Journal of Risk

Risk.net

Liquidity risk management implementation for selected Islamic banks in Pakistan

Omar Masood, Javaria Younas and Mondher Bellalah

The purpose of this particular study is to determine if any liquidity risk exists in the Islamic banks of Pakistan and, if it does, what effect it has on the resilience of the industry in that country. The participants of this study are employees of Islamic banks.  Our primary data was collected from three major cities in the country. This paper sheds light on the current situation in liquidity risk management. Further, we take a look at the attitudes of central and Islamic banks toward liquidity risk management policies. Regression models were applied in order to analyze the impact of liquidity risk management on Islamic banks. The findings show the effect of variables such as rational depositors and training on liquidity risk. The central bank provides Islamic banks with adequate rules and regulations, and the latter aim to control liquidity according to these rules as well as the requirements of depositors. The results of this paper offer useful insights for Islamic banks worldwide.

The first Islamic commercial bank, Dubai Islamic Bank, was opened in 1974 in the United Arab Emirates (UAE). Over the four decades since its inception, the Islamic banking industry has been growing continuously. This sustained increase in the activities of Islamic banks is one of the reasons they have been receiving global attention (Omar and Mondher 2010). The total estimated number of Islamic banks around the world has reached about 400, across fifty-three Muslim and non-Muslim countries. In 2009, the estimated funds of the Islamic banking sector had reached between US$500 billion and US$1 trillion, with an annual growth of 10–20% (Eedle 2009).

The banking industry in Pakistan has seen major changes in the last sixty-two years. It faces several problems, such as capital inadequacy, the socioeconomic condition of the country and uncertainty due to political instability. Changes have been made by the State Bank of Pakistan (SBP) in accordance with the State Bank of Pakistan Act (1956), which motivates the private sector to set up banks and financial institutions. Additionally, in 1992 privatization developments in the banking sector motivated local investors as well as foreign banks (Ahmad et al 2010). On January 31, 2002, Meezan Bank was granted a license by SBP; it started operations as the first Islamic bank in the country on March 20, 2002. Since then, the Islamic banking industry has grown continuously, surpassing the growth rates recorded by conventional banks over the last five years. Now there are six full-fledged Islamic banks (IBs) and twelve conventional banks with Islamic banking branches (IBBs) operating in Pakistan. Currently, the deposit market share stands at 4.2%. There are now more than 358 branches in over fifty cities and towns covering four provinces of the country and Azad Jammu and Kashmir (AJK; Javed 2009).

2 Objectives of the study

This paper aims to analyze liquidity risk management in the Islamic banking industry of Pakistan by measuring the demand for liquidity withdrawal from depositors as well as banks’ allocation of deposits for managing liquidity. In particular, it analyzes the internal Islamic mechanism for liquidity management, with the intention of understanding the liquidity behavior of depositors and the liquidity management of Islamic banks, and offers management recommendations to improve current liquidity management practices. This paper also examines liquidity management practices designed to meet the liquidity demands of depositors, and to what extent the rules and regulations provided by the central bank to control liquidity risk management in Islamic banks are implemented.

3 Liquidity risk in banking institutions

Liquidity risk is a bank’s inability to meet its obligations to depositors due to a shortage of funds or an increase of funds in assets that does not incur any unexpected cost or damages (Ismail 2010). Liquidity risk in banks arises when a depositor withdraws more funds than are available at the bank (Hubbard 2002); it can also occur if a depositor is unable to fulfill its financial obligations to a bank.

A bank may face liquidity problems if a depositor wants to withdraw an amount that the bank cannot afford at that moment in time. Moreover, banks may decide to cease loans when depositors are not in a position to pay them off. Some techniques for managing the regular demand for liquidity are the following: more investment of funds in more liquid loans and/or maintaining more cash in hand, diversifying the sources of funds from various depositors, and using the central bank as a last option for emergency liquidity (Greenbaum and Thakor 2007).

4 Liquidity risk management in Islamic banks

In order to develop a strong liquidity management program, Islamic banks should make arrangements via real business transactions (Antonio 1999). That is because such operations in Islamic banks are based on real and asset-based contracts (which link the business life cycle together), collaboration among business partners and the good conduct of stakeholders. This is the cornerstone of all Islamic banking operations. Therefore, the reasons behind a liquidity problem would be disharmony between business partners or the inevitable downturn of business conditions (Ismal 2010).

