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MACROECONOMIC DETERMINANTS OF COMPETITIVE PRESSURE ON THE CREDIT MARKET IN RUSSIA: AN EMPIRICAL STUDY
F.U. Inalkaeva, student
Supervisor: D.V. Burakov, candidate of economic sciences, associate professor Financial university under the Government of Russian Federation (Russia, Moscow)
DOI: 10.24411/2411-0450-2019-10705
Abstract. This article aims to conduct an econometric assessment of the impact of individual macroeconomic variables on the dynamics of the index of herd behavior of commercial banks on the example of Russia. Based on the analysis of the data on the loan portfolio of commercial banks, the number of registered credit institutions in the Russian Federation, the number of Bank credit institutions with foreign participation in the capital, the dynamics of the refinancing rate and the key rate of the Bank of Russia, as well as the weighted average exchange rate of the ruble to the us dollar for the period from the first quarter of 2009 to the fourth quarter of 2018, using the autoregressive model with distributed lags (ARDL), we established, that the elasticity of the loan portfolios of the sample banks is in co-integration with the explanatory variables. Keywords: competition, bank, lending, regression analysis, determinant
To test the hypothesis of the relationship between the degree of competitive pressure on the credit market, we use the index of herd behavior of commercial banks. We use a common credit channel to assess the index of herd behavior of commercial banks in the sample (Top 10 commercial banks in Russia in terms of the loan portfolio). [1] the following formula is used To build the herd behavior index:
Chb = (1)
71-1 V '
n is the sample size,
x -monthly, annual growth rates of loans issued to borrowers of a commercial Bank; the share of overdue debt in relation to the loan portfolio for the period t,
x - the arithmetic mean value of return on capital (assets); monthly, the annual growth rate of loans to borrowers of a commercial Bank; the specific weight of the overdue debt in relation to the loan portfolio of all commercial banks in the sample over the period t [1].
Table 1. Results of the cointegration test
Then, the value of the variance of our chosen quantities for quantification is a criterion for determining the degree of "herd" behavior, reflecting the spread or concentration of values [1].
To assess the impact of sample variables on the resulting values of the index of herd behavior of sample banks, we transform the linear specification of the model into a loglinear specification. The log-linear specification provides more suitable and efficient results compared to the simple linear functional form of the model. [2] Moreover, the logarithmic form of the variables gives direct elasticities for the interpretations. Thus, we determine the computational equation in loglinear form:
(2)
To test the variables for the existence of a long-term relationship, we use the F-test for equation 2. The boundary test results for the calculated equation are presented in Table 1.
F-statistics 90% LB 90% UB 95% LB 95% UB 99% LB 99% UB
9.05 2.45* 3.52* 2.86* 4.01* 3.74* 5.06*
Source: author calculations
The evaluation equation includes both dependent and independent variables. The results of the F-cointegration test show that the resulting F-statistics are above the upper limit and statistically significant at the significance level of 10%, 5% and 1%.
Given that the sample variables are co-integrated in the long term, we can move on to the next stage, which requires the evalua-
Table 2. ARDL short-run estimates
tion of long-term and short-term coefficients. Given that the ADL model has been evaluated in logarithmic form, we can estimate how the shock in 1% of the explanatory variables affects the resulting variable in both the long and short term.
The results of the evaluation of the immediate effects of variable sampling on an index of herd behavior are presented in table 2.
Regressor Coefficient /-statistics (p-value)
A ln n 3.4534 0.2064 (0.838)
A ln n (-1) -4.6390 -0.2548 (0.800)
A ln f -23.723 -16.6588* (0.031)
A ln k 59.489 28.0062* (0.000)
A ln usd 1.295 1.3455 (0.189)
ECMt_1 -0.179 -25.726* (0.000)
Diagnostic tests results
R2 0.784
DW-statistics 1.92
F-statisics 3.69
RSS 0.01
The results of the construction of an autoregressive model with distributed lags show that there is a long-term and short-term relationship between the resulting and control variables of the sample. This suggests that the herd behavior of commercial banks in Russia is associated with a change in the number of registered and operating credit institutions in the Russian Federation, with a change in the number of Bank credit institutions with foreign participation in the capital, with a change in the interest policy of the Central Bank of the Russian Federation, as well as with a change in the exchange rate of the national currency. As can be seen from the data in table 2, the model is statistically significant, stable and stable.
Table 2 also shows that there is a short-term relationship between the resulting and control sample variables. The meaning of the term error correction is negative and statistically significant. The probability value p is
hypothesis about the absence of a statistically significant relationship between the variables
[3]. The imbalance in the relationship between variables is eliminated every quarter by almost 18% in the short term. Changes in the interest rate policy of the Central Bank, measured by the refinancing rate and the key rate of the Bank of Russia, have a statistically significant impact on changes in the level of herd behavior of a commercial Bank in Russia. For example, a positive shock of the key rate by 1% leads to an increase in the level of dispersion in the loan portfolio of the Top 10 commercial banks in Russia by almost 60%. This result suggests that the reaction of commercial banks to changes in the interest rate policy of the Central Bank is heterogeneous
[4]. In other words, the reaction of commercial banks ' credit policy to the increase in the key rate is multidirectional for banks with state participation in capital on the one hand and private commercial banks on the other.
equal to zero, which allows to reject the null
References
1. Burakov, D. V., 2014. Credit risk and herd behavior: relationships and identification methods. Risk management, 1(69), pp. 58-61.
2. Cameron, S. (1994). "A review of the econometric evidence on the effects of capital punishment." Journal of Socio-Economics, 23, p. 197-214.
3. Goetz, Martin R., 2018. "Competition and bank stability," Journal of Financial Intermediation, Elsevier, vol. 35(PA), p. 57-69.
4. Jimenez, G., Lopez, J.A., Saurina, J., 2013. How does competition impact bank risk- taking? J. Financ. Stab. 9 (2), p. 185-196
МАКРОЭКОНОМИЧЕСКИЕ ФАКТОРЫ КОНКУРЕНТНОГО ДАВЛЕНИЯ НА КРЕДИТНОМ РЫНКЕ РОССИИ: ЭМПИРИЧЕСКОЕ ИССЛЕДОВАНИЕ
Ф.У. Иналкаева, студент
Научный руководитель: Д.В. Бураков, канд. экон. наук, доцент Финансовый университет при Правительстве Российской Федерации (Россия, г. Москва)
Аннотация. В данной статье ставится задача провести эконометрическую оценку воздействия отдельных макроэкономических переменных на динамику индекса стадного поведения коммерческих банков на примере России. Основываясь на анализе данных по кредитному портфелю коммерческих банков, количеству зарегистрированных кредитных организаций на территории Российской Федерации, количеству банковских кредитных организаций с иностранным участием в капитале, динамике ставки рефинансирования и ключевой ставки Банка России, а также средневзвешенному обменному курсу рубля к доллару США за период с первого квартала 2009 по четвертый квартал 2018 года, с применением авторегрессионной модели с распределенными лагами (ARDL) мы установили, что эластичность кредитных портфелей банков выборки находится в коинтеграции с объясняющими переменными.
Ключевые слова: конкуренция, банк, кредит, регрессионный анализ, фактор.
