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PROVING THE RELATIONSHIP BETWEEN THE PARAMETERS OF THE STOCK MARKET AND INTERBANK LENDING MARKET: THE KYRGYZ STOCK EXCHANGE

EKATERINA ROMANOVA,
LOMONOSOV MOSCOW STATE UNIVERSITY, ECONOMICS FACULTY
ROMANOVA.EKO@GMAIL.COM
MARIA ELCHANINOVA,
LOMONOSOV MOSCOW STATE UNIVERSITY, ECONOMICS FACULTY
YULIA LOS,
LOMONOSOV MOSCOW STATE UNIVERSITY, ECONOMICS FACULTY

Abstract. This paper presents calculations reaffirming practical application of an earlier posed theoretical model explaining relationship between the rate of one-day credits in the interbank market, volume of speculative investments and total securities under which transactions have been closed. This article is written based on the Kyrgyz stock exchange data.

Keywords: interbank credit market, equity market, stock market, speculations, trading volumes, KSE

JEL Classification: G12, G14, G17, G21

 

ROMANOVA, EKATERINA. ELCHANINOVA, MARIA. LOS, YULIA (2016) "PROVING THE RELATIONSHIP BETWEEN THE PARAMETERS OF THE STOCK MARKET AND INTERBANK LENDING MARKET: THE KYRGYZ STOCK EXCHANGE". Journal of Russian Review (ISSN 2313-1578), VOL. 1(4), 13-23.

 

1. Introduction

This paper continues the research into the relationships between stock market and interbank lending market, using the database of Stock Exchange of Kyrgyzstan.

The purpose of the study is to check the correctness of the formula [1], according to which the day-rate of loans in the interbank market is inversely proportional to the number of securities traded on the stock exchange:

Where the formula describes the relationship between the parameters:

  • u is the average “loss” per transaction with one security;
  • I is the volume of speculative investments (the volume of monetary funds that are held in accounts in an authorized bank and are intended for speculations);
  • R is the rate of one-day credits in the IM, in shares;
  • U is the total securities under which transactions were closed.

The key point of the formula is the fact that the parameter u remains constant for the duration of the period under review. In this regard, verification of the formula’s compliance with real practice boils down, to showing that the parameter u is indeed constant. In practice, this may indicate a low value of the standard deviation of the parameter. In addition, the use of regression analysis can confirm or disprove the theory about the nature of the relationship between the parameters

2. Literature review

Three articles were published in the period 2013-2016, which tested the applicability of the formula to practice. The first paper [2] uses the data provided by the Moscow Stock Exchange. The authors conclude that the formula as a whole, correctly reflects the relationship between the interbank market parameters and those at Moscow Stock Exchange in 2012.

Another study [3] was carried out on the basis of the Bahrain Stock Exchange data. It has also been noted that the overall formula correctly reflects the real situation, since the parameter u has a low value volatility. In addition, the econometric model confirmed the existence of correlations between variables.

The third work [4] was made on the basis of the Kazakhstan Stock Exchange data. The results are broadly similar to those presented in the other two papers.

Next, the search was carried out for publications, which also address the issue of the relationship between interbank market parameters and the stock market.

The search for Russian language sources was carried out on the site of the Scientific Electronic Library RISC (elibrary.ru), English - the search engine Google Scholar (scholar.google.com) and Online Social Science Research Network International Libraries (ssrn.com).

The search in RSCI was conducted using the keywords stock market, interbank credit market, stock index, overnight rate. As a result, the system showed 47 articles, out of which 2 were chosen after reading the abstracts. However, after the full reading they were dismissed as not relevant to the subject. That is why there are no Russian-language part sources in the literature review.

After looking through SSRN with the keywords: stock market, interbank credit market, rate of one-day credits, lending market, overnight rate, stock index and their different combinations, the websites’ search engine came up with 20 articles, out of which 3 were chosen after analysis of their titles and abstracts for future reading. However, only 1 paper [5] proved to be relevant. It describes the role of the stock market and the market of interbank credits in the measurement of bank performance in Malaysia and Thailand. The author argues that the price of shares in individual banks may reflect the risk in the interbank market.

To search on Google Scholar website the following key words were used: stock market, the market of interbank loans, the rate of one-day loans, stock market index, the overnight rate, the industrial index Dow Jones. As a result 134 works were found, of which after reading the abstracts 12 were selected for the study of the full text. As it turned out, only one of them [6] was relatively close to the subject of the study. This is a fairly popular work, with 72 citations. The authors trace the dynamics of stock prices of Japanese banks during the banking crisis in the mid-1990s. Their research shows that the bank’s shareholders may use financial indicators to quickly differentiate between the activities of banks.

3. Description of the data and methodology

To test the practicability of the formula two approaches were used. The first is to determine whether the standard deviation of the parameter u is of constant or negligible value. As the available data were not detailed or structured they were approached in two steps: first, the analysis of all the available sample and, second, the analysis of the data for December 2015 (for this month the data were full and regular).

The meaning of the second approach is the use of regression analysis to determine the nature of the relationship between the parameters and compare it with the nature of the relationships in the formula itself.

