Abstract. The present paper deals with the analysis of the relationship between interbank loan rate on the one hand and volume of investment and the amount of stocks tradable on the stock exchange on the other hand, as corroborated by calculations performed on Bahrain Stock Exchange data/

Keywords: interbank credit market, equity market, stock market, speculations, trading volumes, BSE
JEL Classification: G12, G14, G17, G21.


1. Introduction

There has been a number of studies into the association between interbank loan rates and stock market parameters (M.Yandiev, 2011; M.Yandiev, A.Pakhalov, 2013), providing both the detailed theoretical rationalisation for such an association and its practical corroboration based on calculations performed on the data provided by the Moscow Stock Exchange. The present paper crosschecks M. Yandiev’s formula explicating the association by applying it to the Bahrain Stock Exchange.

The formula itself is as follows:



The formula can be regarded as adequate as long as u shows little volatility over a significant length of time.

Compared to the prior research, the present study continues calculations of u using a number of approaches. The purpose is to assess the practicability of using each approach and find out which of them produces most accurate results. Also it should be noted that the data series includes isolated instances of excessive surges in trading volumes, possibly distorting the theoretical model’s resulting figures. This required a cross-checking of the two approaches’ results, one including the irregularities and one excluding them.

2. Description of data

The raw data, as indicated above, come from the publications by the Bahrain Stock Exchange and the Reuters news agency:

3. First approach: proving the hypothesis through calculating standard deviation of u

The projected characteristics of u were proved through calculating its standard deviation. Several methods of calculating it were employed for greater precision: with absolute and relative u deviations, as well as using a logarithmic function (see Appendix 11).

Since surges in daily trading are observed throughout the year (on 22.05, 14.06, 20.06, 24.06, and 19.11) (see Appendix 1), their impact on the resulting figures had to be assessed. To that end a second calculation of standard deviation of u was carried out excluding these anomalies.

The results were:

  1. Calculating the standard deviation of u using its absolute values (in both total volume of deposited assets and volume of trading) proved the formula’s applicability: u stayed within a narrow range of low values. Allowing for an accidental nature of surges in daily trading, the standard deviation of u falls significantly, below 1% of a security’s average value. This agrees with the parameter’s low volatility, observed by a narrow range of values on the relevant diagram (see Appendices 5 and 6). Therefore the value of u was assumed to be constant (see Appendix 4).
  2. Calculating the standard deviation of u using its relative values (in both total amount of deposited stocks and volume of trading) proved unusable. This was due to the fact that, even with a small spread in initial values, however small they themselves may be, their ratio is significantly larger, around 1. This, in turn, substantially increases their average value and hence its standard deviation.
  3. Relatively large values of u in cases of surges in speculation can be attributed to the accompanying rise in acceptable levels of speculator risk due to an increase in the total value of deposited assets. By the same logic, it can be assumed that increases in u before holidays (when there is no trade at the exchange) are due to greater uncertainties and a ‘dulling’ of risk awareness, or its underestimation.
  4. Also noteworthy is the excessive volume of deposited assets, of which only an insignificant number was actively traded. In 100 deposited securities only an average of 4.3 were actively traded, with only 20% of a security’s trading value backed by the funds deposited in the exchange (see Appendix 7). This shows an exchange’s balanced risk policy to have the securities on offer exceeding the demand by several times, at the same time zealously attracting the clients’ funds.

4. Second approach: proving the hypothesis through regression analysis

The second method of proving the formula’s applicability required a regression analysis of time series. This enabled the researcher to ascertain the relationship between the theoretical model’s variables, to assess its extent and its conformity to the criterion employed for evaluating the formula.

The time series input used for the model’s 5 variables consisted of 255 observations (one for each working day of 2012, see Appendix 8). The analysis was done for each of the 4 methods of calculating standard deviation. All calculations were made using the Gretl econometrics package.

Since regression analysis of time series requires all of the variables to be stationary (Verbeek, 2004, p. 309-310), the first stage of the analysis included an augmented Dickey–Fuller test (ADF) for each of the variables.

The lag length in each case was established based on the Schwarz information criterion (SIC). All the tests were done after de-trending the time series. The results are presented in Appendix 9.

ADF test showed all the variables except R to be stationary, thus suitable for regression analysis. The variable R had to be confirmed and then cointegrated. According to Verbeek (Marno Verbeek, 2004, p. 314-315), in case of cointegrated variables (with first differences of R being stationary), the theoretical model can yield super consistent estimates, providing for meaningful conclusions.

In this case, in both u for the total amount of stocks and u for the volume of trading the first differences of R are stationary at the 1% level of significance (see Appendix 10). This means that R is cointegrated and can be used in the theoretical model.

The use of regression analysis led to the following conclusions:

  1. Different methods of calculating standard deviation as one of the variables did not have an impact on the result;
  2. Linear regressions using the dependent u_small_vol were generally significant, with R being significant at the 1% level of significance, and I being not significant. Therefore u for the volume of trading is heavily dependent on the volume of assets deposited with the exchange, but independent of the interbank loan rate.
  3. Linear regressions using the dependent u_small_dep were generally not significant, with R being significant at the 1% level of significance, and I being not significant. Therefore u for the volume of trading is heavily dependent on the interbank loan rate, but independent of the volume of assets deposited in the exchange.
  4. Coefficients of R and I were never negative in all of the cases, pointing to their direct relationship with the dependent variables.

5. Conclusion

The result of calculations made in accordance with several approaches have proven that the value of u remained relatively stable and low throughout the whole of 2012. The resulting theoretic econometric model revealed the conjectured relationship between the variables. Therefore, it can be claimed that the formula was able to adequately describe the situation at the Bahrain stock market in 2012.

6. References

  1. Verbeek, Marno. A guide to modern econometrics. 2nd edition. – Chichester^ John Wiley & Sons Ltd, 2004. ISBN 0-470-85773-0
  2. Yandiev, Magomet. The Damped Fluctuations as a Base of Market Quotations. Economics and management, є 16, 2011. ISSN 1822-6515. URL:
  3. Yandiev, Magomet, Pakhalov, Alexander. The Relationship between Stock Market Parameters and Interbank Lending Market: An Empirical Evidence. URL:

7. Appendices




скачать dle 10.2 јвто “юнинг кузова