Journal Issue
Share
Article

Chile

Author(s):
International Monetary Fund
Published Date:
September 2009
Share
  • ShareShare
Show Summary Details

I. The Global Financial Turmoil and Its Impact on Chilean Banks1

1. This chapter assesses the impact of the global financial crisis on Chilean banks and provides a framework for analyzing government measures aimed at reducing systemic risk. Chile, as other open emerging market economies with highly integrated financial systems and capital markets, has been affected by the global financial crisis. The crisis raised concerns about spillovers risks from foreign banks to domestic banks and within the domestic banking system. Corisk analysis, which focuses on conditional measures of default risk, is used to assess the extent of the spillovers and provide a background framework to assess measures implemented to reduce systemic risk. The corisk analysis also provides evidence that could help strengthen the work on vulnerability indicators and off-site supervision.

A. The Potential Exposure to Foreign Banks

2. In Latin America, Chile is the most interconnected country to the global banking system. BIS banking data shows that foreign bank claims on Chile amount to 46 percent of GDP, higher than in other Latin American countries (Brazil 17 percent; Colombia 11 percent), reflecting the openness of the country’s financial system. Spanish banks held most of the claims due to the dominant presence of BBVA and Banco Santander in the Chilean banking system (Table 1). Between them, the two institutions account for 33 percent of the assets in the banking system, 24 percent of the non-derivatives financial instruments and 40 percent of the notional outstanding amount of gross derivatives positions.

Table 1.Foreign Banks’ Consolidated Claims on Chile by Nationality 1/
200220072008
Spain48.056.252.0
United States17.410.76.8
Germany9.66.27.8
Netherlands4.25.26.1
United Kingdom3.80.00.0
Other17.112.127.3
Total100.0100.0100.0
Source: BIS and staff calculations.

On an immediate borrower basis.

Source: BIS and staff calculations.

On an immediate borrower basis.

3. Data on cross-country consolidated claims can be complemented with market-based risk measures to capture both direct and indirect exposures. Cross-border claims cannot identify risks associated with “second round effects.” Even in the absence of cross-border claims, for instance, Chile may be exposed to shocks originating in the U.K. due to the high exposure of Spanish banks to the U.K. economy. In contrast, market-based risk measures, such as Moody’s KMV expected default frequencies (EDFs), should reflect the risk arising from both direct and indirect exposures and can be mapped into credit ratings (Table 2). Figure 1 illustrates the evolution of the average EDFs for Chilean and foreign banks.2

Table 2.EDFs and Equivalent Moody’s Credit Ratings
Moody’s ratingEDFMoody’s ratingEDF
Aaa0.02Ba10.408
Aa10.032Ba20.544
Aa20.04Ba30.848
Aa30.056B11.323
A10.08B22.064
A20.114B34.168
A30.144Caa18.418
Baa10.182Caa217
Baa20.23Caa317.946
Baa30.307Ca, C20
The table shows the equivalence between 5-year EDFs, in percent, and Moody’s credit ratings scale.
The table shows the equivalence between 5-year EDFs, in percent, and Moody’s credit ratings scale.

Figure 1.5-year EDFs

(In percent)

Source: Moody’s KMV and staff calculations.

B. The Impact of the Global Crisis on Chilean Banks

4. Corisk analysis is used to assess the impact of the global crisis on Chilean banks. Figure 1 shows EDFs tend to move together especially during the crisis period. Corisk analysis measures risk codependence, defined as the increase in the risk of one institution conditional on the risk of a peer institution. Corisk analysis is performed for a sample of Chilean and foreign banks using weekly EDF data for the period May 2, 2003–February 27, 2009. The Chilean banks included in the analysis are those for which Moody’s KMV reports EDFs. These banks are: BBVA, Banco de Chile, Banco de Crédito e Inversiones, Banco Santander, Corpbanca, and ScotiaBank. Detailed results are reported in the Annex tables.

5. Banks in Chile have been mainly affected 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 roughly approximated by the difference between the median EDF and the unconditional EDF measured at the 95th percentile (Annex Table 1). 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 and the unconditional EDF. The median corisk EDF exceeds the unconditional 95th percentile EDF by 15 to 100 percent, depending on the institution analyzed.

6. When measured in terms of rating changes, idiosyncratic shocks to foreign banks induced at most a one rating downgrade on Chilean institutions after controlling for the aggregate shock. Annex Table 2 shows the Moody’s 5-year credit ratings implied by the corisk and unconditional EDFs according to the mapping reported in Table 2.3 Compared to 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.

