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Sovereign credit risk, banks’ government support, and bank stock

returns around the world

Richard Correa, Board of Governors Kuan-Hui Lee, Seoul National University Horacio Sapriza, Federal Reserve Board Gustovao Suarez, Federal Reserve Board

March 2012

Abstract

Banking crises have been largely associated with large output and welfare losses, and bank bailouts by the public sector are a recurring feature of financial crises. Such stylized facts underscore the importance of a well-functioning financial system for attaining economic stability and growth, as well as the relevance of understanding the relationship between the economic conditions faced by the government and the banking sector. In particular, differences and changes in explicit (and implicit) government support to banks may affect investors’ incentives to hold bank stocks, and thus impact banks’ external financing costs, which may send ripples through the rest of the economy. Similarly, sovereign debt rating changes may unveil new information about a country’s fundamentals, generating a significant externality for the country’s banking system, and thus they also affect investors’ incentives to hold bank stocks. We explore the joint impact of sovereign debt rating changes and government support on bank stock returns from 36 countries between 1995 and 2011. Our findings show that sovereign rating changes have a significant and robust impact on bank stock returns. The impact is nonlinear and varies across banks and countries. Moreover, we find that the effect is asymmetric and stronger for downgrades than for upgrades, and that large downgrades have a particularly strong negative impact on returns. Importantly, this result is significantly stronger for banks with more ex-ante government support, providing evidence that investors perceive sovereigns and domestic banks as markedly interconnected. Keywords: sovereign bond; credit rating; banks stock returns; government guarantee JEL Classification: G21, G24, H63, G14

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I. Introduction

The banking system is the key source of consumer and corporate financing around the

world, and the 2008 crisis highlighted the extent to which various governments are

willing to support the sector, making it clear that in order to understand the dynamics of

the banking sector, it is important to consider the economic fundamentals of the

sovereign. For instance, the recent European fiscal crisis provides evidence that weak

sovereigns are a drag for the banking sector.1 As public finances deteriorated in some

countries, markets assumed that banks headquartered in these countries would get lower

sovereign support. As a result, these banks’ cost of funding increased and their market

access decreased.

This paper studies the link between government support of individual banks’ and

their stock returns. We identify the effect of government support on bank external

financing by focusing on events when the rating of sovereigns change, as these episodes

provide information about the likelihood of bank support in the future. Additionally, we

test whether bank support has differential effects on banks in emerging and advanced

economies.

Sovereign debt ratings are assessments of the probability of default in government

debt. When rating a sovereign bond, credit rating agencies state that they consider a large

number of economic and political factors and make qualitative and quantitative

evaluations. Via this procedure, sovereign debt rating changes may unveil new

information about a country, imposing a significant externality to the country’s private

sector. For example, Borensztein, Cowan, and Valenzuela (2007) and Gande and Parsley

(2005) document that sovereign debt constitutes a relevant benchmark for domestic

interest rates, affecting the cost of corporate borrowings. For banks, as noted by the

Committee on the Global Financial System (2011), there is a close link between the

sovereign’s rating and domestic banks’ ratings. Sovereign rating downgrades are usually

followed by bank rating downgrades, as these events usually signal a deterioration in

1 Causality may also go the other way, because a sovereign’s finances may deteriorate as it provides support to its troubled banking sector.

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domestic macroeconomic and fiscal conditions and also, a decrease in the sovereign’s

capacity to support domestic banks.

The literature on sovereign rating changes and financial markets has mostly

focused on the effect of such externality on equity market returns (Kaminsky and

Schmukler, 2002; Brooks, Faff, Hilllier, and Hillier, 2002; Martell, 2005; and Lee,

Sapriza, and Wu, 2010), individual stock returns (Martell, 2005; and Lee, Spariza, and

Wu, 2010), or bond yields (Cantor and Packer, 1996; Larrain, Reisen, and von Maltzan,

1997; and Gande and Parsley, 2005). However, there have been no studies considering

the differential effect of changes in government sovereign ratings and support on banks’

stock returns.2 This paper fills this gap by studying the impact of changes in sovereign

credit ratings on daily stock returns at the individual bank level for 36 developed and

emerging markets from January 1995 to May 2011. Controlling for key bank-specific

and country level determinants of bank stock returns, we focus on the differential effect

of sovereign credit events on banks with varying levels of perceived bank-specific

government support. As Gande and Parsley (2005) point out, clustering of events, that is,

changes in sovereign debt ratings may contaminate event-windows, thereby reducing the

validity of analysis in an event study framework. Hence, we take a regression approach

for our empirical analyses.

Our results show that sovereign rating changes have a significant, non-linear and

robust impact on bank excess stock returns. Moreover, we find that the effect is

asymmetric and stronger for downgrades than for positive rating changes and that

downgrades of more than two rating notches also have a particularly strong negative

impact on bank stock returns. We explore how the transmission of the externality is

affected by its interaction with banks’ government support. In response to a negative

sovereign credit event, our results suggest that banks with more government support

before the event, tend to experience a significantly larger fall in excess stock returns.

This result is more pronounced when the sovereign experiences a larger downgrade.

Additionally, we find that the interaction between bank excess stock returns and

government support is stronger in cases when the sovereign rating of an advanced

2 Demirgüç-Kunt and Huizinga (2010) study a related question. Whether the market value of banks that become “too big to save” decreases as the public finances of the home sovereign deteriorate.

