Banks, Government Bonds, and Default
What do the Data Say?
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund
  • | 2 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund
  • | 3 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address:AMartin@imf.org

We analyze holdings of public bonds by over 20,000 banks in 191 countries, and the role of these bonds in 20 sovereign defaults over 1998-2012. Banks hold many public bonds (on average 9% of their assets), particularly in less financially-developed countries. During sovereign defaults, banks increase their exposure to public bonds, especially large banks and when expected bond returns are high. At the bank level, bondholdings correlate negatively with subsequent lending during sovereign defaults. This correlation is mostly due to bonds acquired in pre-default years. These findings shed light on alternative theories of the sovereign default-banking crisis nexus.

Abstract

We analyze holdings of public bonds by over 20,000 banks in 191 countries, and the role of these bonds in 20 sovereign defaults over 1998-2012. Banks hold many public bonds (on average 9% of their assets), particularly in less financially-developed countries. During sovereign defaults, banks increase their exposure to public bonds, especially large banks and when expected bond returns are high. At the bank level, bondholdings correlate negatively with subsequent lending during sovereign defaults. This correlation is mostly due to bonds acquired in pre-default years. These findings shed light on alternative theories of the sovereign default-banking crisis nexus.

1. Introduction

Recent events in Europe have illustrated how government defaults can jeopardize domestic bank stability. Growing concerns of public insolvency since 2010 caused great stress in the European banking sector, which was loaded with Euro-area debt (Andritzky (2012)). Problems were particularly severe for banks in troubled countries, which entered the crisis holding a sizeable share of their assets in their governments’ bonds: roughly 5% in Portugal and Spain, 7% in Italy and 16% in Greece (2010 EU Stress Test, authors’ calculations). As sovereign spreads rose, moreover, these banks greatly increased their exposure to the bonds of their financially distressed governments (2011 EU Stress Test, authors’ calculations; see also Brutti and Sauré (2013)), leading to even greater fragility. As The Economist put it, “Europe’s troubled banks and broke governments are in a dangerous embrace.”1 These events are not unique to Europe: a similar relationship between sovereign defaults and the banking system has been at play also in earlier sovereign crises (IMF (2002)).

Despite the relevance of these phenomena, there is little systematic evidence on them. This paper fills this gap by documenting the link between public default, bank bondholdings, and bank loans. We use the BANKSCOPE dataset, which provides us with information on the bondholdings and characteristics of over 20,000 banks in 191 countries and 20 sovereign default episodes between 1998 and 2012. We address two broad questions:

  1. Does banks’ exposure to sovereign risk affect lending? In particular, do the banks that hold more public bonds exhibit a larger fall in loans when their government defaults?

  2. Why do banks buy public bonds, becoming exposed to default risk in the first place?

The goal of our analysis is to document robust stylized facts regarding these questions, not to identify causal patterns, which our data does not allow us to do. These stylized facts can shed light on the presumption that sovereign defaults damage banks, and thus the real economy, through their bondholdings. Moreover, our analysis allows us to assess whether the dangerous embrace between banks and sovereigns comes about because banks buy and hold public bonds well before sovereign default materializes, or because banks buy many public bonds during the default event itself. Our main findings are:

  • Holdings of public bonds are large in normal times, particularly for banks that make fewer loans and are located in financially less developed countries. In non-defaulting countries, banks hold on average 9% of their assets in public bonds. Among countries that default at least once (which are financially less developed), average bank bondholdings in non-default years are 13.5%. In both groups of countries, bondholdings in non-default years are decreasing in bonds’ expected return.

  • During default years, average bondholdings increase from 13.5% to 14.5% of bank assets. Critically, this increase is concentrated in large banks. Moreover, during default years, bondholdings are increasing in bonds’ expected return.

  • During sovereign defaults, there is a large, negative and statistically significant correlation between banks’ bondholdings and subsequent lending activity. A one dollar increase in bonds is associated with a 0.60 dollar decrease in bank loans during defaults. Strikingly, about 90% of this decline is accounted for by the average bonds held by banks before the default takes place; only 10% of this decline is explained by the additional bonds bought in the run-up to and during default.

These results are very robust to alternative specifications and controls. In particular, results on within country, cross-bank variation are robust to adjusting for any time-varying country-wide shocks. Within the same defaulting country and default year, it is the banks most loaded with government bonds that subsequently cut their lending the most.

Our results support the notion that banks’ holdings of public bonds are an important transmission mechanism of sovereign defaults to bank lending. As we discuss in Section 5, these findings are broadly consistent with the following narrative. Public bonds are very liquid assets (e.g., Holmstrom and Tirole (1998)) that play a crucial role in banks’ everyday activities, like storing funds, posting collateral, or maintaining a cushion of safe assets (Bolton and Jeanne (2012), Gennaioli, Martin, and Rossi (2014)). Because of this, banks hold a sizeable amount of government bonds in the course of their regular business activity, especially in less financially developed countries where alternatives are fewer. When default strikes, banks experience losses on their public bonds and subsequently decrease their lending. During default episodes, moreover, some banks deliberately hold on to their risky public bonds while others accumulate even more bonds. This behavior could reflect banks’ reaching for yield (Acharya and Steffen (2013)), or it could be their response to government moral suasion or bailout guarantees (Livshits and Schoors (2009), Broner et al. (2013)). Whatever its origin, this behavior is largely concentrated in a set of large banks and is associated with a further decrease in bank lending.

Our data suggests that all bondholdings, regardless of whether they are accumulated before or during sovereign default events, contribute to transmitting the effects of defaults to private loans. Critically, though, our analysis also shows the bulk of the drop in lending that takes place during defaults is associated with bond purchases that take place well before the defaults themselves. In Section 5 we discuss the broad implications of these results for recent research on the European crisis and for the design of policy.

Our paper is related to the literature studying the costs of sovereign defaults. Quantitative models like Arellano (2008) typically find that, when calibrated to match the data, exclusion from financial markets is too short to account for the observed low frequency of defaults. In line with her findings, recent work posits that sovereign default is costly because it inflicts a “collateral damage” to the domestic economy. This damage arises because default is assumed to be nondiscriminatory, so that it hurts domestic bondholders as well as foreign ones and this has consequences for domestic financial markets. Some examples of this work are Broner and Ventura (2011), where nondiscriminatory default destroys domestic risk sharing, and Brutti (2011), where it reduces entrepreneurial wealth and investment.

In Gennaioli, Martin, and Rossi (2014), we built a model where nondiscriminatory defaults reduce the net worth of banks holding public bonds and hamper financial intermediation.2 We also provided cross-country evidence that, following a public default, the decline in private credit is larger in those countries where the banking system holds more public bonds.3 In this paper, we substantially extend the evidence by using bank-level data, which enables us to take a granular look at bondholdings and their effect on lending during defaults.