Due to some specific internal and external aspects of Islamic banks, the risk of liquidity is minimal. The values and principles of Sharia prevail inside Islamic banks, which care for a bank’s management, shareholders and stakeholders as valued business partners (Yaqoobi 2007). Thus, a system based on these values and principles ensures cooperation, transparency, symmetric information and equilibrium in allocation on both the asset and liability sides. In the Islamic financial mechanism, external liquidity risk problems are reduced; this is because the mechanism only engages in real business activities, as Sharia requires that real assets are attached to every Islamic financial market contract (Kahf 2000). Islamic banking uses a profit-and-loss sharing (PLS) concept, which reduces liquidity risk by sharing risk among business participants. PLS helps to balance assets and liabilities because the concept of PLS necessitates the full concern and understanding of all parties in the business. If actual return is less than expected return due to the uncertainty of return on deposits, some rational depositors may seek to withdraw their deposits (Ismail 2010).

Banks have to be very careful with their liquidity management, because, once a liquidity risk problem occurs, they will be left with very limited options. Islamic banks can also face reputation risk if they fail in governance management, Sharia compliance, business strategies or operations. In this case, most of the time, the government will come forward to offer immediate liquidity assistance and, in severe cases, take over the bank; this causes a negative public image of the operations of Islamic banks (Antonio 1999). According to Ismal (2010), Islamic banks have organized some internal guidelines and principles based on Islamic Financial Services Board (IFSB) guides to improve liquidity management. These guidelines include

  • establishing appropriate liquidity risk management policies,

  • measuring and monitoring liquidity risk, and

  • carrying out prudential and Sharia-compliant Islamic banking operations.

Islamic banks have to make appropriate decisions regarding business and the selection of entrepreneurs, and they must balance liquidity-establishing relationships with other Islamic banks.

5 Research methodology

This paper incorporates the empirical data analysis approach; the practices of Islamic banks with regard to managing liquidity risk, the factors affecting both the liability and asset sides as well as the liquidity behavior of depositors were all analyzed using a survey questionnaire. After conducting different statistical analyses on the facts and figures gathered via our questionnaire, we were able to suggest some guidelines to control liquidity risk and enhance the productivity of the Islamic banking industry in Pakistan.

Our target respondents were those who work in Islamic banks as presidents, directors, general managers or heads of risk management teams or divisions that are involved in the decision-making processes surrounding liquidity risk management in their banks. The responses were collected from Islamic banks in three of Pakistan’s biggest cities: Islamabad, Lahore and Sargodha. The Islamic banks selected for this study were Meezan Bank, Standard Chartered Bank, Bank Alfalah, Dubai Islamic Bank Pakistan, Muslim Commercial Bank and United Bank Limited. The data collected ranges from 2006 to 2011. The banks selected for this process primarily fell into these two categories: full-fledged Islamic banks and banks with a limited Islamic window. The sample size for this study was 100 bankers working in Islamic banks. A total of 120 questionnaires were distributed among the employees of these banks, and 100 employees responded.

Different statistical analyses were used to analyze each response and its potential effect on liquidity risk management. This process included descriptive statistics and multiple regression models for different sets of variables.

In our table of descriptive statistics (see the online appendix), the range, minimum values, maximum values, mean values, value of standard deviation, variance and skewness of the variables are shown. The range gives us an idea of the spread of the values. The mean value offers insight into the central tendency of the values of the variables. The number of observations for each value is 100. The standard deviation and extreme values (minimum compared with maximum values) give an idea about the dispersion of the value of a variable from its mean value. The skewness is used to see the normality of the variables. Since different units of measurement have been used for different variables, the dispersion of a variable using standard deviation cannot be compared with that of another variable unless both the variables have the same unit of measurement. Nevertheless, these statistics are still helpful in informing us about the central tendencies and dispersion of a variable in absolute rather than relative terms.

6 Regression analysis

A simple regression analysis is conducted on different sets of variables to determine the impact of liquidity risk management on selected Islamic banks in Pakistan.

The first regression model is applied on the following set of variables.

Dependent variable

  • Islamic bank practice of regularly calculating and analyzing the pattern of liquidity (Y).

Independent variables

  • Islamic bank practice of relying on cash reserves to meet daily liquidity withdrawal (X1).

  • Islamic bank practice of communicating with depositors who have large amounts of deposits about their withdrawal time/schedule (X2).

  • Islamic bank practice of relying on the capital adequacy, asset quality, management, earnings and liquidity (CAMELS) approach to make liquidity risk management decisions (X3).

