




































INDIAN JOURNAL OF FINANCE AND BANKING 10(1) (2022), 61-74 

61 

 

                       FINANCE AND BANKING 

                                                                  IJFB VOL 10 NO 1 (2022) P-ISSN 2574-6081  E-ISSN 2574-609X 
                                                  

        Available online at https://www.cribfb.com 

                                                                                                                                           Journal homepage: https://www.cribfb.com/journal/index.php/ijfb 
                                                                                                                                                                                                   Published by CRIBFB, USA 

FACTORS IMPACTING LIQUIDITY OF BANKS: AN EMPIRICAL 

STUDY FROM THE BANKING SECTOR IN THE UAE           

 
 Manoj Kapur (a)1    Arindam Banerjee (b)     Kunjana Malik (c) 

 

(a) Researcher, Strategist and Risk Specialist, Mumbai, India; E-mail: drmanojkapur@hotmail.com  
(b) Associate Professor and Assistant Dean, SP Jain School of Global Management, Dubai, United Arab Emirates; E-mail: arin_006@yahoo.com  
(c) Assistant Professor, SP Jain Institute of Management and Research, Mumbai, India; E-mail: kunjanamalik.phd@fms.edu  

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 7th June 2022  

Accepted: 29th July 2022 

Online Publication: 10th August 2022 

 
Keywords: 

 

Liquidity, Idiosyncratic Factors  

Market Related Factors  
Regression Analysis, UAE  

  
JEL Classification Codes: 

 
      E58, G32, G38 

 

  

 
A B S T R A C T 
 
The primary purpose of this research is to substantiate the factors that impact the liquidity of the banks 

in the UAE. This research paper is an extension of the thesis that the primary author has undertaken to 

prove the test of significance and provide concrete evidence that the identified idiosyncratic and market 

related factors have significant impact on the liquidity risk for the banks in the UAE. The primary author 

has performed linear regression to identify the relation of the dependent and independent variables and 

once the test of significance is proved, the factors have been ranked using MURAME approach as part 

of ultimate thesis research objectives (MURAME approach is not forming part of this research paper). 

The research paper focusses on top 10 banks in the UAE and the study spans from 2010 to 2019. The 

study employs idiosyncratic factors like Deposit growth, NPL, CAR, ROA and market factors like GDP, 

Inflation, Unemployment, Oil prices and studies its relationship on dependent factor i.e., liquidity. Series 

of diagnostic tests are performed to find the impact of liquidity on idiosyncratic and market related 

factors. 

 
 

© 2022 by the authors. Licensee CRIBFB, USA. This article is an open access article distributed 
under the terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

Liquidity has different meanings in different contexts. In general, liquidity refers to the amount of cash available for meeting 

the expenses as well as for investment. Within the banking context, liquidity refers to the ability to meet the maturities 

related to debt and credit within defined timelines. Liquidity risk is the absence of liquidity that is required to meet these 

liabilities. Absence of liquidity can be one of the reasons for the banks to fail. Liquidity crunch may be seen as an important 

aspect for alleviating the expected and unexpected balance sheet movements and increasing the resources for targeting the 

growth (Çetinkaya, 2018). The risk is important in meeting the funding needs as well have having sufficient buffers of 

liquidity for meeting the uncertain liabilities as and when they come due. This aids in the sustainable growth of the bank 

and the sector.  

Banks are an important intermediary for facilitating funds to their customers. Banks are able to achieve this 

objective by sometimes using their own equity to collaborate with customers in lending activities and bridging the gap 

(Santomero, 1997). Thus, banks act as an intermediary in facilitating finances among several industries. The performance 

of the banking sector is of prime importance to ensure safeguarded financial performance of the country (Munir et al., 2012). 

Basis traditional view of the banking sector, banks aid in channeling cash from and to the people through custodian and 

lending activities. Hence, banks consider liquidity risk as seriously since the time of their establishment (Hakimi & 

Zaghdoudi, 2017). Liquidity risk for the bank would be the state wherein the bank is unable to meet the customers’ demands 

as and when they arrive. Such a situation would trigger a red alarm that would signal the market that the bank does not have 

sufficient liquidity. There can be several reasons for liquidity risk in the banking sector, the most common being the short-

term assets funding the long-term liability and vice versa. While banks have advanced risk management systems that track 

maturities of assets and liabilities, there may be times when the bank would face liquidity blockages leading to liquidity 

                                                      
1Corresponding author: ORCID ID: 0000-0002-5787-2796 

© 2022 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/ijfb.v10i1.1779 
 

To cite this article: Kapur, M., Banerjee, A., & Malik, K. (2022). FACTORS IMPACTING LIQUIDITY OF BANKS: AN EMPIRICAL STUDY FROM 

THE BANKING SECTOR IN THE UAE. Indian Journal of Finance and Banking, 10(1), 61-74. https://doi.org/10.46281/ijfb.v10i1.1779 

https://orcid.org/0000-0002-5787-2796
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/ijfb.v10i1.1779
https://orcid.org/0000-0002-1880-7625
https://orcid.org/0000-0001-8983-9803


Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

62 

shortfalls (Kumar & Yadav, 2013).  

The banks have major responsibility of managing their own liquidity since they are a source of liquidity for their 

customers. The banks enjoy this pivotal role of creation of liquidity and management of liquidity. Creation of liquidity is 

successfully carried by clearing off liquidity concerns for large business owners by grating finances. This gives rise to 

creation of liquidity risk since the banks have issued large lending to the corporates (Alqemzi et al., 2022). 