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economy is downgraded. However, the total effect of sovereign downgrades is larger for

banks in emerging economies.

A concern of our identification strategy is the potential correlation between a

banks’ perceived level of government support and its holdings of home-country

sovereign debt. Sovereign ratings events affect the value of sovereign debt. If a bank’s

holdings of sovereign debt are correlated with its perceived government support, changes

in its stock price after a sovereign event could be interpreted as being driven by changes

in the value of its assets rather than a change in the government guarantee. Using a

sample of European banks, we find that there is no statistical correlation between our

measure of government support and banks’ holding of their home-country sovereign debt.

The remainder of the paper is organized as follows. Section II reviews related

literature, Section III describes the data on bank returns, government support to banks,

and our definition of sovereign ratings events. Section IV presents the methodology and

the empirical results. In Section V, we analyze the relation between banks’ government

support and their holdings of sovereign debt. Section VI concludes.

II. Literature review

To the best of our knowledge, this study is the first to empirically investigate the joint

impact of sovereign debt rating changes and government support on bank stock returns.

Our work borrows from and contributes to the literature exploring the effect of sovereign

credit events on the private sector via financial markets.

A number of studies explore the effect of sovereign credit rating events on equity

market returns. Kaminsky and Schmukler (2002); Brooks, Faff, Hilllier, and Hillier (2002)

focus on the aggregate stock market implications, and Martell (2005) also addresses the

impact on individual stock returns. Lee, Sapriza and Wu (2010) uses a different

methodology to also study both dimensions, and additionally explores how different

country level characteristics affect the link between the sovereign events and the private

sector variables.

The literature that examines systematically the impact of sovereign debt ratings

expanded rapidly in the 1990s, with an important body of research focusing mainly on

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the effect on the instruments being rated. For instance, Cantor and Packer (1996) and

Larrain, Reisen and von Maltzan (1997) find a significant effect on bond yield spreads.

More recent studies explore the effect of sovereign ratings on private sector’s debt ratings

and interest rates. Borensztein, Cowan, and Valenzuela (2007) and Cavallo and

Valenzuela (2002) document the presence of a “sovereign ceiling lite,” whereby private

sector debt ratings tend to be below the sovereign’s debt rating, especially in emerging

markets where asymmetric information problems are more severe. Borensztein, Cowan

and Valenzuela (2007) highlight three channels through which the creditworthiness of the

government may affect that of the private sector: first, the negative impact that a

sovereign default has on the domestic economy on the whole, which undermines the

financial strength of the private sector broadly; second, a “spillover” effect from the

insolvency of the sovereign to private debtors; third, the imposition of direct capital

controls or other administrative measures that effectively prevent private borrowers from

servicing their external obligations when the sovereign reaches a situation of default or

near default. On account of the imposition of capital controls, the private sector always

defaults on its external obligations when the sovereign defaults, which provides a rational

for a sovereign ceiling.

Gande and Parsley (2005) analyze the effect of a sovereign credit rating shift in a

country on the sovereign credit spreads of other countries, and they find evidence of

cross-country spillover effects, that is, ratings changes in one economy significantly

affect sovereign credit spreads of other countries.

Ferri, Lui, and Majnoni (2001) and Ferri and Liu (2002) study empirically the

impact of sovereign ratings on private ratings directly. In particular, Ferri and Liu (2002)

estimate the impact on the firms’ credit ratings of sovereign ratings and firm-level

financial indicators. They find that sovereign ratings have a significant effect on private

ratings in emerging market economies. Surprisingly, they find that firm-level variables,

which were specified in a weighted average aggregate form, were generally statistically

insignificant. Although these studies investigate firm-level implications of sovereign

rating changes, none of them explores the link between such sovereign rating changes,

banks’ government support, and bank stock returns.

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III. Data

Sovereign events

We collect data on sovereign bond rating changes on long-term foreign currency-

denominated debt from January 1995 to May 2011. We use rating changes from

Standard & Poor’s (S&P), since they appear to be relatively more frequent, they tend not

to be anticipated by the market, and they tend to precede changes of other rating agencies

(Brooks et al., 2004; and Gande and Parsley, 2005).3

The numerical scales of credit ratings and credit outlooks are shown in the

Appendix. To capture any meaningful changes in ratings, we define the total numerical

value of a rating as the sum of the numeric value of an alphabetical rating and that for the

credit outlook. Then, an event is defined as a non-zero change in the total numerical

value of a rating.

The numerical conversion of credit ratings and outlook is similar to Appendix B of

Gande and Parsley (2010). However, we use a slightly different numeric scale for

outlook changes. The advantages of our adjusted scale can be illustrated with the

following example. On April 30, 2008, the rating of Brazil’s long-term foreign-currency

bond was upgraded from BB+/Positive outlook to BBB-/Stable outlook. According to

Gande and Parsley’s (2010) scale, the numeric value of the credit rating prior to April 30,

2008 is 12, which is obtained by the sum of 11 (BB+) and 1 (positive outlook). Despite

the effective upgrade, the numeric value of the rating in Gande and Parsley’s (2010) scale

is also 12 (BBB-) after April 30, 2008, since the numeric adjustment for a Stable outlook

is zero. In Gande and Parsley’s (2010) scale, the change in ratings (from BB+ to BBB-)

on April 30, 2008 would not be considered an event and would be dropped from the

sample. We find that this is not the only such case in our sample.