  • Islamic bank practice of asking depositors for extra days when liquidity demand exceeds the bank’s reserves (X4):

      patternt=β0+β1cash reservest+β2communicatet+β3CAMELSt+β4exceeds reservet.   (6.1)
Table 1: Regression analysis.
R-squared 0.194
F (sig.) 0.000
Constant (β0) -0.210
Cash reserves (β1) 0.256
Communicate (β2) 0.168
CAMELS (β3) 0.197
Exceeds reserve (β4) 0.060

In Table 1, the results of multiple regression analyses are given. A regression model is applied to determine the best predictors of bank practice with regard to regularly calculating and analyzing patterns of liquidity withdrawal in order to manage anticipated liquidity demand from a depositor. The combination of variables to predict relies on bank practices, ie, relying on cash reserve, communicating with depositors, using the CAMELS approach and reacting to withdrawals exceeding the bank’s reserves, and is statistically significant, as depicted by the significance value of the F statistic, ie, 0.000<0.5. The R-squared value indicates that 19.4% of the variation in Islamic banks’ practice of regularly calculating and analyzing the pattern of liquidity management satisfaction is explained by the independent variables in the model.

The regression equation with coefficient values will be as follows:

  patternt=-0.21+0.256cash reservest+0.168communicatet+0.197CAMELSt+0.06exceeds reservet.   (6.2)

Equation (6.2) shows the values of Beta coefficients in the model. It demonstrates that one unit change in Islamic banks’ reliance on cash reserves to meet daily liquidity withdrawal will result in a 0.256-unit increase in the Islamic bank practice of regularly calculating and analyzing the pattern of liquidity. Similarly, one unit change in the Islamic bank practices of communicating with depositors who have large amounts of deposits about their withdrawal time/schedule, relying on the CAMELS approach to make liquidity risk management decisions and asking depositors for extra days when liquidity demand exceeds the bank’s reserves will cause increases of 0.168, 0.197 and 0.06 units, respectively, in the Islamic bank practice of regularly calculating and analyzing the pattern of liquidity.
The next set of variables for our regression model is as follows.

Dependent variable

  • Islamic banks rely on SBP’s rules and regulations for liquidity risk management decisions (Y).

Independent variables

  • Level of accuracy of SBP’s rules and regulations provided for liquidity risk management in Islamic banks (X1).

  • Level of satisfaction with quality of SBP’s rules and regulations for liquidity risk management in Islamic banks (X2).

  • Level of sufficiency of SBP’s rules and regulations provided for liquidity risk management in Islamic banks (X3):

      rely on SBPt=β0+β1accuracyt+β2satisfaction with SBPt+β3sufficiencyt+Ut.   (6.3)
Table 2: Regression analysis.
R-squared 0.279
F (sig.) 0.000
Constant (β0) 1.176
Accuracy (β1) 0.110
Satisfaction with SBP (β2) 0.008
Sufficiency (β3) 0.590

Table 2 shows the regression results of the model, which is applied to investigate the best predictors of reliance on SBP’s rules and regulations for liquidity risk management in Islamic banks. The combination of variables to predict reliance on SBP’s rules and regulations, including the level of accuracy, level of sufficiency and level of satisfaction with the quality of rules and regulations provided by SBP, is statistically significant, as depicted by the significance value of the F statistic, ie, 0.000<0.5. It also shows that the overall model is significant. The R-squared value indicates that 27.9% of the variation in reliance on SBP’s rules and regulations for liquidity risk management in Islamic banks is explained by the independent variables in the model.

The regression equation with coefficient values will be as follows:

  rely on SBPt=1.176+0.11accuracyt+0.008satisfaction with SBPt+0.59sufficiencyt.   (6.4)

Equation (6.4) depicts the values of Beta coefficients in the model and shows that one unit change in the level of accuracy of SBP’s rules and regulations for liquidity risk management in Islamic banks will result in a 0.11-unit increase in Islamic bank reliance on SBP’s rules and regulations for liquidity risk management decisions. Similarly, one unit change in the level of satisfaction with the quality of SBP’s rules and regulations and the level of sufficiency of SBP’s rules and regulations for liquidity risk management in Islamic banks will cause 0.008- and 0.59-unit increases, respectively, in Islamic bank reliance on SBP’s rules and regulations for liquidity risk management decisions.
The next set of variables for our regression model is as follows

Dependent variable

  • Level of satisfaction with availability of alternative investment opportunities (Y).

Independent variables

  • Problems with finding prospective and profitable projects (X1).

  • Problems due to a large portion of short-term deposits in Islamic banks (X2).

  • Effect of nonperforming loans (NPLs) on liquidity risk (X3).

