Information about Western Hemisphere Hemisferio Occidental
A Guide to IMF Stress Testing

Chapter 17. The Global Financial Crisis and Its Impact on the Chilean Banking System

Li Ong
Published Date:
December 2014
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Information about Western Hemisphere Hemisferio Occidental
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Jorge A. Chan-LauThis chapter was previously published as IMF Working Paper 10/108 (Chan-Lau, 2010). It has benefited from detailed comments by and discussions with Rodrigo Alfaro, Pedro de Beltran, Martin Cerisola, Daniel Oda, and Robert Rennhack as well as seminar participants at the Central Bank of Chile and the IMF.

This chapter explores how the global turmoil affected the risk of banks operating in Chile and provides evidence that could help strengthen work on vulnerability indicators and off-site supervision. In addition, it provides a background framework to assess default risk codependence, or CoRisk, between Chilean banks and global financial institutions; and assesses government measures aimed at reducing systemic risk in the domestic banking sector and the recommendations to allocate sovereign wealth fund assets to domestic banks.

Method Summary

Method Summary
OverviewCoRisk analysis applies quantile regressions to determine how the default risk of an institution/sector/country is affected by the default risk of another, after controlling for common sources of risk.
ApplicationThe method is used to measure interconnectedness risk across institutions/sectors/countries.
Nature of approachEconometric analysis.
Data requirementsSecurity prices, risk measures.
StrengthsThe method captures both direct and indirect sources of interconnectedness; it allows for the inclusion of information from other explanatory variables other than security prices and risk measures.
WeaknessesThe method has the same weaknesses as any econometric model, including model misspecification; statistical causality may not correspond to economic causality.
ToolStandard econometrics package.

Financial systems in Latin America have not been immune to the ongoing financial turmoil. The situation has been brought about by the large presence of foreign-owned institutions and substantial cross-border claims. In particular, the crisis has highlighted the significant degree of interconnectedness and risk codependence among financial institutions, a development brought about by direct sources of exposure, such as interbank borrowing, and indirect sources of exposure, such as those related to common business practices like wholesale funding.

Chile, as other open emerging market economies with highly integrated financial systems and capital markets, has been affected by developments in global financial markets (Banco Central de Chile, 2009). Although the financial system has remained resilient to the significant shocks experienced since September 2008, the Chilean authorities took several measures to minimize domestic disruptions and preserve stable conditions in the domestic financial system. These measures included the flexibilization of reserve requirements, swap lines, as well as government auctions of foreign currency denominated deposits for domestic banks. In addition, the Advisory Committee on Sovereign Wealth Funds has put forward a recommendation that domestic banks be eligible institutions for sovereign wealth fund (SWF) deposits, reflecting in part heightened risks in foreign financial institutions.

The analysis in this chapter explores how the global turmoil affected the risk of banks operating in Chile and provides evidence that could help strengthen work on vulnerability indicators and off-site supervision. In addition, it provides a background framework to assess government measures aimed at reducing systemic risk and default codependence (or CoRisk) as well as the recommendations to allocate SWF assets to domestic banks.

1. A First Glimpse on the Potential Exposure to Foreign Banks

Foreign bank claims on the domestic economy are one important source of interconnectedness and direct exposure to shocks affecting the global banking system. Bank for International Settlements (BIS, 2009) banking data show that foreign bank claims on Chile, measured as percent of the recipient country’s GDP, are higher than those on other emerging market countries in Latin America and elsewhere, reflecting the openness of the country’s financial system (Table 17.1). The exception are Eastern European countries, where foreign bank claims are substantial following the wave of acquisitions by Western European banks, especially those based in Austria and Germany.

Table 17.1Foreign Banks’ Claims on Selected Emerging Market Countries

(in percent of GDP1)

Czech Republic24.522.989.681.1
South Korea19.622.232.635.7
South Africa12.712.539.135.1
Sources: Bank for International Settlements (BIS); and IMF staff calculations.

Calculated using end-2008 GDP figures.

Based on external asset positions, as of end-December 2008.

Based on consolidated claims on an immediate borrower basis, as of end-December 2008.

Sources: Bank for International Settlements (BIS); and IMF staff calculations.

Calculated using end-2008 GDP figures.

Based on external asset positions, as of end-December 2008.

Based on consolidated claims on an immediate borrower basis, as of end-December 2008.