Our paper is also related to a recent strand of work that, largely motivated by the European crisis, has studied the two-way link between banking sector fragility and public defaults. Acharya, Drechsler, and Schnabl (2013) study “Irish-style” crises, in which banking sector bailouts raise the likelihood of public defaults. They show that bailouts are associated with subsequent spikes in sovereign spreads and with declines in banks’ stock returns. Other work focuses on how bank bondholdings may expose the financial system to public defaults. Acharya and Steffen (2013) examine holdings of troubled bonds by Eurozone banks in recent years, finding evidence that banks have engaged in ‘carry trade’ by borrowing short-term to invest in risky bonds. Brutti and Saure (2013) study holdings of European bonds by Eurozone banks during the recent sovereign debt crisis, and document that the share of troubled bonds held by domestic banks has been increasing in the bonds’ risk. Battistini, Pagano, and Simonelli (2013) also study Eurozone banks and obtain similar results. Finally, Reinhart and Sbrancia (2011) study the increase in aggregate bondholdings around defaults and attribute it to financial repression. All of these papers focus on specific crisis episodes and find support for the view that banks have incentives to accumulate bonds precisely when they are risky.4

Our paper contributes to these works by providing a panoramic view of bank-level bondholdings around the world, both during normal times and during default episodes. Moreover, while these works focus on the behavior of bank bondholdings in periods of high risk, we are also interested in the implications of these bondholdings for lending once a sovereign default takes place.

The paper proceeds as follows. Section 2 describes the data. Section 3 analyzes the patterns of bondholdings and Section 4 studies the relationship between bondholdings and lending during sovereign defaults. Section 5 discusses our findings in light of alternative hypotheses and concludes.

2. Data

We build a dataset that includes bondholdings and lending activity at the bank level, as well as a large set of bank-level characteristics and macroeconomic indicators that are meant to capture the state of a country’s economy. We explain each of our data sources below.

We obtain bank-level data from the BANKSCOPE dataset, which contains information on the holdings of public bonds for 20,337 banks in 191 countries over the period 1998-2012 (99,328 bank-year observations). This dataset, which is provided by Bureau van Dijk Electronic Publishing (BvD), provides balance sheet information on a broad range of bank characteristics: bondholdings, size, leverage, risk taking, profitability, amount of loans outstanding, balances with the Central Bank and other interbank balances. The nationality of the bonds is not reported. We shall return to this last issue later on. The information in BANKSCOPE is suitable for international comparisons because BvD harmonizes the data.

All items are reported at book value, including bonds.5 This implies that variations in the bonds-to-assets ratio, both within and across countries, to a large extent capture variations in the relative quantity of public bonds held by banks, particularly in country-years away from sovereign crises. During sovereign crises, however, large declines in the prices of bonds and of other bank assets can contaminate the bonds-to-assets ratio that we use as a measure of bondholdings. For instance, if the price of bonds drops more (less) than the price of other bank assets, book value reporting may overstate (understate) the market value of the bonds-to-assets ratio. This aspect should be borne in mind when interpreting our results. It should be noted, though, that book value estimates significantly shape the actions and beliefs of regulators, markets, and bank managers, so they provide a good measure of bondholdings for our purposes.6

We start with the full sample of banks in BANKSCOPE and examine their unconsolidated accounts. We construct our dataset by assembling the annual updates of BANKSCOPE.7 We filter out duplicate records, banks with negative values of all types of assets, banks with total assets smaller than $100,000, and years prior to 1997 when coverage is less systematic.8 This procedure results in 99,328 observations of the bondholdings variable at the bank-year level over 1998-2012. For our regression analysis, we impose two additional requirements on the remaining banks: first, that we observe at least two consecutive years of data, so that we can examine the banks’ changes in lending activity; and second, that data is available on all of the other main variables such as leverage, profitability, cash and short term securities, exposure to Central Banks, and interbank balances. Our constant-continuing sample for the regression analysis then consists of 7,391 banks in 160 countries for a total 36,449 bank-year observations. We take the location of banks to be the one reported in Bankscope, which coincides with the location of the bank’s headquarters. Commercial banks account for 33.2% of our sample; cooperative banks for 38.2%; savings banks for 20.6%; investment banks for 1.6%; the rest includes holdings, real estate banks, and other credit institutions.

Data on the macroeconomic conditions of the different countries is obtained from the IMF’s International Financial Statistics (IFS) and the World Bank’s World Development Indicators (WDI). Table AI in the Appendix describes these variables. To measure the size of financial markets we use the ratio of private credit provided by money deposit banks and other financial institutions to GDP, which is drawn from Beck et al. (2000). This widely used measure is an objective, continuous proxy for the size of the domestic credit markets.

We follow the existing literature and proxy for sovereign default with a dummy variable based on Standard & Poor’s, which defines default as the failure of a debtor (government) to meet a principal or interest payment on the due date (or within the specified grace period) contained in the original terms of the debt issue. According to this definition, a debt restructuring under which the new debt contains less favorable terms to the creditors is coded as a default. The Greek bond swap that was launched in February of 2012, for instance, is identified as a default by Standard & Poor’s because the retroactive insertion of collective action clauses was deemed to materially change the original contract terms. According to this definition, our sample contains 20 sovereign defaults of different duration in 17 countries, which are listed in Table AI of the Appendix.9

In our robustness tests, we complement our analysis by using two alternative measures of sovereign default, namely, i) a monetary measure of creditors’ losses given default, i.e., “haircuts”, from the work of Cruces and Trebesch (2013) and Zettelmeyer, Trebesch, and Gulati (2012) and; (ii) a market-based measure, whereby a country is defined to be in default if it is in default according to S&P, or if its sovereign bond spreads relative to the U.S. or German bonds exceed a given threshold (using extreme value theory, Pescatori and Sy (2007) identify such a threshold to be approximately 1000 basis points). These measures cover dimensions of sovereign risk that are not captured by the S&P default dummy, such as spikes in credit spreads and the economic magnitude of creditors’ losses. As we show in Section 4, our results are robust to these alternative measures. In our main analysis, however, we stick to the S&P default dummy because these measures have problems of their own. In particular, measures of haircuts depend heavily on the assumptions one makes about counterfactuals (e.g., Sturzenegger and Zettelmeyer (2008)), and measures based on sovereign bond spreads require observing reliable data on secondary market trading, which limits our sample size.

Table AI shows that the default episodes included in our sample contain large variations both in the size of defaulting countries and in the extent of bank coverage. A few countries such as Argentina, Russia, Nigeria, Kenya and Honduras have the lion’s share of banks; at the other end of the spectrum, there are eight defaulting countries in which our data covers five banks or less. One concern is that countries that are small and have few banks might drive our results. In our robustness tests we re-estimate our regressions focusing on large defaulting countries and discarding countries with fewer than five (or ten, or fifteen) banks during a default episode, respectively, and we show that our results are unaffected.

Before concluding, we comment on two other important data series that we use, those measuring the realized and the expected returns of sovereign bonds. Realized bond returns in emerging countries are obtained from the J.P. Morgan’s Emerging Market Bond Index Plus file (EMBIG+). For developed countries, we use the J.P. Morgan’s Global Bond Index (GBI) file (see Kim (2010) for a detailed description; see also Levy-Yeyati, Martinez-Peria, and Schmukler (2010)). These indices aggregate the realized returns of sovereign bonds of different maturities and denominations in each country. Returns are expressed in dollars. The index takes into account the change in the price of the bonds and it assumes that any cash received from coupons or pay downs is reinvested in the bond. This data on returns is available for 68 countries in our sample and it covers 7 default episodes in 6 countries (Argentina, Russia Greece, Cote d’Ivoire, Ecuador, and Nigeria), so that any exercise involving bond returns reduces sample size.