To overcome this problem, we define the numeric scale of outlook categories

differently from Gande and Parsley (2010) by assigning smaller numbers whose absolute

value is less than one. Specifically, we assign the values from -0.2 to 0.2 in increments

of 0.1 to five different outlook stages ascending from negative to positive outlook.

3 In estimations that are not shown, we also use sovereign rating assigned by Moody’s and determine that our results are robust. We use the same methodology to define an event.

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Bank stock returns

We calculate daily returns using the daily total return index from Datastream for

all stocks from 36 countries from January 1995 to May 2011.4 Out of 36 countries, 13

are advanced economies (Australia, Belgium, Canada, Denmark, Finland, Greece, Hong

Kong, Ireland, Italy, Japan, Spain, Sweden, and the United Kingdom) and the remaining

are emerging markets (Argentina, Brazil, Chile, China, Colombia, Hungary, India,

Indonesia, Israel, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Portugal, Russia,

South Africa, South Korea, Taiwan, Thailand, Turkey, and Venezuela).5 We also obtain

a global market return index from Datastream.

For a stock to be included in the sample, it should have market capitalization data

at the end of the year prior to the event year together with previous year-end book-to-

market ratio. We choose only stocks traded on major exchanges that have the majority of

stocks for that country.6 We use only common stocks by excluding stocks with special

features such as Depository Receipts (DRs), Real Estate Investment Trusts (REITs), and

preferred stocks.7, 8

Delisted stocks are maintained in the sample to avoid survivorship bias. Similar

to Ince and Porter (2006), we set the daily return as missing if any daily return above

100% (inclusive) is reversed the following day.9 The daily return is set to missing if

either the total return index for the previous day or that of the current day is less than 4 The return index for each stock is built under the assumption that dividends are re-invested. It is also adjusted for stock-splits. 5 The categorization of countries into developed or emerging markets follows the definition of the International Financial Corporation (IFC) of the World Bank Group. 6 Most countries have one major exchange except China (Shanghai and Shenzhen stock exchanges) and Japan (Osaka and Tokyo stock exchanges). 7 The exclusion of stocks with special features is performed manually by examining the names of the securities, given that Datastream/Worldscope do not provide any code for discerning such stocks from common stocks. Some examples of ‘name filters’ are as follows: in Belgium, shares of the types, AFV and VVPR (the types are named by Datastream/Worldscope), are dropped since they have preferential dividend or tax incentives; in Canada, income trusts are excluded by deleting stocks with names that include “INC.FD”; in Mexico, shares of the types, ACP and BCP, are removed since they have the special feature of being convertible into series A and B shares, respectively, after one year; in Italy, RSP shares are dropped due to the non-voting provisions. 8 Worldscope usually tracks one share for each firm and it is mostly the PN share in Brazil. Although PN shares are preferred stocks, they are not excluded in Brazil since they account for the majority of stocks in that country. 9 Specifically, the daily returns for both days t and t-1 are set to missing if 5.011,, ≤−−titi RR , where

tiR , and

1, −tiR are the gross returns for day t and t-1, respectively, and if at least one of the two is 200% or greater.

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0.01. We truncate 1 percent extreme observations based on daily return or trading

volume. The foreign exchange rate data from WM/Reuters are also obtained through

Datastream.10

Table 1 shows the number of events and the number of stock-day observations for

each country in our sample. To examine asymmetric effects of rating changes, we

decompose events into positive (upgrade), negative (downgrade), big-positive (upgrade

by 2 or more numeric scales), and big-negative (downgrade by 2 or more numeric scales)

events. Turkey experienced more rating changes than any other country in our sample

(28 times), followed by Argentina (19 times), Greece (16 times), and Malaysia (16 times).

Five countries experienced big-positive events, while seven countries have big-negative

events in the sample. In Table 2, we present counts of the number of banks per country

in the sample. Banks from Japan, Italy, and South Korea account for about 30 percent of

the sample.

INSERT TABLE 1 HERE

INSERT TABLE 2 HERE

Table 3 shows descriptive statistics of sample banks on event dates by country.

The numbers are averages across stock-day observations. As expected, average

sovereign ratings are higher for advanced economies. However, average stock returns on

event dates are mixed, with some advanced and emerging economy banks showing large

drops.

INSERT TABLE 3 HERE

Bank support

We measure bank support using bank-specific ratings information from Moody’s

Investors Service. Since 1995, this rating agency has assigned bank financial strength

ratings (BFSR) to banks in about 90 countries. According to Moody’s, BFSRs “are

10 Since the exchange rate against the U.S. dollar does not cover all the sample periods for some countries but the rate against the U.K. sterling does so, the U.S. dollar exchange rate is calculated by using the cross-rates through the pound sterling.

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intended to provide investors with a measure of a bank’s intrinsic safety and soundness

on an entity-specific basis” (Moody’s Investors Service, 2007). More importantly, this

measure does not include any external support that a bank may receive from its parent,

other institutions under a cooperative or mutual arrangement, or the government.

Moody’s also assigns a bank deposit rating to the banks it rates. This is the rating

agency’s opinion on a bank’s ability to repay its deposit obligations punctually. As such,

they incorporate both the bank’s BFSR rating and Moody’s opinion of any external

support.