Most of the claims on Chile are held by Spanish banks (Table 17.2). The dominant presence of Spanish banks is explained by the important role played by Banco Bilbao Vizcaya Argentaria (BBVA) and Banco Santander in the Chilean banking system. Between them, the two institutions account for 33 percent of the assets in the banking system, 24 percent of the nonderivatives financial instruments, and 40 percent of the notional outstanding amount of gross derivatives positions. Furthermore, because of the scale of its operations, measured in terms of assets, Banco Santander is required to hold regulatory capital in excess of 11 percent compared with 8 percent for other banks.

Table 17.2Foreign Banks’ Consolidated Claims on Chile by Nationality on an Immediate Borrower Basis
United States17.410.76.8
United Kingdom3.80.00.0
Sources: Author; and Bank for International Settlements.
Sources: Author; and Bank for International Settlements.

Spanish and U.S. banks have been losing market share to other European banks, and unallocated claims have become more important. U.S. bank lending has further declined because of the subprime crisis (Figure 17.1). Unallocated claims, as share of total claims, are up to 13 percent at end-2008 from 8.9 percent at end-2007. Assessing the risks from these claims is difficult because their geographic origin is not known.

Figure 17.1Chile: Foreign Bank Claims, in Percent of Total

Sources: Author; and Bank for International Settlements.

Cross-country consolidated claims offer only a partial view on potential cross-border vulnerabilities in a country’s banking system. In the specific case of Chile, they suggest that the domestic economy and banking system are vulnerable to adverse shocks to the Spanish banking system. Conversely, claims data suggest that Chile should be relatively insulated from adverse shocks to the British and Canadian banking systems, because cross-border claims ceased to exist from early 2003.

The claims data, however, may not be sufficient to identify risks associated with “second-round effects.” In particular, Spanish banks are highly exposed to developments in the British economy because one-third of their claims are on the United Kingdom. Similarly, British banks hold one-tenth of their claims on Spain. The cross-border claims between Spain and the United Kingdom create exposures for the Chilean economy, as shocks in the United Kingdom will be transmitted via Spanish banks. Similarly, negative shocks to the Chilean economy can be transmitted to the United Kingdom with Spanish banks serving as conduits. The next section explains how to capture these second-round effects using market-based information.

2. Assessing the Impact of the Crisis on Chilean Banks Using Market Information: Risk Measures, Data, and Empirical Method

The assessment of the exposure of banks operating in any given jurisdiction is not a straightforward exercise owing to data limitations. Institutional data on cross-market, cross-currency, and cross-country linkages are, at best, not readily available. As the previous section discussed, aggregated consolidated claims data provide only a snapshot into potential cross-country exposures. In addition, the recent crisis has shown that linkages are not restricted exclusively to direct exposures but can arise from indirect channels. One example of such a channel is the homogeneity of banks’ balance sheets that make them vulnerable to a marked-to-market shock. In the case of an open economy like Chile, where foreign banks play an important role in intermediating funds, indirect exposures may be more closely associated with the linkages of parent banks with other global financial institutions or the use of similar investment and lending strategies in domestic markets.1

Information from security prices helps to deal with data limitations and imperfect knowledge about exposures across financial institutions. When market prices are available, it is possible to use different econometric and empirical methods to measure the CoRisk, or risk codependence across financial institutions, while accounting for potential nonlinear effects. This section starts by describing the market-based risk measure used in this study, describes the data used in the analysis, and concludes by explaining how to construct the CoRisk measures using quantile regressions.2

A. Expected default frequency measures

The market-based risk measure used here is the expected default frequency (EDF), a measure reported by Moody’s KMV (MKMV) for a worldwide sample of banks and financial institutions. Although there are several risk measures based on market information, such as bond or credit default swap (CDS) spreads, EDFs offer some advantages over them. Foremost among them is that EDFs are objective or real-world default probabilities. In contrast, default probabilities measures extracted from bond or CDS spreads are risk neutral and tend to overstate their real-world counterparts because of the presence of a default risk premium.3 EDFs also combine both market information from equity prices and the nonmarket information from the liability side of the balance sheet of the firm. Finally, empirical studies have shown that EDF-like measures can explain default risk in equity returns and forecast financial institutions’ failures.4

Conceptually, the EDF is based on the distance to default.5 The distance-to-default measure is built on the insight that the default of a firm occurs when the asset value of the firm is less than what the firm owes to its debtors. What the firm owes is commonly referred to as the default barrier, and in practice, it is calculated as the face value of short-term liabilities plus half of the face value of long-term liabilities. The wider the gap between the asset value and the default barrier, the safer the firm is. Similarly, the less volatile the asset value is, the safer the firm is because the likelihood of hitting the default barrier diminishes. The distance to default, therefore, can be expressed simply as

The distance-to-default measure can be constructed using information from equity prices and balance sheet information. This information in turn helps to determine the market value of assets and the asset volatility of a firm. For a given firm, MKMV obtains the firm’s EDF from its distance-to-default measure from the empirical calibration of various levels of distance to default to actual default probabilities based on a proprietary historical database. EDFs, therefore, are equivalent to objective default probabilities and can be associated with credit ratings, as shown in Table 17.3. The mapping between EDFs and Moody’s ratings facilitates comparison with institutions rated by rating agencies but not yet included in the MKMV database.