Obtaining data for expected returns is more problematic, because this variable is not directly observable, and standard proxies such as yield-to-maturity are clearly not appropriate for studying default episodes. We construct our series of expected returns using a two-step process. In the first step, we regress returns on a set of country-specific economic, financial, and political risk factors:

Rc,t=γt+β0+β1Zc,t1+ui,c,t,(1)

where Rc,t is the realized return of public bonds in country c at time t, γt are time dummies, which capture variations in the global risk-free rate, and Zc,t−1 is a vector of political, economic and financial risk ratings compiled by the International Country Risk Guide. These ratings provide a comparable measure of political stability and of economic and financial strengths in many countries, and they have been shown to be strong predictors of bond returns (see e.g. Comelli (2012)). In the second stage, we define expected returns as the fitted values of this first-stage regression. We describe this data, as well as all variables used in the analysis, in Table AII in the Appendix.10

2.1 Bondholdings and Returns Data

The BANKSCOPE dataset is widely used and has an established track record, but there is one important dimension along which its reliability has not been scrutinized: its measure of government bondholdings.11 To check the quality of this measure, we compare it to other data sources on bondholdings: the country-level measure of “banks’ net claims on the government” from the IMF, and the bank-level data from the recent European Stress Test.

Table I–

Bank’s Holdings of Government Bonds in Bankscope and IMF data, by year

The table reports summary statistics of bank bondholdings as a percentage of total assets for various samples over 1998-2012. Panel A reports statistics on the full Bankscope universe; Panel B reports statistics on the constant-continuing sample from Bankscope, defined as the sample for which data on other bank characteristics is available; Panel C reports bank-level statistics for the countries covered by the IMF; Panel D reports aggregate country-level statistics from the IMF.

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Table I compares the BANKSCOPE data on bondholdings with the IMF measure. Panel A contains the mean, the median, and the standard deviation of bondholdings (as a share of total assets) in the full BANKSCOPE sample. Mean bondholdings are at 9.3% of assets, while median bondholdings are approximately half as high. The standard deviation of bondholdings in the sample is also high.12 Panel B reports somewhat lower figures for the constant-continuing sample, where we observe also the covariates and that we use in our regression analysis. Panel C reports the same information, but only for the subset of countries for which the IMF also reports banks’ bondholdings. Panel D reports the IMF measure of “financial institutions’ net claims to the government,” computed as a share of total assets.13 Mean, median and standard deviation of the IMF measure are close to the BANKSCOPE data. The IMF data gives a slightly higher mean bondholdings, but measurement in the two datasets converges towards the end of the sample, particularly when examining the subsample of banks in countries covered by IMF. Any discrepancy between IMF and BANKSCOPE data is likely due to the fact that the former also captures non-bond finance and to the fact that the banks used to compute the IMF measure may differ from those in BANKSCOPE.

The IMF data cannot address the quality of the BANKSCOPE data on a bank-by-bank basis. We thus compare our measure of bondholdings to the one reported by the European stress test of 2010. This also allows us to evaluate the mismeasurement that may arise because, differently from the stress test, BANKSCOPE does not break down bonds by nationality.

Table II reports bondholdings from the European stress tests of 2010 and 2011. Panel A of the table reports bondholdings for the full sample contained in the stress test, whereas Panel B reports bondholdings for the subset of the banks in the stress test sample that is contained in BANKSCOPE. The bondholdings reported by BANKSCOPE are shown in Panel C. The data from both sources are highly comparable. The bank-by-bank correlation between the bondholdings reported by BANKSCOPE and by the stress test is 80%. The small discrepancies between our measure and the stress test measure are thus most likely due to differences in the time at which the measurement itself took place.14

Table II–

Banks’ Holdings of Government Bonds–Comparing the EU Stress Tests and Bankscope

The table reports summary statistics of bank bondholdings as a percentage of total assets for various samples over 2010-2011. Panel A reports statistics from the EU stress tests of 2010 and 2011 on the full sample of banks involved in the EU stress tests; Panel B reports statistics from the EU stress tests of 2010 and 2011 on the constant sample, defined as the sample for which data is available from both Bankscope and the EU stress tests; Panel C reports statistics from Bankscope on the constant sample; Panel D reports statistics from both Bankscope and EU stress tests on the constant sample for selected countries.

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The evidence is reassuring. Even in highly integrated European markets, where domestic and foreign bonds are in many cases treated symmetrically by the regulatory framework, more than 75% of bank bondholdings correspond to domestic bonds. This share is in all likelihood much larger in the subset of developing countries that provide most of our observations on sovereign defaults. In sum, the BANKSCOPE measure is a good proxy for the domestic public bonds held by banks around the world, and we use it as such in the rest of the paper.

Table III reports descriptive statistics on these bondholdings around the world. Panel A shows that in the full sample, in non-defaulting countries banks hold on average 9% of their assets in public bonds. Among countries that default at least once in our sample, this average is 13.5% in non-default years, and increases to 14.5% of bank assets during default years. Panel A further shows that bondholdings are much larger in financially less developed countries, as the average bondholdings is 8.4% of assets in OECD countries and 12.4% in non-OECD countries. Panel B reports similar, albeit somewhat smaller figures in the constant-continuing sample that we use in our regression analysis.

Table III–

Banks’ Holdings of Government Bonds Around the World

The table reports summary statistics of the banks’ holdings of government bonds, computed as a percentage of total assets. Panel A reports statistics on the Bankscope universe and Panel B on the constant-continuing sample.

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To conclude, consider our data on the realized returns of public bonds. Table AIII in the Appendix contains descriptive statistics on these returns. The average annual return of public bonds is 9.81%, with a large standard deviation of 21.37%. Countries that experience at least one default episode in the sample have average annual returns of 14.46%, as compared with 9.70% for countries that do not experience any defaults. OECD countries have average annual returns of 7.62%, much lower than the non-OECD annual returns of 11.61%.

Bond returns vary substantially over time. To show this, Figure 1 plots sovereign bond prices for six countries that experienced at least one default over 1998-2012. The Figure depicts a window centered on the day of the default, and bond prices are standardized to begin at 100.

Figure 1.
Figure 1.

Sovereign Bond Prices in Defaulting Countries.

The figure plots the average bond prices over 7 default episodes in 6 countries (Argentina 2001-2004, Russia 1998-2000, Cote d’Ivoire 2000-2004, Ecuador 1998-2000, Ecuador 2009, Nigeria 2002, Greece 2012), from day -1,000 to +1,000, whereby day 0 is the day in which default is announced.

Citation: IMF Working Papers 2014, 120; 10.5089/9781498391993.001.A001

Across these six countries, bond prices exhibit the characteristic V-shaped pattern: in particular, prices deteriorate steadily in the year prior to the default, they reach a minimum in the months immediately after the default, and they pick up thereafter.

