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Comments on “Measuring Banks Insolvency Risk in CEE Countries” Ivicic, Kunovac, Ljubaj by Neven Mates Senior Resident Representative, IMF Moscow Office

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The main conclusions: The stability of banking sector in all CEE countries is improving: Favorable macroeconomic developments have resulted in higher and less volatile returns on assets; Stability increased: Risk of a systemic crisis only 0.1 percent; Increased concentration reduces stability; Low inflation improves stability; Rising loan provisions are a sign of increased vulnerability.

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The Method: Z-score as a measure of distance-to-insolvency. Let assume that the return on assets R is a random variable with mean My and standard deviation Sigma. R=My+Z*Sigma

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The bankruptcy threshold: A border case when the return on assets is so negative that it would exhaust capital in one year: R=-K where K is the capital to asset ratio. Z-score triggering the bankruptcy Zb is then equal to: Zb=-(My+K)/Sigma

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Chebyshev theorem tell us that the following inequality applies, regardless of a specific distribution function of R: P{R≤ -K} ≤ Sigma 2 /(My+K) 2 Or P{R≤-K} ≤ 1/Zb 2

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How far can the Z-scores bring us to? Intuitively, an attractive measure of a “distance to bankruptcy”; Can be used to compare various banks, or their groups; But can we make conclusions on the probability of the bankruptcy?

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The authors think that Chebyshev inequality allows them to establish a maximum probability, without specifying the underlying probability distribution. Indeed, Chebyshev produces the result that is not dependent on a specific probability function … … but it assumes that you exactly know the mean and variance of this function. If you do not know these, Chebyshev is of little help.

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Monte Carlo simulations How precisely can the authors’ procedure estimate parameters that enter into Z-score calculation, i.e. mean and standard deviation of return to assets variable? Model 1: My=0.02Stdev=0.03 K=0.10Zb=4 (true value) Assuming R~iid N(0.02, 0.03), we generated 10,000 observations of Rs. We used those Rs to estimate My, Sygma, and Zbs: Average estimated Zb= 7.015 (almost twice as large) Median of estimated Zb=4.765

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Monte Carlo simulations Model 2: The same, but we introduced a serial correlation between Rs. Average estimated Zb= 11.08 (almost 3 times higher than the true value) Median of estimated Zb=7.45 (twice as high) Upper limit of the probability of default 1/Zb 2 =0.063 Average of estimated 1/Zb 2 =0.033 (about a half) Median of estimated 1/Zb 2 =0.018 (about a third). But what if the sampling takes us 1 sd. from the sample mean? Zb=28, 1/Zb2=0,1 percent.

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Monte Carlo simulations

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Predicting Zs: Which factors matter? Regression of Z-s on macroeconomic and microeconomic variables for each of 7 CEE countries separately. Absence of robustness in the regressions for the whole period 1998-2006.

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Predicting Zs: Which factors matter? Macroeconomic variables: GDP growth is significant and has an expected sign in only 3 out of 7 countries; Inflation is significant and has an expected sign in 5 countries; Concentration index: In two countries the coeficient is positive and significant, in two it is negative and significant; Libor: The coeficient is significant with a right sign in 3 countries (but large differences in the size), it has a wrong sign in one. Microeconomic (banks-specific) variables: Credit growth: Significant and right sign in 4 countries; Total assets: Not significant in any country; Loans to assets ratio: Negative and significant in 2 countries, positive and significant in 1; Loan provisions to net-interest income: Positive significant in one, negative in one; Liquid assets to customer and short-term funding: Not significant.

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Predicting Zs: Which factors matter? 5-year Rolling regressions: Even less robustness; Wild gyrations of coefficients consecutive regressions; In one case, coefficient for GDP goes from -68 to +69 in two consecutive regressions (2004 and 2005), but in both cases it is significant at 1 percent.

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