. ttest sk_ta, by(default)

Two-sample t test with equal variances

____________________________________________________________

Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ____________

0 | 10527 .6683736 .004982 .511154 .658608 .6781392

1 | 134 .4388908 .0430718 .498592 .3536965 .5240852 _______________

combined | 10661 .6654892 .004955 .5116144 .6557765 .6752019 ______

diff | .2294828 .0444237 .1424041 .3165615 _______________________

diff = mean(0) - mean(1)

t = 5.1658

Ho: diff = 0

degrees of freedom = 10659

Ha: diff <> 0

Pr(|T| > |t|) = 0.0000

Both for sk_ca and sk_ta p-values are very small. This means that the null hypothesis of equal means is rejected in both cases at 1% significance level. The decision to use sk_ca was made, as it was found that this variable has lower correlation with other explanatory variables that could be potentially included in the regression. Bp_ca and roa are both profitability ratios. T-tests for equal means between default and non-default groups were rejected for both variables. However, roa tends to improve the quality of regression better if we look at the proportions of truly predicted bankrupts, so bp_ca will be excluded. All the explanatory variables were checked for the equality of means using t-tests for difference of means and in all the cases the null hypothesis was rejected showing that there exists a significant difference in means between the two defaulters and non-defaulters for all variables used in this work. Following the same logic as before la_ta is excluded, being less appropriate for the model than la_ca. For the same reason norm_lam_ta is decided not to be included into the regression. Previous studies indicated the importance of liquidity on the bank balances. Liquid assets can be easily transferred into cash and thus it seems reasonable to include la_ca. Ln_ca is used as a proxy for the size of the bank. It is logically accepted that larger banks have higher operational profits and relatively lower operational costs since they can exploit economies of scale and economies of scope thus being more efficient in their operations. The size and the ratio so_ta are highly correlated. The bigger the bank is, the more liabilities it can have, since agents are not afraid that the bank will default and they will loose money. Ln__ca showed to be highly significant in the analysis and will be included, while other factors correlated with this variable are decided not to be included to avoid the potential problem of multicoliniarity.

Model construction

After determining first explanatory variables that could be included the following regression is obtained:

Model 1 (base model)

Logit (default) = 12,7136 - 0,8156*ln__ca - 2,9251*la_ca - 2,4653*sk_ca - 2,1053*roa

Table 3 (Model 1 coefficients)

Variable	Coefficient	Influence on PD
Constant	12,7136
Ln_ca	- 0,8156	Negative
La_ca	- 2,9251	Negative
Sk_ca	- 2,4653	Negative
Roa	- 2,1053	Negative

For more information see Appendix 1 Box 9 and Box 10. The signs of coefficients correspond with expected ones. Pseudo R2 (Pseudo R2 is actually not interpretable) is equal to 0,2835 and the model only predicts 16% of bankrupts. This means that some other factors should be included or some changes to the data set should be done to improve the model.

First thing to do is to decide on additional factors to be included. To do this each factor is added and the change in percentage of identified banks is looked at. Variable res_ca is highly correlated with res_ta. Not a surprise, but the ratio that contains net assets better fits the model than the ratio of reserves to total assets does. Variable res_ca is decided to be better. However, it does not seem to improve the model much. This could be due to the fact that information provided in accounting sheets differs from real picture. Many banks lowered the value of reserves in their reports during analysed period, as higher reserves mean that fewer opportunities to invest this amount of money are available (information suggested on the Internet resource banki.ru in the news section). With lowering economic activity, this places banks in unfavourable condition. So, the variables containing reserves were not taken in the model to avoid biasedness due to inappropriate data. One by one other factors were included in the model and their influence on model quality was analysed. Odb_cp, rk_ta, cp_so rk_ta and sp_so provided almost no changes to the model and for this reason were not included. Ratios pd_pr and pdfl_prfl bring very similar idea, but pdfl_prfl showed to be absolutely insignificant, for this reason it was not included. Inclusion of roe brought about worse results than roa, so the decision was made to include roa, but not roe. The variables that had a good effect on the explanatory power of the regression are: ncb_ca, ke_f_ca, przdl, gdo_ca and pd_pr. Variable ke_f_ca was the best among these characteristics for increasing predictive power of the model. Seems that proportion of loans given to individuals in the value of bank assets lowers the probability of default, as the coefficient before this variable is estimated by the model has negative sign. This could be explained as follows: loans to individuals are quite risky, stable banks that are confident in their positions are more likely to give more such loans as they know that they are able to diversify risk or to cover losses that may be associated with such activity.

So, the second, wider model looks as follows:

Model 2 (Wide model)

logit (default) = 11,33709 - 0,7129*ln__ca - 2,4314*la_ca - 2,3065*sk_ca - 2,9429*roa + 3,0700*przdl - 2,0544*ncb_ca - 1,7704*ke_f_ca + 3,6367*pd_pr

Table 4 (Model 2 coefficients)

Variable	Coefficient	Influence on PD
Constant	11,33709
Ln_ca	- 0,7129	Negative
La_ca	- 2,4314	Negative
Sk_ca	- 2,3065	Negative
Roa	- 2,9429	Negative
Przdl	3,0700	Positive
Ncb_ca	- 2,0544	Negative
Ke_f_ca	- 1,7704	Negative
Pd_pr	3,6367	Positive

Pseudo R2 = 0.3005. Model correctly classifies 16,67% of bankrupts.

For more information see Appendix 1 Box 11 and Box 12

Coefficient signs correspond with expected except for pd_pr. The positive sign could be explained as follows: higher interest income means that bank gives wore risky loans. This increases its exposure to credit risk and increases probability of default.