To verify the applicability of the formula the data were used for the research during the period 2010-2015 years provided by the Kyrgyz Stock Exchange:

  • The total amount of money deposited in KSE, thsd KGS (I, refer to Appendix 1);
  • The number of stocks in KSE, pcs (U, refer to Appendix 2);
  • KSE one-day loan interest rates, % (R, refer to Appendix 3).

4. The first approach

The calculations show that the standard variations are below one hundredth of the mean price of a single stock. It allows to recognize u parameter as a generally constant value (refer to Appendix 4 and 5). In addition, visual examination of the daily u parameter value shows that it is slightly volatile (refer to Appendices 6-9). Because of the small amounts of data, the first approach consists of two parts: one is dealing with all data that we have and the other dealing with the data for December 2015.

From a visual assessment of the u parameter dispersion it is obvious that in general it is insignificant. Thus, it can be argued that the u parameter has low volatility and can be considered as a value close to a constant.

5. The second approach

The second approach includes the use of regression analysis of time series in order to verify the relationships between the variables of the model again.
The time series input consists of 76 observations for each of the 5 variables (refer to Appendix 10). All calculations are made in the Gretl econometrics package.

The first stage of the econometric analysis is checking the stationarity of all the variables according to the regression analysis of time series. For this reason an augmented Dickey–Fuller test (ADF) is used. At present it is one of the most popular tests for a unit root in a time series sample. All variables are examined for stationarity with constant. The results of the testing procedure are given in Appendices 11-14.

ADF test has shown all variables except R to be stationary. Due to the similarity of the R indicators during the last periods there is no point in testing its stationarity in this particular period. However, the first 10 observations are different. Therefore the ADF test is held separately for the first 10 observations of parameter R. This test shows non-stationarity of the variable. (refer to Appendix 15) The use of non-stationary variables in linear regressions may lead to invalid estimators. For this reason, an additional test of the R first differences (d_R) is arranged and finally the stationarity of the variable is revealed at 1% level of significance (refer to Appendix 16).

Taking everything into consideration it is possible to formulate several conclusions from analyses of these regressions. Both equations are completely significant, and the relationships between parameter u and other variables correspond to logic of the theoretical formula. The R and I variables have positive coefficients (it means the direct relationship with the dependent parameter), and the U variable has a negative coefficient (it means the inverse relationship with the dependent parameter) (refer to Appendices 17, 18). This kind of analysis also proves the significance of the observed formula.

6. Conclusion

The results of calculations made in both cases prove constant relative stability and general correctness of the tested formula. The relationships between parameters correspond to reality. Therefore, it can be argued that the formula can adequately describe the situation at the Kyrgyz Stock Exchange during the period 2010-2015 years and can be useful for financial market regulation.

7. References

  1. Matveev, Aleksandr, Proving the Association between Stock Market and Interbank Lending Market Parameters: The Bahrain Stock Exchange (April 21, 2014). Available at SSRN: http://ssrn.com/abstract=2427196 or http://dx.doi.org/10.2139/ssrn.2427196
  2. Yandiev, Magomet and Andzhaeva, Altana, Confirmation of the Relationship between Stock Market Parameters and Interbank Credit Market on the Example of the Kazakhstan Stock Exchange (March 17, 2016). Available at SSRN: http://ssrn.com/abstract=2749242
  3. Yandiev, Magomet and Pakhalov, Alexander, The Relationship between Stock Market Parameters and Interbank Lending Market: An Empirical Evidence (September 23, 2013). Available at SSRN: http://ssrn.com/abstract=2329871 or http://dx.doi.org/10.2139/ssrn.2329871
  4. Yandiev, Magomet. The Damped Fluctuations as a Base of Market Quotations. Economics and Management, No 16, 2011. ISSN 1822-6515. URL: http://ssrn.com/abstract=1919652
  5. Masahiro Inoguchi, Interbank Market, Stock Market, and Bank Performance in East Asia (November 1, 2013). Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2378725
  6. Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman, Does the Japanese Stock Market Price Bank-Risk? Evidence from Financial Firm Failures (February 2003). Available online at researchgate.com: https://www.researchgate.net/publication/5168302_Does_the_Japanese_Stock_Market_Price_Bank-Risk_Evidence_from_Financial_Firm_Failures

8. Appendices

Appendix 1

Appendix 2

Appendix 3

Appendix 4

"u" parameter, KSE, Dec. 2015 With U as volume of stocks traded With U as total amount of deposited stocks
Average, KSE 0,0000061900 0,0022593348
Volatility, KSE 0,0000004862 0,0001755129

Appendix 5

"u" parameter, KSE, 2010 - 2015 With U as volume of stocks traded With U as total amount of deposited stocks
Average, KSE 0,0000053277 0,0019446140
Volatility, KSE 0,0000052658 0,0019220080

Appendix 6

The whole period (2010-2015)

Appendix 7

The whole period (2010-2015)