Table 17.3EDFs and Equivalent Moody’s Credit Ratings
Moody’s RatingEDFMoody’s RatingEDF
Source: Moody’s KMV.Note: The table shows the equivalence between five-year EDFs, in percent, and Moody’s credit ratings scale. EDF = expected default frequency.
Source: Moody’s KMV.Note: The table shows the equivalence between five-year EDFs, in percent, and Moody’s credit ratings scale. EDF = expected default frequency.

B. Data

This study uses EDFs to measure the CoRisk induced by Latin American and global financial institutions on their Chilean counterparts. Table 17.4 shows the financial institutions included in the analysis. In addition to financial institutions operating in Chile, the sample includes financial institutions in Brazil, Colombia, and Peru; and major global banks in Canada, Europe, and the United States.6 For each of these institutions, weekly five-year EDF series were constructed from daily data for the period May 2, 2003 to February 27, 2009.7

Table 17.4Financial Institutions Included in the Analysis
Chilean institutionsDutch institutionsSpanish institutions
Scotia Bank ChileABN AmroBBVA
CorpBanca1 NG GroppeBanco Santander
Banco de Creditoe
Banco de ChileFrench institutionsSwiss institutions
Banco BBVA ChileBanque Nationale ParibasCredit Suisse
Credit AgricoleUBS
Brazilian institutionsSociete Generale
Banco BradescoU.K. institutions
Banco ItauGerman institutionsHSBC
Colombian institutionsDeutsche BankRoyal Bank of Scotland
Banco BBVA ColombiaStandard Chartered
Banco de BogotaPeruvian institutions
BanColombiaBanco ContinentalU.S. institutions
Banco de OccidenteBanco de CreditoBank of America
Banco Santander ColombiaScotiaBank PeruMorgan Stanley
Corporacion FinancieraGoldman Sachs
Grupo AvalItalian institutionsCitigroup
Banca IntesaWells Fargo
Canadian institutionsMedioBancaJP Morgan
Bank of Nova ScotiaUnicreditoBear Stearns
CIBCLehman Brothers
Royal Bank of CanadaMerrill Lynch Wachovia
Source: Author.
Source: Author.

The analysis includes a subset of Chilean institutions that represent a large share of the systemic core of the banking system. Only 6 Chilean institutions out of a total of 25 banks reporting to the Banking Supervisory Agency/Super-intendencia de Bancose Instituciones Financieras (SBIF) are included. But as of end-January 2009, the Chilean institutions analyzed accounted for 70 percent of the assets in the banking system, 56 percent of nonderivatives financial instruments positions, and 65 percent of the gross derivatives positions as measured by notional outstanding amounts (SBIF, 2009a, 2009b).8

Default risk increased rapidly because of problems in the U.S. banking system. Figure 17.2 shows how default risk in each country, measured as the average five-year EDF for the country’s banks, has evolved since early 2006.9 In general, EDFs were rather compressed until early 2008 but started widening in 2008 following the failure of Bear Stearns (March 2008), the bankruptcy of Lehman Brothers, and the bailout of American International Group in September 2008. The data seem to show that the crisis originated in the United States, where EDFs widened earlier than in other countries with the exception of Germany. Note that the German banks included in the sample, as the U.S. institutions, were reportedly important players in the structured credit market. Based on Table 17.3, at end-February 2009 the average implied rating of U.S. and German institutions was B3 but inching dangerously downward to Caa1.

Figure 17.2Five-Year EDFs, in Percent

Sources: Author; and Moody’s KMV.

Spanish institutions, among European ones, were among the least affected by the crisis. Arguably more conservative lending practices vis-à-vis other European banks, limited or no exposure to structured credit products referred to as “toxic assets” nowadays, and the use of regulatory dynamic provision requirements contributed to partly insulate Spanish banks from the stresses experienced by financial institutions in other countries.