The model still does not work well, that could be seen after looking at proportion of truly predicted defaults. So this means that some changes should be done again.

Earlier it was discussed, that it is necessary to balance the data. Different types of balancing could be used. A random sample of bankrupts and non-bankrupts with demanded proportions could be selected, number of bankrupts could be synthetically increased (Karminsky, Kostrov, 2013) or number of non-bankrupts could be reduced (Peresetsky, 2009). In this work the third option will be used. For this reason a random sample is constructed. (The procedure of making a random sample is as follows: for each non-bankrupt bank a random number is generated using Excel software. The first column refers to bank indicator, the second refers to the random number. Then the obtained numbers are sorted in an ascending order and the demanded number of banks is taken starting from those corresponding to lower generated number).

If the proportion of banks that became bankrupt (168 banks) during 2012 - 2014 is set to be 20% or 30% of total banks, the quality of the model increases. The model where bankrupts are 30% is better. This conclusion is made based on the results provided by ROC curves (Apendix 1 Box 13, Box 14). The larger the area below the ROC curve the better is the model (Sorokin, 2014). Model that includes 30% has area under ROC curve equal to 0,8461, while model with 20% bankrupts has a lower area 0,8401. So it seems that increasing proportion of bankruptcies improves regression. An important thing, however that should be taken into account is that the data is constructed in such a way that the bank that defaulted in period t_i does not provide any information in that period. In period t_i-1 it is said to be bankrupt, and in period t_i-2 it is indicated as a bank that is functioning. For this reason the number of observations of bankrupt banks is even lower. Further changes in the proportion of default observations are discussed later on.

To improve the quality of the model one more step was done. 35 banks were excluded. These banks were deprived license for reason of terrorism financing and money laundering, the list of excluded banks can be observed in Appendix 3 Table 5 (the remaining number of defaulted banks became 133). The predictive power of the model improved after exclusion. This shows that banks that become bankrupts due to illegal actions have different factors of influence.

Another step that should be done to improve the model is to introduce macro variables, as most previous studies suggest high importance of macroeconomic characteristics. After some attempts and analysis the decision was made to include variable barrel (that corresponds to the price of 1 barrel of oil mark Brent measured in US dollars) and sanctions (as a dummy variable that takes 1 from the second quarter of 2014). Year 2014 was absolutely insignificant and was not included in regression. Another factors appeared to be less appropriate after looking at changes in proportion of predicted bankrupts and thus were not included. Moreover all macroeconomic factors are highly correlated and can not appear in the model together to avoid problem of multicollinearity (look at Table 6 below).

Table 6 (Correlation of macro variables) __________________________

| gdp gdp_gr USD barrel ______________________________________

gdp | 1.0000

gdp_gr | -0.7282 1.0000

USD | 0.4712 -0.6197 1.0000

barrel | -0.4463 0.6438 -0.9757 1.0000

After all these changes the quality of estimated model improves significantly. Especially high influence on the predictive power of the model refers to inclusion of variable barrel. This means that under current economic situation lower oil prices hurt the banking system significantly. Russian economy is highly dependent on oil exports and lower prices of this resource reduce economic activity in the country and hurt current account component in GDP. Lower oil prices lead to exchange rate depreciation and consequently to high level of inflation that increases interest rates. This limits the ability of individuals to take loans and as a result hurts banks lowering the amount of available funds.

Returning to the question of reducing the number of non-bankrupt observations, for this aim several random samples were made. The best results were obtained for the following samples:

1^st includes all bankrupts and 312 non-bankrupts, so that bankrupts are 30% in total number of banks included (2% defaults in total number of points)

Model predicts 32,33% of defaults.

2^nd includes all bankrupts and 167 non-bankrupts, so that bankrupts are 40% in total number of banks included (4% defaults in total number of points) (300 banks : 133 bankrupts and 167 good banks)

Model predicts 37,59% of defaults.

For more detailed information look Box 15 below and Appendix 1 Box 16.

3^rd includes all bankrupts and 67 non-bankrupts, so that there are 40% in total number of banks included (7% defaults in total number of points) (200 banks: 133 bankrupts and 67 good banks)

Model predicts 39,85% of defaults.

For more detailed information look Box 17 below and Appendix 1 Box 18.

Box 15 (Model 3.1) (300 banks : 133 bancrupts and 167 good banks) (4%points with default variable = 1)

Logistic regression Number of obs = 3026

LR chi2(10) = 400.49

Prob > chi2 = 0.0000

Log likelihood = -345.36755 Pseudo R2 = 0.3670 ____________________

default | Coef. Std. Err. z P>|z| [95% Conf. Interval] __________________

ln__ca | -.8325414 .0887103 -9.38 0.000^1% -1.00641 -.6586724

la_ca | -2.124287 .2516004 -8.44 0.000^1% -2.617415 -1.63116

sk_ca | -2.468692 .3347952 -7.37 0.000^1% -3.124878 -1.812505

roa | -2.749678 .9056978 -3.04 0.002^1% -4.524813 -.9745428

przdl | 2.657201 1.186951 2.24 0.025^5% .3308206 4.983582

ncb_ca | -1.421409 .5251123 -2.71 0.007^1% -2.45061 -.392208

ke_f_ca | -1.830118 .350382 - 5.22 0.000^1% -2.516854 -1.143382

pd_pr | 4.617182 1.321257 3.49 0.000^1% 2.027565 7.206799

barrel | -.0831911 .0292903 -2.84 0.005^1% -.1405989 -.0257833

sanctions | .7762436 .2479982 3.13 0.002^1% .2901761 1.262311

_cons | 22.52812 3.563409 6.32 0.000^1% 15.54396 29.51227 ___________

(^1%; ^5%; ^10%) are for significance levels of coefficients

banking financial stability default

Box 17 (Model 3.2) (200 banks: 133 bancrupts and 67 good banks) (7% points with default variable = 1)