In the main specifications, the bank-specific government support measure is

defined as the difference (in rating notches) between a bank’s BFSR and its long-term

foreign currency deposit rating. As a robustness check, we also define support as the gap

between a bank’s BFSR and its long-term local currency deposit rating. However, this

measure is missing for many banks because the local currency deposit rating is

unavailable.11

Figure 1 shows the evolution of average and median government support since

1996 for all banks included in the sample. Support tends to increase during periods of

economic distress, as it was the case during the East Asian and Russian crises of the late

1990s or the recent financial crisis.

INSERT FIGURE 1

IV. Results

Instead of estimating the effect sovereign rating changes on raw bank stock returns, we

focus on excess stock returns. This allows us to control for the systematic component of

returns. We estimate the market model with the global market return defined as a daily

stock return measure computed from Datastream’s global market index. The model is

11 Other studies have used a similar measure from Fitch to analyze, amongst other topics, the relation between government support, competition, and bank risk taking (Gropp, Hakenes, and Schnabel, 2011) and the usefulness of equity signals, controlling for government support, as an indicator of banks distress (Gropp, Vesala, and Vulpes, 2006).

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estimated for each bank and event in the window [-75,-6] day (past 70 days) with the

restriction that the stocks must have at least 50 observations in the estimation window.12

Then, we study the impact of sovereign ratings on bank stock returns using the

following regression equation:

, , , , , , ,i j t j t i j t j t i j tr Event Xα β δ θ µ ε= + + + + + (1)

where ��,�,� represents the excess stock return of bank i located in country j in period t in

the [-1,1] window (in days).13 Event is the numeric change of sovereign ratings and their

outlook. In some specifications, we evaluate the separate impact of upgrades (Event+)

and downgrades (Event-), where Event+ (Event-) is defined as the absolute value of

rating and outlook changes if the change is positive (negative), and zero otherwise. ��,�,�

denotes stock-specific controls: the lagged log value of banks’ market capitalization in

U.S. dollars, the lagged log value of the book-to-market ratio, and a measure for the

volatility of each banks’ stock return. Finally, �� and �� are country- and time-fixed

effects. We estimate equation (1) using ordinary least squares (OLS) and weighted least

squares (WLS).

The impact of changes in sovereign ratings on bank excess returns estimated with

equation (1) is illustrated in Table 4. The OLS and WLS regressions shown on columns

(2) and (6), respectively, indicate that sovereign rating events are associated with changes

in bank stock returns that are significant at the 1 percent level, after controlling for bank-

specific controls, year dummies, and country dummies.14

INSERT TABLE 4 HERE

The effect of sovereign rating changes on bank excess returns is, however,

asymmetric, as indicated by the results in columns (3,4) and columns (7,8) for the OLS

12 Our results are robust to using raw individual bank stock returns. 13 Our results are robust to wider windows including: [-1,2], [-1,3], and [-1,4]. 14 These results are comparable in magnitude to those found after bank M&A announcements (Cybo-Ottone and Murgia, 2000) and bank rating changes (Gropp and Richards, 2001).

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and WLS regressions, respectively: on the one hand, negative sovereign rating events

(Event (-)) are statistically significantly associated with lower bank excess returns. On

the other hand, for positive sovereign rating moves (Event (+)), changes in bank excess

returns are positive but not even significant at the 10 percent level in any of the

regressions. In other words, the results for all events (upgrades and downgrades) are

mostly explained by the negative effects of downgrades.

The government is the largest agent in most economies, and it may become a

source of stability when the financial sector is under stress, but such support may also

render the financial sector more dependent on the events affecting the sovereign. The net

effect of their link is explored by means of the following regression:

, , 1 , 2 , , 3 , , , , , , ,i j t j t i j t j t i j t i j t j t i j tr Event Support Event Support Xα β β β δ θ µ ε= + + + × + + + + (2)

where Support is the numeric difference, in notches, between the foreign currency long-

term deposit rating assigned to a bank by Moody’s, and the bank’s financial strength

rating, and all other variables are defined as in equation (2). In parallel with the results

presented in Table 4, we consider the asymmetric effects of downgrades and upgrades in

equation (2) by including Event+ and Event- and their interactions with the Support

variable.

The results of estimating equation (2) are summarized in Table 5. Columns (1,2)

and (5,6) indicate that during sovereign credit events, the presence of government support

has a statistically significant impact on the changes in bank excess returns. The

coefficient is significant for both OLS and WLS estimations, as well as for different

controls. Columns (3,4) and (7,8) show that, consistent with the results in Table 4,

negative sovereign rating events are significantly associated with lower bank excess stock

returns, but positive events are not. In addition, those banks that receive more support

from the government seem to experience significantly more negative returns.

INSERT TABLE 5 HERE

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The differential effect of sovereign rating events on bank excess stock returns for

banks with different perceived levels of government support are both statistically and

economically significant. Using the results from column (6) in Table 5, we find that a

bank in the 75th percentile of the government support distribution (2 notches of support)

would suffer a drop in its excess returns, after a 1 notch sovereign downgrade, that is 50

percent larger compared to a bank in the 25th percentile of the distribution (0 notches of

support).