To avert liquidity crises, Regulatory bodies and Central Banks have implemented strict levels of liquidity to be 

always maintained. This level of cushion is required to maintain liquidity at a certain level. The banks in the UAE are subject 

to minimum cash reserve requirements that is part of fiscal and monetary policies of the central bank. Policy makers suggest 

that banks must be able to maintain higher level of liquid assets so that they can hedge against untoward negative market 

movements. This led to an international debate to create standards that would optimize liquidity risks. The 2008 financial 

crises shocked the world and resulted in a deep concern among the Central Banks around the world. Consequently, the 

BASEL committee pronounced BASEL III with an aim to accumulated reserves that would specifically address the liquidity 

related issues. Liquidity crises is viewed as a serious issue in developed and developing countries. Liquidity assets include 

cash and cash equivalents that are less profitable and generate lower yields. Thus, the banks have to maintain an equilibrium 

between liquid assets and others as it would lose on opportunity costs. Banks try to maintain minimum level of liquidity that 

does not impact their profitability. Hence, the framework created by BASEL III ensured adequate liquidity levels to be 

maintained to avoid liquidity crises (Mwangi, 2014). Several studies have been performed in the past that have specifically 

focused on measuring credit risk as a yardstick for banks performance. However, the same is not true for liquidity risk as 

not many studies are available. There are few studies that have studied the relationship of liquidity and performance (Claeys 

& Vennet, 2008; Trujillo-Ponce, 2013). While some researchers found positive relationship between liquidity and 

performance, others did not conclude a significant relationship. Some researchers view that a decrease in liquidity risk 

positively affects a bank’s performance (Bourke, 1989; Bordeleau & Graham, 2010; Lartey et al., 2013). However, some 

have found the opposite to be true (Konadu, 2009). Furthermore, there are some studies that do not find any significant 

relationship between the two variables (Lamberg & Valming, 2009). All these studies have different results as these have 

been performed in different jurisdictions and in several times. Further, since different dependent as well as independent 

factors were used, the impact on liquidity risk is different. Liquidity risk is studied by using different variables. Alzorqan 

(2014) in its study used variables like ROA and ROI to ascertain the Jordanian banks performance. Similarly, Rahman and 

Saeed (2015) also used ROA and ROE to research on the Malaysian banks’ performance. Hakimi and Zaghdoudi (2017) 

used NIM to measure Tunisian banks performance. Thus, each researcher was able to ascertain the performance of the banks 

in different ways and its impact on the liquidity. LD ratio, liquid assets ratio, assets quality ratio and several other ratios are 

useful to ascertain banking liquidity positions (Chowdhury & Zaman, 2018; Hakimi & Zaghdoudi, 2017; Ferrouhi, 2014). 

One of the notable researchers Mwangi (2014) quoted that the impact of liquidity on the performance of the bank can also 

be determined through the business model and the liquidity cycle the bank faces. This may lead to further research in this 

area. This can be proven from the fact that macro-economic variables like GDP, inflation, oil price movement in the UAE 

will also have an impact on the liquidity of the banks in the UAE. The current study is, therefore, focused on providing 

insights into the impact of the liquidity risks on the banking sector in the UAE. The independent factors are chosen from 

idiosyncratic and macro-economic factors that have been selected through thorough literature review. The current research 

is a piece of a wider research by Kapur (2020), that focusses on ranking the identified idiosyncratic and market related 

factors using MURAME approach. The current research aims at identifying the impact of the factors using regression 

analysis.  

Liquidity as well as performance of the bank can be measured using several parameters. Idiosyncratic factors can 

be measured using financial ratios like Return on Asset (ROA), Return on Equity (ROE), Deposit growth, Capital Adequacy 

Ratio (CAR), etc. (Murthy & Sree, 2003). 

The current research has been performed on top 10 banks in the UAE. Since UAE has more than 50 banks, it can 

be said that the market is fragmented. Hence, in order to provide conclusive evidence of the impact of the liquidity risk, the 

sample is limited to top 10 banks. 

 

LITERATURE REVIEW 

There have been numerous studies conducted that aim to quantify liquidity risk; these have performed analyses of several 

banks around the world. In the UAE, however, very limited literature is available on this subject. Most of the times the 

research is conducted using ratio analysis as an indicator to judge the banks’ performances. During the 1990s, the concept 

of liquidity began to gain popularity through the collapse of Enron. Healy and Palepu (2003) have studied the fall of Enron 

in detail; they question the role played by market intermediaries, governance torchbearers and regulators involved in 

maintaining the sound function of the stock market. While Enron was a classic example of fraud and the manipulation of 

stock market, the fact that the company did not build up liquidity was also one of the reasons for its collapse. Healy and 

Palepu’s research proposes that certain system-level and capital-market changes were required to avert the recurrence of 

fraud like that seen in the Enron case.  

Molyneux and Thornton (1992) conducted an analysis based on ratios, in which liquidity risk was considered as 

an independent factor. The objective of the research was to ascertain the determinants of the profitability of European banks. 

The study was comprehensively conducted and included 600-plus banks across 18 European countries. Specifically, the 

dependent variables included net profit before and after taxes, linked to capital/reserves; total assets; staff operating 

expenses; and provision for loss loans. The independent variables included government policies, the concentration of 

depositors, the IMF-denominated long-term bank market rate in each country, the IMF-denominated money supply in each 

country, cash/marketable securities, the consumer price index (CPI) and staff expenses. The study was conducted using a 



Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

63 

simple linear equation for the period between 1986 to 1989 and concluded by stating that all the factors had a positive 

relationship, apart from the relationship between the government and profitability. The results were comparable to those 

concerning US markets, and the authors did not seem to find any evidence of risk aversion by the banks. The gearing in the 

bank was found to be high, which seemed to affect the liquidity of the banks.  