Finally, we comment briefly on our two-stage process for the construction of a series of expected returns. Table AIV in the Appendix shows the results of the first-stage estimation of Equation (1), in which we regress bond returns on country risk-ratings. As the first three columns of the table shows, there is a strong negative correlation between the risk ratings at time t and realized returns at time t + 1.15 Taking into account that these ratings are decreasing in risk, this result is exactly what one would expect from theory: the positive coefficients are consistent with the notion that high bond returns compensate investors for economic, financial, and political risk. In the second stage, we define expected returns as the fitted values of this first-stage regression.16 This is the series that we use in our regressions.

2.2 Summary Statistics of other Bank-Level Variables

We consider the distribution of bank characteristics in BANKSCOPE, focusing on: (i) bank size as measured by total assets, (ii) non-cash assets, measured as the investment in assets other than cash and other liquid securities, (iii) leverage as measured by one minus shareholders’ equity as a share of assets, (iv) loans outstanding as a share of assets, (v) profitability as measured by operating income over assets, (vi) exposure to the Central Bank as measured by deposits in the Central Bank over assets, (vii) balances in the interbank market, and (viii) government ownership, a dummy that equals one if the government owns more than 50% of the bank’s equity. To neutralize the impact of outliers, all variables are winsorized at the 1st and 99th percentile. Table IV provides descriptive statistics for these variables in our sample.

Table IV–

Descriptive Statistics

The table reports summary statistics of the main variables used in the empirical analysis. Assets is the total book value in million $ of intangible, tangible and other fixed assets; non-cash assets is total assets minus cash and due from banks, divided by total assets; leverage is one minus book value of equity (issued share capital plus other shareholders fund) divided by total assets; loans is total loans outstanding divided by total assets; profitability is operating income divided by total assets; exposure to central bank is total exposure to central bank divided by total assets; interbank balances is interest-earning balances with central and other banks divided by total assets; government owned is a dummy that equals one if the government owns more than 50% of the bank’s equity. Panel A reports statistics on the Bankscope universe and Panel B on banks involved in the EU stress test of 2010. For details on the construction of all variables see Table AI in the Appendix.

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Panel A shows that there is a fairly large variation in bank characteristics within the BANKSCOPE sample. The average bank invests roughly 96% of its resources in non-cash assets (60% of which are loans, and the rest includes government bonds, debentures and other securities), obtains 91% of its financing in the form of debt, which includes deposits (for an average leverage ratio assets/equity of about 10), and holds 3% of its assets in central bank reserves.17 Table AV in the appendix reports the correlations between different bank characteristics in our sample. All correlations are statistically significant. Bank profitability is positively correlated with size, exposure to the central bank and interbank balances, while it is negatively correlated with non-cash assets, leverage, and loans outstanding.

3. Determinants of Banks’ Bondholdings

This section addresses our first question: what determines bank bondholdings? We have already mentioned that average bondholdings are high in our data: they account for 9.3% of bank assets in the entire sample. Moreover, there is also substantial variation in bondholdings over time. In countries that experience at least one default, average bondholdings during default years represent 14.5% of assets as opposed to 13.5% in non-default years. Figure 2 illustrates this by depicting the average evolution of bondholdings across six defaulting countries, during a seven-year window centered on the year of default.

Figure 2.
Figure 2.

Bondholdings in Defaulting Countries.

The figure plots the average annual bondholdings in seven default episodes in six countries (Argentina 2001-2004, Russia 1998-2000, Cote d’Ivoire 2000-2004, Ecuador 1998-2000, Ecuador 2009, Nigeria 2002, Greece 2012), from three years prior to default to three years after. The within-country averages are normalized at 0. Year 0 is the first year of default.

Citation: IMF Working Papers 2014, 120; 10.5089/9781498391993.001.A001

The figure shows that bondholdings follow a V-shaped pattern. Starting from their initial level, they first decrease gradually as the default is approached. From there, bondholdings rise after reaching a minimum on the year of the default itself.

Thus, the raw data already provides two interesting facts regarding bondholdings: banks hold substantial amounts of public bonds in non-default years and they hold even more bonds during sovereign defaults. To delve deeper into these facts and see how they relate to bank- and country-characteristics, we turn to regression analysis.

3.1. Methodology

Let Bi,c,t denote the ratio of government bonds over assets held at time t by bank i located in country c. We think of Bi,c,t as being chosen by banks in period t–1, so that bondholdings at time t are a function of the bank’s balance sheet and of the state of the economy at time t–1.18 We then run the following regression:

Bi,c,t=α0+α1Xi,c,t1+α2Xc,t1+α3Defc,t1+α4Defc,t1Xi,c,t1++α5Defc,t1Xc,t1+εi,c,t,(2)

where Defct–1 is a dummy variable taking value 1 if country c is in default at t−1 and value 0 otherwise, Xi,c,t–1 is a vector of bank characteristics, and Xc,t–1 is a vector of country characteristics. We run this regression in specifications that include country dummies, time dummies, and also their interaction. Standard errors are clustered at the bank level throughout.19

Coefficients α1 and α2 respectively capture the effect of bank- and country-factors on a bank’s holdings of public bonds when the government is not in default (i.e., in “normal times”). Coefficient α3 captures the average impact of default on bondholdings, while α4 and α5 indicate whether the association between default and bonds is heterogeneous across banks and countries. Equation (2) thus allows us to test whether bondholdings behave differently in years of default relative to all other years. For example, if α3 > 0, all banks tend to increase their bondholdings during default events.

Vector Xi,c,t–1 includes bank characteristics that may affect the demand for bonds, such as loans outstanding (which proxies for a bank’s investment opportunities), non-cash assets, exposure to central bank, interbank balances, profitability, size, whether or not the bank is owned by the government, and lagged bondholdings to control for persistence. Vector Xc,t–1 includes instead country-level factors that may affect the demand for bonds, such as a country’s financial development (as measured by Private Credit to GDP and banking crises), GDP growth, and inflation. One interesting variable to consider is the expected return of public bonds denoted by Rc,te, which captures the expectation (at time t–1) of the time-t return of public bonds of country c. As explained in Section 2, we proxy this variable with the fitted value of realized returns when regressed on lagged country-specific risk factors, and we estimate the two-stage model with GMM.

3.2. Results

Table V reports the estimates of different specifications of Equation (2). Columns (1)-(3) assess the patterns of bondholdings without accounting for the interactive effects of default and by using only time dummies. Column (4) includes interactive effects, column (5) includes country dummies, and finally column (6) includes country*time dummies. The inclusion of dummies is important because it allows us to control–among other things–for variations in the supply of government bonds in a country.20 Table V only reports coefficients of variables that are systematically significant.