Next, we consider nonlinear effects by analyzing the separate effects of large and

small rating changes. We define a large rating change as a movement of at least two

notches along our rating scale. The results of taking into account possible nonlinear

effects are presented in Table 6, with WLS as the estimating method. In essence, the

findings in Tables 4 and 5 are even more pronounced when governments experience

larger sovereign credit events. When sovereign credit ratings change by two or more

notches, events have more dramatic effects on bank stock returns, and government

support remains a relevant conditioning factor.

INSERT TABLE 6 HERE

Lastly, in Table 7 we divide the sample of banks between those located in

advanced (columns 1 through 4) and emerging (columns 5 through 8) economies. We

estimate the same specifications as in Table 5. As in the previous estimations, we find

that sovereign rating events mostly affect the excess returns of banks with more

perceived government support. The interaction between sovereign support and the

ratings event proxy (which includes both upgrades and downgrades) is significant for

banks in advanced and emerging economies. However, when we break down positive

and negative sovereign events (columns 3, 4, 7, and 8), we find that this interaction is

only statistically significant for the latter. Note that the direct effect of sovereign events

on excess stock returns is very weak for banks in advanced economies. This could be the

results of a larger degree of diversification for banks headquartered in these countries.

Thus, sovereign events would mostly affect banks through the government guarantee

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rather than through a direct effect of the sovereign’s policies (for example, changes in

domestic taxes or spending) on the profitability of the banks.

INSERT TABLE 7 HERE

V. Government support and banks’ holdings of home-country sovereign debt

As noted by the Committee on the Global Financial System (2011), the main

transmission channel of sovereign distress to banks is their holdings of sovereign debt. A

concern for our identification strategy is the potential correlation between our measure of

government support and banks’ holdings of their home-country’s sovereign debt. Any

changes in the rating or outlook of a country’s sovereign debt should affect its value. If a

bank has large holdings of its home-country sovereign debt, its stock return should fall

after a sovereign event due to the change in value of its sovereign holdings. Thus, we

have to check whether banks’ home-country holdings of sovereign debt are correlated

with the rating agency’s expectations of government support for banks.

There is very little information on banks’ geographical breakdown of their

holdings of sovereign debt. To test whether government support and sovereign debt

holdings are correlated, we focus on a sample of European banks that disclosed this

information in the 2011 E.U.-wide stress test coordinated by the European Banking

Authority (EBA). Banks reported the gross and net value of sovereign debt holdings

broken down by maturity and country of issuer as of end-2010. Out of the 90 banks that

participated in the stress test, Moody’s issues ratings information for 78 banks. Figure 2

shows, for this sample of banks, the ratings-based measure of government support in the

horizontal axis and the share of gross sovereign debt holdings divided by assets in the

vertical axis. The flat black line shows the regression line for these two variables, which

statistically implies that the correlation coefficient is not significantly different from zero.

INSERT FIGURE 2

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As noted above, data limitations prevent us from controlling for sovereign debt

holdings in our main estimations. However, the results shown for the sample of 78

European banks provide evidence that our measure of government support is not

statistically correlated with banks’ sovereign debt holdings. Thus, we can attribute the

differential effect of sovereign events on banks’ stock returns to expectations of

government support rather than to changes in values of sovereign debt holdings in banks’

balance sheets.

VI. Conclusion

The level of government support plays a significant role in determining the effect of

sovereign ratings events on bank stocks returns. We examine the presence of such effects

using bank-level data for a panel of countries over the last two decades. We find that

even after controlling for time, country, and bank specific factors, sovereign rating events

have a significant, non-linear and robust impact on bank stock returns, and that

government support to banks can significantly magnify or dampen the effect.

Additionally, we find that the effect is much stronger for sovereign downgrades than for

positive rating changes (asymmetry) and that large downgrades in sovereign ratings have

a particularly strong negative impact on bank stock returns (nonlinearity).

Thus, in focusing on the implications of changes in public debt conditions on

bank excess returns, our study highlights an additional transmission channel through

which the management of public debt affects the private sector. These findings are

particularly relevant in the context of the current European fiscal crisis. As the sovereign

finances of several countries deteriorated, the outlook of domestic banks in these

economies was negatively affected. In particular, access to market funding became

tighter for several banks. Part of this increase in external financing constraints is

explained by a lower market-assessed level of banks’ sovereign support. The rating of

several banks has been downgraded after the rating of their own sovereign was lowered.

Finally, the results in this paper also show that government support of banks not

only benefits senior bank debt holders but also their equity holders. By reducing banks’

cost of debt funding, government support may increase bank profitability and, in some

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cases, induce banks to take greater risk, increasing their equity holders’ returns.

However, if economic conditions deteriorate, these same banks may be vulnerable to

large losses and become a fiscal drag for their sovereign.

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Appendix: Numerical scales of credit ratings

The table shows numerical scales of S&P sovereign bond rating together with credit outlook. The overall numerical value of credit ratings is the sum of numeric value for sovereign ratings and that for credit outlook.