Logistic regression Number of obs = 1826

LR chi2(10) = 324.02

Prob > chi2 = 0.0000

Log likelihood = -314.42359 Pseudo R2 = 0.3400

_____________________________________________________________

default | Coef. Std. Err. z P>|z| [95% Conf. Interval]

_____________________________________________________________

ln__ca | -.7044004 .0896124 -7.86 0.000^1% -.8800375 -.5287634

la_ca | -1.83776 .2360142 -7.79 0.000^1% -2.300339 -1.37518

sk_ca | -2.169613 .3285581 -6.60 0.000^1% -2.813575 -1.525651

roa | -2.620563 .8980722 -2.92 0.004^1% -4.380752 -.8603734

przdl | 2.301808 1.184949 1.94 0.052^10% -.0206494 4.624265

ncb_ca | -.9842073 .5165546 -1.91 0.057^10% -1.996636 .028221

ke_f_ca | -1.50798 .342046 -4.41 0.000^1% -2.178378 -.8375824

pd_pr | 4.720428 1.41502 3.34 0.001^1% 1.947041 7.493816

barrel | -.0847524 .0287742 -2.95 0.003^1% -.1411488 -.0283561

sanctions | .9149861 .2467949 3.71 0.000^1% .4312771 1.398695

_cons | 20.43789 3.514845 5.81 0.000^1% 13.54892 27.32686

_____________________________________________________________

(^1%; ^5%; ^10%) are for significance levels of coefficients

Model that is constructed based on the 3^rd sample showed the best result. This model will be named Model 3.2. The expression for this model is provided below.

Model 3.2.

logit (default) = 20,4379 - 0,7044* ln__ca - 1,8378*la_ca - 2,1696*sk_ca - 2,6206*roa + 2,3018*przdl - 0,9842*ncb_ca - 1,5080*ke_f_ca + 4,7204*pd_pr - 0,0848*barrel + 0,9150*sanctions

However, model that is constructed based on the 2^nd sample (it will be named Model 3.1) predicts very little worse. It includes a larger number of observations that means that better estimates must be received as less information is omitted.

Testing the models

The last step that should be done to decide on the final version of the model is to look at how models work for year 2015 that is the one with tested data. For this reason post estimation was conducted for Model 3.1, 3.2 and 2. The results could be seen in tables below.

Table 7 Testing Model 3.1 (For more detailed information look Appendix 1 Box 20)

	Bankrupt	Non-bankrupt
Predict default	97	1829 (Type 1 error)
Predict non-default	7 (Type 2 error)	1196

Table 8 Testing Model 3.2 (For more detailed information look Appendix 3 Box 21)

	Bankrupt	Non-bankrupt
Predict default	99	2343 (Type 1 error)
Predict non-default	5 (Type 2 error)	675

Table 9 Testing Model 2 (For more detailed information look Appendix 3 Box 22)

	Bankrupt	Non-bankrupt
Predict default	91	851 (Type 1 error)
Predict non-default	13 (Type 2 error)	2167

H₀ : bank is not bankrupt

H₁ : not H₀

Type 1 error : predict default for non-bankrupt bank

Type 2 error : not predict default for bankrupt bank

Type 2 errors bring about much more serious problems than errors of Type 1 do. That is why higher attention should be paid for them.

Percentage of non-bankrupt banks predicted as bankrupts (Type 1 error) is quite high for all models. One possible explanation for this situation may be that problematic banks experience problems from time that is more than one quarter of the year. This means that indicated in the data used as a non-bankrupt, bank is already experiencing serious problems and is already in pre-bankrupt condition.

Model 2 has the highest percentage of total predictability for year 2015 (72%), but predicts 85,5% of bankruptcies. Model 3.1 predicts 93% of bankruptcies and has 41% total predictability power. Model 3.2 is the best, it predicts default in a right way in 95% cases, however the total prediction is only 24%. Among all the three models Model 3.2 is the most appropriate. Even though it predicts 22% of non-bankruptcies, it allows to identify most banks that are going to become bankrupt and those banks that should be controlled more precisely to avoid their default (less Type 2 errors). Moreover, it should be remembered that bankrupt banks are included as healthy ones before they default. It could be the case that the model predicts bankruptcies long before they occur. This could allow authorities to pay more attention to such banks and to improve their stability.

To be sure that the model does not have such problems as multicollinearity or heteroskedasticity, that adversely influence the quality of the model, some tests should be conducted. Although variables that are highly correlated were not included in the model it is better to check once again. Multicollinearity will be tested using the method described in online sources of PennState Eberly College of Science - the Variation Inflation Factor (VIF) method. As suggested, if value of VIF is higher than 4, the factors should be checked more precisely, if higher than 10, this must suggest of strong multicollinearity. Table 10 below suggests VIFs for all the variables included in the model. All of them are less than 4. This suggests that multicolliniarity should not be a case in this model.

Table 10 (Variation Inflation Factors)

Variable | VIF 1/VIF __________________________________

ln__ca | 2.03 0.492161

sk_ca | 1.49 0.670617

la_ca | 1.30 0.767570

barrel | 1.24 0.807267

pd_pr | 1.24 0.809181

sanctions | 1.23 0.813862

ke_f_ca | 1.16 0.864306

ncb_ca | 1.15 0.870951

roa | 1.04 0.962330

przdl | 1.00 0.995572 __________________________________

Mean VIF | 1.29

Heteroskedasticity is controlled in STATA software using robust option. After adding the option the results did not change at all. Heteroskedasticity is not a problem in the obtained model.

