, the

sample size decreases by about 25%. The adjustment is as follows. First, for banks that report

trading VaRs for other than a one-day period, I divided the reported VaR by the square root of the

holding period. Second, following the approach in Linsmeier and Pearson (2000), I normalize all

trading VaRs to a 99% confidence level.25

Table 10 panel A provides the regression results for equation (1), after controlling for VaR.

The significantly positive coefficient on VaR suggests a positive relation between the risk taking

and VaR disclosure informativeness. The coefficient on CompCG remains significantly positive.

25 The loss with a probability of 1% is larger than the loss with a probability of 5%. If the portfolio profit or loss is normally distributed, it is 1.414 times as large because the loss exceeds 1.645 (2.326) times one standard deviation with a probability of 5% (1%). The ratio of these is 1.414 = 2.326/1.645.

42

Model 1 in Panel B provides the regression results for equation (2), after controlling for VaR. In

model 1, I find that VaRDIS is still significantly negatively associated with CofC. In addition,

consistent with the findings in Jorion (2002) and Liu et al. (2004), the positive coefficient on VaR

implies that VaR is an important risk factor. 26

5.4.3 Alternative Measures of the Cost of Equity Capital

Here, I consider the sensitivities of the main results to firm-level cost of equity capital

estimates derived from the modified PEG approach in Easton (2004), the residual income valuation

approach in Gebhardt et al. (2001), and the abnormal earnings growth valuation approach in Gode

and Mohanram (2003). I provide details of each approach on the estimation of the cost of equity

capital measures in Appendix B.27 In Panel B of Table 10, models 2 to 4 present the regression

results for equation (2) using different estimates of the cost of equity capital and controlling for

VaR. The number of observations varies across models due to data availability. Consistently, the

coefficients on VaRDIS across the three models are significantly negative, supporting the finding in

the main test.

I further evaluate the sensitivities of the main results to portfolio-level CofC estimates, based

on O’Hanlon and Steele (2000), as adapted by Easton and Sommers (2007). I provide details of a

portfolio-level approach on the estimation of the CofC measures in Appendix B. I separate my

sample banks into two groups based on VaRDIS to test the impact of VaR disclosure on the cost of

equity capital between high and low VaRDIS groups. Untabulated results show that, after

controlling for the Fama-French risk factors and other variables, is negative at a smaller than

26 The correlation between VaR and Beta in the sample is 13.2% at a significance level of 1.49%. 27 The correlation coefficients between CofC and

MPEGr , between CofC and

GLSr , and between CofC and

GMr are

93.01%, 57.82%, and 39.23%, all at a significance level of <.0001.

43

1% significance level. It is consistent with informativeness of VaR disclosure having a positive

impact on the cost of equity capital in the banking industry.

5.4.4 Firm Fixed Effects Specification

When analyzing the effect of corporate governance on VaR disclosure informativeness,

endogeneity concern arises because of omitted unobservable firm characteristics. Omitted variables

affecting both corporate governance quality and the level of VaR disclosure informativeness could

lead to spurious correlations between corporate governance and VaR disclosure informativeness.

Similarly, endogeneity issues arise when analyzing the impact of VaR disclosure informativeness

on the cost of equity capital. To examine the robustness of our results, I use firm fixed effects to

address the concern that omitted time-invariant firm characteristics are driving the results.

In Table 11, Panels A and B show the results of the regression equations (1) and (2) with firm

fixed effects specification. The coefficient on CompCG in Panel A is still positive at the one-tailed

significance level of 6.15%. Panel B shows that VaRDIS is still significantly negatively associated

with CofC.

5.4.5 Principal Component Analysis

The measure of VaR disclosure informativeness in this paper is aggregated on a rich set of

VaR disclosure items using equal weighting. It is possible that equal weighting of individual items

may not represent the real weighting assigned by market participants in evaluating the VaR

disclosure. To alleviate some concerns, I use principal component analysis (PCA) to create the first

principal component reflecting the common variation of VaR disclosure across bank-year

observations. The top 3 factor loadings in the first principal component are daily VaR figures

44

(Category V.), Trading Revenues (Category VI.) and Backtesting (Category VII). I use annual

percentile ranks of the first principal component (First Principal Component – VaRDIS) in place of

VaRDIS in the regression equations (1) and (2). The results in Panel A of Table 12 show that the

coefficient on the CompCG remains significantly positive. Panel B shows that First Principal

Component – VaRDIS is still significantly negatively associated with CofC.

5.4.6 U.S. and Non-U.S. Incorporated Banks

I separate the sample into the two subsamples of U.S. and non-U.S. incorporated banks, and

estimate the regression equations (1) and (2) for the two subsamples, respectively. Panel A of Table

13 provides the results of regression equation (1) for each of the subsamples. For the U.S.

subsample, the coefficient on CompCG is positive at the one-tailed significance level of 6.2%. For

the non-U.S. subsample, the coefficient on CompCG is significantly positive at the one-tailed

significance level of 3.5%. Panel B of Table 13 provides the results of regression equation (2) for

each of the subsamples. For the U.S. subsample, the impact of VaRDIS on CofC is negative at the

significance level of 8.9%. For the non-U.S. subsample, the relation between VaRDIS and CofC is

negative but insignificant.

5.4.7 Other Tests

An additional robustness test controls for all six individual governance characteristics

simultaneously in the regression equation (2). Table 14 shows that the coefficient on VaRDIS is still

significantly negative at a 7.8% significance level, and its magnitude is about the same.

A couple of control variables (e.g. Beta, BM and Size) in regression equation (1) are also used

in regression equation (2). It is possible that the impact of VaRDIS on CofC is partially due to its

45

correlation with the control variables. Thus, in regression equation (2), I replace VaRDIS with its

orthogonalized transformation (Orthogonalized_VaRDIS), which is the residual from regression

equation (1) using CompCG as the governance attribute. Table 15 shows that the coefficient on the

Orthogonalized_VaRDIS is still significantly negative.

I also run the regression equations (1) and (2) as pooled regressions controlling for clustering

by firm and year. Panels A and B in Table 16 present the results of estimating the two regression

equations. Panel A shows that VaRDIS remains positively associated with most of the governance

measures at the significance level of 10%. Panel B shows that, after controlling for each of the

governance measures separately, VaRDIS remains strongly negatively associated with CofC.

46

Chapter 6: Conclusion

In response to the financial disasters in the 1990s, one of the most important initiatives by the

Basel Committee of the Bank for International Settlements, government regulators, accounting

standard setters, and other important constituents around the world was the conception and

development of value at risk (VaR) and its disclosure in the external reports of institutions. In 1997,

the SEC issued FRR No. 48 mandating VaR disclosure as one of the important disclosure

alternatives for market-risk quantitative information. Today, VaR disclosure is the most commonly

used approach for measuring the risk of trading positions by large financial institutions around the

world.

This study is the first in the literature to empirically examine the economic determinants and

consequences of VaR disclosure informativeness in the banking industry. First, this study finds that

more informative VaR disclosure is associated with more effective corporate governance, including

better protection of minority shareholders, a larger and more independent board of directors, the

presence of a separate risk committee, a more independent risk committee, higher institutional

ownership and a better overall governance environment. Together, these results are consistent with

the notion that corporate governance mechanisms are important determinants of the

informativeness of VaR disclosure. Second, the evidence shows that the cost of equity capital is

significantly negatively associated with the informativeness of VaR disclosure, implying that more

informative VaR disclosure can effectively reduce the information asymmetry between

management and outside investors about both a bank’s market risk exposure and its risk

management system. This effect is robust to controls for the magnitude of disclosed VaR figures as

well as to alternative proxies for the cost of equity capital.

Additional evidence during the financial crisis of 2007-2009 also shows a strong negative

47

association between VaR disclosure informativeness and the cost of equity capital. Further, a

comparison of VaR disclosure informativeness between the two groups of banks with large and

small financial-crisis losses clearly demonstrates the greater informativeness of VaR disclosure in

the group with small financial-crisis losses. Thus, the findings during the recent crisis suggest the

importance of VaR disclosure informativeness to capital markets as a strong signal reflecting the

efficacy of risk management practices and the quality of the risk governance mechanism.

Nevertheless, I still find that a large proportion of sample banks choose not to disclose information

with respect to some important disclosure items (e.g., quantitative stress-test results, and non-

trading portfolio VaR), suggesting that government regulators ought to re-consider the current

regulation on VaR disclosure in the external reports of the banking industry.

The findings in this study should be useful to regulators, standard setters, investors, and

others. It provides direct and recent evidence about the determinants of and the impact of unaudited

VaR disclosure on the capital market during the past decade, consistent with SEC FRR No. 48 and

“Pillar III - Market Discipline” in Basel II to increase publicly available information about firms’

market risk. In addition, this study further sheds light on the role of VaR disclosure in the financial

crisis of 2007-2009. Thus, this study should be useful for academics, standard setters and regulators

in analyzing how to mitigate or avoid similar global financial crises in the future.