7. Higher corisk EDFs are associated with highly levered banks and those with high external debt ratios. This is the case for some domestic’ owned banks. Among foreign-owned banks, the percent difference between the corisk EDF and the unconditional EDF risks also appear to reflect the strength of the parent institution.

8. Even in the absence of cross-border claims, there are second-round effects that affect the Chilean banking system. 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.

Box 1.Corisk Analysis 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. Following the insights of Adrian and Brunnermeier (2008), the analyisis in this chapter uses the corisk analysis, a method based on quantile regressions (Chan-Lau, 2008).

Quantile regression makes possible to evaluate the response of the independent variable on particular segments of the conditional distribution and captures some of the nonlinearities associated with risk spillovers, e.g. the response may differ across different quantiles of the conditional distribution. Specifically, in a quantile regression 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 (Koenker and Bassett, 1978). 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 k-th common aggregate risk factor, and Clean EDFj is the component of EDFj that is orthogonal to the common aggregate risk factors Rk’s. By using the orthogonal component, equation (2) isolates the idiosyncratic effect of institution j on institution i. The fitted values using equation (2) will be referred to as corisk EDF. Rather than relying on economic theory, the aggregate risk factor in this study was set equal to the first principal component corresponding to the EDFs of all institutions in the sample excluding the Chilean institutions.

9. The resilience of Chilean banks to institution-specific shocks is partly explained by limited reliance on external financing sources and low exposure to market and counterparty risk. The domestic banking system is mainly funded through domestic deposits (60 percent of assets) and domestic securities issuance (13 percent of assets) while external funding accounting for only minor share (5 percent of assets). Market risk is limited due to the small size of the trading book (4 to 5½ percent of assets), the amount of securities available for sale (7 to 8 percent of assets) and the derivatives net open position (less than 1½ percent of assets). Finally, counterparty risk within the domestic system appears limited. counterparty exposure can be roughly estimated 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 ratings downgrade conditional on other banks’ increase in default risk (Annex Table 3).

C. Conclusions

10. Due to the interconnectedness of the domestic banking system, it would be important to continue advancing the agenda on cross-border supervision and crisis management. The analysis suggests that the Chilean banks are resilient to global and regional shocks. However, even in the absence of direct exposures with other countries in the region, there may be risk spillovers from other banks in the region and in advanced economies. Continued cross-border supervision (such as through regular contacts with foreign supervisors) and crisis management coordination with other bank supervisory agencies in the region and advanced economies is key to preserve the stability of the Chilean financial system.

11. Although a formal analysis was not conducted, measures enacted by the authorities may have contributed to offset the surge in risk in the banking system. These measures included the flexibilization of reserve requirements, swap lines, as well as government auctions of foreign currency denominated deposits for domestic banks. Indeed, as Figure 1 shows, the implementation of the measures kept the average EDF in Chile mostly flat during most of the second half of 2008, contrary to what was observed in other countries in Latin America.

12. While the analysis would provide some support to the recommendation by the Financial Advisory Committee to make domestic banks eligible for SWF deposits, some caveats remain. Chilean institutions appear less vulnerable to risk spillovers than foreign banks but the empirical analysis can only offer so much support especially since it did not consider a number of relevant factors and alternatives. For instance, deposits with domestic banks may contribute to “Dutch disease” problems. From a risk perspective, the recommendation should also be balanced against the alternative to invest the SWF assets or deposits in other riskless assets, such as government bonds and bills despite their lower returns. Finally, 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.