Table V–

Banks’ Demand for Government Bonds

The table presents coefficient estimates from pooled OLS regressions. The dependent variable is bank bondholdings, and it is computed as bondholdings divided by total assets. Size is the natural logarithm of total assets; non-cash assets is total assets minus cash and due from banks, divided by total assets; leverage is one minus book value of equity (issued share capital plus other shareholders fund) divided by total assets; loans is total loans outstanding divided by total assets; profitability is operating income divided by total assets; exposure to central bank is total exposure to central bank divided by total assets; interbank balances is interest-earning balances with central and other banks divided by total assets; government owned is a dummy that equals one if the government owns more than 50% of the bank’s equity. Sovereign default is a binary variable that equals 1 if the sovereign is in default in year t-1 and 0 otherwise; GDP growth is natural logarithm of GDP in year t minus natural logarithm of GDP in year t-1; aggregate leverage is the country-year average of bank leverage; banking crisis is a binary variable that equals 1 if the country is in a banking crisis in year t-1 and 0 otherwise, private credit is the ratio of credit from deposit taking financial institutions to the private sector to GDP, expressed as a percentage. Standard errors (in parentheses below the coefficient estimates) are adjusted for heteroskedasticity using the Huber (1967) and White (1980) correction, as well as for clustering at the bank level using the Huber (1967) correction. *** indicates significance at the 1% level; ** indicates significance at the 5% level; * indicates significance at the 10% level.

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Consider first columns (1) and (2). Bondholdings decrease with outstanding loans, while they increase with bank size and government ownership. In terms of country factors, bondholdings fall with private credit and GDP growth, and increase with banking crises. The variables with greatest explanatory power in terms of marginal R2 are private credit and outstanding loans. Column (3) adds expected returns to the regression.21 We do so in a separate column because bond returns are available only for a subset of countries, so the number of observations drops accordingly. Over the full sample, expected bond returns are negatively and significantly correlated with bondholdings. If we think of expected returns as a compensation for risk, this means that bondholdings are higher when bonds are safest.

Next, we examine whether these patterns differ in default relative to non-default years. To assess the importance of default, we include our default dummy in columns (4)-(6). These columns teach us two critical features of the data.

First, columns (4)-(6) show that the accumulation of bonds during default years is very unequal across banks. Relative to non-default years, large banks are systematically more likely to increase their exposure to public bonds, while banks with more outstanding loans are less likely to do so. The non-interacted default dummy is often insignificant (or even negative in column (6)), implying that the average increase in bondholdings during defaults entirely comes from a selected set of banks. Column (6) shows that these results hold when controlling also for country and country*year fixed effects. Quite strikingly, this indicates that within-country-year bank heterogeneity is critical in explaining the variations in the data. Quantitatively, this heterogeneity across banks is large. During a default year, for instance, banks in the lowest size decile decrease their bondholdings by 4.2% of assets, while banks in the highest decile increase their bondholdings by 4.5% of assets.22

The second and perhaps most interesting message of columns (4)-(6) is that bondholdings behave differently during default and non-default years. Consider the role of expected returns in column (5). While in non-default years expected bond returns are associated with lower bondholdings, this correlation is reversed during default years. A similar reversal arises with respect to Private Credit to GDP. Columns (4) and (5) show that banks in countries with more developed financial markets, as measured by Private Credit to GDP, hold fewer bonds in normal times but pile up more bonds during default events.

How can we interpret Table V? The evidence suggests a simple narrative. In non-default years, the demand for bonds is consistent with their role as providers of liquidity. Banks that already have many good investment opportunities available (i.e., banks with many outstanding loans) do not need safe and liquid public bonds to ‘store’ their funds. Banks that operate in financially developed economies do not need to buy many public bonds because private alternatives are available. Finally, bondholdings are low when expected bond returns are high, because high-risk, high-return bonds do not provide a good store of liquidity.

One caveat to this interpretation is that bondholdings in the year immediately before default need not necessarily represent banks’ “normal” demand for bonds. Indeed, they may already reflect some risk taking if signs of future default have already materialized. Prima facie, this possibility seems unlikely. As shown in Figure 1, the main defaults that we consider are characterized by abrupt drops in bond prices that take place when the defaults are just 3-4 months away, on average. Additionally, as Figure 2 shows, average bondholdings tend–if anything–to slightly decrease as a default approaches (consistent with them being decreasing in expected bond returns in normal times). In this respect, our representative default is very different from the Greek default of 2012, as Greek bond spreads started to rise already in 2009 and Greek banks accumulate public bonds during these years. Figure 3 indeed shows the different paths of bondholdings in Greece with respect to the main defaulters in our sample.23

Figure 3.
Figure 3.

Bondholdings in Selected Defaulting Countries by Bank Size.

The figure plots the average bondholdings by large (above-median total assets) and small (below-median total assets) banks in selected countries.

Citation: IMF Working Papers 2014, 120; 10.5089/9781498391993.001.A001

As Table V and Figure 3 show, in fact, during default episodes bondholdings change behavior. In those times, high expected returns correlate with higher bondholdings, implying that demand for high-risk, high-return bonds is higher during years of default. Moreover, higher bondholdings in default years are largely concentrated in the hands of large banks. This is consistent with the possibility that these banks have an incentive to take risk in the sovereign bond market owing to implicit government bailout guarantees or to direct moral suasion.24

The analysis of this provides a general overview of the behavior of bank bondholdings. But do these bondholdings matter for bank lending? We turn to this question next.

4. Default, Bondholdings and Loans

Equipped with the results of the previous section, we now address our second question: what is the relationship between bondholdings and lending during default events?

4.1. Methodology

Let Λi,c,t denote the change in loans over assets made by bank i in country c between time t–1 and t. We run the following regression:

Λi,c,t=γ0+γ1Bi,c,t1+γ2Defc,t1+γ3Defc,t1Bi,c,t1+γ4Xi,c,t1+γ5Defc,t1Xi,c,t1+γ6Xc,t1+γ7Defc,t1Xc,t1+μi,c,t.(3)

Coefficient γ2 captures the average effect of default on bank loans. A negative value of γ2 suggests that, all else equal, sovereign defaults are associated with a subsequent reduction in bank lending. The main focus of our analysis is on coefficient γ3. A negative value of γ3 is consistent with the hypothesis that default reduces bank lending through government bondholdings: it implies that, when governments are in default, banks that hold more public bonds are the ones that reduce their lending the most.

Once again, controlling for vectors Xi,c,t–1 and Xc,t–1 and for their interactions with the sovereign default dummy allows us to control for cross-bank and cross-country variation in the proclivity of banks to make loans. Together with country and country*time dummies, these controls reduce the likelihood that our results are due to omitted variables, like recession-induced drops in the demand for loans by firms. They also reduce the likelihood of identifying spurious correlations, like the ones that would arise if larger banks both hold more bonds and make more loans during default years.

The interpretation of coefficient γ3 raises an interesting question. If higher bonds are indeed associated with a stronger drop in loans (i.e. γ3 < 0) in default years, is this drop related to the bonds that banks normally purchase in non-default years or to the bonds purchased during the default events themselves? As we discuss in Section 5, this distinction is important: shedding light on whether the dangerous embrace between the government and banks originates in normal times or in the proximity of sovereign defaults has important positive and normative implications. We address this question in three alternative ways.

First, we run a cross sectional version of Equation (3) focusing on the change in loans around default episodes. In this regression, the dependent variable is the change in a bank’s loan-to-asset ratio occurring in the first two years of default, while the main explanatory variable is the bank’s bondholdings in the year prior to default. This is the simplest way to check whether pre-default bondholdings matter. One shortcoming here is that bondholdings in the year before default may be influenced by the anticipation of default by the bank. As a result, we perform a second test in which the explanatory variable is a bank’s average bondholdings in the three years prior to the default. This test allows us to establish whether or not the change in a bank’s lending behavior around a default event is related to the public bonds that the bank has well before the default event, before sovereign risk materializes.