  • Potential risk problems due to a rational depositor (X4):

      alt investment oppst=β0+β1projectst+β2short-term depositst+β3NPLst+β4rational depositort+Ut.   (6.5)
Table 3: Regression analysis.
R-squared 0.319
F (sig.) 0.000
Constant (β0) 1.463
Projects (β1) 0.384
Short-term deposits (β2) 0.470
NPLs (β3) -0.191
Rational depositor (β4) 0.222

Table 3 shows the results for our multiple regression model. This model is applied to test the best predictors of the level of satisfaction with the availability of alternative investment opportunities. The combination of independent variables to predict problems with finding prospective and profitable projects, problems due to a large portion of short-term deposits in Islamic banks, the effect of NPLs on liquidity risk and potential risk problems due to rational depositors is statistically significant, as depicted by the significance value of the F statistic, ie, 0.000<0.5. The R-squared value indicates that 31.9% of the variation in the dependent variable is explained by the independent variables in the model.

The regression equation for this model with coefficient values will be as follows:

  alt investment oppst=1.463+0.384projectst+0.47short-term depositt-0.191NPLst+0.222rational depositort.   (6.6)

Equation 6.6 depicts the values of Beta coefficients in the above-mentioned regression model. It shows that one unit change in problems with finding prospective and profitable projects will result in a 0.384-unit increase in the level of satisfaction with the availability of alternative investment opportunities. Similarly, one unit change in problems due to a large portion of short-term deposits in Islamic banks and potential risk problems due to rational depositors will cause 0.47- and 0.222-unit increases, respectively, in the dependent variable. However, with one unit change in the effect of NPLs on liquidity risk, the level of satisfaction with the availability of alternative investment opportunities will decrease by -0.191 units.
The last set of variables for our regression model is as follows.

Dependent variable

  • Level of satisfaction with techniques currently in use to manage market pressure (Y).

Independent variables

  • Problems with finding prospective and profitable projects (X1).

  • Problems due to a large portion of short-term deposits in Islamic banks (X2)

  • Effect of NPLs on liquidity risk (X3)

  • Potential risk problems due to a rational depositor (X4):

      market pressuret=β0+β1projectst+β2short-term depositt+β3NPLt+β4rational depositort+Ut.   (6.7)
Table 4: Regression analysis.
R-squared 0.321
F (sig.) 0.000
Constant (β0) 0.645
Projects (β1) 0.181
Short-term deposit (β2) 0.293
NPL (β3) -0.091
Rational depositor (β4) 0.218

In Table 4, the multiple regression model is applied to measure the best predictors of level of satisfaction with the techniques currently in use to manage market pressure. The independent variables in this model, ie, problems with finding prospective and profitable projects, problems due to a large portion of short-term deposits in Islamic banks, the effect of NPLs on liquidity risk and potential risk problems due to rational depositors, are statistically significant, as shown by the significance value of the F statistic, ie, 0.000<0.5. The R-squared value shows that 32.1% of the variation in the level of satisfaction with techniques currently in use to manage market pressure is explained by the independent variables in the model.

The regression equation with coefficient values will be as follows:

  market pressuret=0.645+0.181projectst+0.293short-term depositt-0.091NPLt+0.218rational depositort.   (6.8)

Equation (6.8) shows the values of Beta coefficients in the regression model. It demonstrates that one unit change in problems with finding prospective and profitable projects will result in a 0.181-unit increase in the level of satisfaction with the availability of alternative investment opportunities. Similarly, one unit change in problems due to a large portion of short-term deposits in Islamic banks and potential risk problems due to rational depositors will cause 0.293- and 0.218-unit increases, respectively, in the dependent variable. However, with one unit change in the effect of NPLs on liquidity risk, the level of satisfaction with the availability of alternative investment opportunities will decrease by -0.191 units.

7 Conclusions

This study proposes several conclusions based on the analysis conducted. The following are our key findings and recommendations for the development of liquidity risk management.

Our analysis revealed that there is a satisfactory level of availability for alternative investment opportunities in the Islamic banking market. However, Islamic banks are not availing these opportunities to invest because of rational depositors. Islamic banks are compelled to stay away from such opportunities to avoid liquidity risk. Whenever an investment opportunity is available, banks tend to pass on those opportunities for fear of sudden demands for liquidity from rational depositors. Our analysis depicts that Islamic banks have a large portion of short-term deposits, which creates problems for Islamic banks trying to fulfill rational depositors’ demands. Further, if the liquidity demand exceeds the Islamic bank’s reserves, this will affect not only the bank’s liquidity but also the good reputation of the Islamic bank and its customers’ belief in it. This situation could lead to the loss of valuable customer accounts.