Appendix 8

01.12.15-31.12.15

Appendix 9

01.12.15-31.12.15

Appendix 10

Variable name in the theoretical model Variable model in Gretl Definition
u u_small_vol Mean loss per deal (calculated using the volume of trade)
u u_small_dep Mean loss per deal
(calculated using the amount of deposited stocks)
I I Volume of speculative investment (amount of money in the bank autorised by the Exchange)
R R One-day loan interest rates
R d_R First difference of R

Appendix 11

Unit root test for u_small_vol

Results of the ADF test

Augmented Dickey-Fuller test for u_small_vol

including 0 lags of (1-L)u_small_vol

(max was 11, criterion AIC)

sample size 76

unit-root null hypothesis: a = 1

test with constant

model: (1-L)y = b0 + (a-1)*y(-1) + e

estimated value of (a - 1): -0.589324

test statistic: tau_c(1) = -5.58178

p-value 9.829e-06

1st-order autocorrelation coeff. for e: -0.005

The variable is stationary at the 1% level of significance.

Appendix 12

Unit root test for u_small_dep

Results of the ADF test

Augmented Dickey-Fuller test for u_small_dep

including 0 lags of (1-L)u_small_dep

(max was 11, criterion AIC)

sample size 76

unit-root null hypothesis: a = 1

test with constant

model: (1-L)y = b0 + (a-1)*y(-1) + e

estimated value of (a - 1): -0.589324

test statistic: tau_c(1) = -5.58178

p-value 9.829e-06

1st-order autocorrelation coeff. for e: -0.005

The variable is stationary at the 1% level of significance.

Appendix 13

Unit root test for I

Results of the ADF test

Augmented Dickey-Fuller test for I

including 0 lags of (1-L)I

(max was 11, criterion AIC)

sample size 74

unit-root null hypothesis: a = 1

test with constant

model: (1-L)y = b0 + (a-1)*y(-1) + e

estimated value of (a - 1): -0.542084

test statistic: tau_c(1) = -5.17475

p-value 4.32e-05

1st-order autocorrelation coeff. for e: -0.019

The variable is stationary at the 1% level of significance

Appendix 14

Unit root test for R
The ADF test (R, d_R) cannot be held because most dependent variables are constant.

Appendix 15

Unit root test for R (first 10 observations)

Results of the ADF test

Augmented Dickey-Fuller test for R

including one lag of (1-L)R

(max was 2, criterion AIC)

sample size 10

unit-root null hypothesis: a = 1

test with constant

model: (1-L)y = b0 + (a-1)*y(-1) + ... + e

estimated value of (a - 1): -0.414669

test statistic: tau_c(1) = -1.12293

asymptotic p-value 0.7091

1st-order autocorrelation coeff. for e: 0.425

Appendix 16

Unit root test for d_R

Results of the ADF test

Augmented Dickey-Fuller test for d_R

including 2 lags of (1-L)d_R

(max was 2, criterion AIC)

sample size 8

unit-root null hypothesis: a = 1

test with constant

model: (1-L)y = b0 + (a-1)*y(-1) + ... + e

estimated value of (a - 1): -1.90414

test statistic: tau_c(1) = -4.27636

asymptotic p-value 0.0004832

1st-order autocorrelation coeff. for e: -0.288

lagged differences: F(2, 4) = 2.359 [0.2105]

Appendix 17

Calculation with volume of trade

Linear regression of u_small_vol using I and R

Model 2: OLS, using observations 1-75

Dependent variable: u_small_vol

  Coefficient Std. Error t-ratio p-value

const -4.4881e-06 9.46334e-07 -4.7426 <0.0001 ***
I 7.48608e-11 1.51878e-12 49.2899 <0.0001 ***
R 4.36902e-07 7.87213e-08 5.5500 <0.0001 ***

 

Mean dependent var 5.33e-06   S.D. dependent var 6.80e-06
Sum squared resid 9.66e-11   S.E. of regression 1.16e-06
R-squared 0.971755   Adjusted R-squared 0.970970
F(2, 72) 1238.561   P-value(F) 1.71e-56
Log-likelihood 920.2480   Akaike criterion -1834.496
Schwarz criterion -1827.544   Hannan-Quinn -1831.720
rho 0.862350   Durbin-Watson 0.274967

Appendix 18

Calculation with amount of deposited funds

Linear regression of u_small_dep using I and R

Model 3: OLS, using observations 1-75

Dependent variable: u_small_dep

  Coefficient Std. Error t-ratio p-value

const 0.00163816 0.000345412 -4.7426 <0.0001  ***  
I 2.73242e-08 5.54356e-10  49.2899 <0.0001  ***  
R 0.000159469 2.87333e-05  5.5500 <0.0001  ***  

 

Mean dependent var 0.001945    S.D. dependent var 0.002482
Sum squared resid 0.000013   S.E. of regression 0.000423
R-squared 0.971755   Adjusted R-squared 0.970970
F(2, 72) 1238.561   P-value(F) 1.71e-56
Log-likelihood 477.7557   Akaike criterion -949.5115
Schwarz criterion -942.5590   Hannan-Quinn -946.7354
rho 0.862350   Durbin-Watson 0.274967

 

 

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