1 / 13

Measuring Banks' Insolvency Risk in CEE Countries: Stability and Predictive Factors

This article analyzes the stability of the banking sector in Central and Eastern European countries (CEE) and identifies factors that can predict banks' insolvency risk. The study utilizes the Z-score as a measure of distance-to-insolvency and applies Monte Carlo simulations to estimate parameters. The analysis examines macroeconomic variables such as GDP growth and inflation, as well as microeconomic variables including credit growth and loan provisions.

ehorner
Download Presentation

Measuring Banks' Insolvency Risk in CEE Countries: Stability and Predictive Factors

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related