While useful, this last test has still two shortcomings. First, its cross sectional nature does not allow us to control for a full set of country*time dummies. Second, by considering only bonds accumulated for the most part well before default, this test does not allow us to properly assess the impact of bondholdings accumulated in the run-up to and during the default itself, which may be an important part of the story.

We address these concerns by running yet another specification of Equation (2), in which we decompose a bank’s holdings of public bonds Bi,t into: (i) a “normal-times” average component bi,n,t measuring a bank’s average bondholdings in all non-default years up to year t, and; (ii) a “residual” component bi,t = Bi,t –bi,n,t, which captures any differential take-up in public bonds relative to the normal-times average. We then use these components as separate explanatory variables in Equation (2). Because this regression uses the full panel structure of our data, it can include a full set of country*time dummies.

To interpret this regression, we view the component bi,n,t as capturing a bank’s average demand for bonds in the course of its everyday business activity. Hence, the interaction of bi,n,t with the default dummy proxies for the effect of sovereign defaults that is transmitted through the bonds that are normally held by bank i for its regular operations.25 According to this interpretation, the residual bi,t captures any discrepancy between observed bondholdings and typical bondholdings in normal times. This discrepancy may be due to a number of reasons, including–as we have mentioned–distorted incentives to accumulate bonds precisely when they are risky.

4.2. Results

Table VI reports our estimates. Columns (1)-(4) include as explanatory variables the total bondholdings of bank i in year t–1, Bi,c,t–1, as well as our sovereign default dummy, various bank-level controls, the realized return of bonds,26 and their interactions. Column (1) report results of a specification without any fixed effects. It shows that bondholdings have a large negative effect on subsequent lending during default years.

Table VI–

Bondholdings and Changes in Loans

The table presents coefficient estimates from pooled OLS regressions. The dependent variable changes in loans is computed as loans outstanding in year t minus loans outstanding in year t-1, divided by total assets. The main independent variables are bank bondholdings, computed as bondholdings divided by total assets; pre-default bank bondholdings, coimputed as bondholdings in the year prior to the first year of a sovereign default, divided by total assets; average pre-default bank bondholdings, computed as the average of bondholdings divided by total assets in the last three years prior to the first year of a sovereign default; bank average non-default years bondholdings, computed as the average of bank bondholdings in all the non-default years prior to and including year t–1, bank time-varying bondholdings, computed as bank bondholdings minus bank average non-default years bondholdings. Standard errors (in parentheses below the coefficient estimates) are adjusted for heteroskedasticity using the Huber (1967) and White (1980) correction, as well as for clustering at the bank level using the Huber (1967) correction. *** indicates significance at the 1% level; ** indicates significance at the 5% level; *indicates significance at the 10% level.

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Column (2) presents estimation results with year dummies but without country dummies; column (3) presents results with year and country dummies, to control for time-invariant country-level differences in the quality of economic policy and other institutional differences; column (4) presents estimation results with year, country, and country*year dummies, to control for uniform demand shocks at the country-year level. The results confirm a strong negative effect of bondholdings on subsequent lending during default years. Column (5) repeats this test in the full sample, i.e., including also the countries for which we do not observe sovereign bond returns, and shows that our result is, if anything, stronger. Remarkably, columns (4) and (5) show that within the same defaulting country-year, it is the banks most loaded with government bonds that reduce their lending the most. This is the basic result of this section. This raises a question: is this association driven by the bonds accumulated in non-default years or by those accumulated during the default event itself?

Columns (6) and (7) address this question by looking at the cross-sectional variation in changes in loans around a default and seeing how it correlates with bonds held before the default. Column (6) shows that the bonds held in the year before the default have a strong negative association with the subsequent decrease in lending during the first two years of a default event. This finding suggests that the bonds accumulated prior to default matter for the decrease in lending. However, it could still be that bank purchases of bonds in the year prior to default reflect the deteriorating prospects of sovereign risk and not their regular business activity.

To address this possibility, in column (7) we focus on the average bondholdings held by the banks in the three years prior to the beginning of default, to attempt to better capture the effect of bondholdings held during the course of banks’ ‘normal’ business activity. Column (7) shows a strong negative association between a bank’s average bondholdings in the three years prior to a default and its change in loans during the first two years of a default event. The effects are quantitatively large: a 10% increase in the average level of bondholdings in the three years before default is associated with a 3.6% cumulative reduction in loans during the first two years in default. This result is consistent with a standard balance sheet effect, whereby losses on preexisting government bonds reduce bank capital, forcing the bank to deleverage and thus reducing its ability to intermediate funds towards investment. It is important to stress that these tests require bank data for a five-year window around a default, so that they effectively focus on large banks in large defaulting countries such as for example Argentina, Greece, and Ecuador. These results suggest that the effects of bondholdings on lending are pervasive, long-lasting and not limited to small banks that may go bust during the crisis.

Finally, columns (8) and (9) address this question in an alternative way, by splitting bonds into their “normal-times” and “residual components” as defined in Section 4.1. Column (8) introduces both variables while controlling for country dummies, time dummies, bank controls, and expected returns, as well as for their interactions with default. Thus, column (8) effectively amounts to a ‘decomposed version’ of column (3). Column (9) then adds country*year fixed effects, so it effectively amounts to a decomposed version of columns (5). We obtain two important results.

First, higher normal-times bonds are indeed associated with significantly fewer loans during default events. Second, the interaction of the residual component of bonds and the default dummy is also negative and significant, indicating that banks holding abnormally many bonds during default years are systematically less likely to make new loans. This negative association is interesting because, as we documented in Section 3, it is the large banks that are most likely to accumulate bonds during default years. Presumably, these banks also face strong investment opportunities. As a result, the drop in their loans during default seems likely to be induced by the bonds that they hold, and not by a drop in their relative demand for credit.

The estimates of column (7) may be contaminated by country-level unobserved shocks, though, such as a pre-existent decline in demand for credit by firms in the country. To rule out this possibility, column (8) adds a full set of country*time dummies. The coefficients of both components of bondholdings remain economically large and strongly statistically significant.

The economic effects of both the normal-time and residual component of bondholdings are large. A 10% annual increase in the normal-time component of bondholdings within a defaulting country is associated with a 2.1% decrease in lending; and a 10% annual increase in the residual component of bondholdings within a defaulting country is associated with a 2.0% decrease in lending.

The estimated marginal effects of the normal-time and the residual component of bondholdings on loans are thus similar in magnitude. To properly assess the contribution of these components, however, one needs to consider that in our sample banks tend to accumulate a much larger proportion of bonds in the years prior to default relative to those accumulated in the default years. In particular, in our sample of defaulting countries, average bank bondholdings during non-default years (13% of assets) represent 87% of their average bondholdings during default years (14.9% of assets). Coupled with the fact that in our sample banks loans as a share of assets are four times larger than bondholdings (in particular, loans represent approximately 53% of total assets), our estimates imply that a one-dollar increase in bonds translate into a 60-cent decrease in lending during default years; and that about 90% of this effect is due to the normal-time component of bondholdings, i.e. to the average bondholdings held by banks before the default took place.