The evolution of default risk in Latin America, where Spanish institutions have a substantial footprint, is rather similar to that of Spain. The average EDF remained in the range of 1 to 1½ percent by end-February 2009. Overall, banks in Latin America have little or no exposure to subprime assets and have small trading books relative to their banking book, which helps to insulate them from market shocks. Among Latin American countries, Chile has been the least affected by the global turmoil. The next section formally analyzes the comovements between individual institutions.

C. Empirical method: CoRisk estimation using quantile regressions

CoRisk, or risk codependence, can be defined as the increase in the risk of one institution conditional on the risk of a peer institution. Although there is no unique or generally accepted method to measure CoRisk, this study uses CoRisk analysis, a method based on quantile regressions motivated by the insights of Adrian and Brunnermeier (2008) and introduced in Chan-Lau (2009).10

The basic question to answer is how the default risk of an institution is affected by the default risk of another institution, after controlling for common sources of risk. In statistical terms, the goal is to learn f (y | x,θ), the conditional distribution of the default risk of institution y, given common drivers of default risk and the default risk of the other institution, which are denoted by x, where θ is a set of parameters that needs to be inferred from observed realizations of x. Ordinary least squares (OLS) is a useful technique to extract this information. However, OLS can provide information only about the mean relationship across institutions’ default risk. Because this relationship is nonlinear, OLS may have some serious limitations.

Quantile regression is an alternative to the use of nonlinear models that can capture some of the nonlinearities of the relationship across institutions’ default risk. Quantile regression, first introduced by Koenker and Bassett (1978), extends the OLS intuition beyond the estimation of the mean of the conditional distribution f (y | x,θ). It allows the researcher to “slice” the conditional distribution at the quantile (percentile) of interest, τ, and obtain the corresponding cross-section of the conditional distribution fτ (y | x,θ).

Quantile regression makes it possible to evaluate the response of the independent variable on particular segments of the conditional distribution. For instance, when a linear model is used to analyze systemic interlinkages, it is expected that the coefficients of the regressors change with the level of risk. In terms of model estimation, quantile regression does not consist of estimating a number of separate OLS regressions in nonoverlapping samples after sorting the data by quantiles (or percentiles), though the use of the term “quantile” may suggest so. Using nonoverlapping samples could introduce a small sample problem when dealing with lower and/or higher quantiles of the data, a problem often found when analyzing data with extreme value theory techniques.

Quantile regressions make the best use of all data available in the sample by weighting each available observation. In a quantile regression, the parameters are obtained by solving an optimization program that uses all the information contained in the data sample. The parameters are obtained from the minimization of the sum of residuals, y, where the latter are weighted by a check function, ρτ, that depends on the quantile of interest, τ:

where y is the dependent variable, ξ(xi,β) is a linear function of the parameters β and the exogenous variables xi, and ρτ(.) is a weighting function for each observation. More specifically, the function assigns a weight equal to the quantile τ if the residual is positive or a weight equal to τ – 1 if the residual is negative. The minimization can be solved using standard linear programming methods. The covariance matrices are usually estimated using bootstrap techniques and remain valid even if the residuals and explanatory variables are not independent (Koenker, 2005).

For analyzing CoRisk between Chilean and global banks, the following equation was estimated for τ set equal to the 95th quantile:

where EDFi is the EDF of institution i, Rk denotes the kth common aggregate risk factor, and Clean EDFj is the component of EDFj that is orthogonal to the common aggregate risk factors Rks, which is referred to here onward as the orthogonal EDF component. By using the orthogonal component, equation (17.3) isolates the idiosyncratic effect of institution j on institution i. The fitted values using equation (17.3) will be referred to as CoRisk EDF.

Economic theory can be used to guide the choice of aggregate risk factors. Usually, the common risk factors include variables such as the slope of the term structure of interest rates and the implied volatility index (VIX) as a proxy for investor sentiment. The aggregate risk factor in this study, however, was constructed by extracting the main principal components corresponding to the EDFs of all institutions in the sample excluding the Chilean institutions.11 Only the first principal component was included as an aggregate risk factor because it accounted for close to 95 percent of the total variability in the data. Furthermore, as Figure 17.3 illustrates, the first principal component can be roughly identified with default risk in the global banking system.

Figure 17.3Aggregate Risk Factor

Sources: Author; and Moody’s KMV.

Note: The aggregate risk factor corresponds to the first principal component extracted from the EDFs of all institutions in the sample excluding the Chilean institutions. EDF = expected default frequency.