The findings clearly indicate that, due to the presence of rational depositors, Islamic banks are having problems with investing in potentially profitable projects. The rational depositor can, at any time, ask for their deposits back, and they do not want to wait for funds to be made available. As a consequence, Islamic banks tend to keep more reserves compared with conventional banks. This results in lost opportunities with regard to investing in potentially favorable long-term projects. Our analysis also demonstrates that, in order to manage market pressure, increasing the adequacy level of rules and regulations for liquidity risk management in Islamic banking is important. Improving the adequacy level of rules and regulations for managing liquidity risk will help Islamic banks to invest in the available opportunities.

According to our analysis, rational depositors are affecting liquidity risk management in Islamic banks. Although the SBP has provided rules and regulations for liquidity risk management in Islamic banks, the current practices in Islamic banks are not adequate for managing rational depositors’ effect on liquidity risk. SBP is responsible for making the rules and regulations for liquidity risk management in the banking industry. However, Islamic banks in Pakistan have to communicate with depositors to realize any anticipated demand for liquidity. For this reason, Islamic banks are also doing calculations and analyses on liquidity patterns on a regular basis.

7.1 Recommendations

There is an adequate level of rules and regulations in place to manage liquidity risk in Islamic banks; however, due to the improper implementation of practices, it is difficult to manage rational depositors. So, we suggest that Islamic banks implement the rules and regulations effectively. This will help them to manage the demands from depositors.

Due to poor liquidity risk management, Islamic banks are not able to invest in profitable projects. Unexpected demands from rational depositors prevent them from availing investment opportunities; they need to consider liquidity risk management as an essential factor in their growth. Islamic banks are experiencing more distress than commercial banks due to rational depositors. Islamic banks must consider the rational depositor as an integral part of liquidity risk management while developing strategies for liquidity risk management in Islamic banking. Further, to improve liquidity risk management, Islamic banks need to provide their employees with regular training. This training will help bankers to understand the importance of liquidity risk management and allow them to learn new techniques to perform their duties. It will also boost performance, which should ultimately help to control liquidity risk management in Islamic banks.

SBP is responsible for addressing the problems arising in the banking industry in Pakistan. SBP needs to improve the rules regarding the incentives provided for liquidity risk management in Islamic banks. The reserves affect the circulation of liquidity, which leads to liquidity risk in Islamic banks. In such situations, SBP needs to come forward and help Islamic banks by providing them with support. Holding large amounts of cash in reserves also affects a bank’s ability to utilize this cash to pay debts and results in poor working capital management. The central bank may want to consider establishing a separate funding system that is just for fulfilling urgent liquidity requirements in Islamic banks.

Finally, the recommended policies are constructed with an expectation of growth in the Islamic banking industry of Pakistan. It is assumed that the growth of the industry will continue and the number of depositors will increase. So, these recommendations should be viewed as a basis for improvement in liquidity risk management; future areas of development should not involve making major policy changes but rather fortifying and working together with current policies.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper. This paper was submitted at the 8th International Finance Conference (IFC8) in Paris, France. All papers were reviewed by the conference committee.

References

Ahmad, A., Malik, M. I., and Humayoun, A. A. (2010). Banking developments in Pakistan: a journey from conventional to Islamic banking. European Journal of Social Sciences 17(1), 12–17.

Antonio, S. (1999). Sharia Bank for Bankers and Practitioners, 1st edn. Bank Indonesia and Tazkia Institute, Jakarta.

Eedle, S. (2009). A Global Bank’s View of the Evolution of Islamic Finance. Adrian Hornbrook, Colchester.

Greenbaum, S. I., and Thakor, A. V. (2007). Contemporary Financial Intermediation, 2nd edn. Elsevier.

Hubbard, G. R. (2002). Money, the Financial System, and the Economy. Addison Wesley Series in Economics. Pearson Education.

Ismail, A. G. (2010). Money, Islamic Banks and the Real Economy, pp. 93–96. Cengage Learning Asia, Singapore.

Ismal, R. (2010). Volatility of the returns and expected losses of Islamic bank financing. International Journal of Islamic and Middle Eastern Finance 3(3), 267–279 (http://doi.org/b9x9hr).

Javed, M. (2009). Islamic banking in Pakistan. The Nation. URL: http://www.nation.com.pk.

Kahf, M. (2000). Treatment of excess liquidity in the Arab Gambian Islamic Bank. Unpublished Paper. URL: http://bit.ly/2nCZTgU.

Omar, M., and Mondher, B. (2010). General introduction and main concepts in Islamic banking and finance. In The Handbook of Islamic Banking and Islamic Finance. World Scientific.

Yaqoobi, N. (2007). Shariah finance: questions and answers. Islamic Finance Review, Euromoney Year Books 2007/08.

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net to find out more.

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here