It is now possible to look at marginal effects each variable brings to probability of default, as coefficients obtained earlier gave only the sign of the effect, but not the strength of the influence. The results can be seen in Box 23 below.

Box 23 (Marginal effects after logit)

_____________________________________________________________

variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X

_____________________________________________________________

ln__ca | -.0150049 .00266 -5.64 0.000^1% -.020224 -.009786 15.861

la_ca | -.0391473 .00627 -6.24 0.000^1% -.051435 -.02686 1.03289

sk_ca | -.0462164 .0084 -5.50 0.000^1% -.062673 -.029759 .724191

roa | -.0558223 .02082 -2.68 0.007^10% -.096626 -.015019 .011869

przdl | .0490323 .02587 1.90 0.058^10% -.001676 .09974 .002355

ncb_ca | -.0209653 .01124 -1.86 0.062^10% -.043004 .001073 .176467

ke_f_ca | -.0321225 .00789 -4.07 0.000^1% -.047596 -.016649 .403445

pd_pr | .1005530 .03269 3.08 0.002^5% .036481 .164625 .02504

barrel | -.0018054 .00061 -2.94 0.003^5% -.003009 -.000602 110.532

sancti~s* | .0201058 .00598 3.36 0.001^1% .008393 .031819 .498905

_____________________________________________________________

(*) dy/dx is for discrete change of dummy variable from 0 to 1

(^1%; ^5%; ^10%) are for significance levels of coefficients

A unit change in sk_ca reduces PD by approximately 0.0462164 %

A unit change in la_ca reduces PD by approximately 0.0391473 %

A unit change in roa reduces PD by approximately 0.0558223 %

A unit change in ln__ca reduces PD by approximately 0.0150049 %

A unit change in przdl increases PD by approximately 0.0490323 %

A unit change in ncb_ca reduces PD by approximately 0.0209653 %

A unit change in sanctions* increase PD by approximately 0.0201058 %

A unit change in ke_f_ca reduces PD by approximately 0.0321225 %

A unit change in pd_pr increases PD by approximately 0.100553 %

A unit change in barrel price reduces PD by approximately 0.0018054 %

Introduction of sanctions increased PD by 0.0201058 %

Prediction on possible bankruptcies

The last thing to do in this work is to construct the rating of banks. For this reason data on last quarter of year 2015 will be used. Banks that were earlier said to be too big to fail and those said to be too small and specific are excluded. The values of function Z (2) and corresponding probabilities of default (1) were estimated. The full list of these parameters could be obtained in Appendix 3 Table 12. Table 11 bellow presents a list of most risky banks that are extremely likely to bankrupt. After results are obtained cut points should be determined. This could be done either for the value of Z after which the bank is said to be bankrupt or for the value of probability of default. After looking at obtained results it is seen that for Z < 1,6823 (or PD < 0,8432) all the banks that did not become bankrupt are located. When Z takes value of 1,6823 (PD = 0,8432) bank Уралсиб is located. As Russian information agency ТАСС suggests on its official Internet resource, this bank was on 27^th place according to the value of assets and its default was a shock for the banking system. This means that taking this point as a cut off is not a good idea. Default of this bank was something unexpected and unclear. The next default only occurs to the bank Инвесткапиталбанк. This point has values: Z = 2,7941 and PD = 0,9424. Below this point 44 banks are located, 34 among them are banks that experienced default. This shows that if the cut point is set to be Z cut point = 2,7 and PD cut point = 0,94, the model predicts 92,2% of defaults and 22% of banks are mistakenly referred to defaulters. So, if the purpose is to identify which bank will become bankrupt during the observed quarter, the cut points Z = 2,7 and PD = 0,94 are appropriate. Earlier it was mentioned that banks often experience problems long before the bankruptcy. It is interesting to look how the model works for predicting defaults in 2016 using data from last quarter of 2015. Bankruptcies that occurred before 08.06.16 were analysed. Model did put values -0,7490 and 0,3210 to the bank Океан. As said on the official Internet resource of newspaper Ведомости, the bank was ranked only on the 444^th place by the size, but was the largest bank used for online payments. So, this bank is f very special one. Bank Интеркоммерц (Z = -0,4861; PD = 0,3808), as said by РИА Новости (information available on the official Internet resource) was in top 100 credit organisations. The reason for deprivation is set by Russian CB as money laundering. Similar reason was for Внешпромбанк, Банк Динамичные Системы and most other banks that defaulted in 2016 and model predicted quite low probability of default for them. The fact of licence deprivation is impossible to predict that is why it is ok that the model did not catch them. Most banks that became bankrupt due to financial problems have Z > 1,0325 and P > 0,7374. Note that 8 among 10 banks earlier discussed being below Z = 2,7941 and PD = 0,9424 and still being non-bankrupt in 2015 became bankrupt in 2016. Two other are said to experience difficulties and have low credit ratings. So, if the aim is to identify most potentially problematic banks cut points of Z > 1,0325 and P > 0,7374 should be used. If it is necessary to determine maximum number of banks that will become bankrupt soon (within half a year) and at the same time to make the sample include the minimum number of banks that will not default, the following cut points are appropriate: Z > 1,8275 and P > 0,8615.