3. Results

Banks in Chile have been affected mainly by aggregate risk in the global financial system and to a lesser extent by idiosyncratic shocks affecting regional and international banks. The impact of changes in aggregate risk can be approximated roughly by the difference between the median EDF and the unconditional EDF measured at the 95th percentile (Table 17.5). For Chilean banks, the unconditional EDF is two to three times higher than the median EDF. The impact of idiosyncratic shocks can be gauged from the difference between the conditional EDF, or CoRisk EDF, obtained from equation (17.3) and the unconditional EDF. The median CoRisk EDF exceeds the unconditional 95th percentile EDF by 15 to 100 percent, depending on the institution analyzed.

Table 17.5CoRisk between Financial Institutions Abroad and Those Operating in Chile, Measured as Expected Default Frequency(in percent)
Bank 1Bank 2Bank 3Bank 4Bank 5Bank 6
Median EDF0.710.
95th percentile EDF2.680.240.240.500.230.76
Latin American institutions
Banco Bradesco, Brazil5.940.270.310.710.300.91
Banco Itau, Brazil5.750.270.300.680.100.88
Banco BBVA, Colombia3.
Banco de Bogotá, Colombia5.870.320.300.700.330.83
Banco de Occidente, Colombia3.510.300.270.590.390.85
Banco Santander, Colombia3.840.310.270.550.460.73
BanColombia, Colombia5.
Corporacion Financiera, Colombia5.860.270.320.670.350.92
Grupo Aval, Colombia5.520.340.340.700.410.85
Banco Continental, Perú5.290.320.310.680.310.89
Banco de Credito, Perú5.300.310.250.680.370.90
Scotiabank, Peru5.980.370.310.720.200.69
U.S. institutions
Bank of America2.890.430.310.730.580.62
Morgan Stanley4.020.310.310.680.330.90
Goldman Sachs5.520.320.290.660.370.91
Wells Fargo3.700.320.250.540.350.79
Bear Stearns5.420.260.240.680.370.80
Lehman Brothers6.140.430.420.760.451.01
Merrill Lynch6.250.370.390.790.510.92
JP Morgan5.440.320.340.690.350.88
Canadian institutions
Bank of Nova Scotia3.440.340.290.540.310.80
Royal Bank of Canada5.580.260.240.680.380.78
European institutions
BBVA, Spain4.440.280.200.520.390.88
Banco Santander, Spain4.
Banque Nationale Paribas, France3.820.330.290.590.300.89
Credit Agricole, France5.730.280.330.690.390.89
Societe Generale, France5.550.270.300.700.330.89
Commerzbank, Germany6.080.270.330.720.300.91
Deutsche Bank, Germany5.960.320.310.720.310.92
Banca Intesa, Italy5.900.290.310.700.260.87
Mediobanca, Italy5.990.290.320.700.300.90
Unicredito, Italy5.540.300.310.700.420.87
Credit Suisse, Switzerland5.510.290.190.600.190.84
UBS, Switerzerland4.490.320.270.710.350.84
Barclays, United Kingdom3.730.310.280.630.380.84
HSBC, United Kingdom4.290.310.280.620.370.86
Lloyds, United Kingdom5.510.260.260.580.290.91
Royal Bank of Scotland, United Kingdom4.410.360.310.620.360.84
Standard Chartered, United Kingdom3.710.260.200.680.370.80
ABN Amro, Netherlands4.
ING, Netherlands3.490.310.270.600.320.88
Sources: Author; and Moody’s KMV.
Sources: Author; and Moody’s KMV.

Bank vulnerability to adverse idiosyncratic shocks affecting other banks seems related to leverage, external debt and/or obligations, and the strength of the parent institution in the case of foreign-owned banks. Higher unconditional and CoRisk EDFs are associated with highly levered banks and those with high external debt ratios, where the latter are measured as debt owed to foreign banks. This is the case, for instance, for Banco de Crèdito e Inversiones. Among foreign-owned banks, the percent difference between the CoRisk EDF and the unconditional EDF risks are higher for Bank 1 and Bank 5, owned by Bank of Nova Scotia and Citibank, respectively, than for the Spanish-owned institutions. When the CoRisk EDF is measured as percent changes vis-à-vis the unconditional EDF, ScotiaBank and Banco de Chile are the two institutions most affected by institution-specific shocks.

Changes in implied ratings also highlight the relative importance of aggregate shocks vis-à-vis bank-idiosyncratic shocks. Table 17.6 shows the Moody’s five-year credit ratings implied by the CoRisk and unconditional EDFs according to the mapping reported in Table 17.3.12 Compared with the median rating, the unconditional 95th percentile EDF implies a downgrade of three to four notches, which can be attributed to the aggregate shock. In contrast, idiosyncratic shocks to foreign institutions induce, on average, at most one conditional rating downgrade on Chilean institutions from the rating implied by its unconditional 95th percentile EDF.