The above study by Molyneux and Thornton (1992) was an extension to the research already conducted by Bourke 

(1989), which utilized factors such as the profitability of international banks and simulated previous research to conclude 

that the concentration of liquidity is positively correlated to profitability. These results also suggested that banks were subject 

to a high degree of risk avoidance and that they had high market power. The quantification and analysis of data was achieved 

through a pooled time-series method that estimated linear equations for several bank-specific factors such as staff expenses 

and capital and liquidity ratios. External factors were also used to determine the profitability of the banks, including 

concentration, government ownership, inflation, market growth and interest rates.  

Demirgüç-Kunt, Laeven, and Levine (2003) have studied the impact of and correlation between bank regulations, 

market structure, and national institutions and the effect it has on net interest margins and the banks’ overhead costs. 

Specifically, the analysis studies the liquid assets to total assets ratio to quantify the impact that concentration has on a 

bank’s interest margin. The analysis was conducted using data from more than 1,400 banks spanning across 72 countries, 

and mainly concerns bank-specific factors such as profitability, total assets, depositors and concentration. The research uses 

the linear equation model; the results indicate that strict laws from the regulator enhance bank activities and thereby boost 

financial interbank-intermediary costs. In terms of external factors, the study finds that inflation causes a positive correlation 

between the profitability of a bank and its operational costs. Furthermore, it is observed that concentration is positively 

correlated with net interest margins; however, this correlation is not effective when controlling factors such as regulatory 

impact and competition are introduced. In addition, individual regulation for banks is insignificant when determining the 

correlation between market factors such as inflation and global assets and liabilities. These would, however, be considered 

significant when calculating interest margins and operating costs. The research concludes by stating that regulations that are 

prescribed by the regulators cannot be viewed in isolation, as they are made while keeping monetary policies, national 

interest and competition from foreign banks in view.  

Kosmidou and Zopounidis (2008) measure the performance of commercial and co-operative banks in Greece using 

the PROMETHEE method and specific financial ratios. The research includes ratios for return on equity, ROA, net profit 

before taxes, gross profit, loan to deposit, equity-to-assets ratio, NPL and loan loss provisions to total assets. The objective 

of the research is to ascertain the performance and efficiency of co-operative banks and compare them with those of the 

commercial banks during the period 2003–2004. As PROMETHEE is a ranking method, the results rank the banks’ strengths 

and weaknesses and compare the competition among them. The results of the method indicate that commercial banks are in 

a better position to maximize their profits and attract higher customer deposits due to lower operational costs and higher 

margins. Additionally, the results indicate that co-operative banks are not as symmetrical as those banks that had an 

increasing profits and market share reported deterioration in the financial conditions, and thus had higher NPL ratios. The 

combined results indicate that liquidity is a factor of ROA and NPL: the higher the ROA, the higher the liquidity; conversely, 

NPL higher would be the available liquidity. Kosimidou states that liquidity risk is the relationship between liquid assets 

and a combination of short-term funds and deposits (customer deposits). The ratio is termed as deposit run-off ratio, which 

highlights the bank’s capacity to meet its short-term funding requirements by selling assets in situations where short-term 

liabilities are not available. Some ratios that require liquidity to be more than 100% of the total liabilities expose banks to a 

higher risk of liquidity due to the cost of holding liquidity.  

Several studies were conducted by Demirgüç-Kunt and Huizinga (1999) that examine the ratio between loans to 

total assets to measure the extent of its effect on the liquidity of the banks. This ratio of loans to total assets is considered 

more appropriate than the ratio of loans to deposits because the latter does not consider the impact on total liabilities. In 

addition, the new generation of authors (Shen, Chen, & Kao, 2009; Soula, 2015) are very vocal in terms of their presentation 

of liquidity, using ratios as proxies for its computation. Chen et al. (2018) utilize data from 12 local commercial banks 

during the period 1994–2006 to ascertain the impact of liquidity on the bank performance. The authors employ a two-stage 

least squares regression method as part of their analysis, concluding that they have proved that liquidity risk is not only 

dependent on HQLAs, which are internal to the bank, but also on supervisory and regulatory changes and macroeconomic 

factors, which are external influences. In addition, they state that liquidity risk affects a bank’s profits by significantly 

reducing the return on average assets and return on average equity and increasing the cost of deposits for the bank. 

Conversely, the model used by Soula (2015) integrates the risk factor model to ascertain the impact of liquidity risk for the 

sampled banks between the period 2005–2012. The results indicate that idiosyncratic factors are a major cause of liquidity 

risk, and that liquidity has high heterogeneity during normal periods. However, during stressed periods, the risk is systemic 

in nature, with a significant reduction in heterogeneity.  

The objective of each approach is to assess the impact of liquidity risk on other components and assess its stability. 

Under the balance sheet analysis approach, liquidity is evaluated based on balance sheet components. The items in the 

balance sheet on the asset side that directly have a consequential effect on liquidity include the receivables, loans and 

advances and investment, the latter of which is categorized into trading and available for sale, repo and reverse repo bonds 

and collateralized borrowings. On the liability side, the items include customer deposits, repos and reverse repos, other long- 

and short-term liabilities and equity. The liquidity analysis for the balance sheet consists of comparing the asset side with 

the liability side, looking for available liquid assets to fund the liabilities. In either case, the objective is to ensure the stability 

of funding sources, which are required to meet obligations as they become due.  

The peer-group ratio comparison involves comparing several ratios from the balance sheets of different banks. This 

method relies on collecting all significant sources of liquidity and their uses by the bank. An illustration of this method 



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would be to compare the use of ratios relating liquid assets to total assets by several banks. One of the limitations of this 

ratio comparison is that banks need to have similar business models, equivalent asset sizes, similar geographies, etc. Other 

ratios under the peer-group analysis could include loans to deposits or loans to total assets.  

