CC BY
1
dynamics:Monography. M.: Finance Academy, 2009. 120 p. УДК 336.71.078.3
Al Khasan I.D. master student of 1st year IFF group 1-2m Scientific advisers: Tregub I. V.
Ph.D. in Technics, Sc.D. in Economics, Professor
Fedunin A.S.
Ph.D. in Economics, docent Financial University under the Government of the RF
Moscow, Russia
Аль Хасан И.Д. студент 1 курса магистратуры МФФ группа 1-2м Трегуб И.В., к.тн., д.э.н. научные руководитель, профессор Федюнин А.С., к.э.н. научные руководитель,, доцент Финансовый университет при Правительстве РФ
Россия, г. Москва DETERMINATION OF ADDITIONAL BANKING STABILITY REGULATION FACTORS TO BASEL III STANDARD ОПРЕДЕЛЕНИЕ ДОПОЛНИТЕЛЬНЫХ ФАКТОРОВ РЕГУЛИРОВАНИЯ БАНКОВСКОЙ СТАБИЛЬНОСТИ К СТАНДАРТУ БАЗЕЛЬ III Annotation: Banking regulation is very complex process which is regulated basing on international standards such as Basel standards with the newest Basel III. However, there may be some additional elements not considered or weakly controlled by Basel III. In this article such factors are considered and explained using econometrics methods.
Key words: Basel III, Econometrics, Banking, Regulation Аннотация: Банковское регулирование - очень сложный процесс, который регулируется на базе международных стандартов, таких как стандарты Базель, где Базель III является новейшим стандартом. Однако могут существовать дополнительные элементы, которые не рассматриваются или слабо контролируются стандартом Базель III. В этой статье рассматриваются и объясняются такие факторы, используя эконометрические методы.
Ключевые слова: Базель III, Эконометрика, Банковское дело, Регулирование
Nowadays regulation of banking sphere is based on Basel standards which are developed by Committee on Banking Supervision of the Bank for International Settlements.
The last set of standards is called Basel III which is revision of Basel II done after financial crisis of 2008. This standard is applied in Russia as well as all over the world. [1]
Basel III is aimed at strengthening regulation, supervision and banking sector risk management (better transparency and disclosures). Generally, it means that the general aim is to make banking sector as strong as possible. [7]
Basel III has three main principles: capital requirements, leverage ratio, and liquidity requirements.
Central Bank of Russia is working on implementation of these principles. For example, Bank of Russia developed approaches to define systemically important financial institutions. The CBR defined all changes and implementations in a very detailed way. For example, the minimum level of the short-term liquidity, set at 60% (2015), said to increase in equal annual parts up to the value of 100% (1 January 2019). The same is done with capital conservation buffer and the level for the base capital, which are also minutely explained, i.e. annual level until they reach 2.5% of RWAs (from 0.625% in 2016) and 1% of RWAs (from 0.15% at 2016) respectively. Moreover, there are some additions such as possibility without any punishment to use highly liquid assets in order to cover cash outflows and then usage of the CBR irrevocable loan lines to fill back highly liquid assets. [4]
Basing not only on these measures, but on other as well, BIS reported that Russia is compliant with minimum Basel capital and liquidity standards and their framework components. [6]
BIS decisions are seen by the CBR and then published so that everybody can get the understanding. For example, in one of the reports it was announced and described that Regulatory Consistency Assessment Program recognized the CBR regulations as relevant with BCBS Basel II and Basel III standards in all aspects. [5]
However, what if these requirements are not the only one which should be paid attention to. Maybe there are some other aspects that influence Russian banks, but are not regulated. That is why there is an idea to construct an econometric model to evaluate influence of different indicators on the possibility that bank will close. Such modeling and prognostication are quite developed nowadays, and there special computer software, such as Eviews which is used for calculations shown below. Some of the indicators are included in current Basel standards and some are not. And these not included in Basel standards factors are our main aim as they could be added to the current considered indicators. [2]
The equation model and results of its estimation look like the following:
Table 1
Method: Least Squares Included observations: 85 CL B=C(1)+C(2)*BANKROLL BOX ATM+C(3)*FUNDS SETTLEMENT+C(4)*R ES HIGH LIQ ASSET+C(5)*RES IBL+C(6)*BONDS+C(7) *RES BONDS+C(8)*PR NOTE+C(9)*RES PR NOTE+C(10) *LOAN LE RESIDENT GE+C(11)*LOAN PP RES+C(12) *IBL NONRESIDENT+C(13)*RE
Coefficient Std. Error t- Statistic Prob.