48

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60

Appendix A

VaR Disclosure Informativeness Measure

The items included in the VaR disclosure informativeness measure (VaRDIS_Index) are:

Category I. VaR Characteristics

a. Holding Period (e.g., 1 day, 1 month)

b. Confidence Level (e.g., 99%, 95%)

c. Model Type (e.g., Historical simulation, Monte Carlo simulation, Delta-normal)

d. Time Frame of Data

Category II. Summary VaR Statistics

a. Annual average VaR

b. Minimum (low) VaR over the year

c. Maximum (high) VaR over the year

d. Year-End VaR

e. Diversification Effect

f. VaR Limit

Category III. VaR Statistics by Risk Type

a. Annual average VaR by risk type

b. Minimum (low) VaR over the year by risk type

c. Maximum (high) VaR over the year by risk type

d. Year-End VaR by risk type

Category IV. Intertemporal Comparison

a. Summary Information about the Previous Year VaR

Category V. Daily VaR Figures

a. Histogram of Daily VaRs (with 2 points for Plot of Daily VaRs)

61

Category VI. Trading Revenues

a. Hypothetical Revenues

b. Revenues without Trading Fees

c. Histogram of Daily Revenues (with 2 points) for Plot of Daily Revenues

d. Trading Revenue by Risk Type

e. Discussing the number of times (days) actual portfolio loss

f. Largest daily loss over the year.

Category VII. Backtesting

a. Number of Exceptions (with 2 points for no exceptions for Category VII)

b. Explanation of Exceptions

Category VIII. Stress Testing

a. Stress tests done

b. Stress tests described qualitatively

c. Stress-test results reported

Category IX. Limitations of VaR

a. Limitations of VaR

Category X. Market Risk Control Structure

a. Market risk control structure

Category XI. Non-trading VaR disclosure

a.Non-trading VaR disclosure

62

Appendix B

Alternative Measures for Cost of Equity Capital

Price-Earnings-Growth (PEG) and Modified PEG (MPEG) approaches by Easton (2004):

2

0 2 1 1( ) /MPEG MPEG

p feps r dps feps r= + −,

where 0p is current price per share, 2feps

is a two-period-ahead median forecast of accounting

earnings per share, 1feps is a one-period-ahead median forecast of accounting earnings per share,

dps1 is a one-period-ahead median forecast of dividends per share, for which we use actual

dividend per share in year 1 from Compustat, and rMPEG is the implied cost of equity capital (MPEG

estimate). When dps1 is equal to 0, rMPEG becomes PEG estimate ( 2 1 0( ) /feps feps p− ) in the main

test.

Gebhardt, Lee, and Swaminathan [2001] (GLS):

1 0 2 1 12 11 13 120 0 2 12 12

...1 (1 ) (1 ) (1 )

GLS GLS GLS GLS

GLS GLS GLS GLS GLS

feps r B feps r B feps r B feps r Bp B

r r r r r

− − − −= + + + + +

+ + + +,

where B0 is current book value of equity per share, Bt is future book value of equity per share at

period t estimated using a clean surplus assumption, 0p is current price per share, t

feps (t=1, 2, and

3) is a t-period-ahead median forecast of accounting earnings per share, and GLS

r is implied cost of

equity capital. The approach uses actual book values per share and forecasted earnings per share up

to three years ahead to derive future expected residual income for a first three-year period. The

approach assumes clean surplus. Moreover, dividends are set equal to a constant fraction of

forecasted earnings. After the first three years, feps4 to feps12 is forecasted such that ROE gradually

63

(linearly) converges to industry ROE in the 12th year. Industry ROE is estimated as the five-year

moving average median of past ROEs of all firms in the same industry. I use a numerical

approximation program to identify GLS

r within a .005 difference between the actual and fitted value

of p0.

Gode and Mohanram [2003] (GM):

2 1 1 110

( ( ))

( )GM

GM GM GM

feps feps r feps dpsfepsp

r r r g

− − −= +

−,

where 0p is current price per share, 2feps

is a two-period-ahead median forecast of accounting

earnings per share, 1feps is a one-period-ahead median forecast of accounting earnings per share,

dps1 is a one-period-ahead median forecast of dividends per share, for which we use actual

dividend per share in year 1 from Compustat, g is the long-term growth in abnormal earnings

changes, and GM

r is the implied cost of equity capital. The following expression describes the

solution for GM

r :

2 12

0

( ( 1))GM

fepsr A A g

pγ= + + − − ,

where 1 2 12

0 1

1(( 1) );

2

dps feps fepsA g

p fepsγ

−≡ − + = .

Gode and Mohanram (2003) estimate γ (the long-term growth in abnormal earnings changes) as the

real risk-free rate: 1 3%f

Rγ − = − , and use the ten-year T-bond rate at the date of estimation to

proxy for the risk-free rate.

Portfolio-Level approach by Easton and Sommers (2007):

64

Easton and Sommers (2007) show that O’Hanlon and Steele (2000) can be used to estimate the

expected rate of return for any group or portfolio of stocks:

0 1

1 1

jt jt jt

jt

jt jt

eps p bps

bps bpsα α ς

− −

−= + + ,

where eps is earnings per share, p is stock price per share and bps is book value of equity per share.

Here, 0α is the expected rate of return for the portfolio.

Based on Easton and Sommers (2007), the following regression equation can further be used to

compare the difference in the expected rate of return across the two different disclosure regimes:

0 1 2 3

1 1 1

jt jt jt jt jt

jt

jt jt jt

eps p bps p bpsD D controls

bps bps bpsς

− − −

− −= ∂ + ∂ + ∂ + ∂ + + ,

where D is equal to 1 for a high disclosure regime, and 0 otherwise. In the equation, captures

the difference in the expected rate of return across the two regimes.

65

Appendix C

Variable Definitions

Variable definitions are set out below;

VaRDIS _Index VaR disclosure informativeness measure in the Appendix A.

VaRDIS The percentile rank of VaRDIS _Index within each year.

Antidir Anti-director index in Djankov, La Porta, Lopez-de-Silanes, and Shleifer

(2008) to measure the protection of minority shareholder rights.

BoardSize The number of board members for each bank-year observation.

BoardIndep The percentage of independent directors on the board of directors.

RiskCom 1 if the board of directors of the sample bank has a risk committee, and 0

otherwise.

RiskComIndep The percentage of independent directors on the risk committee.

InstiOwner The percentage of the firm’s equity held by institutional investors.

CompCG The average of the percentiles of the six governance measures above.

CofC 2 1 0( ) /feps feps p− .

0p Current price per share as of the end of 3 months after fiscal year.

2feps A two-period-ahead median forecast of accounting earnings per share.

1feps A one-period-ahead median forecast of accounting earnings per share.

Beta Market beta from a regression of prior 60 monthly returns on the

corresponding value-weighted CRSP market return.

BM The ratio of firm’s book value of equity divided by its market value of

equity at the beginning of the fiscal year.

Size The natural log of the firm’s market value at the beginning of fiscal year.

66

Trading Assets Ratio The ratio of Trading/Dealing Account Securities (total) from Compustat

Bank, divided by total assets at the end of the fiscal year.

Capital Ratio The ratio of Tier 1 and Tier 2 capital to total risk-weighted assets

multiplied by 100.

Liquidity The ratio of liquid assets to total deposits and borrowing from Bankscope.

Loans Ratio The ratio of loans, net of total allowance for loan losses, to total assets.

Tangible Equity Ratio The ratio of tangible equity to total liabilities.

Depository Funding The ratio of depository funding over total assets from Bankscope.

Issue Equal to 1 if the firm has an equity or debt issuance in the current or

following year, and 0 otherwise.

News 1 if the firm’s current EPS is greater than previous-year EPS, and 0

otherwise.

Numest The natural log of the number of analysts that issue earnings forecasts for

the firm during the fiscal year.

VaR The self-reported adjusted ending trading VaR amount of each bank,

scaled by market value of equity.

Sensitivity 1 if a bank uses sensitivity analysis in one year, and 0 otherwise.

Tabular 1 if a bank uses tabular format in one year, and 0 otherwise.

Moral Hazard An indicator of the generosity of the deposit insurance regime in the

country.

Crisis 1 for the financial crisis period of the years 2007 and 2008, and 0

otherwise.

Non_Crisis 1 for the non-crisis period from the years 1998 to 2006, and 0 otherwise.

67

rMPEG Implied cost of equity capital based on Easton (2004).

GLSr Implied cost of equity capital based on Gebhardt et al. (2001).

GMr Implied cost of equity capital based on Gode and Mohanram (2003).

dps1 A one-period-ahead median forecast of dividends per share.

B0 The current book value per share.

Bi Book value per share at the end of period i.

ifeps A i-period-ahead median forecast of accounting earnings per share.

eps Earnings per share excluding extraordinary items.

bps Book value of equity per share.

D 1 if bank-year VaRDIS is greater than median VaRDIS each year, and 0

otherwise.

Forecast Dispersion The inter-analyst standard deviation of the most recent forecasts for each

bank-year, deflated by the stock price at the end of fiscal year.