Table 17.6CoRisk between Financial Institutions Abroad and Those Operating in Chile Measured as Moody’s Credit Ratings
Bank 1Bank 2Bank 3Bank 4Bank 5Bank 6
Median ratingBa2A2Aa3Baa1A2Baa3
Unconditional rating, 95th percentileB2Baa2Baa2Ba1Baa2Ba2
Latin American institutions
Banco Bradesco, BrazilB3Baa2Baa3Ba2Baa2Ba3
Banco Itau, BrazilB3Baa2Baa2Ba2Baa3Ba3
Banco BBVA, ColombiaB2Baa2Baa1Ba2Baa3Ba3
Banco de Bogotà, ColombiaB3Baa3Baa2Ba2Baa3Ba2
Banco de Occidente, ColombiaB2Baa2Baa2Ba2Baa3Ba3
Banco Santander, ColombiaB2Baa3Baa2Ba2Ba1Ba2
BanColombia, ColombiaB3Baa2Baa2Ba2Baa3Ba2
Corporacion Financiera, ColombiaB3Baa2Baa3Ba2Baa3Ba3
Grupo Aval, ColombiaB3Baa3Baa3Ba2Ba1Ba3
Banco Continental, PerúB3Baa3Baa3Ba2Baa3Ba3
Banco de Credito, PerúB3Baa3Baa2Ba2Baa3Ba3
Scotiabank, PeruB3Baa3Baa3Ba2Baa1Ba2
U.S. institutions
Bank of AmericaB2Ba1Baa2Ba2Ba2Ba2
Morgan StanleyB2Baa2Baa2Ba2Baa3Ba3
Goldman SachsB3Baa3Baa2Ba2Baa3Ba3
Wells FargoB2Baa3Baa2Ba2Baa3Ba2
Bear StearnsB3Baa2Baa2Ba2Baa3Ba2
Lehman BrothersB3Ba1Ba1Ba2Ba1Ba3
Merrill LynchB3Baa3Baa3Ba2Ba1Ba3
JP MorganB3Baa3Baa3Ba2Baa3Ba3
Canadian institutions
Bank of Nova ScotiaB2Baa3Baa2Ba1Baa2Ba2
Royal Bank of CanadaB3Baa2Baa2Ba2Baa3Ba2
European institutions
BBVA, SpainB3Baa2Baa1Ba1Baa3Ba3
Banco Santander, SpainB3Baa2Baa2Ba2Baa3Ba3
Banque Nationale Paribas, FranceB2Baa3Baa2Ba2Baa2Ba3
Credit Agricole, FranceB3Baa2Baa3Ba2Baa3Ba3
Societe Generale, FranceB3Baa2Baa2Ba2Baa3Ba3
Commerzbank, GermanyB3Baa2Baa3Ba2Baa2Ba3
Deutsche Bank, GermanyB3Baa3Baa3Ba2Baa2Ba3
Banca Intesa, ItalyB3Baa2Baa3Ba2Baa2Ba3
Mediobanca, ItalyB3Baa2Baa3Ba2Baa2Ba3
Unicredito, ItalyB3Baa2Baa3Ba2Ba1Ba3
Credit Suisse, SwitzerlandB3Baa2Baa1Ba2Baa1Ba2
UBS, SwiterzerlandB3Baa3Baa2Ba2Baa3Ba2
Barclays, United KingdomB2Baa3Baa2Ba2Baa3Ba2
HSBC, United KingdomB3Baa2Baa2Ba2Baa3Ba3
Lloyds, United KingdomB3Baa2Baa2Ba2Baa2Ba3
Royal Bank of Scotland, United KingdomB3Baa3Baa3Ba2Baa3Ba2
Standard Chartered, United KingdomB2Baa2Baa1Ba2Baa3Ba2
ABN Amro, NetherlandsB3Baa2Baa1Ba2Baa3Ba3
ING, NetherlandsB2Baa3Baa2Ba2Baa3Ba3
Sources: Author; and Moody’s KMV.
Sources: Author; and Moody’s KMV.

Santander appears more resilient to external shocks affecting the parent bank and its subsidiaries in the region than BBVA. The rating of Santander Chile remains unchanged while BBVA experiences a one rating downgrade. Furthermore, Santander Chile ratings improve when BBVA and its subsidiaries are adversely affected by shocks. Arguably, this result suggests that Santander Chile gains market share at the expense of BBVA.