Table 11 (List of most risky banks)

Региональный коммерческий банк
Финансбизнесбанк
Первый чешско-российский банк
Интехбанк
Иканобанк
Прискокапиталбанк
Анкорбанк
Вуз-банк
Мпсб
Евроазиатский инвестиционный банк
Новый московский банк
Русский трастовый банк
Вологдабанк
Элита
Гарант-инвест
Кубанский универсальный банк
Бум-банк
Солидарность
Тамбовкредитпромбанк
Ринвестбанк
Межрегиональный клиринговый банк

Conclusion

This work provided an analysis on what factors do influence the stability of Russian banking system. The model that was constructed has shown to give sufficiently good results while measuring probability of default of the bank. After looking at post-estimation results a big proportion of non-defaulters was set to be bankrupt. This error however brings not as serious problems as if the model worked in a way that high proportion of defaulters was not caught. Moreover, the model enables to determine potential defaulters that do not become bankrupt in the next quarter of the year, but are very likely to do so a bit later.

Results suggest that among the most influential accounting factors are those that measure capital adequacy, liquidity and quality of assets and the size of the bank that is approximated in the work as logarithm of assets of the bank. Although GDP is one of the most important features of economic health, this parameter was much less significant than prices of oil. Previous works indicated the importance of export to import ratio and exchange rate, however it should be understood that Russian rubble as well as the whole Russian economy is highly dependent on exports of primary recourses, and mostly oil. Oil prices influence Russian economy very much. That is why this parameter must be paid a great attention. Sanctions also appeared to be significant in the model; however, they do not increase probability of default much. An unexpected result was that reserves do not appear to work well in the model. This was explained by the problem of lack of precision of information on reserves provided by banks. For this reason ratios of reserves were not included in the model.

As it was mentioned many times, Russian economy is very volatile. Current economic and geo-political situation makes it change and adjust to new conditions. The model provided in this work showed good results in predicting defaults of banks, however it should be understood that it will not work well forever and constant adjustments should be made.

Never the less, model allowed to construct the rating of banks according to their probability of default and could be used further to identify banks that need to be paid higher attention.

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13. Martin, D., Early warning of bank failure: A logit regression approach // Journal of Banking & Finance, 1977 // vol. 1, issue 3, p. 249-276

14. Rostami. M., CAMELS' analysis in banking industry // Global Journal of Engineering Science and Research Management // November, 2015 // p. 10

15. Van Greuning H., Brajovic-Bratanovic S., Analyzing and Managing Banking Risk: A Framework for Assessing Corporate Governance and Financial Risk // The World Bank, 2009. - 442 p. - 3rd ed.

16. Wyan J. Basic econometrics for students and non-studentd alike // Published by Jerry Wyant at Smashwords Copyright. 2013 t.p. 59

Documents

17. Конституция Российской Федерации // Статья 75 // от 12 декабря 1993 года

18. Федеральный закон от 02.12.1990 N 395-1 (ред. от 05.04.2016) "О банках и банковской деятельности"

19. Федеральный закон от 10.07.2002 N 86-ФЗ (ред. от 30.12.2015) "О Центральном банке Российской Федерации (Банке России)" (с изм. и доп., вступ. в силу с 09.02.2016)

20. Федеральный закон от 07.08.2001 N 115-ФЗ (ред. от 30.12.2015) "О противодействии легализации (отмыванию) доходов, полученных преступным путем, и финансированию терроризма" (с изм. и доп., вступ. в силу с 29.03.2016)

21. Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework - Comprehensive Version (June 2006) // Bank for International Settlements Press & Communications CH-4002 Basel

22. International convergence of capital measurement and capital standards // Basle Capital Accord // (July 1988, updated to April 1998)

Other resources

23. Course work 3^rd Year, 2014 «The development of the energy market and economic growth in Russia»

Online resources

24. http://www.cbr.ru/

25. http://www.gks.ru/

26. http://www.banki.ru/

27. http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=rbrte&f=D

28. http://www.stata.com/

29. Prof. Sharyn O'Halloran Sustainable Development U9611 Econometrics II. Available at: http://www.columbia.edu/~so33/SusDev/Lecture_9.pdf

30. Newspaper ВЗГЛЯД Available at: http://www.vz.ru/economy/2016/5/18/811270.html

31. Online sources of PennState Eberly College of Science - the Variation Inflation Factor (VIF) Available at: https://onlinecourses.science.psu.edu/stat501/node/347

32. http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm

33. ТАСС Internet resource: http://tass.ru/ekonomika/2407735

34. Ведомости Internet resource: https://www.vedomosti.ru/finance/articles/2016/04/07/636759-tsb-otklyuchil-sistemi-raschetov-okean-bank

35. РИА НОВОСТИ Internet resource: http://ria.ru/economy/20160505/1426738081.html

Appendix 1

Box 1. (Russian GDP, bln. RUB, 2011 - 2015)

(Source: http://www.gks.ru/)

Box 2. (Russian GDP growth rate, %, 2012 - 2015)

(Source: http://www.gks.ru/)

Box 3. (Rubble exchange rate fluctuations 2012 - 2015)

(Source: http://www.cbr.ru/)

Box 4. (Oil price dynamics, USD per barrel, 2012 - 2015)

(Source: http://www.eia.gov/)

Box 5 (Russian unemployment level, %, dynamics 2008 - 2015)

(Source: http://www.gks.ru/)