The liquidity index indicates the latent losses that a bank might derive from an immediate sale or disposal of an 

asset in the open market. The ratio is calculated as the ‘weighted sum of the price of each asset in the event of fire sale to 

its fair market price (the price it would receive in the market under normal situation’. The bank must construct stressed 

scenarios that cater to these types of distress sales if it is to have an understanding of the liquidity skewness in the market. 

One of the limitations of this ratio is that it considers the assets side of the balance sheet; hence, it can be used to gauge 

market risk liquidity rather than funding liquidity. 

The financing gap is a measure that requires mature judgement concerning deposit withdrawals made by the deposit 

holders. Mathematically, it is expressed as the difference between average total loans and average total deposits. The bank 

must create scenarios that depict the expected withdrawals under both normal and stressed situations, which will allow it to 

understand the resulting financing gap. The larger the finance gap, the more liquidity problems, as a larger gap would 

indicate that the bank does not have enough core deposits to meet its obligations. In such a situation, a bank would have to 

use cash and/or other liquid assets or borrow funds in the interbank market. A continuous incremental finance gap indicates 

chronic liquidity issues, which may lead to insolvency if not properly addressed through a contingency funding plan.  

The financing requirement refers to summing up the financing gap using readily available and convertible HQLAs. 

An additional objective of this study is to consider how it is that market-related events have a strong impact on the 

liquidity risk management of a particular bank. The 2008 liquidity crisis, as well as the 2020 COVID-19 pandemic (even 

though both have a low probability of recurrence), are systemic issues that had/are having a devastating effect on the entire 

banking system. Brunnermeier and Cheridito (2019) state that aggregate fiscal and liquidity policies by the central banks 

are important when determining any market-related data points, particularly when these policies are made during economic 

growth and downturn phases. The authors initiate the development of several risk indicators with practical relevance, in that 

they quantify bank-specific liquidity risk indicators and systemic factors. The research further substantiates its position by 

considering two moments in time: the time ‘t’, an ex-ante time period during which the bank manages to generate cash flows 

on the assets and liabilities that assume liquidity risk, and the time period ‘t+1’, which reflects the economic conditions. 

The rationale behind the creation of this model using the time periods ‘t’ and ‘t+1’ is that it allows an explanation of the 

liquidity index in several economic conditions in ‘t+1’ time, while the total liquidity index equates to the starting time period 

‘t’. Each asset and liability in the balance sheet is assigned to the respective cash index to achieve the equivalent cash asset 

and liability indices. During the methodology phase, the liquidity mismatch is generated by calculating the difference 

between the asset cash index and the liability cash index and assigning equivalent weights for each index. Subsequently, the 

VaR method is used to estimate the probability of liquidity risk at time period ‘t’, with a confidence interval of 5%. It is 

important to note that during the methodology stage, it is vital to consider the variation in the assets and liabilities for each 

time period (‘t’ or ‘t+1’, respectively). These factors can then be used to conduct an analysis of liquidity exposure for a 

standalone single sector. 

Drehmann and Nikolau (2013) observe that liquidity risk can be bifurcated into two distinct components: 

inflows/outflows and financing (funding) pricing. The authors put forward the theory of the funding premium approach, 

whereby the interest rates applied to the refinancing options are provided by the central banks. The authors use the difference 

between the final bid and the minimum bid price as a liquidity risk proxy (LRP), using the assumption that the markets will 

remain tight, and the bank has a risk-averting strategy. In practical terms, banks exposed to a higher funding risk would 

generally be willing to pay higher prices for liquidity from the central bank; as such, funds would be highly stable in nature. 

These proxies can be used as a single source of spread, allowing the development of further liquidity risk measures. The 

conclusion of the research notes the opportunity cost between the bid price and marginal interest rate, allowing the accurate 

calculation of the LRP. In mathematical terms, this LRP is characterized by the variation of spread for every bank that 

participates in the bid auction.  

Singh and Sharma (2016) have studied the bank-specific and macroeconomic factors that affect the liquidity of the 

banks in India. They perform an exploratory study into the association between these factors as well as their random and 

fixed-term effects, for 59 banks in India for the period 2000–2013. The study includes bank-specific factors such as bank 

size (in terms of the bank’s total assets), profitability in each of the years under study, the cost of deposit and funding, CAR 

and total deposits. Regarding the systemic factors, the authors study India’s GDP, its inflation and its unemployment rate. 

The findings indicate that management is also a factor that affects the banks’ liquidity. None of the bank-specific factors 

(except the cost of deposit) had a significant impact on liquidity; neither did any of the macroeconomic factors (except 

unemployment). Specific factors included the size of the bank, the deposit size, the profitability of the bank, CAR and 

India’s GDP and inflation. Hence, in the final analysis, the size of the bank and India’s GDP were seen to have a negative 

effect on the liquidity of the bank, while a positive effect occurred because of the total deposits, profits and CAR. The results 

are very useful as they enable a judgement of the impact of liquidity on emerging countries such as India.  

Mahmood et al. (2019) have studied the impact of macro- and bank-specific factors on bank liquidity using the 

fully modified OLS approach. The study is based on a previous investigation conducted by Gafrej and Abbes (2017), which 

conducted a macroeconomic examination of the determinants of commercial bank liquidity factors in the Czech Republic. 