First Principal The annual percentile rank of the first principal component created from

Component – VaRDIS The measure of VaR disclosure informativeness by the Principal

Component Analysis.

Orthogonalized_VaRDIS The residual from regression equation (1) using CompCG as the governance attribute.

68

Figure 1: VaR Disclosure Trend lines from 1997 to 2008

69

Figure 2: VaR Model Type

This pie chart displays the relative frequency of each VaR model type used by the sample banks

from the years 1997 to 2008.

70

TABEL 1. The Sample of U.S. Listed Banks (Grouped by Incorporation

Regions)

U.S.

Canada

J.P. Morgan Chase & Co Toronto-Dominion Bank

Bank of America Scotia Bank

Citigroup

Bank of Montreal

US Bancorp Canadian Imperial Bank of Commerce

Countrywide Financial Corp Royal Bank of Canada

Bank of New York Mellon Corporation

Mellon Financial Corporation Europe

Bank of New York Company HSBC

State Street Corporation Royal Bank of Scotland

Capital One Financial Corporation Barclays

SunTrust Banks Inc Lloyds

KeyCorp

BNP

Bank One Corp Societe Generale

Northern Trust Corp Deutsche Bank

National City Corp Commerzbank

UnionBanCal Corporation BBVA

Popular Inc Banco Santander

BOK Financial Corporation ABN AMRO

PNC Financial Services Group Inc ING Group

Wachovia Corporation UBS

Wells Fargo & Company Credit Suisse

FleetBoston Financial Corp Bank of Ireland

BankBoston Corp Allied Irish Banks

AllFirst Financial National Bank of Greece

Bank of Hawaii

Regions Financial Corp Asia-Pacific

Fifth Third Bancorp National Australia Bank

Susquehanna Bancshares Inc ANZ Banking Group

Zions Bancorporation Westpac Banking Corporation

First Horizon National Corp Mitsubishi Tokyo Financial Group

Comerica Inc Mizuho Financial Group

Nomura Holdings

Latin America Kookmin Bank

CrediCorp Ltd Woori Finance Holdings

CorpBanca Shinhan Financial Group

Banco De Chile ICICI Bank

71

TABLE 2. VaR Disclosure Informativeness Measure (VaRDIS_Index) from the Years 1997 to 2008

This table provides mean scores for the VaRDIS_Index, each subcategory of the VaRDIS_Index, and each disclosure item

from each subcategory in Appendix A for 66 sample banks over 12 years. The mean score for each disclosure item

represents the proportion of banks with the disclosed item.

YEAR 1997 98 99 2000 01 02 03 04 05 06 07 08

VaRDIS_Index 6.6 9.4 10.9 12.4 13.2 14.0 14.8 15.0 14.8 15.0 15.4 16.1

Category I. VaR Characteristics 1.45 1.69 1.90 1.87 1.81 2.05 2.16 2.33 2.34 2.38 2.59 2.93

Holding Period 0.50 0.54 0.58 0.56 0.53 0.58 0.63 0.70 0.69 0.68 0.73 0.84

Confidence Level 0.50 0.59 0.62 0.61 0.58 0.64 0.67 0.75 0.75 0.72 0.76 0.82

Model Type 0.30 0.34 0.43 0.43 0.43 0.49 0.51 0.54 0.55 0.58 0.62 0.7

Time Frame of Data 0.15 0.21 0.27 0.27 0.29 0.34 0.36 0.34 0.36 0.39 0.48 0.56

Category II. Summary VaR Stats 0.73 1.44 1.82 2.20 2.11 2.34 2.53 2.85 2.87 2.88 3.06 3.35

Annual Average VaR 0.08 0.31 0.40 0.49 0.46 0.49 0.52 0.60 0.58 0.59 0.61 0.68

Min. VaR over the year 0.07 0.20 0.32 0.40 0.38 0.40 0.47 0.52 0.51 0.52 0.56 0.6

Max. VaR over the year 0.08 0.26 0.33 0.41 0.40 0.43 0.49 0.55 0.55 0.57 0.59 0.63

Year-end VaR 0.32 0.38 0.47 0.49 0.46 0.52 0.55 0.57 0.60 0.59 0.64 0.7

Diversification Effect 0.12 0.18 0.20 0.33 0.30 0.36 0.37 0.43 0.45 0.45 0.52 0.58

VaR Limit 0.07 0.11 0.10 0.09 0.11 0.13 0.13 0.18 0.18 0.16 0.15 0.16

Category III. VaR Stats by Risk Type 0.72 1.10 1.27 1.43 1.38 1.48 1.60 1.84 1.93 1.99 2.15 2.39

Annual Average VaR by Risk Type 0.17 0.30 0.37 0.41 0.41 0.42 0.44 0.49 0.51 0.52 0.55 0.6

Min. VaR over the year by Risk Type 0.15 0.26 0.32 0.36 0.34 0.36 0.40 0.46 0.49 0.51 0.53 0.6

Max. VaR over the year by Risk Type 0.17 0.26 0.32 0.36 0.34 0.36 0.40 0.46 0.49 0.51 0.55 0.6

Year-end VaR by Risk Type 0.23 0.28 0.27 0.30 0.29 0.34 0.36 0.42 0.43 0.45 0.53 0.6

Category IV. Intertemporal VaR

Comparison 0.12 0.34 0.43 0.43 0.43 0.53 0.56 0.60 0.61 0.64 0.68 0.75

Category V. Daily VaR Figures 0.10 0.20 0.32 0.40 0.48 0.52 0.55 0.63 0.63 0.61 0.61 0.77

Category VI. Trading Revenues 0.38 0.39 0.60 0.74 0.95 1.00 1.32 1.54 1.37 1.41 1.44 1.63

Hypothetical Revenues 0.03 0.00 0.02 0.03 0.05 0.10 0.15 0.18 0.19 0.19 0.21 0.25

Revenues without Trading Fees 0.00 0.00 0.02 0.01 0.01 0.01 0.03 0.04 0.03 0.03 0.02 0.11

Daily Revenues 0.12 0.18 0.28 0.34 0.43 0.45 0.56 0.63 0.58 0.57 0.59 0.6

Trading Revenues by Risk Type 0.03 0.02 0.02 0.03 0.04 0.04 0.04 0.03 0.03 0.03 0.03 0.05

The Number of Times Actual Loss 0.12 0.11 0.13 0.17 0.20 0.18 0.25 0.28 0.25 0.28 0.29 0.3

Largest Daily Loss 0.08 0.08 0.13 0.16 0.23 0.21 0.29 0.37 0.28 0.32 0.30 0.33

Category VII. Backtesting 0.13 0.30 0.37 0.39 0.45 0.48 0.61 0.64 0.67 0.70 0.70 0.89

Exceptions 0.12 0.25 0.37 0.39 0.40 0.48 0.56 0.64 0.66 0.68 0.55 0.72

Explanation of Exceptions 0.02 0.05 0.00 0.00 0.05 0.00 0.05 0.00 0.01 0.01 0.15 0.18

Category VIII. Stress Testing 0.43 0.56 0.67 0.77 0.76 0.91 1.00 1.12 1.15 1.14 1.23 1.49

Stress Tests Done 0.25 0.31 0.37 0.41 0.40 0.45 0.49 0.55 0.57 0.58 0.61 0.7

Qual. Description of Stress Tests 0.17 0.21 0.25 0.27 0.26 0.32 0.36 0.39 0.43 0.42 0.47 0.6

Stress Test Results 0.02 0.03 0.05 0.09 0.10 0.13 0.15 0.18 0.15 0.14 0.15 0.19

Category IX. Limitations of VaR 0.12 0.23 0.25 0.27 0.29 0.30 0.33 0.36 0.37 0.39 0.42 0.58

Category X. Market Risk Control

Structure 0.75 0.75 0.78 0.76 0.73 0.75 0.79 0.87 0.88 0.88 0.91 1

Category XI. Non-Trading VaR 0.05 0.07 0.13 0.16 0.18 0.16 0.17 0.22 0.25 0.25 0.23 0.3