Box 6. (Correlation matrix of financial ratios)

| sk_ca bp_ca ncb_ca pzs_ke odb_cp res_ca sk_ta sk_nor~r la_ta la_ca gdo_ca ke_f_ca _________________________________________________

sk_ca | 1.000

bp_ca | 0.142 1.000

ncb_ca | -0.085 -0.027 1.000

pzs_ke | 0.067 -0.082 0.034 1.000

odb_cp | -0.026 -0.016 0.015 0.000 1.000

res_ca | 0.037 -0.129 -0.104 0.195 0.010 1.000

sk_ta | 0.930 0.126 -0.061 0.075 -0.026 0.065 1.000

sk_norm_ar | 0.071 0.035 0.010 0.030 -0.000 0.053 0.070 1.000

la_ta | 0.152 0.057 0.089 0.040 -0.016 -0.224 0.234 -0.009 1.000

la_ca | 0.154 0.107 0.004 0.009 -0.010 -0.314 0.063 -0.006 0.712 1.000

gdo_ca | -0.099 0.003 0.142 0.000 -0.004 -0.085 -0.101 0.004 0.165 0.192 1.000

ke_f_ca | -0.042 -0.016 -0.171 -0.015 -0.009 0.102 -0.006 -0.004 -0.257 -0.344 -0.096 1.000

rk_ta | 0.028 0.047 0.037 0.005 0.008 0.150 0.056 0.011 0.125 0.046 0.000 -0.063

roa | 0.123 0.978 -0.023 -0.087 -0.014 -0.137 0.108 0.032 0.043 0.090 -0.001 -0.014

roe | -0.023 0.014 0.007 0.115 -0.001 0.005 -0.023 -0.000 -0.008 -0.004 0.007 -0.003

cp_so | -0.016 0.137 -0.008 0.002 -0.000 -0.051 0.011 -0.001 0.015 0.006 0.003 0.006

odb_orb | -0.271 0.119 0.157 -0.009 0.042 0.123 -0.203 0.019 -0.027 -0.147 0.070 -0.121

pdfl_prfl | -0.009 0.009 -0.012 0.004 -0.001 0.016 -0.008 0.024 -0.022 -0.022 -0.007 0.076

pd_pr | -0.213 -0.028 0.112 -0.004 0.001 -0.011 -0.199 -0.018 -0.129 -0.162 0.073 0.054

norm_lam_ta | 0.058 0.032 -0.081 0.069 0.007 0.020 0.088 0.039 0.328 0.226 0.019 -0.090

res_ta | 0.026 -0.118 -0.102 0.193 0.011 0.984 0.073 0.052 -0.213 -0.318 -0.084 0.102

so_ta | -0.666 -0.109 0.083 -0.082 0.016 -0.187 -0.534 -0.076 0.139 -0.227 0.070 0.161

przdl | -0.010 -0.030 -0.011 0.042 -0.002 0.031 -0.009 -0.001 -0.020 -0.021 -0.004 0.002

ln__ca | -0.573 -0.022 0.173 -0.006 0.025 0.041 -0.492 0.004 -0.171 -0.301 0.157 0.095

| rk_ta roa roe cp_so odb_orb pdfl_prfl pd_pr norm_l~a res_ta so_ta przdl ln__ca ____________________________________________________________

rk_ta | 1.000

roa | 0.032 1.000

roe | 0.000 0.013 1.000

cp_so | 0.005 0.148 0.007 1.000

odb_orb | 0.126 0.113 0.006 0.001 1.000

pdfl_prfl | 0.004 0.011 0.001 0.000 -0.026 1.000

pd_pr | -0.039 -0.021 0.009 0.003 0.153 -0.006 1.000

norm_lam_ta | 0.083 0.020 -0.001 0.005 0.157 -0.007 -0.088 1.000

res_ta | 0.156 -0.124 0.006 0.009 0.129 0.016 -0.009 0.025 1.000

so_ta | -0.040 -0.087 0.011 0.026 0.266 0.008 0.175 0.027 -0.164 1.000

przdl | 0.013 -0.031 0.058 0.000 0.001 -0.000 0.000 -0.007 0.032 -0.010 1.000

ln__ca | 0.024 -0.011 0.030 0.015 0.502 0.026 0.484 0.023 0.051 0.515 0.007 1.000

Box 9. (Base Model)

Logistic regression Number of obs = 14100

LR chi2(4) = 761.50

Prob > chi2 = 0.0000

Log likelihood = -962.50192 Pseudo R2 = 0.2835 _________________

default | Coef. Std. Err. z P>|z| [95% Conf. Interval] __________________

ln__ca | -.8156089 .0456693 -17.86 0.000^1% -.9051192 -.7260987

la_ca | -2.925145 .2118287 -13.81 0.000^1% -3.340322 -2.509969

sk_ca | -2.465306 .1997352 -12.34 0.000^1% -2.85678 -2.073832

roa | -2.105254 .5660555 -3.72 0.000^1% -3.214702 -.9958057

_cons | 12.71363 .7779809 16.34 0.000^1% 11.18881 14.23844 __________ (^1%; ^5%; ^10%) are for significance levels of coefficients

Box 10 (Base model post estimation)

Classified | D ~D | Total ________________________________________

+ | 44 20 | 64

- | 228 13808 | 14036 ___________________________________________

Total | 272 13828 | 14100

Classified + if predicted Pr(D) >= .5

True D defined as default != 0 --___________________________

Sensitivity Pr( +| D) 16.18%

Specificity Pr( -|~D) 99.86%

Positive predictive value Pr( D| +) 68.75%

Negative predictive value Pr(~D| -) 98.38% ______________________

False + rate for true ~D Pr( +|~D) 0.14%

False - rate for true D Pr( -| D) 83.82%

False + rate for classified + Pr(~D| +) 31.25%

False - rate for classified - Pr( D| -) 1.62% __________________________

Correctly classified 98.24% __________________________________

Box 11. (Model 2)