Both studies suggest that macroeconomic factors have a significant impact on bank liquidity; specifically, they indicate that 

factors such as inflation not only impact the liquidity of the banks but also have a detrimental impact on the economy. The 

study also finds that other macro-economic indicators such as interest rates, NPLs, and GDP serve as factors that have a 

significant impact on bank liquidity risks. It has been seen in the market that NPLs have a positive correlation with liquidity, 

in that whenever NPLs rise, liquidity also rises, as it induces the bank to preserve liquidity for any unforeseen circumstances. 



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65 

A similar situation is seen in relation to GDP growth, in that during a period of GDP growth, many borrowers are willing to 

take out more loans; this, by default, forces banks to maintain an adequate level of liquidity. The interbank rate has a positive 

relationship with liquidity, as banks invest excess liquidity as interest rates increase. As regards bank size in terms of total 

assets, the stated principle supports the ideology of ‘too big to fail’ and the possibility of support from the lender of last 

resort. Massoc (2020) studies competition in retail banking and structural banking reform in relation to the ‘too big to fail’ 

concept. The research focuses on a single global and domestic bank in Europe, examining how the market dynamics have 

affected it since the 2008 financial crisis. Based on a competitive analysis of bank structure and regulatory banking policies 

in the UK, France and Germany, the study explains how vulnerable banks can be in the wake of a global financial crisis. 

Gafrej and Abbes (2017) firmly concludes by stating that interest margins, monetary policies and interest margins do not 

have a significant impact on the liquidity of banks in the Czech Republic.  

Rani and Zergaw (2017) and Trenca et al. (2015) echo this assessment of the impact macroeconomic variables 

have upon banking system liquidity. The studies are very similar to that conducted by Gafrej and Abbes (2017), concluding 

by saying there is a negative relationship between liquidity and financial crises. Banks’ liquid assets, which are received 

from total assets and deposits as well as from short-term funding, reduce in line with the higher profitability, increased 

capital adequacy and size of the bank. However, the abovementioned studies challenge Gafrej and Abbes (2017) findings 

in respect of unemployment and the public deficit. The study also found that GDP and unemployment have an effect on the 

bank liquidity in that region.  

This literature review includes several studies that consider idiosyncratic and market-related factors. 

Macroeconomic factors are also important, as these can be determinant towards gathering liquidity during both normal and 

stressed market conditions. While idiosyncratic factors can be gathered from financial statements, macroeconomic indicators 

arise from a country’s economic, monetary and fiscal policies. We have already seen, in the earlier sections, how the 2008 

financial crisis that emanated from one financial institution spread to other financial markets and thus became a systemic 

issue. Hence, it can clearly be concluded that factors affecting liquidity not only arise from individual banks but also from 

systemic market factors, which may be related to the quantity and quality of liquidity in the interbank market.  

Over the last four decades, a major attribute of the economic success of the UAE is the positive movement of oil 

prices. The discovery and export of oil changed the face of UAE, from a fishing economy to an oil giant, ultimately creating 

a revenue-rich country. The GDP of the UAE is one of the highest in the Middle East, standing at USD 421 billion as of 31 

December 2019. In contrast, it stood at merely USD 17 million in 1960.  

El-Chaarani (2019) documents the influence of oil prices by studying the balance sheet and income statements of 

several banks in the Middle East. Of the many countries considered, one country selected is the UAE. Banks from several 

other countries (such as Saudi Arabia, Qatar, Bahrain, Kuwait, Jordan, Oman and Iran) were also studied. Since the drop in 

oil prices in 2014, the central and federal governments of the Middle Eastern countries that are mainly engaged in oil-

producing businesses have tried to diversify into hydrocarbons and trading activities. Other non-oil sectors include 

manufacturing, tourism, health/ medical activities and finance. The study also propounds the idea that many scholars have 

ventured into analyzing the correlation of the banking sector with oil price fluctuations; however, no clear propensity of the 

nature of their relationship has been determined.  

A study conducted by El Mahmah and Trabelsi (2021) focuses on the impact of the drop in oil prices on the 

financial stability of GCC banks over the period 2000–2014. The method used by the study is a panel VAR model that is 

specifically constructed to show when the price drops below 1%. The results of the study reveal that “Oil price shocks were 

a determinant of the overall financial stability of banking sector in the region. The oil price drop in the middle of 2014 was 

associated with high level of non-performing loans and a low level of financial stability”. 

The study was further done in 2019, when the oil prices had fallen significantly, to negative levels. This study highlights 

serious financial concerns for banks in GCC countries, as oil-producing companies withdrew massive deposits in order to 

continue as going concerns. 

Tabash and Khan (2018) reinforce the long- and short-term correlation between oil price movements and the 

financial performance of Islamic banks in the UAE between the end of 1990 and mid-2015. Their research clearly shows 

that the movement of oil prices, as well as the shocks, resulted in a significantly large negative correlation with the banking 

sector in an emerging economy such as the UAE; however, it also shows that the Islamic banks were not significantly 

impacted due to their Sharia lending principles and additional capital requirements. The research gap, identified by the study 

itself, is that it does not consider conventional banks, as Islamic banks were the focus of the research. 

A more recent study has been undertaken by Musa (2021); this examines the relationship between oil prices and 

Islamic bank performance in OPEC members for the period 2007 to 2016. The authors observe that each country has specific 

oil-price management characteristics and that the impact of oil prices is different in every country. Based on the results, the 

study notes that 

Given the importance of oil price movement in the Middle East, there are very few research conducted that have 

measured its impact on the liquidity of the banks?.  

 

MATERIALS AND METHODS 
A regression analysis predicts a continuous variable from a set of independent variables. The independent variables can be 

either dichotomous or continuous. A multiple regression analysis provides information about the interaction between 

dependent and independent variables, or between several independent variables, which can then be used to enable further 

manipulation (Kroll & Chesler, 1992). Through regression, the determination of the statistical significance of a coefficient 

is possible. It is also possible to retain those factors that are statistically significant while rejecting those that are not. In this 

study, the regression is performed on naturally occurring variables that have been calculated from the balance sheets found 



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66 

in the banks’ annual financial statements (for bank-specific factors) and in the UAE’s national statistics register (for market-

related factors).  