72

TABLE 3. Descriptive Statistics

This table presents descriptive statistics of the main variables in this study for the sample of 617 observations from fiscal years 1997 to 2008. For each year, VaRDIS is the percentile rank of a bank’s VaRDIS_Index, VaR disclosure informativeness measure in Appendix A. Governance Attribute is measured by: 1) anti-director index (Antidir) of La Porta et al. (1998) as revised in Djankov et al. (2008); 2) BoardSize, measured as the number of board members for each bank-year observation; 3) BoardIndep, defined as the percentage of independent directors on the board of directors; 4) RiskCom, coded as 1 if the board of directors of a bank has a risk committee as one of the standing committees responsible to the board, and 0 otherwise; 5) RiskComIndep, computed as the percentage of independent directors on the risk committee; 6) InstiOwner, defined as the percentage of the firm’s equity held by institutional investors; and 7) CompCG, measured as the average of the percentiles of the six governance measures above for each sample observation. Every year, each of the six governance metrics is sorted in ascending order before computing the percentile values. Hence, high values of the summary measure represent an overall strong governance environment. CofC is PEG estimate in Easton (2004). Beta is market beta from a regression of prior 60 monthly returns on the corresponding value-weighted CRSP market return. BM is the ratio of the firm’s book value of equity divided by its market value of equity at the beginning of the fiscal year. Size is the natural log of a firm’s market value at the beginning of the fiscal year. Trading Assets Ratio is the ratio of trading/dealing account securities, divided by total assets at the end of the fiscal year. Capital Ratio is the ratio of Tier 1 and Tier 2 capital to total risk-weighted assets multiplied by 100. Liquidity is liquid assets, which include cash, interbank lending, government bonds and, where appropriate, the trading portfolio, scaled by total deposits and borrowing. Loans Ratio is defined as the ratio of loans, net of total allowance for loan losses, to total assets. Tangible Equity Ratio is defined as the ratio of tangible equity to total liabilities, as a balance sheet measure of a bank’s risk taking. When we do not have data for intangible assets, we use total equity in the numerator. Depository Funding is a ratio of depository funding over total assets. Issue is equal to 1 if the firm has an equity or debt issuance in the current or following year, and 0 otherwise. News is equal to 1 if the firm’s current EPS is greater than previous-year EPS, and 0 otherwise. Numest is the natural log of the number of analysts that issue earnings forecasts for the firm during the fiscal year. VaR is defined as the self-reported adjusted ending trading VaR amount of each bank, scaled by market value of equity, in each year.

Variable Mean Min. 25% Median 75% Max. Std. Dev

VaRDIS_Index 13.738 1.000 6.000 13.000 22.000 29.000 8.935

VaRDIS 0.498 0.016 0.270 0.508 0.754 1.000 0.302

Governance Attribute

Antidir 3.560 2.000 3.000 3.000 4.000 5.000 0.791

BoardSize 15.206 5.000 12.000 15.000 18.000 33.000 4.361

BoardIndep 0.764 0.133 0.667 0.813 0.889 1.000 0.174

RiskCom 0.569 0.000 0.000 1.000 1.000 1.000 0.496

RiskComIndep 0.457 0.000 0.000 0.571 1.000 1.000 0.455

InstiOwner 0.524 0.026 0.286 0.519 0.769 1.000 0.286

CompCG 0.540 0.140 0.396 0.560 0.675 0.907 0.170

Firm Characteristic

CofC 0.106 0.000 0.077 0.092 0.111 0.723 0.070

Beta 0.933 0.000 0.633 0.854 1.188 3.893 0.804

BM 0.743 0.129 0.368 0.468 0.625 11.621 2.112

SIZE 25.041 20.192 23.644 25.057 26.348 28.958 1.822

Trading Assets Ratio 0.097 0.000 0.008 0.041 0.151 0.733 0.128

Capital Ratio 12.249 8.700 11.110 11.950 13.000 23.290 1.755

Liquidity 0.155 0.000 0.041 0.086 0.220 0.705 0.191

Loans Ratio 0.520 0.017 0.427 0.549 0.643 0.934 0.169

Tangible Equity Ratio 0.105 0.002 0.037 0.050 0.068 0.707 0.296

Depository Funding 0.618 0.000 0.576 0.649 0.701 0.876 0.144

ISSUE 0.432 0.000 0.000 0.000 1.000 1.000 0.496

NEWS 0.603 0.000 0.000 1.000 1.000 1.000 0.490

NUMEST 1.724 0.000 0.693 1.609 2.890 3.555 1.200

VaR 0.001 0.000 0.000 0.000 0.001 0.047 0.003

73

TABLE 4. Pearson Correlations This table presents Pearson correlation coefficients among the main variables for the sample of 617 observations from the years 1997 to 2008. All variables are defined in Appendix C. Spearman correlation statistics provide similar results. ***, **, * represent significance levels (two-tailed) at 1%, 5% and 10%, respectively.

VaRDIS Antidir BoardSize BoardIndep RiskCom RiskComIndep InstiOwner CompCG CofC Beta BM SIZE

Trading

Assets Ratio

VaRDIS_Index 0.94*** 0.44*** 0.18*** -0.24*** 0.17*** 0.23*** 0.08* 0.30*** -0.02 0.10** 0.08** 0.61*** 0.47***

VaRDIS - 0.39*** 0.27*** -0.21*** 0.10** 0.17*** 0.10** 0.26*** -0.06 0.14*** 0.04 0.56*** 0.47***

Antidir - 0.09** -0.41*** 0.13*** 0.07* -0.58*** 0.22*** -0.05 0.06 0.19*** 0.53*** 0.25***

BoardSize - -0.07* 0.07* 0.16*** 0.02 0.36*** -0.15** 0.06 0.01 0.11** 0.15***

BoardIndep - 0.16*** 0.19*** 0.35*** 0.27*** -0.02 -0.09** -0.15*** -0.39*** -0.28***

RiskCom - 0.82*** 0.02 0.78*** 0.05 0.02 -0.09** 0.10** 0.01

RiskComIndep - 0.06 0.81*** 0.09** 0.04 -0.09** 0.14*** 0.13***

InstiOwner - 0.09** 0.01 0.07* -0.05 0.03 0.06

CompCG - 0.01 0.06 -0.04 0.24*** 0.09**

CofC - -0.06 0.65*** 0.16*** -0.03

Beta - -0.04 0.11** 0.14***

BM - 0.19*** 0.02

SIZE - 0.47***

Trading Assets

Ratio -

Capital Ratio -

Liquidity -

Loans Ratio -

Tangible

Equity Ratio -

Depository

Funding -

ISSUE -

NEWS -

Numest -

VaR -

74

Capital

Ratio Liquidity

Loans

Ratio

Tangible

Equity

Ratio

Depository

Funding ISSUE NEWS NUMEST VaR

VaRDIS_Index -0.13*** 0.27*** -0.26*** -0.54*** -0.15*** 0.11*** 0.03 -0.41*** 0.03

VaRDIS -0.15*** 0.29*** -0.27*** -0.50*** -0.12*** 0.03 0.01 -0.34*** 0.06

Antidir -0.19*** -0.14*** 0.16*** -0.33*** -0.10** 0.12*** 0.06 -0.65*** 0.06

BoardSize 0.00 0.07* -0.14*** -0.26*** 0.09** -0.08** -0.02 0.08** -0.03

BoardIndep 0.09** -0.05 0.08** 0.28*** 0.19*** 0.05 -0.03 0.42*** -0.11**

RiskCom -0.12** 0.01 0.05 -0.11*** -0.06 0.18*** 0.01 -0.02 -0.17***

RiskComIndep -0.12*** 0.09** -0.04 -0.13*** -0.06 0.19*** 0.00 0.00 -0.10**

InstiOwner 0.18*** 0.38*** -0.37*** 0.02 0.08* 0.09** 0.04 0.25*** 0.05

CompCG -0.13*** 0.11*** -0.12*** -0.20*** -0.07 0.20*** 0.02 -0.05 -0.13***

CofC 0.12** 0.01 0.07 0.09** -0.06 0.24*** -0.19*** -0.05 0.13**

Beta 0.02 0.20*** -0.11*** 0.06 -0.18*** 0.02 -0.01 0.01 0.00

BM -0.01 -0.04 0.06 -0.12*** -0.06 0.09** -0.06 -0.14*** 0.61***

SIZE -0.22*** 0.22*** -0.17*** -0.52*** -0.31*** 0.20*** -0.05 -0.41*** 0.05

Trading Assets

Ratio -0.04 0.56*** -0.44*** -0.37*** -0.38*** 0.08** 0.02 -0.30*** 0.04

Capital Ratio - 0.15*** -0.29*** 0.29*** 0.07* 0.05 0.05 0.19*** 0.02

Liquidity - -0.72*** -0.15*** -0.36*** 0.05 0.03 0.01 0.01

Loans Ratio - 0.22*** 0.43*** -0.04 -0.04 -0.07 0.00

Tangible

Equity Ratio - -0.05 0.02 0.05 0.37*** -0.09**

Depository

Funding - -0.20*** -0.02 0.04 -0.07

ISSUE - -0.03 -0.13*** 0.03

NEWS - 0.00 -0.13***

Numest - -0.02

VaR -

75

TABLE 5. VaR Disclosure Informativeness and Corporate Governance Each year from 1997 to 2008, I estimate the cross-sectional relation between VaR disclosure informativeness (VaRDIS) and corporate governance mechanisms (Governance Attribute): VaRDISi, t = α0 + α1Governance Attributei,t + α2Betai,t + α3BM i,t + α4Sizei,t (1) + α5 Trading Assets Ratioi,t + α6Capital Ratioi,t + α7Liquidityi,t + α8Loans Ratioi,t + α9Tangible Equity Ratioi,t + α10Depository Fundingi,t + α11Issuei,t + α12Newsi,,t + α13Sensitivityi,t + α14Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent

