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The paper was prepared while Tigran Poghosyan was an intern at the IMF. We would like to thank Ales Bulir, Jakob De Haan, Enrica Detragiache, Michaela Erbenová, Luc Everaert, Olivier Frecaut, Tomislav Galac, Thomas Harjes, Heiko Hesse, Luc Laeven, Klaus Schaeck, Iman van Lelyveld, Thomas Walter, and participants in an IMF seminar for useful comments. All remaining errors are our own.
Examples of this research include Martin (1977) and Calomiris and Mason (2000) for the United States, Gonzalez-Hermosillo, Pazarbasioglu, and Billings (1997) for Mexico, Persons (1999) for Thailand, and Kraft and Galac (2007) for Croatia.
There are of course some limits to rules-based systems. For example, banks may be able to bypass the rules, especially if the rules become too cumbersome. But this is an argument for designing rules that are relatively simple and easy to enforce, rather than for moving from rules to discretion.
This section provides a brief overview of this body of work; additional information is provided in Appendix I. There is a related, but separate, literature on early warning systems for predicting currency crises and systemic banking crises. For a survey, see, e.g., Berg, Borensztein, and Pattillo (2004).
Rating agencies’ assessments could also be considered in this category, even though these are typically based on a combination of financial ratios and market indicators.
This finding is consistent also with recent surveys of supervisory and regulatory practices by Čihák and Tieman (2006 and 2008), showing that there are still substantial differences between the regulations “on the book” and their implementation in the field.
The EU has a developed banking system with some 8,000 banks. Within this group, major LCFIs are emerging. Forty-six LCFIs hold about 68 percent of EU banking assets; of these, 16 key cross-border players account for about one-third of EU banking assets, hold an average of 38 percent of their EU banking assets outside their home countries, and operate in just under half of the other EU countries (Appendix II).
A possible counterargument is that if “too much” information on bank soundness becomes publicly available, it might trigger bank runs. This argument would be relevant if there were a sudden release of detailed and damaging information about a bank. However, the “benchmarks” described here represent an evolutionary, rather than revolutionary, change towards more transparency. The very point of the increased transparency is that, over the medium term, it will push banks into behaving more prudently, in order to limit the risk of runs.
In the baseline estimate, we lagged the explanatory variables by one period, i.e., one year. As a robustness check, we also experimented with two-year and three-year lags. These checks yielded very similar results, but weaker in terms of statistical significance (especially for the three-year lags), suggesting that the predictive power of the explanatory variables declines as we attempt to predict failures further into the future.
Romania and Bulgaria are excluded, since they joined the EU only in 2007. As regards the “new EU member states” that entered the EU in 2004, the benchmark specification includes all their observations, because their economies were characterized by a high degree of integration with the “old” EU countries even prior to their entry. One of the robustness checks we do consists of excluding pre-2004 observations in these countries.
The Factiva contains a collection of 14,000 sources, including the Wall Street Journal, the Financial Times, Dow Jones and Reuters newswires, and the Associated Press, as well as Reuters Fundamentals, and D&B company profiles (for details, see www.factiva.com).
Relatedly, Gropp and Heider (2008), examining a sample of banks and nonbank corporations in Europe and the United States, and using a simple leverage ratio, are unable to detect first order effects of capital regulation (imposed on the risk-weighted capital adequacy ratio) on the capital structure of banks. They find that the standard cross-sectional determinants of firms’ capital structures valid for nonbank corporations also apply to large, publicly traded banks.
We performed two robustness checks. First, we defined “similar size” as ±100 million Euro rather than ±200 million euro. Second, we used the share of loans to total assets, with a ± 5 percent band, as an alternative measure of similarity. Our estimation results do not change when these alternative definitions of similarity are used to evaluate the impact of contagion (the results are available upon request).
To alleviate the impact of extreme observations and errors in the sample, all these independent variables are winsorized at the 1 percent level.
Observations for individual banks may be correlated. To take this into account, we drop the standard assumption that errors are independent within each bank and use a variance-covariance matrix that is robust to clustering of errors.
To keep Table 3 legible, we show just the three macroeconomic factors discussed in the previous paragraph. We also tested the other macroeconomic variables that come out in the studies on systemic distress, such as Čihák and Schaeck (2007), and they were not significant. Results are available upon request.
Results for this iteration of the robustness check are not shown in Table 3, but are available upon request.
The intercept becomes insignificant when macroeconomic variables enter the specification, which may reflect a complex relationship between the contagion dummy, macroeconomic shocks and the baseline hazard.
There are 21 repetitive distress bank-year observations in total. The remaining 54 distress events correspond to the number of distressed banks in the sample.
Listed banks were identified from BankScope by their International Securities Identification Number (ISIN). Daily series of bank stock prices and the FTSE-100 index are taken from Datastream. The market information variable takes a value of 0 for the nonlisted banks. Because the logit estimate is based on annual data, we use yearly averages of the daily stock price data. We also experimented with different approaches to mapping the daily data into yearly data, but this had little impact on the results.
We also experimented with shortening the lag of the stock market prices below one year in estimate (VIII), using the high frequency with which stock market data are available. This allows us to further improve the predictive power of the model, but the weakness of this approach is that it would not provide a sufficiently early warning; moreover, reliable stock price data are available only for a minority of the banks in our sample.
See also De Haan, Oosterloo, and Schoenmaker (2009) for an extensive discussion of the European financial stability framework.