The author has performed the routine diagnostics for regression by ensuring the accuracy of the data, accounting 

for missing data, confirming the number of cases and noting any outliers. All diagnostics have been performed using SPSS 

software for data cleaning and analysis. The data for the annual financial statements was plotted on an Excel sheet; this data 

was then uploaded into SPSS using an .xls file.  

 

Diagnostic Validation for Bank-Specific Factors Using Regression Analysis 

Table 1. Dependent and Independent variables 

 
Dependent Variable Liquidity Ratio 

Independent variables  Deposit growth, NPL, CAR, ROA 

 

The dependent and independent variables have been derived from the published annual financial statements of the 

sampled banks for the period 2010–2019.  

 

Test of Normality 

The test of normality can be judged through Histogram, P-P plot, Q-Q plot and test of normality. Elliott & Woodward (2007) 

stated that the normality tests are also supplemented with the graphical assessment of normality.  

 

Figure 1. Normal distribution and PP Plot diagram for idiosyncratic factors 

 

Based on figure 1, the visual representation of the dependent variable shows a normally distributed bell curve while 

the variable also aligns reasonably perfect on the P-P plot.  

 

Table 2. Test of Normality for Idiosyncratic factors 

 
Test of Normality 

  Kolmogorov-Smirnova Shapiro-Wilk 

  Statistic df Sig. Statistic df Sig. 

Liquid_Ratio 0.184 91 .200* 0.898 91 0.207 

*. This is a lower bound of the true significance. 

a. Lilliefors Significance Correction 

 

The Shapiro and Wilk (1965) test of normality states that if p > 0.05, then the dependent variable is normally 

distributed. Since in the given case, the significance is at 0.207, it satisfies the Shapiro-Wilk test for normality.  

 

Homoscedasticity Test 

The test highlights if the residuals are equally distributed or if the accumulate together at specific values or if they are far 

apart. The test is to verify if the errors have the same (but unknown) variance. This is constant variance assumption also 

known as homogeneity of error variance or error variance homoscedasticity assumption. The plotted diagram is shown 

below and visually we can see that the data is randomly distributed. 

 
                                Figure 2. Scatter plot for idiosyncratic factors 



Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

67 

Test of Linearity  

Linearity refers to the predictor variables having a linear relationship with the outcome variable.  

  

 

Figure 3. Linearity test for idiosyncratic factors 

 

Multicollinearity Test 

Table 3 calculated from SPSS indicates the correlation coefficient using the Pearson correlation. 

 

Table 3. Correlation for Idiosyncratic factors 

 
Correlations 

    Liquid_Ratio Deposit_Growth NPL CAR ROA 

Pearson Correlation Liquid_Ratio 1 -0.282 0.461 -0.32 -0.568 

  Deposit_Growth -0.282 1 0.283 -0.172 -0.06 

  NPL 0.461 0.283 1 -0.099 -0.563 

  CAR -0.32 -0.172 -0.099 1 0.114 

  ROA -0.568 -0.06 -0.563 0.114 1 

Sig. (1-tailed) Liquid_Ratio 1 0.003 0 0.001 0 

  Deposit_Growth 0.003 1 0.003 0.051 0.287 

  NPL 0 0.003 1 0.176 0 

  CAR 0.001 0.051 0.176 1 0 

  ROA 0 0.287 0 0.142 1 

N Liquid_Ratio 91 91 91 91 91 

  Deposit_Growth 91 91 91 91 91 

  NPL 91 91 91 91 91 

  CAR 91 91 91 91 91 

  ROA 91 91 91 91 91 

 

If the correlation coefficient in the Pearson correlation is greater than 0.8 it would indicate that independent 

variables are multicollinear as 0.8 would be strong high correlation. Based on the table above all the independent variables 

have correlation coefficient less than 0.8, which indicates absence of multicollinearity.  

The second indicated method to verify multicollinearity is to analyze the VIF values.  

 

Table 4. Coefficient for Idiosyncratic factors 

 
Coefficientsa 

Model  Unstandardized 

B 

Coefficients 

Std. Error 

Standardized 

Coefficient 

Beta 

t Sig. Collinearity 

Statistics 

Tolerance VIF 

1. (Constant) 117.093 11.407   10.265 0     

Deposit_Growth -88.025 13.854 -0.461 -6.354 0 0.882 1.134 

NPL 1.262 0.305 0.358 4.144   0.62 1.612 



Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

68 

CAR -1.415 0.304 -0.324 -4.655   0.959 1.042 

ROA -6.126 1.43 -0.357 -4.284   0.666 1.501 

a. Dependent Variable: Liquid_ratio 

 

Table 4 shows that VIF values are less than 2 indicating absence of multicollinearity.  

 

Error Term Test 

The test assumes that the error term has mean equal to zero. 

 

Table 5. Descriptive statistics for idiosyncratic factors 

 
Descriptive Statistics 

  N Minimum Maximum Mean  Std. Deviation 

Standardized Residual 91 -2.98371 2.42146 0 0.977525 

Valid N (list wise) 91         

 

                 Table 5 shows that the mean is equal to zero. Hence, the assumption is satisfied that error term has mean equal 

to zero. Further, as per Standard residual test, the residual should be between +3 and -3. As per table 8, currently it is at -

2.984 and +2.421 and hence the negative residual assumption is met.  