Annex I. Corisk analysis results
Table 1.Corisk Between Financial Institutions Abroad and Those Operating in Chile, Measured as Expected Default Frequency (EDF)(In percent)
Bank ABank BBank CBank DBank EBank F
Median EDF0.710.120.080.200.120.32
95th percentile EDF2.680.240.240.500.230.76
Latin American institutions
Banco Bradesco, Brazil5.940.270.310.710.300.91
Banco Itau, Brazil5.750.270.300.680.310.88
Banco BBVA Colombia3.020.260.230.600.360.86
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, Colombia5.070.270.240.670.360.83
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
Scotia Bank Peru5.980.370.310.720.200.69
Minimum3.020.260.230.550.200.69
Median5.410.310.300.680.360.85
Maximum5.980.370.340.720.460.92
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
Citigroup6.500.440.460.940.560.87
Wells Fargo3.700.320.250.540.350.79
Bear Steanrs5.420.260.240.680.370.80
Lehman Brothers6.140.430.420.760.451.01
Merrill Lynch6.250.370.390.790.510.92
Wachovia4.520.250.170.650.380.85
JP Morgan5.440.320.340.690.350.88
Minimum2.890.250.170.540.330.62
Median5.430.320.310.690.380.88
Maximum6.500.440.460.940.581.01
Canadian institutions
Bank of Nova Scotia3.440.340.290.540.310.80
CIB3.330.400.350.550.370.84
Royal Bank of Canada5.580.260.240.680.380.78
Minimum3.330.260.240.540.310.78
Median3.440.340.290.550.370.80
Maximum5.580.400.350.680.380.84
European institutions
BBVA Spain4.440.280.200.520.390.88
Banco Santander, Spain4.250.290.280.560.370.92
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, Switzerland4.490.320.270.710.350.84
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, Netherlands4.240.270.200.680.370.87
ING, Netherlands3.490.310.270.600.320.88
Minimum3.490.260.190.520.190.80
Median4.490.290.280.680.350.88
Maximum6.080.360.330.720.420.92
Source: Moody’s KMV and staff calculations.
Source: Moody’s KMV and staff calculations.
Table 2.Corisk Between Financial Institutions Abroad and Those Operating in Chile, Measured as Moody’s Credit Ratings
Bank ABank BBank CBank DBank EBank F
Median ratingBa2A2Aa3Baa1A2Baa3
Unconditional rating, 95th percentileB2Baa2Baa2Ba1Baa2Ba2
Latin American institutions
Banco Bradesco, BrazilB3Baa2Baa3Ba2Baa2Ba3
Banco Itau, BrazilB3Baa2Baa2Ba2Baa3Ba3
Banco BBVA ColombiaB2Baa2Baa1Ba2Baa3Ba3
Banco de Bogotá, ColombiaB3Baa3Baa2Ba2Baa3Ba2
Banco de Occidente, ColombiaB2Baa2Baa2Ba2Baa3Ba3
Banco Santander, ColombiaB2Baa3Baa2Ba2Ba1Ba2
BanColombia, ColombiaB3Baa2Baa2Ba2Baa3Ba2
Corporacion Financiera, ColombiaB3Baa2Baa3Ba2Baa3Ba3
Grupo Aval, ColombiaB3Baa3Baa3Ba2Ba1Ba3
Banco Continental, PerúB3Baa3Baa3Ba2Baa3Ba3
Banco de Credito, PerúB3Baa3Baa2Ba2Baa3Ba3
Scotia Bank PeruB3Baa3Baa3Ba2Baa1Ba2
MinimumB2Baa2Baa1Ba2Baa1Ba2
MedianB3Baa2Baa2Ba2Baa3Ba3
MaximumB3Baa3Baa3Ba2Ba1Ba3
U.S. Institutions
Bank of AmericaB2Ba1Baa2Ba2Ba2Ba2
Morgan StanleyB2Baa2Baa2Ba2Baa3Ba3
Goldman SachsB3Baa3Baa2Ba2Baa3Ba3
CitigroupB3Ba1Ba1Ba3Ba2Ba3
Wells FargoB2Baa3Baa2Ba2Baa3Ba2
Bear SteanrsB3Baa2Baa2Ba2Baa3Ba2
Lehman BrothersB3Ba1Ba1Ba2Ba1Ba3
Merrill LynchB3Baa3Baa3Ba2Ba1Ba3
WachoviaB3Baa2A3Ba2Baa3Ba3
JP MorganB3Baa3Baa3Ba2Baa3Ba3
MinimumB2Baa2A3Ba2Baa3Ba2
MedianB3Baa3Baa2Ba2Baa3Ba3
MaximumB3Ba1Ba1Ba3Ba2Ba3
Canadian institutions
Bank of Nova ScotiaB2Baa3Baa2Ba1Baa2Ba2
CIBB2Baa3Baa3Ba2Baa3Ba2
Royal Bank of CanadaB3Baa2Baa2Ba2Baa3Ba2
MinimumB2Baa2Baa2Ba1Baa2Ba2
MedianB2Baa3Baa2Ba2Baa3Ba2
MaximumB3Baa3Baa3Ba2Baa3Ba2
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, SwitzerlandB3Baa3Baa2Ba2Baa3Ba2
Barclays, United KingdomB2Baa3Baa2Ba2Baa3Ba2
HSBC, United KingdomB3Baa2Baa2Ba2Baa3Ba3
Lloyds, United KingdomB3Baa2Baa2Ba2Baa2Ba3
Royal Bank of Scotland, United KingdomB3Baa3Baa3Ba2Baa3Ba2
Standard Chartered, United KingdomB2Baa2Baa1Ba2Baa3Ba2
ABN Amro, NetherlandsB3Baa2Baa1Ba2Baa3Ba3
ING, NetherlandsB2Baa3Baa2Ba2Baa3Ba3
MinimumB2Baa2Baa1Ba1Baa1Ba2
MedianB3Baa2Baa2Ba2Baa3Ba3
MaximumB3Baa3Baa3Ba2Ba1Ba3
Source: Moody’s KMV and staff calculations.
Source: Moody’s KMV and staff calculations.
Table 3.Corisk Between Chilean Banking Institutions, Measured as Expected Default Frequency and Implied Moody’s Ratings 1/
Bank ABank BBank CBank DBank EBank F
Median EDF0.710.120.080.200.120.32
95th percentile EDF2.680.240.240.500.230.76
Bank A0.300.260.550.360.81
Bank B5.410.260.600.320.90
Bank C3.460.330.570.350.84
Bank D3.980.330.270.370.80
Bank E3.730.330.280.580.81
Bank F4.660.320.310.620.38
Minimum3.460.300.260.550.320.80
Median3.980.330.270.580.360.81
Maximum5.410.330.310.620.380.90
Median ratingBa2A2Aa3Baa1A2Baa3
Unconditional rating, 95th percentileB2Baa2Baa2Ba1Baa2Ba2
Bank ABaa2Baa2Ba2Baa3Ba2
Bank BB3Baa2Ba2Baa3Ba3
Bank CB2Baa3Ba2Baa3Ba2
Bank DB2Baa3Baa2Baa3Ba2
Bank EB2Baa3Baa2Ba2Ba2
Bank FB3Baa3Baa2Ba2Baa3
MinimumB2Baa2Baa2Ba2Baa3Ba2
MedianB2Baa3Baa2Ba2Baa3Ba2
MaximumB3Baa3Baa2Ba2Baa3Ba3
Source: Moody’s KMV and staff calculations.