C( 1) 14.13933 3 .965159 3 565892 0.0006
C(2) -0.027109 0.006588 - 4 114988 0.0001
C(3) -0.007409 0.004768 - 1 723394 0.0892
C(4) 0.433648 0.221420 1 958484 0.0540
C(5) 0.251764 0.047526 5 .297454 0.0000
C(6) 0.003568 0.001196 2 .983602 0.0039
C(7) 0.341321 0.090874 3 755971 0.0003
C(8) -0.018320 0.006923 - 2 646094 0.0100
C(9) -0.930969 0.201234 4 .626307 0.0000
C(10) 0.009895 0.004907 2 016400 0.0475
C(11) -0.017484 0.005647 - 3 .096243 0.0028
C(12) -0.006301 0.001863 - 3 .381861 0.0012
C(13) -0.006330 0.001623 3 900602 0.0002
R-squared 0. 709154 Mean dependent var 5.623529
Adjusted R-squared 0.660680 S.D. dependent var 3.814061
S.E. of regression 2.221736 Akaike info criterion 4.574352
Sum squared resid 355.4000 Schwarz criterion 4.947934
Log likelihood -181.4100 Hannan-Quinn criter. 4.724617
F-statistic 14.62948 Durbin-Watson stat 2.549582
Prob(F-statistic) 0.000000
The equation is highlighted by the yellow colour, where CL_B is the number of closed banks in Russia,
BANKROLL_BOX_ATM is bankroll held in ATMs of all banks (assets, in billion roubles),
FUNDS_SETTLEMENT is funds in settlement (assets, in billion roubles), RES_HIGH_LIQ_ASSET is reserve on highly liquid assets (assets, in billion roubles),
RES_IBL is reserve on interbank loans (assets, in billion roubles), BONDS is the value of bonds (assets, in billion roubles), RES_BONDS is reserves on bonds (assets, in billion roubles), PR_NOTE is the value of promissory notes (assets, in billion roubles), RES_PR_NOTE is reserves on promissory notes (assets, in billion roubles), LOAN_LE_RESIDENT_GE is the volume of loans given to governmental
legal entities (assets, in billion roubles),
LOAN_PP_RES is the volume of loans given to people (assets, in billion roubles),
IBL_NONRESIDENT is amount of interbank loans taken from nonresidents (liabilities, in billion roubles),
RE is retained earnings (equity, in billion roubles).
Results are based on monthly data taken from January 2010 until January 2017, which means that we have good pool of data for modern time. Data was taken from data services: 1) www.kuap.ru (financial data); and 2) www.banki.ru (data on closed banks). It is important to add that data was taken for all Russian banks.
Now let us say several words on the model. It has high R2 which means that equation is good because the regression line well fit the data. Supporting this, result of F-statistic probability proves that equation is of high quality and R2 is not random.
All variables present in equation are significant basing on results of probability and t-test, so we should not exclude any variables.
Furthermore, using White-test, it appeared that model is not subject to heteroscedasticity, and using Durbin-Watson test for autocorrelation we cannot be sure if there is some autocorrelation or not. However, such result means that we are not obliged to reject the model, and it is possible to continue to use it.
Additionally, model performed well in adequacy test which gives us final confirmation that model is of good quality, and we can use it. [3]
If we interpret the equation we will see that if there are no changes, the number of closed banks in Russia increases by 14.14 of bank. It may be explained by the fact that there should be continuous changes in order to have stable situation without closing banks. If bankroll held in ATMs increases by 1 billion roubles, then the number of closed banks in Russia decreases by 0.027 of bank. Bigger amount of money in ATMs means that banks are popular and people trust banks as it is sign of more active behavior of people. If funds in settlement increase by 1 billion roubles, the number of closed banks in Russia decreases by 0.007 of bank. It means that banks are active and do not have problems in doing business with other entities. If reserve on highly liquid assets increases, the number of closed banks in Russia decreases by 0.434 of bank. This goes in line with theory and with Basel standards. If reserve on interbank loans increases, the number of closed banks in Russia decreases by 0.252 of bank. This means that banks should keep reserves for bank loans as if they fail to they may become insolvent. If amount of bonds held by banks increases by 1 billion roubles, the number of closed banks in Russia decreases by 0.004 of bank. It means that when banks start investing more in bonds there is possibility for more failures. If reserve on bonds held by banks increases, the number of closed banks in Russia increases by 0.341 of bank. This is interesting outcome. If we add it with the previous one, we will see when banks buy more bonds and create for them more reserves, they become subject to insolvency. If amount of promissory notes increases, the
number of closed banks in Russia decreases by 0.018 of bank. That means promissory notes are more reliable for banks, and with them they are more stable. If reserves promissory notes increase, the number of closed banks in Russia decreases by 0.931 of bank. This normal, especially related to previous point on increase of promissory notes, as there should be reserves for such purposes which ensure banks position. It is interesting that it appears banks should rely more on promissory notes and not on bonds. If amount of loans, given to governmental legal entities, increases, the number of closed banks in Russia increases by 0.01 of bank. Governmental companies look like more reliable than ordinary, but in our model we received that increase of government share in bank loans is harmful. If volume of loans, given to people, increase, the number of closed banks in Russia decreases by 0.018 of bank. This shows the situation when people believe banks and are confident in them, so there are more loans and banks do not have lack of liquidity and the operation is more active. If amount of interbank loans, taken from nonresidents, increases, the number of closed banks in Russia decreases by 0.063 of bank. It may be explained by the fact that foreign capital is cheaper for Russian banks, i.e. if they get it, they have more liquidity at lower price, which is beneficial and gives some freedom in operations. Finally, if banks retained earnings increase, the number of closed banks in Russia decreases by 0.063 of bank. This is logical because bigger retained earnings show better performance of banks, which is sign of lower possibility for insolvency.
