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Comments on “Measuring Banks Insolvency Risk in CEE Countries” Ivicic, Kunova c , Ljubaj

Comments on “Measuring Banks Insolvency Risk in CEE Countries” Ivicic, Kunova c , Ljubaj. by Neven Mates Senior Resident Representative, IMF Moscow Office. The main conclusions: . The stability of banking sector in all CEE countries is improving:

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Comments on “Measuring Banks Insolvency Risk in CEE Countries” Ivicic, Kunova c , Ljubaj

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

  2. 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.

  3. 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

  4. 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

  5. Chebyshev theorem tell us that the following inequality applies, regardless of a specific distribution function of R: P{R≤-K} ≤ Sigma2/(My+K)2 Or P{R≤-K} ≤ 1/Zb2

  6. 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?

  7. 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.

  8. 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=0.02 Stdev=0.03 K=0.10 Zb=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

  9. 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/Zb2 =0.063 Average of estimated 1/Zb2 =0.033 (about a half) Median of estimated 1/Zb2 =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 .

  10. Monte Carlo simulations

  11. 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.

  12. 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.

  13. 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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