Logistic regression Number of obs = 10978

LR chi2(8) = 522.23

Prob > chi2 = 0.0000

Log likelihood = -607.77956 Pseudo R2 = 0.3005 ____________________

default | Coef. Std. Err. z P>|z| [95% Conf. Interval] ________________

ln__ca | -.7128877 .0600729 -11.87 0.000^1% -.8306284 -.5951469

la_ca | -2.431432 .2245221 -10.83 0.000^1% -2.871487 -1.991376

sk_ca | -2.306506 .2655541 -8.69 0.000^1% -2.826982 -1.786029

roa | -2.942902 .7225298 -4.07 0.000^1% -4.359035 -1.52677

przdl | 3.06999 1.13293 2.71 0.007^1% .8494888 5.290492

ncb_ca | -2.054379 .5317536 -3.86 0.000^1% -3.096597 -1.012161

ke_f_ca | -1.77044 .2823739 -6.27 0.000^1% -2.323882 -1.216997

pd_pr | 3.636677 1.069908 3.40 0.001^1% 1.539697 5.733657

_cons | 11.33709 .9991244 11.35 0.000^1% 9.378842 13.29534

(^1%; ^5%; ^10%) are for significance levels of coefficients

Box 12 (Model 2 post estimation)

_____________________ True __________________

Classified | D ~D | Total ________________________________________

+ | 28 19 | 47

- | 140 10791 | 10931 ___________________________________________

Total | 168 10810 | 10978

Classified + if predicted Pr(D) >= .5

True D defined as default != 0

Sensitivity Pr( +| D) 16.67%

Specificity Pr( -|~D) 99.82%

Positive predictive value Pr( D| +) 59.57%

Negative predictive value Pr(~D| -) 98.72% __________________________

False + rate for true ~D Pr( +|~D) 0.18%

False - rate for true D Pr( -| D) 83.33%

False + rate for classified + Pr(~D| +) 40.43%

False - rate for classified - Pr( D| -) 1.28% __________________________

Correctly classified 98.55%

Box 13 (Model 2 with 20% bankrupt banks)

Box 14 (Model 2 with 30% bankrupt banks)

Box 16 (Model 3.1 post estimation)

_____________________ True __________________

Classified | D ~D | Total ________________________________________

+ | 50 9 | 59

- | 83 2884 | 2967 _____________________________________________

Total | 133 2893 | 3026

Classified + if predicted Pr(D) >= .5

True D defined as default != 0 ___________________________________

Sensitivity Pr( +| D) 37.59%

Specificity Pr( -|~D) 99.69%

Positive predictive value Pr( D| +) 84.75%

Negative predictive value Pr(~D| -) 97.20% _________________________

False + rate for true ~D Pr( +|~D) 0.31%

False - rate for true D Pr( -| D) 62.41%

False + rate for classified + Pr(~D| +) 15.25%

False - rate for classified - Pr( D| -) 2.80% _________________________

Correctly classified 96.96%

Box 18. (Model 3.2 post estimation)

_____________________ True __________________

Classified | D ~D | Total _______________________________________

+ | 53 9 | 62

- | 80 1684 | 1764 _____________________________________________

Total | 133 1693 | 1826

Classified + if predicted Pr(D) >= .5

True D defined as default != 0 ___________________________________

Sensitivity Pr( +| D) 39.85%

Specificity Pr( -|~D) 99.47%

Positive predictive value Pr( D| +) 85.48%

Negative predictive value Pr(~D| -) 95.46% _________________________

False + rate for true ~D Pr( +|~D) 0.53%

False - rate for true D Pr( -| D) 60.15%

False + rate for classified + Pr(~D| +) 14.52%

False - rate for classified - Pr( D| -) 4.54% __________________________

Correctly classified 95.13%

Box 20. Model 3.1 post estimates

_____________________ True _____________________

Classified | D ~D | Total ________________________________________

+ | 97 1829 | 1926

- | 7 1189 | 1196 _____________________________________________

Total | 104 3018 | 3122

Sensitivity Pr( +| D) 93.27%

Specificity Pr( -|~D) 39.40%

Positive predictive value Pr( D| +) 5.04%

Negative predictive value Pr(~D| -) 99.41% _________________________

False + rate for true ~D Pr( +|~D) 60.60%

False - rate for true D Pr( -| D) 6.73%

False + rate for classified + Pr(~D| +) 94.96%

False - rate for classified - Pr( D| -) 0.59% __________________________

Correctly classified 41.19% ___________________________________

Box 21. Model 3.2 post estimates

_____________________ True_____________________

Classified | D ~D | Total _______________________________________

+ | 99 2343 | 2442

- | 5 675 | 680 _____________________________________________

Total | 104 3018 | 3122

Sensitivity Pr( +| D) 95.19%

Specificity Pr( -|~D) 22.37%

Positive predictive value Pr( D| +) 4.05%

Negative predictive value Pr(~D| -) 99.26% __________________________

False + rate for true ~D Pr( +|~D) 77.63%

False - rate for true D Pr( -| D) 4.81%

False + rate for classified + Pr(~D| +) 95.95%

False - rate for classified - Pr( D| -) 0.74% __________________________

Correctly classified 24.79%

Box 22. Model 2 post estimates

_____________________ True _____________________

Classified | D ~D | Total _________________________________________

+ | 91 851 | 942

- | 13 2167 | 2180 _____________________________________________

Total | 104 3018 | 3122

Sensitivity Pr( +| D) 87.50%

Specificity Pr( -|~D) 71.80%

Positive predictive value Pr( D| +) 9.66%

Negative predictive value Pr(~D| -) 99.40% ___________________

False + rate for true ~D Pr( +|~D) 28.20%

False - rate for true D Pr( -| D) 12.50%

False + rate for classified + Pr(~D| +) 90.34%

False - rate for classified - Pr( D| -) 0.60% _________________________

Correctly classified 72.33% _____________________________

Appendix 2

Box 24 (DATA description for defaulters)