Sovereign bond rating NumericAAA 21AA+ 20AA 19AA- 18A+ 17A 16A- 15BBB+ 14BBB 13BBB- 12BB+ 11BB 10BB- 9B+ 8B 7B- 6CCC+ 5CCC 4CCC- 3CC 2C 1SD, D 0Outlook NumericPositive 0.2Watch Developing 0.1Stable 0Watch Negative -0.1Negative -0.2

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20

Table 1 Number of events by country

The table shows the number of sovereign rating changes (events) by country between January 1995 and May 2011. We also report counts of the events decomposed as follow: Positive (upgrade), negative (downgrade), big positive (upgrade by more than or equal to two notches), big negative (downgrade by more than or equal to two notches), and events when the sovereign is upgraded to investment grade or downgraded to non-investment grade. The last column shows the sum of the number of stocks on any event day by country. Country N of Events N of

Positive

Events

N of

Negative

Events

N of Big

Positive

Events

N of Big

Negative

Events

N of Events

(from Inv

to Non-Inv)

N of Events

(from Non-

Inv to Inv)

N of Banks N of Bank-

Event Day

Observations

ARGENTINA 19 6 13 1 1 0 0 5 40

AUSTRALIA 3 3 0 0 0 0 0 10 22

BELGIUM 1 0 1 0 0 0 0 1 1

BRAZIL 14 12 2 0 0 0 1 12 71

CANADA 2 2 0 0 0 0 0 6 12

CHILE 5 5 0 0 0 0 0 2 9

CHINA 7 7 0 0 0 0 0 3 14

COLOMBIA 4 4 0 0 0 0 0 2 6

DENMARK 2 2 0 0 0 0 0 3 5

FINLAND 5 5 0 0 0 0 0 2 7

GREECE 16 6 10 1 3 1 0 6 69

HONG KONG 12 10 2 0 0 0 0 9 76

HUNGARY 10 5 5 0 0 0 0 1 10

INDIA 12 7 5 0 0 0 1 14 90

INDONESIA 15 10 5 1 1 1 0 7 37

IRELAND 7 1 6 0 0 0 0 4 12

ISRAEL 7 4 3 0 0 0 0 6 42

ITALY 6 1 5 0 0 0 0 21 77

JAPAN 10 3 7 0 0 0 0 30 193

MALAYSIA 16 8 8 0 1 0 0 7 53

MEXICO 9 6 3 0 0 0 1 1 9

PAKISTAN 15 8 7 1 1 0 0 4 35

PERU 4 3 1 0 0 0 0 2 5

PHILIPPINES 12 5 7 0 0 0 0 7 53

POLAND 12 8 4 0 0 0 0 9 72

PORTUGAL 11 3 8 0 2 0 0 5 36

RUSSIA 9 6 3 0 0 0 1 5 20

S.AFRICA 6 5 1 0 0 0 1 1 6

S.KOREA 13 8 5 1 2 1 1 16 125

SPAIN 9 4 5 0 0 0 0 10 64

SWEDEN 3 3 0 0 0 0 0 4 10

TAIWAN 7 2 5 0 0 0 0 3 16

THAILAND 11 6 5 0 0 0 0 9 78

TURKEY 28 15 13 0 0 0 0 14 182

UK 2 1 1 0 0 0 0 4 7

VENEZUELA 2 1 1 0 0 0 0 1 2

Total 326 185 141 5 11 3 6 246 1,566

21

Table 2 Number of banks by country

This table shows the number of banks in the sample by country.

Country N of Banks % of sample

ARGENTINA 5 2.1

AUSTRALIA 10 4.2

BELGIUM 1 0.4

BRAZIL 12 5.0

CANADA 6 2.5

CHILE 2 0.8

CHINA 3 1.3

COLOMBIA 2 0.8

DENMARK 3 1.3

FINLAND 2 0.8

HONG KONG 9 3.8

HUNGARY 1 0.4

INDIA 14 5.9

INDONESIA 7 2.9

IRELAND 4 1.7

ISRAEL 6 2.5

ITALY 21 8.8

JAPAN 30 12.6

MALAYSIA 7 2.9

MEXICO 1 0.4

PAKISTAN 4 1.7

PERU 1 0.4

PHILIPPINES 7 2.9

POLAND 9 3.8

PORTUGAL 5 2.1

RUSSIA 5 2.1

S.AFRICA 1 0.4

S.KOREA 16 6.7

SPAIN 10 4.2

SWEDEN 4 1.7

TAIWAN 3 1.3

THAILAND 9 3.8

TURKEY 14 5.9

UK 4 1.7

VENEZUELA 1 0.4

Total 239 100

22

Table 3 Descriptive statistics

This table shows country averages of daily returns in percentage, volatility of returns, numeric sovereign rating (as defined in the appendix), market capitalization one year prior to the event (MV), the book to market ratio one year prior to the event (B/M), and the return on assets on the year of the event (ROA).

Country Avg Return Avg of Volati lity Avg Sov. Debt

Rating

Avg of logMV

(t-1)

Avg of log(B/M)

(t-1)

