sovereign credit risk, banks’ government support, and bank
TRANSCRIPT
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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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References
Amihud, Y., 2002, "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets 5, 31-56.
Amihud, Y., and H. Mendelson, 1989, "The Effects of Beta, Bid-Ask Spread, Residual
Risk, and Size on Stock Returns," Journal of Finance 44, 479-486. Bernardo, A. E., and I. Welch, 2004, "Liquidity and Financial Market Runs," Quarterly
Journal of Economics 119, 135-158. Borensztein, Eduardo, Kevin Cowan, and Patricio Valenzuela, 2007, "SovereignCeilings
‘Lite’? The Impact of Sovereign Ratings on Corporate Ratings inEmerging Market Economies", IMF Working Paper WP/07/75.
Brennan, M. J., T. Chordia, and A. Subrahmanyam, 1998, "Alternative factor
specifications, security characteristics, and the cross-section of expected stock returns," Journal of Financial Economics 49, 345-373.
Brennan, M. J., and A. Subrahmanyam, 1996, "Market microstructure and asset pricing:
On the compensation for illiquidity in stock returns," Journal of Financial Economics 41, 441-464.
Brooks, R., R. W. Faff, D. Hillier, and J. Hillier, 2004, "The national market impact of
sovereign rating changes," Journal of Banking and Finance 28, 233-250. Brunnermeier, M. K., and L. H. Pedersen, 2009, "Market Liquidity and Funding
Liquidity," Review of Financial Studies 22, 2201-2238. Cantor, R., and F. Packer, 1996, "Determinants and impact of sovereign credit ratings,"
Federal Reserve Bank of New York Policy Review, 37-54. Chordia, T., A. Sarkar, and A. Subrahmanyam, 2005, "An empirical analysis of stock and
bond market liquidity," Review of Financial Studies 18, 85-129. Committee on the Global Financial System (CGFS), 2011, “The impact of sovereign
credit risk on bank funding conditions,” Working Paper 43. Cybo-Ottone, A., and M. Murgia, 2000, “Mergers and shareholder wealth in European
banking,” Journal of Banking and Finance 24, 831-859. Demirgüç-Kunt, A., and H. Huizinga, 2010, “Are banks too big to fail or too big to save?
International evidence from equity prices and CDS spreads”, European Banking Center Discussion, No. 2010-15.
18
Dichev, I. D., and J. D. Piotroski, 2001, "The Long-Run Stock Returns Following Bond Ratings Changes," Journal of Finance 56, 173-203.
Doron, K., and S. Oded, 2000, "The Information Value of Bond Ratings," Journal of
Finance 55, 2879-2902. Faccio, M., 2006, "Politically Connected Firms," American Economic Review 96, 369-
386. Gande, A., and D. C. Parsley, 2005, "News spillovers in the sovereign debt market,"
Journal of Financial Economics 75, 691-734. Gande, A., and D. C. Parsley, 2010, "Sovereign credit ratings, transparency and
international portfolio flows," Working paper, Vanderbilt University. Goh, J. C., and L. H. Ederington, 1993, "Is a Bond Rating Downgrade Bad News, Good
News, or No News for Stockholders?," Journal of Finance 48, 2001-2008. Gromb, D., and D. Vayanos, 2002, "Equilibrium and welfare in markets with financially
constrained arbitrageurs," Journal of Financial Economics 66, 361-407. Gropp, R., H. Hakenes, and I. Schnabel, 2011, “Competition, Risk-shifting, and Public
Bail-out Policies,” The Review of Financial Studies 24, 2084-2120. Gropp, R., and A. Richards, 2001, “Rating Agency Actions and the Pricing of Debt and
Equity on European Banks: What Can We Infer About Private Sector Monitoring of Bank Soundess?,” Economic Notes 30, 373-398.
Gropp, R., J. Vesala, G. Vulpes, 2006, “Equity and Bond Market Signals as Leading
Indicators of Bank Fragility,” Journal of Money, Credit, and Banking 38, 399-428. Hameed, A., W. Kang, and S. Viswanathan, 2010, "Stock Market Declines and
Liquidity," Journal of Finance 65, 257-293. Holthausen, R. W., and R. W. Leftwich, 1986, "The effect of bond rating changes on
common stock prices," Journal of Financial Economics 17, 57-89. Huang, R. D., and H. R. Stoll, 2001, "Exchange rates and firms' liquidity: evidence from
ADRs," Journal of International Money and Finance 20, 297-325. Ince, O., and R. B. Porter, 2006, “Individual equity return data from Thompson
Datastream: handle with care!,” Journal of Financial Research 29, 463-479. Jorion, P., and G. Zhang, 2007, "Information Effects of Bond Rating Changes: The Role
of the Rating Prior to the Announcement," Journal of Fixed Income 16, 45-59.
19
Kaminsky, G., and S. L. Schmukler, 2002, "Emerging market instability: Do sovereign ratings affect country risk and stock returns?," World Bank Economic Review 16, 171.
Kim, S.-J., and E. Wu, 2008, "Sovereign credit ratings, capital flows and financial sector
development in emerging markets," Emerging Markets Review 9, 17-39. Kyle, A. S., and W. Xiong, 2001, "Contagion as a wealth effect," Journal of Finance 56,
1401-1440. La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. W. Vishny, 1998, "Law and
finance," Journal of Political Economy 106, 1113-1155. Leuz, C., D. Nanda, and P. D. Wysocki, 2003, "Earnings management and investor
protection: an international comparison," Journal of Financial Economics 69, 505-527.
Moody’s Investors Services, 2007, “Incorporation of joint-default analysis into Moody’s
bank ratings: A refined methodology”. Moody’s Investors Services, 2010, “Rating symbols and definitions”. Mora, N., 2006, "Sovereign credit ratings: Guilty beyond reasonable doubt?," Journal of
Banking and Finance 30, 2041-2062. Morris, S., and H. S. Shin, 2004, "Liquidity black holes," Review of Finance 8, 1-18. Reinhart, C. M., 2002, "Default, currency crises, and sovereign credit ratings," World
Bank Economic Review 16, 151. Rigobon, R., 2002, "The curse of non-investment grade countries," Journal of
Development Economics 69, 423-449. Stulz, R. M., 2005, "The limits of financial globalization," Journal of Finance 60, 1595-
1638. Vayanos, D., 2004, Flight to quality, flight to liquidity, and the pricing of risk, NBER
Working paper, National Bureau of Economic Research, Cambridge, MA.
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)