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The authors especially thank Pamela Madrid for helping to inspire and conceptualize the study, and for many useful references and comments, during its gestation; Kiran Sastry and Nada Oulidi, for their invaluable data assistance; Jochen Andritzky, Martin Cihak, Vidhi Chhaochharia, Gianni De Nicolo, Alain Ize, Inutu Lukonga, Kathleen McDill, Franziska Ohnsorge, David Parker, Mark Swinburne, Jan-Willem van der Vossen, and Francesco Vasquez; and participants at the Second Annual DG ECFIN Research Conference, for helpful suggestions. We are also indebted to Richard Podpiera, for providing material for the computation of the BCP indices. The authors are responsible for any remaining errors.
The EU10 comprise Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic, and Slovenia. The S8 are Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Moldova, Romania, and Serbia and Montenegro.
Adopting the terminology introduced by Schadler et al. (2004), the three “non-core” countries refer to Greece, Portugal, and Spain, as these countries joined the EU much later than the rest of their western European counterparts.
A higher z—and higher log(z)—implies a lower upper bound of insolvency risk and hence a lower probability of bank insolvency. Ideally, the z-score should be computed based on the market values of shareholders’ equity and assets rather than the book value of banks’ balance sheets, as is done here to overcome the lack of data on market capitalization of most of the banks in our sample. Since book values are invariably lower than market values, our measure of z gives a more conservative (but less volatile) measure of risk than would in fact exist.. For other studies using a similar methodology, see, for example, De Nicolo (2000), Altman and Saunders (1998), and Lin, Penm, Gong, and Chang (2005).
Studies of rapid credit growth include IMF (2004a)—which also considers this phenomenon in East Asia— Schadler, Drummond, Kuijs, Murgasova, and van Elkan (2004); Coricelli, and Masten (2004); Cottarelli, Dell’Ariccia, and Vladkova-Hollar (2003); IMF (2005); IMF (2004b); and Borio and Lowe (2002), who deal with this issue in a more general context.
A few studies, however, have found limited evidence of credit boom-induced banking crises (IMF, 2004a, and Tornell and Westermann, 2001).
Both countries are now pegged to the euro.
See the Bank for International Settlements (2005) for an analysis of the experiences of Asia, Central and Eastern Europe, and Latin America.
A discussion on the role of foreign banks as a risk transmission mechanism in emerging Europe can be found in Sorsa et al., (forthcoming). The risk implications of the centralization of operational functions in cross-border bank groups are discussed in IMF (2007).
See, for example, Schinasi (2006). For an in-depth discussion on the impact of rising vulnerabilities on the macroeconomic and financial sector stability of Emerging Southeastern European countries, see Sorsa et al., forthcoming,
There are other sources of risk which we were unable to explore for lack of data, including issues of financial integration among European countries (Manna, 2004; the European Commission, 2004; and Corker, de Nicolo, Tieman, and van der Vossen, 2005); capital flows, including spillovers and sudden large scale reversals (IMF, 2005; Kóbor and Székely, 2004; Vincze, 2001; and Portes and Rey, 2001); and direct and indirect euroization risks.
All variables are taken as natural logarithms, except for the dummy variables. For variables that can take 0 or negative values, we have used a transformation when taking logs as follows: ln(1+x), for small x (expressed as fraction).
The presence of time-invariant and country-specific supervisory variables (the CPs) makes it difficult to use a (fixed-effect) panel estimation model, which would drop a number of relevant bank-specific variables. A pooled OLS, however, enables us to exploit both variations within and between banks as well as regional variations.
We interact the supervisory scores with a bank-specific variable to avoid losing too many degrees of freedom.
NPLs would have been a good indicator, but using this would have led to a sharp decline in our sample size due to missing observations on most banks.
As Fitch (2005) notes, prudential behavior of banks could be a risk factor if banks’ risk behavior is procyclical—excessively optimistic or pessimistic prudential behavior could amplify the business cycle and result in higher risk of bank failure.
Countries with real credit growth exceeding 16.8 percent (y-o-y) on an average between 2000 and 2004. However, the banks included in the High Credit Growth countries are not necessarily the ones with the highest average bank-by-bank nominal loan growth because of differences in their inflation rates.
Because of inflation, some countries in High Credit Growth and Surroundings overlap.