 

Tale 6. Residual statistics for idiosyncratic factors 

 
Residual Statisticsa 

  Minimum Maximum Mean Std. Deviation  N 

Predicted Value 15.9363 60.4023 30.1831 8.54599 91 

Std. Predicted Value -1.667 3.536 0 1 91 

Standard Error of Predicted Value 0.838 4.102 1.598 0.48 91 

Adjusted Predicted Value 15.5269 58.6238 30.1617 8.49537 91 

Residual -21.23173 17.23087 0 6.95596 91 

Std. Residual -2.984 2.421 0 0.978 91 

Stud Residual -3.109 2.588 0.001 1.007 91 

Deleted Residual -23.057 19.67585 0.02145 7.38969 91 

Stud. Deleted Residual -3.281 2.679 -0.002 1.024 91 

Mahal. Distance 0.259 28.916 3.956 3.722 91 

Cook's Distance 0 0.19 0.013 0.031 91 

Centered Leverage Value 0.003 0.321 0.044 0.041 91 

a. Dependent Variable: Liquid_ratio 

 

Residual Independence Test 

We will use Durbin Watson Test (DWT is measured on a scale of 0 to 4. We want to be as close to 2 as possible and avoid 

figures below 1 and above 3) 

If d<2 positive auto correlation 

If d>2 negative auto correlation  

If d=2 no autocorrelation 

 

Table 7. Residual Independence test for idiosyncratic factors 

 
Model Summaryb 

Model R R 

Square 

Adjusted 

R Square 

Std. Error of  

the Estimate 

R Square 

Change 

F Change Df Df2 Sig F Change Durbin-

Watson 

1 .776a 0.602 0.583 7.11589 0.602 32.453 4 86 0 0.805 

a. Predictors : (Constant), ROA, Deposit_growth, CAR, NPL 

b. Dependent Variable: Liquid_ratio 

 

Table 7 shows that since, d<2 i.e., at 0.805, hence there is positive auto correlation 

 

Outlier Test 

Table 8. Frequencies for Idiosyncratic factors 

 
Frequencies 

Statistics 

    Standardized 

DFBETA Intercept 

Standardized DFBETA 

Deposit_growth 

Standardized 

DFBETA NPL 

Standardized 

DFBETA CAR 

Standardized 

DFBETA ROA 

N Valid 91 91 91 91 91 

  Missing 0 0 0 0 0 

Minimum -0.48636 -0.22685 -0.86069 -0.39028 -0.306 

Maximum 0.20809 0.4866 0.68963 0.22538 0.27233 

 



Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

69 

Table 8 shows that all the betas for dependent and independent variables are between +2 and -2 range indicating 

that there are no significant outliers.  

 

Regression Model for Bank Specific Factors 

Table 9. Regression analysis for idiosyncratic factors 

 
Model Summaryb 

Model R R 

Square 

Adjusted R 

Square 

Std. Error of  

the Estimate 

R Square 

Change 

F Change Df Df2 Sig F 

Change 

Durbin-

Watson 

1 .776a 0.602 0.583 7.11589 0.602 32.453 4 86 0 0.805 

a. Predictors : (Constant), ROA, Deposit_growth, CAR, NPL 

b. Dependent Variable: Liquid_ratio 

 

Table 9 shows that adjusted R square is at 58.3% indicating 58.3% of the variation is caused due to independent 

variables on dependent variables.  

The Regression Model is statistically significant as F change is less than 0.05.  

 

Table 10. Regression model for idiosyncratic factors 

 
Coefficientsa 

Model Unstandardized 

B 

Coefficients 

Std. Error 

Standardized 

Coefficient 

Beta 

t Sig. Collinearity Statistics 

Tolerance VIF 

1. (Constant) 117.093 11.407   10.265 0     

Deposit_Growth -88.025 13.854 -0.461 -6.354 0 0.882 1.134 

NPL 1.262 0.305 0.358 4.144   0.62 1.612 

CAR -1.415 0.304 -0.324 -4.655   0.959 1.042 

ROA -6.126 1.43 -0.357 -4.284   0.666 1.501 

a. Dependent Variable: Liquid_ratio 

 

Further, based on the table 10, all independent variables are statistically significant as “sig” is less than 0.05. 

  

Table 11. ANOVA for Idiosyncratic factors 

 
ANOVAa 

Model Sum of Squares df Mean Square F Sig. 

Regression 6573.058 4 1643.265 32.453 .000b 

Residual 4354.688 86 50.636     

Total 10927.746 90       

a. Dependent Variable: Liquid_ratio 

b. Predictors : (Constant), ROA, Deposit_growth, CAR, NPL 

 

In ANOVA table 11, since sig < 0.05, it is statistically significant, and we reject the null hypothesis 

 

Diagnostic Validation for Market-Related Factors Using Regression Analysis 

Table 12. Dependent and Independent variables 

 
Dependent variable:  Liquidity ratio 

Independent variables:  GDP, inflation, unemployment, oil prices 

 

In order to arrive at the liquidity ratio, the author has averaged the liquidity ratio for all the sampled banks for each 

of the 10 years. The market indicators have been taken from the National Statistics Centre of the UAE. 

When doing regression, the cases-to-independent variables ratio should ideally be 20:1; that is 20 cases for every 

independent variable in the model. The lowest your ratio should be is 5:1 (i.e., 5 cases for every independent variable in the 

model). The study has taken 10 years of market related data. However, the data is considered inadequate for conclusive 

regression analysis unlike bank specific factors. Nevertheless, the regression on the limited data showcases, that there is 

statistical significance between the independent and dependent variable for market related data. 

 

Test of Normality 

Table 13. Test of Normality for market-related factors 

 
Test of Normality 

  Kolmogorov-Smirnova Shapiro-Wilk 

  Statistic df Sig. Statistic df Sig. 