Thus, the model shows that some of the reserves (word "some" is used because not all types of reserves were used) proposed by Basel III standard, such as, reserve on highly liquid assets, are really important. However, during regulation and analysis of banking, attention should also be paid to such areas as bankroll held in ATMs of all banks, funds in settlement, amount of bonds held and reserves for them, amount of promissory notes and reserves for them also, and other indicators shown and explained before. All these additional elements may be included or may be controlled more strictly in the regulation standards, as these elements influence banks in one or another way and show the level of banks healthiness and stability.
Sources:
1. Альберт Бикбов, Базелева болезнь, Реальное время, 11.01.2016 URL: http://realnoevremya.ru/articles/20985
2. Трегуб А.В., Трегуб И.В. Методика прогнозирования показателей стохастических экономических систем //Вестник Московского государственного университета леса - Лесной вестник. - 2008. - №2 (59). -С. 144-152.
3. Трегуб И.В., Хацуков к.л. Проверка применимости модели для прогнозирования экономических показателей // Экономика и социум. 2014. № 4-4 (13). С. 1345-1349.
4. Центральный банк Российской Федерации (Банк России), Пресс-служба, О мерах по реализации Базеля III и о регулировании деятельности системно значимых банков URL:
https://www.cbr.ru/press/PR.aspx?file=15072015_190947ik2015-07-15T19_06_47.htm
5. Центральный банк Российской Федерации (Банк России), Пресс-служба, О соответствии нормативной базы Банка России стандартам Базеля II, Базеля 2,5 и Базеля III URL: http ://www.cbr.ru/press/pr.aspx?file=15032016_163316ik2016-03 -15T16_30_17.htm
6. Basel Committee on Banking Supervision, Implementation of Basel standards, A report to G20 Leaders on implementation of the Basel III regulatory reforms, August 2016 URL: http://www.bis.org/bcbs/publ/d377.pdf
7. Bank for International Settlements, Basel Committee on Banking Supervision, Basel III: international regulatory framework for banks URL: http://www.bis.org/bcbs/basel3.htm
8. Banks Financial Data URL: http://kuap.ru/banks/9999/balances/
9. Data on closed banks URL: http://www.banki.ru/banks/memory/?by=PROPERTY_date&order=desc
УДК 336.642
Bibik Yuriy master student International Finance Faculty Financial University under the Government of the Russian Federation
Russia, Moscow Scientific Supervisor: I. V. Tregub REAL ESTATE VALUATION USING THE METHOD OF CORRELATION AND REGRESSION ANALYSIS Annotation: One of the directions of the formalized scientific approach of methods of mathematics in real estate valuation. The history of development of domestic estimative activities has shown the problems, which exist in this sphere. So, for example, selective check revealed distinction in the cost of the same property estimated by different appraisers. The independent appraiser is urged to resolve this conflict of interest, whose task is to estimate object competently and without prejudice, with deep arguments to convince participants of transaction that the size calculated by it and is that objective cost which reflects object value in the market of time in this place at present. Thus, for the determination of market value of the premises, the correlation and regression model describing interaction of the major factors was constructed.
Key words: Real estate, valuation, regression, correlation, comparative approach, income approach, cost approach, Subject property, object-analogue.
Application of mathematical methods is an essential element of modern economics as well as application of real estate valuation. The contents and the main purpose of valuation - calculation of the most probable price of the object ownership in a free competitive market. The methodological basis of the valuation