Variable Dependent Variable: VaRDIS

Alternative Measures of Corporate Governance

Antidir BoardSize BoardIndep RiskCom RiskComIndep InstiOwner CompCG

Governance

Attribute

0.040 0.009 0.161 0.051 0.046 0.062 0.158

(0.026) (<.0001) (0.083) (0.035) (0.064) (0.064) (0.019)

Beta 0.042 0.048 0.050 0.042 0.047 0.057 0.042

(0.106) (0.013) (0.054) (0.050) (0.041) (0.122) (0.047)

BM 0.267 0.241 0.279 0.259 0.241 0.203 0.238

(0.016) (0.030) (0.021) (0.021) (0.032) (0.004) (0.024)

Size 0.084 0.085 0.091 0.088 0.089 0.084 0.086

(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

Trading Assets

Ratio

0.589 0.586 0.645 0.587 0.564 0.597 0.581

(0.018) (0.006) (0.005) (0.012) (0.018) (0.007) (0.007)

Capital Ratio -0.016 -0.021 -0.018 -0.015 -0.015 -0.011 -0.015

(0.176) (0.072) (0.089) (0.236) (0.266) (0.295) (0.238)

Liquidity 0.107 0.102 0.062 0.075 0.068 0.025 0.034

(0.677) (0.713) (0.803) (0.777) (0.793) (0.916) (0.883)

Loans Ratio -0.371 -0.311 -0.395 -0.381 -0.359 -0.252 -0.387

(0.012) (0.043) (0.002) (0.014) (0.013) (0.115) (0.002)

Tangible Equity

Ratio -0.186 0.032 -0.202 -0.208 -0.198 -0.273 -0.445

(0.502) (0.897) (0.391) (0.442) (0.473) (0.440) (0.322)

Depository Funding 0.155 0.162 0.160 0.174 0.178 0.045 0.140

(0.312) (0.267) (0.254) (0.192) (0.186) (0.747) (0.295)

Issue -0.035 -0.057 -0.077 -0.065 -0.070 -0.021 -0.078

(0.306) (0.019) (0.004) (0.010) (0.013) (0.594) (0.008)

News 0.032 0.036 0.034 0.029 0.029 0.038 0.033

(0.286) (0.051) (0.183) (0.229) (0.187) (0.102) (0.162)

Sensitivity -0.106 -0.119 -0.155 -0.133 -0.131 -0.092 -0.121

(0.004) (0.004) (0.007) (0.003) (0.004) (0.001) (0.001)

Tabular 0.261 0.231 0.262 0.221 0.201 0.316 0.226

(0.095) (0.100) (0.088) (0.087) (0.090) (0.066) (0.108)

Constant -1.826 -1.777 -1.900 -1.747 -1.753 -1.794 -1.717

(0.001) (0.001) (0.000) (0.002) (0.003) (0.000) (0.001)

Adj. R2 52.59% 52.90% 52.70% 52.44% 52.39% 50.15% 53.89%

N 617 617 617 617 617 606 606

76

TABLE 6. VaR Disclosure Informativeness and Cost of Equity Capital

Each year from 1997 to 2006, I estimate the cross-sectional relation between cost of equity capital estimate (CofC) and VaR disclosure informativeness (VaRDIS): CofCi,t = β0 + β1VaRDISi,t + β2Governance Attributei,t + β3Betai,t + β4BMi,t + β5Sizei,t (2) + β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Numesti,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent

Variable Dependent Variable: CofC

Alternative Measures of Corporate Governance

Antidir BoardSize BoardIndep RiskCom RiskComIndep InstiOwner CompCG

VaRDIS -0.029 -0.019 -0.027 -0.025 -0.027 -0.026 -0.020

(0.000) (0.176) (<.0001) (0.004) (0.001) (0.000) (0.001)

Governance

Attribute -0.021 -0.001 0.039 -0.001 0.003 0.032 0.008

(0.252) (0.574) (0.332) (0.760) (0.412) (0.087) (0.423)

Beta 0.014 0.017 0.017 0.014 0.013 0.014 0.014

(0.040) (0.008) (0.008) (0.045) (0.048) (0.033) (0.035)

BM 0.078 0.066 0.081 0.075 0.077 0.082 0.075

(0.000) (0.004) (0.001) (0.000) (0.000) (0.001) (0.001)

Size 0.006 0.009 0.005 0.006 0.007 0.007 0.006

(0.001) (0.016) (0.005) (<.0001) (0.000) (0.006) (0.004)

Trading Assets

Ratio -0.015 -0.063 -0.026 -0.049 -0.054 -0.048 -0.044

(0.540) (0.012) (0.018) (0.009) (0.015) (0.015) (0.026)

Capital Ratio -0.005 -0.003 -0.004 -0.005 -0.004 -0.004 -0.004

(0.116) (0.039) (0.074) (0.089) (0.098) (0.040) (0.112)

Liquidity 0.005 -0.012 0.009 0.001 0.006 0.000 0.004

(0.477) (0.388) (0.392) (0.844) (0.514) (0.989) (0.610)

Loans Ratio -0.027 -0.022 -0.014 -0.026 -0.025 -0.014 -0.021

(0.034) (0.103) (0.294) (0.029) (0.094) (0.308) (0.092)

Tangible Equity

Ratio

0.251 0.184 0.203 0.263 0.252 0.177 0.275

(0.103) (0.089) (0.119) (0.097) (0.092) (0.141) (0.080)

Depository Funding 0.043 0.019 0.034 0.021 0.030 0.025 0.028

(0.131) (0.340) (0.192) (0.388) (0.280) (0.342) (0.276)

Issue 0.005 0.003 0.012 0.003 0.006 0.003 0.004

(0.346) (0.486) (0.127) (0.471) (0.361) (0.431) (0.435)

News -0.004 -0.007 -0.004 -0.006 -0.005 -0.005 -0.006

(0.369) (0.281) (0.332) (0.296) (0.396) (0.193) (0.223)

Numest -0.007 -0.009 -0.006 -0.008 -0.008 -0.011 -0.007

(0.001) (0.003) (<.0001) (0.001) (0.000) (0.009) (0.002)

Moral Hazard -0.001 0.014 0.008 0.011 0.011 0.007 0.011

(0.801) (0.073) (0.003) (0.055) (0.031) (0.035) (0.045)

77

Sensitivity -0.010 -0.012 -0.009 -0.011 -0.011 -0.022 -0.020

(0.162) (0.114) (0.195) (0.124) (0.136) (0.142) (0.164)

Tabular 0.000 0.000 0.002 0.000 0.000 0.002 0.000

(0.984) (0.820) (0.402) (0.861) (0.820) (0.267) (0.958)

Constant 0.041 -0.166 -0.095 -0.075 -0.103 -0.089 -0.065

(0.691) (0.099) (0.168) (0.064) (0.051) (0.232) (0.273)

Adj. R2 28.22% 28.33% 28.15% 28.10% 28.13% 28.40% 28.48%

N 417 417 417 417 417 414 414

78

TABLE 7. Additional Evidence from the Financial Crisis of 2007-2009

I provide additional evidence of the impact of VaR disclosure informativeness (VaRDIS) on the cost of equity capital (CofC) during the 2007-2009 financial crisis by estimating the following pooled regression equation: CofCi,t = β0 + β1VaRDISi,t * Crisis + β2VaRDISi,t * Non_Crisis + β3Crisis (3) + β4CompCG + β5 Betai,t + β6BMi,t + β7Sizei,t + β8Trading Assets Ratioi,t

+ β9Capital Ratioi,t + β10Liquidityi,t + β11Loans Ratioi,t + β12Tangible Equity Ratioi,t

+ β13Depository Fundingi,t + β14Issuei,t + β15Newsi,t + β16Numesti,,t

+ β17Moral Hazardi,t + β18 Sensitivityi,t + β19Tabulari,t + εi,t

Crisis is a dummy variable equal to 1 for the financial crisis period of years 2007 and 2008, and 0 otherwise. Non_Crisis is a dummy variable equal to 1 for the non-crisis period from years 1997 to 2006, and 0 otherwise. Pooled regression controls for clustering by firm. The p-values, in parentheses, are based on two-tailed tests. All other variables are defined in Appendix C.

Independent Variable Dependent Variable: CofC

Coefficient p value

VaRDIS*Crisis -0.058 (0.083)

VaRDIS*Non_Crisis -0.024 (0.013)

Crisis 0.039 (0.058)

CompCG -0.012 (0.482)

Beta 0.000 (0.877)

BM 0.130 (<.0001)

Size 0.011 (<.0001)

Trading Assets Ratio -0.040 (0.056)

Capital Ratio 0.002 (0.093)

Liquidity -0.013 (0.148)

Loans Ratio -0.004 (0.779)

Tangible Equity Ratio 0.017 (0.159)

Depository Funding 0.004 (0.846)

Issue -0.003 (0.416)

News -0.006 (0.094)

Numest -0.002 (0.394)

Moral Hazard 0.009 (0.058)

Sensitivity -0.009 (0.241)

Tabular 0.014 (0.008)

Constant -0.282 (0.001)

Adj. R2 50.42%

N 511

79

TABLE 8. Comparisons of VaRs and VaR Related Disclosure Scores between the Banks with Large Financial-crisis Losses and the

Banks with Small Financial-crisis Losses

Panel A:

The sample banks partitioned by the median percentage change in the provision of credit loss from fiscal years 2006 to 2007.