There are second-round effects on the Chilean banking system even in the absence of reported cross-border banking claims. For instance, since 2003 there have been no cross-border claims between Chile and the United Kingdom. Shocks affecting British banks, however, cause a one rating conditional downgrade in Chilean banks. This is also true, to a lesser extent, in the case of Canadian banks. Put together, these results suggest how information on direct exposures, such as consolidated claims, and market-based information, such as EDFs, complement each other and are useful for assessing risks in the financial sector.

Although Chilean banks are vulnerable to aggregate financial shocks, several factors may have contributed to make them relatively resilient to institution-specific shocks. First, reliance on external financing sources is limited because the domestic banking system funds its operations mainly through domestic deposits (60 percent of assets) and by issuing domestic securities (13 percent of assets) while external funding is small (5 percent of assets).13

Domestic funding, however, may be affected negatively going forward. Retail funding is being eroded by increased equity investment and the emergence of alternative investment vehicles targeted to retail customers, such as pension funds. Increased investment abroad by pension funds following the relaxation of foreign investment limits has gradually reduced the domestic investor base for bank securities (Saldías and others, 2008). These trends may foster increased external financing and, as the results show, lead to higher exposure to foreign banks’ idiosyncratic shocks.

Second, market risk in the system is limited. The trading book, mostly in government securities, accounted for 4 to 5½ percent of assets, and securities available for sale for 7 to 8 percent of assets. Although derivatives are held mostly for trading purposes, the net open position in the system was at most 1½ percent of assets during the period. Furthermore, derivatives trading mainly involves trading forward and swaps, so notional amounts may certainly overstate losses due to market risk. A back-of-the-envelope calculation suggests that at most 13 percent of assets are affected directly by market risk.

Third, credit risk remains the main source of vulnerability in the banking system. Banks are somewhat insulated from problems affecting other banks because credit risk is driven predominantly by developments in the domestic economy. According to aggregate balance sheet data, the banking book usually accounts for 67 percent of the assets in the banking system. Therefore, vulnerabilities are more likely to arise from the deterioration of creditworthiness in the corporate and household sectors.

Finally, counterparty risk within the domestic system appears limited and reduces exposures to international institutions through second-round-effects channels. Counterparty exposure can be estimated roughly as the sum of the trading exposure (4 to 5½ percent of assets), interbank lending (less than ½ percent of assets), and derivatives net open positions (1½ percent of assets). The reduced counterparty exposure translates into limited CoRisk exposure within Chilean banks, with banks experiencing at most a one rating downgrade conditional on other banks’ increase in default risk (Table 17.7).

Table 17.7CoRisk between Chilean Banking Institutions Measured as Expected Default Frequency (EDF) and Implied Moody’s Ratings
ScotiaBankCorpbancaBanco SantanderBanco de Credito

e Inversiones
Banco de ChileBBVA
Medium EDF0.710.
95th percentile EDF2.680.240.240.500.230.76
Banco Santander3.460.330.570.350.84
Banco de Credito3.980.330.270.370.80
e Inversiones
Banco de Chile3.730.330.280.580.81
Median ratingBa2A2Aa3Baa1A2Baa3
Unconditional rating,B2Baa2Baa2Ba1Baa2Ba2
95th percentile
Banco SantanderB2Baa3Ba2Baa3Ba2
Banco de CreditoB2Baa3Baa2Baa3Ba2
e Inversiones
Banco de ChileB2Baa3Baa2Ba2Ba2
Sources: Author; and Moody’s KMV.
Sources: Author; and Moody’s KMV.

Spanish banks are the more resilient ones to risks from other Chilean banks. As noted by rating agencies repeatedly, a source of strength for subsidiaries of foreign banks operating in Chile is the implicit support from the parent bank. As a result, conditional EDFs for Santander Chile and BBVA remain mostly unchanged. In contrast, ScotiaBank is highly vulnerable partly because of the high exposure of its parent bank, Bank of Nova Scotia, to the global shocks. Finally, in the case of Santander Chile, the bank may not be affected as much by shocks to other Chilean banks because its loan portfolio is quite different from those of other banks and by being required to hold a larger capital requirement than other banks.14

A recent trend worth monitoring, however, is the sudden pickup in claims on foreign banks that started only in October 2008. Although still relatively small, at less than 1 percent of assets, they may reflect increased deposits with parent banks and suggest that subsidiaries could be propping up liquidity and/or capital in parent companies.