Variable | Obs Mean Std. Dev. Min Max ________________________

sk_ca | 134 .4523991 .4995218 -.6089102 2.796816

bp_ca | 134 -.0496909 .224189 -1.097293 .6489729

ncb_ca | 134 .0987109 .1988475 0 1.059545

pzs_ke | 134 .1149649 .2099586 0 1.635509

odb_cp | 134 40.95893 273.7552 -184.0262 3102.182 _________________

res_ca | 134 .2267124 .2443718 0 1.113472

la_ca | 134 .5937856 .6621962 0 2.883226

gdo_ca | 134 .0329298 .1403746 0 .9590247

ke_f_ca | 134 .2220679 .3034411 0 1.430239

ke_f_ca | 134 .2220679 .3034411 0 1.430239________________________

pd_pr | 134 .0312049 .0818937 0 .3224706

roa | 134 -.0552083 .21708 -1.099809 .4958689

odb_orb | 134 .5899375 .3340197 0 1.355246

pdfl_prfl | 134 15.07665 103.6832 0 1048

pd_pr | 134 .0312049 .0818937 0 .3224706 __________________________

norm_lam_ta | 134 .074096 .1829634 0 1.003923

przdl | 134 .0134035 .0970278 0 .9509674

ln__ca | 133 15.19406 2.189195 9.47685 21.74257

gdp | 134 61751.88 25009.78 14925.02 77945.07

gdp_gr | 134 .0933806 .025913 .0611095 .1619989 __________________

USD | 134 33.36004 2.319611 29.3282 36.0501

barrel | 134 108.4292 3.654587 103.23 121.61

Box 25 (DATA description for non-defaulters)

Variable | Obs Mean Std. Dev. Min Max __________________________

sk_ca | 10527 .6868581 .5157368 -.3666239 2.993241

bp_ca | 10527 .025829 .0706397 -1.265946 1.182064

ncb_ca | 10527 .2319616 .3348408 -.0020494 2.879131

pzs_ke | 10527 .1293901 .544009 0 30.5702

odb_cp | 10527 84.7268 1197.582 -12831 99635.25 ___________________

res_ca | 10527 .1757317 .1694834 0 2.164978

la_ca | 10527 1.023692 .6031247 0 9.164169

gdo_ca | 10527 .0518101 .1261256 0 2.33544

ke_f_ca | 10527 .4456208 .4783353 0 2.766715

ke_f_ca | 10527 .4456208 .4783353 0 2.766715 ______________________

pd_pr | 10527 .0337649 .0891265 0 .5935943

roa | 10527 .0196779 .0640937 -1.267089 1.146029

odb_orb | 10527 .7385665 .2358996 0 3.947618

pdfl_prfl | 10527 1521.531 80595.8 0 6986887

pd_pr | 10527 .0337649 .0891265 0 .5935943 ______________________

norm_lam_ta | 10527 .1598204 .3269278 0 2.938305

przdl | 10527 .0002443 .0166384 0 1.62463

ln__ca | 10500 16.32638 1.841168 8.965207 22.82683

gdp | 10527 54469.29 27564.26 14925.02 77945.07

gdp_gr | 10527 .0942347 .0299582 .0611095 .1619989 ____________

USD | 10527 32.40609 2.369268 29.3282 36.0501

barrel | 10527 110.214 4.774643 103.23 121.61

Box 26 (MACRO DATA description)

Variable | Obs Mean Std. Dev. Min Max ______________________

gdp | 10661 54560.83 27544.51 14925.02 77945.07

gdp_gr | 10661 .0942239 .0299099 .0611095 .1619989

USD | 10661 32.41808 2.370927 29.3282 36.0501

barrel | 10661 110.1915 4.766217 103.23 121.61

Appendix 3

Table 5 (Banks excluded due to terrorism financing and money laundering)

Ассигнация	Новый коммерческий банк
Банк24.ру	Один банк
Бузулукбанк	Онлайн банк
Дагестан	Пк-банк
Диг-банк	Принтбанк
Донинвест	Пурпе
Европейский экспресс	Радиан
Имбанк	Рингкомбанк
Кип-банк	Русский земельный банк
Кредитбанк	Сберинвестбанк
Кредитимпэксбанк	Северинвестбанк
Линк-банк	Совинком
Мастер-банк	Спецсетьстройбанк
Мгмб	Стройиндбанк
Месед	Сулак
Навигатор	Сунжа
Надежность	Торгово-строительный банк
	Эсидбанк

Table 12 (Total rating of Russian credit organisations)

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Z	Pd	Default	Bank	Default in 2016
-7,348	0,001	0	Финтрастбанк
-5,791	0,003	0	Банккредитсвисс(Москва)
-5,684	0,003	0	Банк""СКС""
-5,379	0,005	0	Фридомфинанс
-5,175	0,006	0	Капитал
-4,316	0,013	0	Банкпсафинансрус
-4,288	0,014	0	Нрд
-4,138	0,016	0	Национальный клиринговый центр
-4,093	0,016	0	Аверс
-4,084	0,017	0	Северстройбанк
-4,081	0,017	0	Фольксвагенбанкрус
-4,072	0,017	0	Платежный центр
-4,025	0,018	0	Российский национальный коммерческий банк
-3,892	0,020	0	Королевский банк Шотландии
-3,881	0,020	0	Ситибанк
-3,838	0,021	0	Морганстэнлибанк
-3,757	0,023	0	Башпромбанк
-3,727	0,023	0	Руснарбанк
-3,563	0,028	0	Ингбанк (Евразия)
-3,538	0,028	0	Сетелембанк
-3,519	0,029	0	Русфинансбанк
-3,506	0,029	0	Чайнасельхозбанк
-3,503	0,029	0	Дойчебанк
-3,495	0,029	0	Юбиэсбанк