Appendix I. Variable Definitions And Data Sources
Change in international interbank borrowing: percent change (y-o-y) in quarterly international claims vis-à-vis banks. Source: BIS Consolidated Banking Statistics on immediate borrower basis.
Credit risk index: Moody’s KMV one-year median Expected Default Frequency (EDF) for banking groups. Source: Moody’s KMV Creditedge database. For some banking systems with missing data, the median EDF of the “financials” group was used instead.
Indirect credit risk index: sum of the banking system’s foreign claims, weighted by borrowers’ one-year median EDFs, and divided by the banking system’s total credit to the private sector. Sources: BIS Consolidated Banking Statistics on immediate borrower basis, Moody’s KMV Creditedge database, and IMF International Financial Statistics.
Liquidity risk index: sum of the banking system’s foreign liabilities, weighted by creditors’ one-year median EDFs, divided by the banking system’s total credit to the private sector. Sources: BIS Consolidated Banking Statistics on immediate borrower basis, Moody’s KMV Creditedge, and IMF International Financial Statistics.
Indirect liquidity risk index: sum of the banking system’s foreign liabilities, weighted by creditors’ indirect credit risk index, and divided by the banking system’s total credit to the private sector. Sources: author’s calculations based on the Bank for International Settlements Consolidated Banking Statistics on immediate borrower basis, Moody’s KMV Creditedge, and IMF’s International Financial Statistics.
Depreciation: quarterly percent change in the nominal exchange rate against the U.S. dollar, defined as U.S. dollars per unit of national currency. Source: IMF Global Data Source.
Real GDP growth: percent change in quarterly real GDP. Source: IMF Global Data Source.
Fiscal balance to GDP: quarterly fiscal balance as a percent of nominal GDP. Source: IMF Global Data Source. For Austria, Belgium, Greece and Portugal net operating balance since other data were not available. Source: IMF International Financial Statistics.
Statistical break dummy: the dummy variable identifies statistical breaks in the series based on a table published by BIS as an annex to the consolidated banking statistics. Source: BIS.
Appendix II. Data Caveats
The analysis faced some constraints due to data limitations.
First, the variables measuring international borrowing and cross-border counterparty risk are not fully comparable. While the borrowing variable is compiled on a “residency” basis and includes borrowing by domestic banks and foreign subsidiaries, the indexes measure the risks faced by domestic-owned banks.
Second, the numerator and denominator of the indexes are compiled on different bases. The numerator represents consolidated data for domestic banks, while credit to the private sector in the denominator includes lending by all resident banks, including foreign subsidiaries. However, excluding foreign subsidiaries from the definition of the banking system may seriously underestimate the size of systems with large foreign ownership.
Third, there are inconsistencies with regard to the composition of the indirect credit risk index. The international credit variable includes claims on banks, nonbanks and sovereigns, while its level of risk was measured with banking system EDFs only since EDFs for the other sectors were not available. Nevertheless, since during the period under investigation the perception of country risk was dominated by banking problems, the bank EDFs would be a reasonable proxy and correlated with the EDFs for other sectors.
Fourth, there may be noise in the data due to exchange rate effects. BIS data are expressed in U.S. dollars and may change from period to period even if the actual positions remain unchanged because claims in other currencies are converted into U.S. dollars. Although it was not possible to adjust for exchange rate conversions since there is no data on currency composition, the exchange rate in the model controls to some extent for such effects. The results should not be considerably affected by exchange rate effects since international interbank transactions are typically contracted in U.S. dollars (see BIS Economic Paper 8).
Finally, the use of aggregate data may affect the reliability of the results, since the relationships may be driven by different banks. For example, the deterioration in aggregate banking system soundness may be attributable to one bank, while the decline in international borrowing may be due to reduced borrowing by another bank.
Appendix III. Supplementary Figures and Tables
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The author wishes to thank, for helpful advice, Graciela Kaminsky, Michael Bradley, Jay Shambaugh, Iman van Lelyveld, Eugenio Cerutti, Aaron Jackson, reviewers at the Bank of England and Bank of Spain and participants in the 2012 Economists International Conference in Amsterdam. All remaining mistakes are the responsibility of the author.
The median EDF is provided by Moody’s KMV as a measure of overall risk. Medians are commonly used to measure centrality of skewed distributions since unlike averages they are to a smaller degree influenced by outliers.
Since EDFs are based on stock market data they reflect the collective judgment of a broad pool of market participants, which according to the efficient market hypothesis is based on all available information. However, due to noise in market data they may not always correctly reflect the intrinsic soundness of a particular institution.
The significance of the composite EDF variable in specifications that include also the international and macroeconomic risk factors validates this approach.
Assuming a loss given default of one, which is a reasonable assumption, if lending is unsecured. Due to noise in market data they may not always correctly reflect the intrinsic soundness of particular exposures.
Data on ultimate risk (ownership) basis are also compiled by the BIS but for a somewhat smaller sample of countries and for a shorter time period. Under this approach, borrowing, for example, by the Polish subsidiary of a German bank would be classified as a claim on the German banking system.
Bilateral credit is available only for six emerging banking systems (Brazil, Chile, Mexico, Panama, Taiwan and Turkey). Panama and Taiwan were left out of the analysis because of missing EDF data.
Coverage of other systems’ portfolios was at least 80 percent.
June 2010 was chosen as a cut-off point because interbank credit to emerging markets started to recover afterward, whereas sovereign debt problems emerged in the euro zone.
Large outliers were left out of the regression analysis. Israel, Mexico, Peru and Poland emerged as outliers in the first period, whereas Colombia, Israel, Peru, and Venezuela are outliers in the second period. Japan is an outlier among advanced banking systems because of large positive interbank credit inflows at the height of the crisis, possibly related to a reversal of the yen carry trades. The overall findings are not sensitive to these outliers.
The groups of advanced and European banking systems overlap substantially.
A one percentage point change in the index corresponds to three standard deviations.
A one percentage point change in the index roughly corresponds to three standard deviations.
A one percentage point change in the index roughly corresponds to one standard deviation.