The table reports corisk measured as the EDF and rating of banks listed in the upper row conditional on the EDF and rating of the banks listed in the first column.

Source: Moody’s KMV and staff calculations.

The table reports corisk measured as the EDF and rating of banks listed in the upper row conditional on the EDF and rating of the banks listed in the first column.

References

    Adrian,T., and M.K.Brunnermeier,2008, “CoVaR,” Staff Report No. 348(New York:Federal Reserve Bank).

    Chan-Lau,J.A. 2008, “Default Risk Codependence in the Global Financial System: Was the Bear Stearns Bailout Justified?,” in G. Gregoriou,Banking Crises,Chapman-Hall, forthcoming.

    • Search Google Scholar
    • Export Citation

    ———,2009, “The Global Financial Turmoil and its Impact on the Chilean Banking System,” mimeo(Washington, D.C.:International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    Chan-Lau,J.A.,A.Jobert, and J.Kong,2004, “An Option-based Approach to Bank Vulnerabilities in Emerging Markets,” IMF Working PaperNo 04/33(Washington, D.C.:International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    Crosbie,P., and J.Bohn,2003, “Modeling Default Risk,” mimeo(San Francisco:Moody’s KMV).

    Gropp,R.,J.Vesala, and G.Vulpes,2006, “Equity and Bond Market Signals as Leading Indicators of Bank Fragility,” Journal of Money, Credit and Banking, vol. 38,No.2,pp. 399–428.

    • Search Google Scholar
    • Export Citation

    Koenker,R., and G.Bassett, Jr.,1978, “Regression Quantiles,” Econometrica, vol. 45, No. 1,pp. 33–50.

1

This chapter was prepared by Jorge A. Chan-Lau (Ext. 34271).

2

EDFs are based on the distance-to-default measure, which is a measure of the equity value of a firm normalized by the asset volatility (Crosbie and Bohn, 2003). Distance-to-default measures tend to forecast the failure of financial institutions with some success (Gropp and others, 2004, and Chan-Lau and others, 2004).

3

The shift in ratings 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.

Other Resources Citing This Publication