Our data thus shows that, when a default takes place, there is a strong negative correlation between a bank’s bondholdings and the loans that it extends. In our sample, though, the bulk of the correlation is explained by the bonds accumulated in normal times. We discuss the economic implications of these findings in Section 5.

Before concluding our statistical tests, we mention two robustness tests that address important concerns regarding the results of this section. A first concern is that these results may be driven by relatively “unimportant” defaults, because approximately one half of the default episodes in our sample involve either small countries, or countries with a small banking sector, or both. We address this concern thoroughly by redoing our estimation in various possible ways: (i) we exclude the smaller defaulting countries in our sample, both as measured by GDP per capita, and by the economic magnitude of the debt defaulted, and; (ii) we exclude the defaulting countries with fewer than 5, 10, and 15 banks, respectively in our sample. As Table VII shows in columns (1)-(10), these exercises strongly confirm our main results, which–if anything–become both statistically and economically stronger. A second concern is that our default dummy is too blunt a variable to capture default crises. We repeat our analysis using the haircut measure of default constructed by Cruces and Trebesch (2013) and Zettelmeyer et al. (2012), which capture the severity of a default. As Table VII, columns (11)-(11) show, our main results are again confirmed and if anything the economic magnitude of the results is stronger. Finally, we repeat our analysis using the augmented measure of default that, in addition to the default identified by S&P, includes defaults identified as situations in which sovereign spreads exceed 1,000 basis points.27 As Table VII, columns (13)-(14) show, our main results are again confirmed.

Table VII–

Bondholdings and Changes in Loans: Robustness Tests

The table presents coefficient estimates from pooled OLS regressions. The dependent variable changes in loans is computed as loans outstanding in year t minus loans outstanding in year t-1, divided by total assets. The main independent variables are bank average non-default years bondholdings, computed as the average of bank bondholdings in all the non-default years prior to and including year t–1, bank time-varying bondholdings, computed as bank bondholdings minus bank average non-default years bondholdings. Largest defaults are Argentina’s, Russia’s Ukraine’s and Greece’s; Large defaults are Argentina’s, Russia’s Ukraine’s, Greece’s, Ecuador’s, Nigeria,’s, and Kenya’s. Standard errors (in parentheses below the coefficient estimates) are adjusted for heteroskedasticity using the Huber (1967) and White (1980) correction, as well as for clustering at the bank level using the Huber (1967) correction. *** indicates significance at the 1% level; ** indicates significance at the 5% level; * indicates significance at the 10% level.

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5 Interpretations and Implications of our Findings

What do we learn from our empirical analysis? While the main goal of our paper is descriptive, the correlations that we document are consistent with a simple narrative of the sovereign default-banking crisis nexus. As we already discussed at the end of Section 3, the demand for public bonds behaves differently in default and non-default years. During non-default years, banks’ bondholdings are consistent with the liquidity services of public bonds (Holmstrom and Tirole (1998)): banks demand low-risk public bonds when they have few investment opportunities, particularly in less financially-developed countries. During sovereign crises, instead, bondholdings patterns change. In those times, it is predominantly large banks that accumulate high-risk public bonds, consistent with an important role of bailout guarantees or moral suasion.

The evidence analyzed in Section 4 then seems to indicate that all bondholdings, regardless of their origin, hurt the ability or willingness of banks to extent new loans when sovereign default materializes. On the one hand, banks holding on average more bonds in the pre-default years significantly contract their loans during the default event. These banks may be cutting their loans for one or more of the following reasons: (i) losses on their existing public bonds force them to deleverage, or relatedly; (ii) they deliberately choose to remain exposed to sovereign risk, or finally because; (iii) the unavailability of safe public bonds prevents them from efficiently managing their liquidity. Either way, this correlation suggests that banks’ regular demand for bonds during normal times induces an adverse effect on bank lending once default strikes. On the other hand, banks with high bondholdings during the default years also significantly contract their loans. Regardless of whether these high bondholdings are due to banks reaching for yield or to government intervention, this correlation suggests that the banks’ demand for bonds during sovereign defaults is also detrimental to lending. The typical explanation for this effect is that purchases of bonds crowd out new loans on the asset side of banks.

One important feature of our dataset is that, by covering a wide sample of default and non-default years, it allows us to quantitatively evaluate the relative importance of these different bondholdings in transmitting sovereign defaults. In this respect, our data provide a rather clear result: in the countries and periods that we consider, average bondholdings in non-default years, which reflect banks’ normal activity, play a significantly larger role than bonds accumulated in the run-up to and during default years. First, the marginal adverse effect of bonds accumulated in non-default years is slightly larger than the marginal adverse effect of bonds bought during crises. Second, and most important, banks in our sample of defaulting countries hold many bonds in normal times (13.0%), and the average increase in bondholdings during crises is rather small by comparison (less than 2%).

These results provide a new perspective on the mechanisms whereby the sovereign default-banking crisis nexus comes into existence and operates. Fueled by the recent European sovereign crisis, much of the work on this nexus has focused on risk-taking by European banks (e.g., see Acharya and Steffen (2013)). Although this may well be the right strategy for the European context, our panoramic view of sovereign debt crises calls for paying close attention also to the bonds held by banks in normal times: average bondholdings of banks during non-default years appear to play a very important role in sovereign crises, and neglecting them might be problematic. This insight has both positive and, potentially, normative implications.

From a positive standpoint, our analysis suggests that the unfolding of sovereign crises is qualitatively different in emerging and advanced economies. In emerging economies, financial markets are less developed and banks hold a large amount of bonds in normal times (12.7% of assets in non-OECD countries). It is only natural that these bondholdings generate a large fraction of the adverse effects of sovereign defaults on bank lending. In developed economies, banks hold substantially fewer bonds in normal times (5% of assets in OECD countries). As a result, in these countries, banks’ take-up of public bonds during crises is likely to be more important relative to their total bondholdings. The patterns of bondholdings in our sample confirm this hypothesis. In the defaults by emerging countries in our sample, such as for example Argentina and Russia, banks hold many bonds before the default; if anything, they slightly decrease their bondholding as default approaches and, after default happens, large banks accumulate even more bonds. By contrast, banks in Europe’s more troubled economies held few bonds before 2008, but they accumulated large quantities of them as sovereign risk increased. In our sample, bondholdings between 2008 and 2010 went from 4.4% to 12.3% in Greek banks; from 6.7% to 11% in Irish banks; and from 3% to 8.1% in Portuguese banks.28 It thus seems highly likely that, in more advanced economies, the accumulation of bonds during crises (either due to a search for yield or to moral suasion) is responsible for a substantially larger portion of the adverse costs of default.

Our results also carry some potentially important normative implications. In the context of recent events, conventional wisdom holds that the European sovereign crisis became a banking crisis due to the specifics of bank regulation. In particular, the fact that regulation assigns a low risk weight to sovereign bonds even in times of crisis made it possible for banks to gamble in the sovereign bond market without being penalized by the regulator. This consideration is important, but our results suggest that the link between sovereign risk and banking crisis might result from deeper forces. If banks demand a sizeable amount of government bonds to carry out their normal business activities, as seems to be particularly the case in emerging economies but also in developed ones, sovereign defaults will undermine the functioning of the banking sector and bank lending over and above its risk taking during the crisis itself. In this context, proposed regulations to increase the risk weight of government bonds during sovereign crises may backfire, because they might exacerbate the pro-cyclicality of bank balance sheets without having much of an effect on the link between sovereign risk and the banking sector.