4. Conclusion

Nowadays, domestic banking systems are highly interconnected within the global financial system. The oft-quoted phrase from Donne (1624), “No man is an island, entire of itself … any man’s death diminishes me” rings true in light of the increased globalization and rapid pace of innovation experienced in the international financial system.

The CoRisk analysis in this chapter has shown that, even in the absence of direct exposures with other countries in the region, the domestic banking system in Chile is vulnerable to adverse idiosyncratic shocks affecting other banks in the region as well as in advanced economies. The empirical evidence on the interconnectedness between the domestic banking system and the global banking system makes it imperative to continue advancing the agenda on cross-border supervision and coordinated crisis management with countries in the region and advanced economies.

Notwithstanding the large presence of foreign-owned institutions and spillover risks, it is worth noting the resilience of the system to idiosyncratic shocks. The magnitude of the institution-specific spillovers is relatively constrained as adverse shocks abroad translate, at most, into an implied one rating downgrade for most institutions after accounting for aggregate shocks. Nevertheless, some caution is warranted because increased perceptions of risk can cause substantial increases in funding rates to domestic banks or lead to a loss of confidence by depositors.

Although a formal analysis was not conducted, measures enacted by the government may have contributed to offset the surge in risk in the banking system. These measures include the flexibilization of reserve requirements, swap lines, as well as government auctions of foreign currency denominated deposits for domestic banks. Indeed, as Figure 17.2 shows, the passage and implementation of the measures kept the average EDF in Chile mostly flat during most of the second half of 2008. In contrast, the average EDF in Brazil and Peru, which were below or in line with the EDF of Chile in the first half of 2008, widened rapidly in the second half of 2008. In this regard, the authorities should continue to monitor conditions in the market closely and to ascertain that government-provided liquidity remains onshore.

The analysis provides some support to the recommendation by the Financial Advisory Committee to make domestic banks eligible for SWF deposits. Despite the spillover effects, the CoRisk analysis finds that Chilean institutions may be less vulnerable than banks abroad, especially those in advanced economies. Nevertheless, empirical analysis can offer only so much support, especially because it did not consider a number of relevant factors and alternatives. For instance, deposits with domestic banks may lead to “Dutch disease” problems. Also, the recommendation should be balanced against the alternative to invest in riskless assets, such as government bonds and bills. Finally, in the specific case of foreign-owned institutions, it is necessary to ensure that the domestic subsidiaries are effectively ring-fenced from weaker parent institutions to prevent the latter from draining resources from their subsidiaries.

Finally, going forward, risks in the domestic banking system may be more closely associated with credit risk in the household and corporate sectors rather than risk spillovers from the global financial system. Government support and intervention in the banking sector in advanced economies have helped stabilize the financial system and ease the liquidity problems experienced in the second half of 2008. Spillover risks from the financial system appear contained and have been reflected by a decline in the EDFs of Chilean banks from the high levels observed at end-February 2009, where the sample data end, to values closer to their median levels. This development suggests that in the short and medium term, bank vulnerabilities in Chile are more closely associated to stress scenarios that could affect the banks’ loan portfolio.


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In addition to quantile regressions, as done in this study, it is possible to use extreme value theory and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) and regime switching models.


See Vassalou and Xing (2004) on default risk and equity returns; and Chan-Lau, Jobert, and Kong (2004) and Gropp, Vesala, and Vulpes (2006) on using the distance-to-default to forecast bank failures in advanced and emerging market economies respectively.


Although it is not correct, institutions operating in a given country will be referred to interchangeably as “the country” or the “country’s institutions” for simplicity.


Five-year EDFs correspond to the probability that the institution defaults sometime over a five-year horizon. The choice of the five-year horizon would facilitate contrasting the results presented herein with studies that use five-year CDS spreads, the latter being the most liquid CDS traded maturity in the market.


Calculated as the ratio of the sum of derivatives assets and liabilities positions for the banks in the sample to the corresponding total sum for the banking system.


Some caution is needed when interpreting the figure because the analysis includes only two institutions for some countries.


See Chan-Lau and others (2009) for further specialization of this framework to the analysis of systemic risk and Chan-Lau (2013) for a comprehensive description.


Principal component analysis is a technique widely used to construct factors. For details, see, for instance, Timm (2002).


The reader should keep in mind that the ratings movements are those implied by the changes in the EDFs (or probabilities of default). Therefore, the analysis does not refer to actual upgrades or downgrades by credit rating agencies.


Figures in this section are estimated using publicly available data from the SBIF.


Pizarro (2009) reports that the loan portfolio of Santander Chile is tilted toward lower-income households and small to medium enterprises compared with other peer institutions.

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