ARGENTINA 1.281 0.025 7.084 6.720 -0.382

AUSTRALIA -0.435 0.012 20.067 8.016 -0.610

BELGIUM -1.807 0.019 19.800 9.331 0.261

BRAZIL 3.608 0.022 9.414 7.754 -0.591

CANADA 3.765 0.016 20.500 9.096 -0.501

CHILE 0.548 0.013 16.320 8.595 -1.148

CHINA -0.040 0.025 15.686 9.062 -1.342

COLOMBIA 0.806 0.013 10.600 7.463 -0.732

DENMARK 0.192 0.016 20.600 7.544 -0.323

FINLAND -0.285 0.017 19.880 6.511 -0.237

GREECE -2.244 0.030 13.600 7.360 -0.369

HONG KONG -0.411 0.019 17.942 7.920 -0.516

HUNGARY -0.554 0.035 13.560 8.409 -1.018

INDIA -0.471 0.025 10.750 7.537 -0.121

INDONESIA 2.220 0.031 7.793 7.609 -0.535

IRELAND 0.333 0.054 18.429 7.219 1.264

ISRAEL 1.351 0.021 15.343 7.096 -0.025

ITALY -0.569 0.015 17.900 8.094 -0.094

JAPAN -0.359 0.019 18.620 7.956 0.010

MALAYSIA 0.968 0.031 14.438 7.532 -0.576

MEXICO 0.081 0.031 12.244 7.120 -0.409

PAKISTAN -0.648 0.024 6.227 5.670 -0.492

PERU 0.046 0.009 10.500 6.844 -0.601

PHILIPPINES 0.225 0.028 10.283 6.705 -0.191

POLAND 1.275 0.022 14.067 7.541 -0.761

PORTUGAL -1.967 0.015 16.373 7.775 -0.211

RUSSIA 2.270 0.047 13.089 8.147 -0.366

S.AFRICA -2.274 0.022 13.167 9.070 -0.869

S.KOREA -6.443 0.052 13.077 6.398 0.297

SPAIN -2.257 0.016 19.878 8.771 -0.535

SWEDEN 2.498 0.017 20.400 8.730 -0.560

TAIWAN 0.648 0.028 18.314 7.902 -0.153

THAILAND -1.415 0.035 13.600 7.266 -0.263

TURKEY 2.535 0.037 7.543 7.508 -0.447

UK -0.523 0.035 20.900 10.327 0.314

VENEZUELA -1.392 0.019 7.900 7.507 -1.122

23

Table 4 Sovereign ratings events and banks’ excess returns

The dependent variable is individual banks’ excess returns estimated using a market model with the Global market return, a daily stock return measure computed from Datastream’s global market index, used as the market. Event is the numeric change of sovereign ratings. Event+ (Event-) is defined as an absolute value of rating changes if the rating change is positive (negative), and zero otherwise. Other controls include the lagged log value of banks’ market capitalization in US dollars, the lagged log value of the book-to-market ratio, and a measure for the volatility of each banks’ stock return. Columns (1) through (4) show estimates using ordinary least squares (OLS) and columns (5) to (8) show estimates using weighted least squares (WLS). The asterisk ***, **, and * show significance at the 1%, 5%, and 10%, respectively. t-values are shown in italics.

(1) (2) (3) (4) (5) (6) (7) (8)

Event 0.011*** 0.012*** 0.007*** 0.010***

4.236 4.589 3.175 4.412

Event (+) 0.000 0.001 -0.001 0.000

-0.093 0.160 -0.340 0.024

Event (-) -0.025*** -0.028*** -0.024*** -0.034***

-5.557 -6.007 -5.075 -6.856

Observations 1274 1274 1274 1274 1274 1274 1274 1274

R-square 16 17 17 18 13 17 15 19

Other controls No Yes No Yes No Yes No Yes

Country dummy Yes Yes Yes Yes Yes Yes Yes Yes

Year Dummy Yes Yes Yes Yes Yes Yes Yes Yes

Method OLS OLS OLS OLS WLS WLS WLS WLS

24

Table 5 Sovereign ratings events, government support, and banks’ excess returns

The dependent variable is individual banks’ excess returns estimated using a market model with the Global market return, a daily stock return measure computed from Datastream’s global market index, used as the market. Event is the numeric change of sovereign ratings. Event+ (Event-) is defined as an absolute value of rating changes if the rating change is positive (negative), and zero otherwise. Support is the numeric difference, in notches, between the foreign currency long term deposit rating assigned to a bank by Moody’s, and the bank’s financial strength rating. Other controls include the lagged log value of banks’ market capitalization in US dollars, the lagged log value of the book-to-market ratio, and a measure for the volatility of each banks’ stock return. Columns (1) through (4) show estimates using ordinary least squares (OLS) and columns (5) to (8) show estimates using weighted least squares (WLS). The asterisk ***, **, and * show significance at the 1%, 5%, and 10%, respectively. t-values are shown in italics.

(1) (2) (3) (4) (5) (6) (7) (8)

Support 0.000 0.000 0.001 0.001 -0.001 -0.001* 0.001 0.000

0.129 0.353 1.176 1.311 -0.913 -1.672 1.171 0.169

Event 0.010*** 0.012*** 0.009*** 0.012***

4.098 4.518 3.897 5.366

Event (+) 0.000 0.001 0.000 0.002

0.072 0.219 0.039 0.507

Event (-) -0.019*** -0.022*** -0.017*** -0.028***

-3.814 -4.278 -3.314 -5.271

Event x Support 0.002*** 0.002*** 0.003*** 0.003***

3.382 3.246 4.634 4.947

Event (+) x Support 0.000 0.000 0.000 0.000

-0.186 0.000 -0.105 0.277

Event (-) x Support -0.003** -0.003** -0.006*** -0.005***

-2.576 -2.426 -4.429 -3.865

Observations 1274 1274 1274 1274 1274 1274 1274 1274

R-square 17 17 17 19 15 18 16 20

Other controls No Yes No Yes No Yes No Yes

Country dummy Yes Yes Yes Yes Yes Yes Yes Yes

Year Dummy Yes Yes Yes Yes Yes Yes Yes Yes

Method OLS OLS OLS OLS WLS WLS WLS WLS

25

Table 6 Sovereign ratings “extreme” events, government support, and banks’ excess returns