Panel A reports means and medians of 2007 VaRs and VaR related disclosure scores for each of the two groups of the sample banks with large and small financial-crisis losses in year 2007. The two groups are classified, according to the percentage changes in provision of credit loss from fiscal years 2006 to 2007. VaR is the self-reported adjusted ending trading VaR amount of each bank, scaled by market value of equity. The VaR related disclosure scores include VaRDIS_Index, Stress Testing (Category VIII under VaRDIS_Index) score, and Limitations of VaR (Catergory IX under VaRDIS_Index) score. I use a t-test and a Wilcoxon rank sum test to assess whether the means and medians of VaRs and VaR related disclosure scores are different between the two groups, and report the two-sided p-values in the last two columns.

Group with Large Financial-crisis

Losses

Group with Small Financial-crisis

Losses

Diff. p-value

N Mean Median

N Mean Median

Mean Median

VaR 21 0.0007 0.0004

28 0.0023 0.0004

0.344 0.991

VaRDIS_Index 30 12.93 11

30 17.47 19

0.051 0.053

Category VIII: Stress Test 30 1.1 1

30 1.6 2

0.091 0.113

Category IX: Limitations of VaR 30 0.35 0

30 0.63 1

0.027 0.036

80

Panel B: The sample banks partitioned by the median percentage change in provision of credit loss from fiscal years 2007 to 2008. Panel B reports means and medians of 2008 VaRs and VaR related disclosure scores for each of the two groups of the sample banks with large and small financial-crisis loss in year 2008. The two groups are classified, according to the percentage changes in provision of credit loss from fiscal years 2007 to 2008. VaR is the self-reported adjusted ending trading VaR amount of each bank, scaled by market value of equity. The VaR related disclosure scores include VaRDIS_Index, Stress Testing (Category VIII under VaRDIS_Index) score, and Limitations of VaR (Catergory IX under VaRDIS_Index) score. I use a t-test and a Wilcoxon rank sum test to assess whether the means and medians of VaRs and VaR related disclosure scores are different between the two groups, and report the two-sided p-values in the last two columns.

Group with Large Financial-crisis

Losses

Group with Small Financial-crisis

Losses

Diff. p-value

N Mean Median

N Mean Median

Mean Median

VaR 21 0.0018 0.0015

24 0.0021 0.0014

0.549 0.707

VaRDIS_Index 28 13.21 12

27 18.59 21

0.033 0.028

Category VIII: Stress Test 28 1.14 1

27 1.82 2

0.027 0.031

Category IX: Limitations of VaR 28 0.39 0

27 0.74 1

0.009 0.01

81

TABLE 9. Using Analysts' Forecast Dispersion as an Alternative Measure of Corporate

Information Environment

Each year from 1997 to 2006, I estimate the cross-sectional relation between cost of equity capital estimate (CofC) and VaR disclosure informativeness (VaRDIS): CofCi,t = β0 + β1VaRDISi,t + β2CompCGi,t + β3Betai,t + β4BMi,t + β5Sizei,t

+ β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Forecast Dispersioni,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: CofC

Coefficient p value

VaRDIS -0.017 (0.035)

CompCG 0.016 (0.193)

Beta 0.009 (0.020)

BM 0.060 (0.004)

Size 0.001 (0.140)

Trading Assets Ratio -0.020 (0.115)

Capital Ratio -0.003 (0.188)

Liquidity -0.005 (0.423)

Loans Ratio -0.017 (0.034) Tangible Equity Ratio 0.171 (0.180) Depository Funding 0.030 (0.265)

Issue 0.007 (0.455)

News -0.003 (0.231)

Forecast Dispersion 4.157 (0.008)

Moral Hazard 0.026 (0.044)

Sensitivity -0.014 (0.358)

Tabular 0.002 (0.276)

Constant -0.009 (0.862)

Adj. R2 32.52%

N 344

82

TABLE 10. Using VaR as an Additional Measure of Bank-wide Risk

Panel A:

In this panel, I estimate the cross-sectional relation between VaR disclosure informativeness (VaRDIS) and overall governance mechanism (CompCG) from 1997 to 2008, controlling for VaR: VaRDISi, t = α0 + α1CompCGi,t + α2VaRi,t + α3Betai,t + α4BM i,t + α5Sizei,t

+ α6 Trading Assets Ratioi,t + α7Capital Ratioi,t + α8Liquidityi,t + α9Loans Ratioi,t + α10Tangible Equity Ratioi,t + α11Depository Fundingi,t + α12Issuei,t + α13Newsi,,t + α14Sensitivityi,t + α15Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: VaRDIS

Coefficient p value

CompCG 0.106 (0.042) VaR 29.734 (0.084)

Beta 0.039 (0.141)

BM 0.215 (0.171)

Size 0.079 (<.0001)

Trading Assets Ratio 0.448 (0.005)

Capital Ratio -0.016 (0.129)

Liquidity 0.005 (0.975)

Loans Ratio -0.430 (0.002) Tangible Equity Ratio -0.038 (0.855) Depository Funding 0.408 (0.019)

Issue 0.005 (0.931)

News 0.059 (0.044)

Sensitivity -0.050 (0.195)

Tabular 0.015 (0.263)

Constant -1.488 (0.001)

Adj. R2 55.87%

N 466

Panel B: In this panel, I estimate the cross-sectional relation between cost of equity capital estimate (DepV) and VaR disclosure informativeness (VaRDIS), from 1997 to 2006, by using different cost of equity capital estimates and controlling for VaR: DepVi,t = β0 + β1VaRDISi,t + β2VaRi,t + β3CompCGi,t + β4Betai,t + β5BMi,t + β6Sizei,t

+ β7Trading Assets Ratioi,t + β8Capital Ratioi,t + β9Liquidityi,t + β10Loans Ratioi,t

+ β11Tangible Equity Ratioi,t + β12Depository Fundingi,t + β13Issuei,t + β14Newsi,t

+ β15Numesti,,t + β16Moral Hazardi,t + β17Sensitivityi,t + β18Tabulari,t + εi,t

DepV is CofC (PEG estimate in Easton [2004]), MPEGr

in Easton (2004), GLSr

in Gebhardt et al. (2001) and GMr

in Gode and Mohanram (2003). I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All other variables are defined in Appendix C.

83

Independent Variable Dependent Variable: DepV

Model 1 Model 2 Model 3 Model 4

CofC (PEG estimate) rMPEG rGLS rGM

VaRDIS -0.020 -0.027 -0.015 -0.042

(0.085) (0.052) (0.025) (0.013)

VaR 9.070 6.768 -0.114 0.397

(0.133) (0.289) (0.940) (0.887)

CompCG 0.007 0.0189 0.014 0.0319

(0.407) (0.262) (0.214) (0.130)

Beta 0.014 0.009 0.001 0.017

(0.006) (0.035) (0.909) (0.037)

BM 0.002 0.102 0.097 0.101

(0.968) (0.063) (0.046) (0.114)

Size 0.007 0.001 -0.002 -0.002

(0.094) (0.945) (0.675) (0.749)

Trading Assets Ratio -0.060 0.017 0.024 0.080

(0.043) (0.713) (0.269) (0.183)

Capital Ratio -0.002 -0.003 -0.005 -0.001

(0.509) (0.006) (0.129) (0.439)

Liquidity 0.012 0.021 0.045 0.057

(0.583) (0.533) (0.272) (0.302)

Loans Ratio 0.029 0.033 0.035 0.100

(0.569) (0.504) (0.220) (0.217)

Tangible Equity Ratio -0.096 -0.133 0.032 -0.263

(0.060) (0.045) (0.163) (0.059)

Depository Funding -0.047 0.045 0.047 0.002

(0.514) (0.260) (0.237) (0.964)

Issue -0.003 -0.007 -0.004 -0.020

(0.544) (0.236) (0.379) (0.009)

News -0.002 -0.003 0.002 -0.009

(0.573) (0.193) (0.620) (0.343)

Numest -0.009 0.000 0.001 -0.004

(0.211) (0.964) (0.767) (0.205)

Moral Hazard 0.004 -0.001 0.006 0.003

(0.323) (0.788) (0.092) (0.468)

Sensitivity -0.025 -0.033 -0.006 -0.021

(0.144) (0.121) (0.038) (0.271)

Tabular -0.001 -0.001 -0.001 0.000

(0.311) (0.307) (0.477) (0.266)

Constant -0.041 0.089 0.056 0.092

(0.701) (0.458) (0.516) (0.317)

Adj. R2 18.69% 25.16% 30.94% 19.65%

N 303 301 301 301

84

TABLE 11. Firm Fixed Effects Specification

Panel A:

From 1997 to 2008, I provide the cross-sectional relation between VaR disclosure informativeness (VaRDIS) and overall governance mechanism (CompCG) by estimating the panel data least squares regression equation: VaRDISi, t = α0 + α1CompCGi,t + α2Betai,t + α3BM i,t + α4Sizei,t

+ α5 Trading Assets Ratioi,t + α6Capital Ratioi,t + α7Liquidityi,t + α8Loans Ratioi,t + α9Tangible Equity Ratioi,t + α10Depository Fundingi,t + α11Issuei,t + α12Newsi,,t + α13Sensitivityi,t + α14Tabulari,t + Firm Fixed Effects + εi,t The specification includes firm fixed effects. The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: VaRDIS

Coefficient p value

CompCG 0.096 (0.123)

Beta 0.013 (0.074)

BM 0.000 (0.985)

Size -0.015 (0.099)

Trading Assets Ratio -0.048 (0.655)

Capital Ratio -0.004 (0.264)

Liquidity 0.044 (0.335)

Loans Ratio 0.203 (0.006) Tangible Equity Ratio 0.038 (0.686) Depository Funding

0.072 (0.219)

Issue -0.008 (0.614)

News -0.002 (0.858)

Sensitivity -0.027 (0.421)

Tabular 0.023 (0.707)

Constant 0.264 (0.239)

Firm Fixed Effects Yes

Adj. R2 87.24%

N 606

85

Panel B: From 1997 to 2006, I provide the cross-sectional relation between cost of equity capital estimate (CofC) and VaR disclosure informativeness (VaRDIS) by estimating the panel data least squares regression equation:. CofCi,t = β0 + β1VaRDISi,t + β2CompCGi,t + β3Betai,t + β4BMi,t + β5Sizei,t

+ β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Numesti,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + Firm Fixed Effects + εi,t The specification includes firm fixed effects. The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: CofC

Coefficient p value

VaRDIS -0.023 (0.075)

CompCG 0.016 (0.354)

Beta -0.001 (0.658)

BM 0.055 (<.0001)

Size 0.001 (0.720)

Trading Assets Ratio -0.124 (0.000)

Capital Ratio -0.001 (0.323)

Liquidity -0.004 (0.723)

Loans Ratio -0.002 (0.947) Tangible Equity Ratio 0.003 (0.921) Depository Funding 0.015 (0.381)

Issue 0.001 (0.829)

News -0.005 (0.090)

Numest -0.002 (0.566)

Moral Hazard -0.046 (0.524)

Sensitivity -0.010 (0.199)

Tabular 0.002 (0.919)

Constant 0.294 (0.489)

Firm Fixed Effects Yes

Adj. R2 47.91%

N 414

86

TABLE 12. The First Principal Component of VaR Disclosure Informativeness Measure

Panel A: Each year from 1997 to 2008, I estimate the cross-sectional relation between annual percentile rank of the first principal component of VaRDIS (First Principal Component - VaRDIS) and overall governance mechanism (CompCG): First Principal Component -VaRDISi, t = α0 + α1CompCGi,t + α2Betai,t + α3BM i,t + α4Sizei,t

+ α5 Trading Assets Ratioi,t + α6Capital Ratioi,t + α7Liquidityi,t + α8Loans Ratioi,t + α9Tangible Equity Ratioi,t + α10Depository Fundingi,t + α11Issuei,t + α12Newsi,,t + α13Sensitivityi,t + α14Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: First Principal Component - VaRDIS

Coefficient p value

CompCG 0.366 (<.0001)

Beta 0.055 (0.264)

BM 0.216 (0.055)

Size 0.044 (0.006)

Trading Assets Ratio 0.545 (0.013)

Capital Ratio -0.004 (0.594)

Liquidity 0.044 (0.658)

Loans Ratio -0.411 (0.001) Tangible Equity Ratio 0.307 (0.389) Depository Funding 0.127 (0.444)

Issue -0.119 (0.001)

News 0.043 (0.068)

Sensitivity -0.107 (0.003)

Tabular -0.126 (0.312)

Constant -0.636 (0.116)

Adj. R2 39.39%

N 606

87

Panel B: Each year from 1997 to 2006, I estimate the cross-sectional relation between cost of equity capital estimate (CofC) and annual percentile rank of the first principal component of VaRDIS (First Principal Component - VaRDIS): CofCi,t = β0 + β1First Principal Component-VaRDISi,t + β2CompCGi,t + β3Betai,t + β4BMi,t + β5Sizei,t

+ β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Numesti,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: CofC

Coefficient p value

First Principal Component - VaRDIS -0.015 (0.022)

CompCG 0.013 (0.331)

Beta 0.015 (0.008)

BM 0.081 (0.003)

Size 0.005 (0.013)

Trading Assets Ratio -0.036 (0.038)

Capital Ratio -0.003 (0.170)

Liquidity 0.005 (0.452)

Loans Ratio -0.013 (0.185) Tangible Equity Ratio 0.287 (0.058) Depository Funding 0.028 (0.283)

Issue 0.008 (0.214)

News -0.003 (0.299)

Numest -0.006 (0.001)

Moral Hazard 0.009 (0.001)

Sensitivity -0.018 (0.166)

Tabular -0.001 (0.645)

Constant -0.069 (0.196)

Adj. R2 27.34%

N 414

88

TABLE 13. U.S. and Non-U.S. Incorporated Banks

Panel A: coefficient on CompCG only

Each year from 1997 to 2008, I estimate the cross-sectional relation between VaR disclosure informativeness (VaRDIS) and overall governance mechanism (CompCG) for the two subsamples of U.S. and non-U.S. incorporated banks, respectively: VaRDISi, t = α0 + α1CompCGi,t + α2Betai,t + α3BM i,t + α4Sizei,t

+ α5 Trading Assets Ratioi,t + α6Capital Ratioi,t + α7Liquidityi,t + α8Loans Ratioi,t + α9Tangible Equity Ratioi,t + α10Depository Fundingi,t + α11Issuei,t + α12Newsi,,t + α13Sensitivityi,t + α14Tabulari,t + εi,t I report only the mean of the annual coefficient estimates of CompCG for each subsample, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Sample Dependent Variable: VaRDIS

Coefficient on CompCG p value

U.S. subsample 0.130 (0.124)

Non-U.S. subsample 0.653 (0.071)

Panel B: coefficient on VaRDIS only

Each year from 1997 to 2006, I estimate the cross-sectional relation between cost of equity capital estimate (CofC) and VaR disclosure informativeness (VaRDIS) for the two subsamples of U.S. and non-U.S. incorporated banks, respectively: CofCi,t = β0 + β1VaRDISi,t + β2CompCGi,t + β3Betai,t + β4BMi,t + β5Sizei,t

+ β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Numesti,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + εi,t I report only the mean of the annual coefficient estimates of VaRDIS for each subsample, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Sample Dependent Variable: CofC

Coefficient on VaRDIS p value

U.S. subsample -0.010 (0.089)

Non-U.S. subsample -0.016 (0.205)

89

TABLE 14. VaR Disclosure Informativeness and Cost of Equity Capital, after Controlling for the

Six Governance Characteristics Simultaneously Each year from 1997 to 2006, I estimate the cross-sectional relation between cost of equity capital estimate (CofC) and VaR disclosure informativeness (VaRDIS). CofCi,t = β0 + β1VaRDISi,t + β2Antidiri,t +β3BoardSizei,t + β4BoardIndepi,t + β5RiskComi,t

+ β6RiskComIndepi,t + β7Instiowneri,t + β8Betai,t +β9BMi,t + β10Sizei,t

+ β11Trading Assets Ratioi,t + β12Capital Ratioi,t + β13Liquidityi,t + β14Loans Ratioi,t

+ β15Tangible Equity Ratioi,t + β16Depository Fundingi,t + β17Issuei,t + β18Newsi,t

+ β19Numesti,,t + β20Moral Hazardi,t + β21Sensitivityi,t + β22Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: CofC

Coefficient p value

VaRDIS -0.02 (0.078)

Antidir 0.016 (0.236)

BoardSize -0.001 (0.605)

BoardIndep 0.052 (0.166)

RiskCom -0.002 (0.812)

RiskComIndep 0.005 (0.627)

InstiOwner 0.03 (0.102)

Beta 0.021 (0.002)

BM 0.079 (0.001)

Size 0.009 (0.016) Trading Assets Ratio -0.054 (0.055)

Capital Ratio -0.001 (0.311)

Liquidity -0.021 (0.326)

Loans Ratio -0.017 (0.246) Tangible Equity Ratio 0.056 (0.392) Depository Funding 0.026 (0.350)

Issue 0.016 (0.086)

News -0.005 (0.401)

Numest -0.012 (0.025)

Moral Hazard 0.014 (0.139)

Sensitivity -0.02 (0.136)

Tabular 0.001 (0.556)

Constant -0.281 (0.119)

Adj. R2 20.27%

N 414

90

TABLE 15. Orthogonalized VaR Disclosure Informativeness and Cost of Equity Capital Each year from 1997 to 2006, I estimate the cross-sectional relation between cost of equity capital estimate (CofC) and orthogonalized VaRDIS (Orthogonalized_VaRDIS): CofCi,t = β0 + β1 Orthogonalized_VaRDISi,t + β2CompCGi,t + β3Betai,t + β4BMi,t + β5Sizei,t