References

  • Acharya, Viral V., Itamar Drechsler, and Philipp Schnabl, 2013, A Pyrrhic victory? Bank bailouts and sovereign credit risk, Journal of Finance, forthcoming.

    • Search Google Scholar
    • Export Citation
  • Acharya, Viral V., and Raghuram G. Rajan, 2013, Sovereign debt, government myopia, and the financial sector, Review of Financial Studies 26, 15261560.

    • Search Google Scholar
    • Export Citation
  • Acharya, Viral V., and Sascha Steffen, 2013, The greatest carry trade ever? Understanding Eurozone bank risks, NBER working paper 19039.

    • Search Google Scholar
    • Export Citation
  • Andritzky, Jochen R. 2012, Government bonds and their investors: What are the facts and do they matter?, IMF working paper.

  • Arellano, Cristina, 2008, Default risk and income fluctuations in emerging economies, American Economic Review 98, 690712.

  • Arteta, Carlos, and Galina Hale, 2008, Sovereign debt crises and credit to the private sector, Journal of International Economics 74, 5369.

    • Search Google Scholar
    • Export Citation
  • Baskaya, Yusuf Soner, and Sebnem Kalemli-Ozcan, 2014, Government debt and financial repression: Evidence from a rare disaster, University of Maryland working paper.

    • Search Google Scholar
    • Export Citation
  • Battistini, Niccolò, Marco Pagano, and Saverio Simonelli, 2013, Systemic risk, sovereign yields and bank exposures in the Euro crisis, mimeo, Università di Napoli Federico II.

    • Search Google Scholar
    • Export Citation
  • Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine, 2000, A new database on financial development and structure, World Bank Economic Review 14, 597605.

    • Search Google Scholar
    • Export Citation
  • Berger, Allen, Christa Bouwman, Thomas Kick, and Klaus Schaeck, 2012, Bank risk taking and liquidity creation following regulatory interventions and capital support, mimeo, Case Western Reserve University.

    • Search Google Scholar
    • Export Citation
  • Borensztein, Eduardo, and Ugo Panizza, 2008, The costs of sovereign default, IMF working paper.

  • Broner, Fernando, Alberto Martin, and Jaume Ventura, 2010, Sovereign risk and secondary markets, American Economic Review 100, 15231555.

    • Search Google Scholar
    • Export Citation
  • Broner, Fernando, and Jaume Ventura, 2011, Globalization and risk sharing, Review of Economic Studies 78, 4982.

  • Brutti, Filippo, and Philip Sauré, 2013, Repatriation of Debt in the Euro Crisis: Evidence for the Secondary Market Theory, mimeo, University of Zurich.

    • Search Google Scholar
    • Export Citation
  • Claessens, Stijn, and Luc Laeven, 2004, What drives bank competition? Some international evidence, Journal of Money, Credit and Banking 36, 563583.

    • Search Google Scholar
    • Export Citation
  • Comelli, Fabio, 2012, Emerging market sovereign bond spreads: Estimation and back-testing, IMF working paper.

  • Cruces, Juan J., and Christoph Trebesch, 2013, Sovereign defaults: The price of haircuts, American Economic Journal: Macroeconomics 5, 85117.

    • Search Google Scholar
    • Export Citation
  • Eaton, Jonathan, and Mark Gersovitz, 1981, Debt with potential repudiation: Theoretical and empirical analysis, Review of Economic Studies 48, 289309.

    • Search Google Scholar
    • Export Citation
  • Gelos, R. Gaston, Sahay Ratna and Guido Sandleris, 2011, Sovereign borrowing by developing countries: What determines market access?, Journal of International Economics 83(2), pages 243254.

    • Search Google Scholar
    • Export Citation
  • Gennaioli, Nicola, Alberto Martin, and Stefano Rossi, 2014, Sovereign default, domestic banks, and financial institutions, Journal of Finance 69, 819866.

    • Search Google Scholar
    • Export Citation
  • Greenwood, Robin, and Dimitri Vayanos, 2014, Bond supply and excess bond returns, Review of Financial Studies 27, 663713.

  • Hannoun, Hervé, 2011, Sovereign risk in bank regulation and supervision: Where do we stand?, Bank for International Settlements.

  • Holmström, Bengt, and Jean Tirole, 1993, Market liquidity and performance monitoring, Journal of Political Economy 101, 678709.

  • Kalemli-Ozcan, Sebnem, Bent E. Sorensen and Sevcan Yesiltas, 2012, Leverage across banks, firms and countries, Journal of International Economics 88, 284298.

    • Search Google Scholar
    • Export Citation
  • Kim, Gloria, 2010, EMBI Global and EMBI Global Diversified, rules and methodology, Global Research Index Research, J.P. Morgan Securities Inc.

    • Search Google Scholar
    • Export Citation
  • Krishnamurthy, Arvind and Annette Vissing-Jorgensen, 2012, The aggregate demand for treasury debt, Journal of Political Economy 120, 233267.

    • Search Google Scholar
    • Export Citation
  • Kumhof, Michael, and Evan Tanner, 2008, Government debt: A key role in financial intermediation, in Carmen M. Reinhart, Carlos Végh, and Andres Velasco, eds.: Money, Crises and Transition, Essays in Honor of Guillermo A. Calvo.

    • Search Google Scholar
    • Export Citation
  • Levy-Yeyati, Eduardo, Maria Soledad Martinez Peria, and Sergio Schmukler, 2010, Depositor behavior under macroeconomic risk: Evidence from bank runs in emerging economies, Journal of Money, Credit, and Banking 42, 585614.

    • Search Google Scholar
    • Export Citation
  • Livshits, Igor, and Koen Schoors, 2009, Sovereign default and banking, mimeo, University of Western Ontario.

  • Mengus, Eric, 2012, Foreign borrowing, portfolio allocation, and bailouts, mimeo, University of Toulouse.

  • Opler, Tim, Lee Pinkowitz, Réné Stulz, and Rohan Williamson, 1999, The determinants and implications of corporate cash holdings, Journal of Financial Economics 52, 346.

    • Search Google Scholar
    • Export Citation
  • Ozer-Balli, Hatice, and Bent E. Sorensen, 2013, Interaction effects in econometrics, Empirical Economics, forthcoming.

  • Pescatori Andrea, and Amadou N.R. Sy, 2007, Are debt crises adequately defined? IMF Staff Papers 54(2), 306337.

  • Reinhart, Carmen M., and M. Belen Sbrancia, 2011, The liquidation of government debt, NBER working paper 16893.

  • Sandleris, Guido, 2012, The costs of sovereign defaults: theory and empirical evidence, mimeo, Universidad DiTella.

  • Stock, James, and Motohiro Yogo, 2005, Testing for Weak Instruments in Linear IV Regression. In: Andrews DWK Identification and Inference for Econometric Models. New York: Cambridge University Press; pp. 80108.