The dependent variable is individual banks’ excess returns estimated using a market model with the Global market return, a daily stock return measure computed from Datastream’s global market index, used as the market. Event big + (Event big -) is defined as an absolute value of rating changes if there is an upgrade (downgrade) of the sovereign of two or more notches, and zero otherwise. Event small + (Event small -) is defined as an absolute value of rating changes if there is an upgrade (downgrade) of the sovereign of less than two notches, and zero otherwise. Support is the numeric difference, in notches, between the foreign currency long term deposit rating assigned to a bank by Moody’s, and the bank’s financial strength rating. Other controls include the lagged log value of banks’ market capitalization in US dollars, the lagged log value of the book-to-market ratio, and a measure for the volatility of each banks’ stock return. Equations are estimated using weighted least squares. The asterisk ***, **, and * show significance at the 1%, 5%, and 10%, respectively. t-values are shown in italics.

(1) (2)

Support 0.002 0.001

1.630 0.683

Event (big +) -0.001 0.000

-0.001 0.000

Event (big -) -0.018** -0.027***

-2.166 -3.249

Event (small +) 0.007 0.010*

1.281 1.921

Event (small -) -0.010 -0.021***

-1.620 -3.289

Event (big +) x Support 0.001 0.001

0.446 0.711

Event (big -) x Support -0.006*** -0.006***

-2.925 -2.897

Event (small +) x Support -0.002 -0.002

-1.244 -1.037

Event (small -) x Support -0.006*** -0.005***

-3.830 -3.241

Observations 1274 1274

R-square 16.5 20.3

Other controls No Yes

Country dummy Yes Yes

Year Dummy Yes Yes

26

Table 7 Sovereign ratings events, government support, and banks’ stock returns – advanced vs. emerging

economies

The dependent variable is individual banks’ excess returns estimated using a market model with the Global market return, a daily stock return measure computed from Datastream’s global market index, used as the market. Event is the numeric change of sovereign ratings. Event+ (Event-) is defined as an absolute value of rating changes if the rating change is positive (negative), and zero otherwise. Support is the numeric difference, in notches, between the foreign currency long term deposit rating assigned to a bank by Moody’s, and the bank’s financial strength rating. Other controls include the lagged log value of banks’ market capitalization in U.S. dollars, the lagged log value of the book-to-market ratio, and a measure for the volatility of each banks’ stock return. All specifications are estimated using weighted least squares (WLS). The asterisk ***, **, and * show significance at the 1%, 5%, and 10%, respectively. t-values are shown in italics.

(1) (2) (3) (4) (5) (6) (7) (8)

Support -0.003** -0.005*** 0.002 0.001 0.000 0.000 0.001 0.001

-2.210 -4.032 1.129 0.521 -0.088 -0.226 0.898 0.608

Event -0.019*** -0.005 0.007** 0.009***

-2.709 -0.753 2.531 3.288

Event (+) -0.021* -0.009 0.000 0.001

-1.694 -0.794 0.058 0.306

Event (-) 0.021 0.006 -0.016** -0.023***

1.471 0.428 -2.537 -3.572

Event x Support 0.014*** 0.013*** 0.002*** 0.002***

8.334 8.866 2.672 2.604

Event (+) x Support 0.003 0.002 0.000 0.000

0.793 0.002 0.058 0.017

Event (-) x Support -0.019*** -0.019*** -0.004** -0.003**

-6.506 -7.158 -2.436 -2.201

Observations 416 416 416 416 858 858 858 858

R-square 32 46 34 49 15 18 16 19

Other controls No Yes No Yes No Yes No Yes

Country dummy Yes Yes Yes Yes Yes Yes Yes Yes

Year Dummy Yes Yes Yes Yes Yes Yes Yes Yes

Region Advanced Economies Emerging Economies

27

Figure 1. Government support. This figure shows average and median government support for all banks included in the sample. Support is defined as the difference in notches between a bank’s long term foreign currency deposit rating and its stand-alone rating.

-0.5

0

0.5

1

1.5

2

2.5Ja

n-9

6

Au

g-9

6

Ma

r-9

7

Oc

t-9

7

Ma

y-9

8

De

c-9

8

Jul-

99

Fe

b-0

0

Se

p-0

0

Ap

r-0

1

No

v-0

1

Jun

-02

Jan

-03

Au

g-0

3

Ma

r-0

4

Oc

t-0

4

Ma

y-0

5

De

c-0

5

Jul-

06

Fe

b-0

7

Se

p-0

7

Ap

r-0

8

No

v-0

8

Jun

-09

Jan

-10

Au

g-1

0

Mean Median

28

Figure 2. Government support and home-country sovereign debt holdings. This figure shows holdings of home-country sovereign debt as of end-2010 for 78 banks that participated in the 2011 E.U.-wide stress test coordinated by the European Banking Authority (EBA). Support is defined as the difference in notches between a bank’s long term foreign currency deposit rating and its stand-alone rating.

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8

Ho

me

-so

ve

reig

n d

eb

t h

old

ing

s (%

of

ass

ets

)

Support (ratings notches)