+ β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Numesti,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + εi,t I report the mean of the annual coefficient estimates, and assess statistical significance using the time-series standard errors of these estimates (Fama and MacBeth [1973]). I further adjust the Fama-MacBeth standard errors for autocorrelation following Newey and West (1987). The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent Variable Dependent Variable: CofC

Coefficient p value

Orthogonalized_VaRDIS -0.020 (0.001)

CompCG 0.010 (0.319)

Beta 0.014 (0.025)

BM 0.071 (0.001)

Size 0.003 (0.050)

Trading Assets Ratio -0.048 (0.003)

Capital Ratio -0.004 (0.146)

Liquidity 0.003 (0.604)

Loans Ratio -0.005 (0.366) Tangible Equity Ratio 0.216 (0.217) Depository Funding 0.032 (0.276)

Issue 0.009 (0.247)

News -0.007 (0.106)

Numest -0.007 (0.000)

Moral Hazard 0.012 (0.021)

Sensitivity -0.016 (0.224)

Tabular 0.000 (0.922)

Constant -0.032 (0.554)

Adj. R2 25.66%

N 414

91

TABLE 16. Pooled Regressions Controlling for Clustering by Firm and Year

Panel A:

From 1997 to 2008, I provide the cross-sectional relation between VaR disclosure informativeness (VaRDIS) and corporate governance mechanisms (Governance Attribute) by estimating the following pooled regression equation: VaRDISi, t = α0 + α1Governance Attributei,t + α2Betai,t + α3BM i,t + α4Sizei,t

+ α5 Trading Assets Ratioi,t + α6Capital Ratioi,t + α7Liquidityi,t + α8Loans Ratioi,t + α9Tangible Equity Ratioi,t + α10Depository Fundingi,t + α11Issuei,t + α12Newsi,,t + α13Sensitivityi,t + α14Tabulari,t + εi,t Pooled regression controls for clustering by firm and year. The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent

Variable Dependent Variable: VaRDIS

Alternative Measures of Corporate Governance

Antidir BoardSize BoardIndep RiskCom RiskComIndep InstiOwner CompCG

Governance

Attribute

0.060 0.008 0.272 0.037 0.040 0.088 0.228

(0.042) (0.001) (0.006) (0.096) (0.088) (0.263) (0.038)

Beta 0.023 0.023 0.026 0.024 0.023 0.026 0.024

(0.020) (0.021) (0.010) (0.024) (0.025) (0.013) (0.019)

BM 0.128 0.141 0.147 0.133 0.126 0.145 0.131

(0.011) (0.004) (0.007) (0.009) (0.014) (0.006) (0.009)

Size 0.058 0.065 0.071 0.065 0.065 0.069 0.062

(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

Trading Assets

Ratio 0.586 0.557 0.652 0.591 0.574 0.581 0.602

(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

Capital Ratio -0.029 -0.026 -0.022 -0.024 -0.024 -0.025 -0.021

(0.000) (0.002) (0.004) (0.003) (0.002) (0.001) (0.007)

Liquidity -0.163 -0.158 -0.169 -0.173 -0.174 -0.170 -0.173

(0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000)

Loans Ratio -0.594 -0.527 -0.542 -0.564 -0.558 -0.533 -0.519

(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

Tangible Equity

Ratio -0.629 -0.531 -0.618 -0.601 -0.600 -0.611 -0.572

(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

Depository

Funding 0.091 0.092 0.072 0.083 0.076 0.095 0.095

(0.201) (0.217) (0.360) (0.296) (0.330) (0.217) (0.214)

Issue -0.066 -0.054 -0.070 -0.072 -0.073 -0.075 -0.074

(0.002) (0.014) (0.001) (0.001) (0.001) (0.001) (0.000)

News 0.044 0.047 0.051 0.045 0.045 0.049 0.041

(0.035) (0.023) (0.013) (0.034) (0.033) (0.020) (0.052)

Sensitivity -0.125 -0.134 -0.157 -0.134 -0.135 -0.118 -0.130

(0.010) (0.007) (0.002) (0.008) (0.009) (0.020) (0.013)

Tabular 0.254 0.250 0.274 0.236 0.239 0.265 0.247

(0.113) (0.079) (0.090) (0.142) (0.133) (0.102) (0.122)

Constant -0.820 -0.869 -1.121 -0.739 -0.755 -0.878 -0.828

(0.010) (0.006) (0.001) (0.023) (0.020) (0.007) (0.010)

Adj. R2 51.60% 51.82% 52.02% 50.91% 50.92% 51.42% 52.57%

N 617 617 617 617 617 606 606

92

Panel B: From 1997 to 2006, I provide the cross-sectional relation between cost of equity capital estimate (CofC) and VaR disclosure informativeness (VaRDIS) by estimating the following pooled regression equation: CofCi,t = β0 + β1VaRDISi,t + β2Governance Attributei,t + β3Betai,t + β4BMi,t + β5Sizei,t

+ β6Trading Assets Ratioi,t + β7Capital Ratioi,t + β8Liquidityi,t + β9Loans Ratioi,t

+ β10Tangible Equity Ratioi,t + β11Depository Fundingi,t + β12Issuei,t + β13Newsi,t

+ β14Numesti,,t + β15Moral Hazardi,t + β16Sensitivityi,t + β17Tabulari,t + εi,t Pooled regression controls for clustering by firm and year. The p-values, in parentheses, are based on two-tailed tests. All variables are defined in Appendix C.

Independent

Variable Dependent Variable: CofC

Alternative Measures of Corporate Governance

Antidir BoardSize BoardIndep RiskCom RiskComIndep InstiOwner CompCG

VaRDIS -0.016 -0.014 -0.016 -0.015 -0.015 -0.014 -0.014

(0.005) (0.003) (0.001) (0.003) (0.003) (0.005) (0.006)

Governance

Attribute -0.006 -0.001 0.010 -0.002 -0.001 0.010 0.008

(0.096) (0.308) (0.399) (0.542) (0.883) (0.304) (0.478)

Beta 0.002 0.002 0.002 0.002 0.002 0.002 0.002

(0.258) (0.243) (0.218) (0.240) (0.240) (0.232) (0.261)

BM 0.067 0.070 0.071 0.071 0.071 0.074 0.072

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Size 0.003 0.004 0.004 0.004 0.004 0.004 0.004

(0.037) (0.014) (0.010) (0.012) (0.012) (0.010) (0.012)

Trading Assets

Ratio -0.026 -0.026 -0.025 -0.028 -0.027 -0.030 -0.029

(0.103) (0.217) (0.203) (0.177) (0.185) (0.127) (0.151)

Capital Ratio -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(0.477) (0.576) (0.653) (0.572) (0.595) (0.503) (0.636)

Liquidity -0.005 -0.004 -0.004 -0.004 -0.004 -0.004 -0.004

(0.482) (0.594) (0.650) (0.656) (0.656) (0.670) (0.650)

Loans Ratio -0.006 -0.008 -0.007 -0.006 -0.007 -0.005 -0.006

(0.570) (0.412) (0.458) (0.518) (0.490) (0.620) (0.541)

Tangible Equity

Ratio 0.009 0.011 0.011 0.013 0.013 0.015 0.014

(0.435) (0.244) (0.213) (0.187) (0.180) (0.196) (0.198)

Depository

Funding

-0.005 -0.001 0.001 0.001 0.001 0.001 0.000

(0.699) (0.951) (0.958) (0.965) (0.947) (0.946) (0.975)

Issue -0.002 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001

(0.674) (0.659) (0.792) (0.842) (0.819) (0.776) (0.855)

News -0.004 -0.003 -0.003 -0.003 -0.003 -0.003 -0.003

(0.101) (0.214) (0.206) (0.225) (0.226) (0.195) (0.185)

Numest -0.004 -0.005 -0.006 -0.006 -0.006 -0.007 -0.005

(0.168) (0.023) (0.012) (0.013) (0.017) (0.006) (0.018)

Moral Hazard 0.006 0.007 0.006 0.007 0.007 0.006 0.007

(0.085) (0.085) (0.148) (0.091) (0.106) (0.188) (0.120)

Sensitivity -0.010 -0.009 -0.010 -0.009 -0.009 -0.010 -0.010

(0.041) (0.084) (0.036) (0.069) (0.070) (0.064) (0.066)

Tabular 0.015 0.013 0.015 0.015 0.014 0.014 0.014

93

(0.000) (0.000) (0.001) (0.009) (0.006) (0.001) (0.003)

Constant 0.012 -0.042 -0.061 -0.052 -0.051 -0.050 -0.048

(0.766) (0.442) (0.259) (0.333) (0.341) (0.328) (0.340)

Adj. R2 26.84% 25.75% 25.51% 25.48% 25.46% 25.73% 25.70%

N 417 417 417 417 417 414 414