Liquid_Ratio 0.154 10 .200* 0.893 10 0.184 

*. This is a lower bound of the true significance. 

a. Lilliefors Significance Correction 

 



Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

70 

The Shapiro-Wilk test results in table 13 are based on the correlation between the data and the corresponding 

normal scores and provides better power than the K-S test even after the Lilliefors correction. The Shapiro and Wilk (1965) 

test of normality states that if p > 0.05, then the dependent variable is normally distributed. Since in the given case, the 

significance is at 0.240, it satisfies the Shapiro-Wilk test for normality.  

 
Figure 4. Normal distribution and PP plot for market-related factors 

 

Based on graphs in figure 4, the visual representation of the dependent variable shows a normally distributed bell 

curve while the variable also aligns reasonably perfect on the Q-Q plot.  

 

Homoscedasticity Test 

The plotted diagram is shown in Figure 5 and visually we can see that the data is randomly distributed. 

 
Figure 5. Scatter diagram for market-related factors 

 

Test of Linearity  

Test of linearity for market related factors could not be completed, as there were not sufficient observations to conclude the 

linear relationship. This has been considered as a limitation of the research. 

 

Multicollinearity Test 

Table 14 calculated from SPSS indicates the correlation coefficient using the Pearson correlation.  

 

Table 14. Correlation for market-related factors  

 
Correlations 

    Liquid_Ratio Oil_price_changes Inflation GDP Unemployment 

Pearson Correlation Liquid_Ratio 1 0.71 0.443 0.566 0.401 

  Oil_price_changes 0.71 1 -0.266 0.338 0.568 

  Inflation 0.443 -0.266 1 0.26 0.003 

  GDP 0.566 0.338 0.26 1 0.573 

  Unemployment 0.401 0.568 0.003 0.573 1 

Sig. (1-tailed) Liquid_Ratio 1 0.011 0.1 0.044 0.126 

  Oil_price_changes 0.011 1 0.229 0.169 0.044 

  Inflation 0.1 0.229 1 0.234 0.497 

  GDP 0.044 0.169 0.234 1 0.042 

  Unemployment 0.126 0.044 0.497 0.42 1 

N Liquid_Ratio 10 10 10 10 10 

  Oil_price_changes 10 10 10 10 10 

  Inflation 10 10 10 10 10 

  GDP 10 10 10 10 10 

  Unemployment 10 10 10 10 10 

 



Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

71 

Table 14 shows that all the independent variables have correlation coefficient less than 0.8 that indicates absence 

of multicollinearity.  

 

 Table 15. Coefficients for market-related factors  

 
Coefficientsa 

Model Unstandardized 

B 

Coefficients 

Std. Error 

Standardized 

Coefficient 

Beta 

t Sig. Collinearity Statistics 

Tolerance VIF 

1. (Constant) -1.272 0.102   -12.489 0     

Oil_price_changes 0.012 0.001 0.958 12.841 0 0.595 1.68 

Inflation 30.627 3.116 0.636 9.829 0 0.791 1.265 

GDP 4.24 1.334 0.237 3.178 0.025 0.593 1.685 

Unemployment -2.767 0.79 -0.281 -3.502 0.017 0.514 1.946 

a. Dependent Variable: Liquid_ratio 

 

Table 15 shows that VIF values are less than 2 indicating absence of multicollinearity.  

 

Regression Model for Market Factors 

Table 16. Regression analysis for market-related factors 

 
Model Summaryb 

Model R R Square Adjusted 

R Square 

Std. Error of  

the Estimate 

R Square 

Change 

F Change Df Df2 Sig F 

Change 

Durbin-

Watson 

1 .992a 0.944 0.899 0.654 0.944 74.212 4 5 0 0.941 

a. Predictors : (Constant), Unemployment, Inflation, Oil_price_changes, GDP 

b. Dependent Variable: Liquid_ratio 

 

Table 16 shows that the adjusted R square is at 89.9% indicating almost 90% of the variation is caused due to 

independent variables on dependent variables.  

The Regression Model is statistically significant as F change is less than 0.05.  

 

Table 17. Regression model for market-related factors 

 
Coefficientsa 

Model Unstandardized 

B 

Coefficients 

Std. Error 

Standardized 

Coefficient 

Beta 

t Sig. Collinearity 

Statistics 

Tolerance VIF 

1. (Constant) -1.272 0.102   -12.489 0     

Oil_price_changes 0.012 0.001 0.958 12.841 0 0.595 1.68 

Inflation 30.627 3.116 0.636 9.829 0 0.791 1.265 

GDP 4.24 1.334 0.237 3.178 0.025 0.593 1.685 

Unemployment -2.767 0.79 -0.281 -3.502 0.017 0.514 1.946 

a. Dependent Variable: Liquid_ratio 

 

Further, table 17 shows that all independent variables are statistically significant as “sig” is less than 0.05. GDP 

contribution is highest as it is at 0.997 as compared to other independent variables.  

 

Table 18. ANOVA for market-related factors 

 
ANOVAa 

Model Sum of 

Squares 

df Mean 

Square 

F Sig. 

Regression 0.188 4 0.047 74.212 .000b 

Residual 0.003 5 0.001     

Total 0.191 9       

a. Dependent Variable: Liquid_ratio 

b. Predictors : (Constant), Unemployment, Inflation, 

Oil_price_changes, GDP 

 

In ANOVA table 18, since sig < 0.05, it is statistically significant, and we reject the null hypothesis. 

 

Regression Analysis for Idiosyncratic and Market-Related Factors 

Table 19 shows the overall regression calculation for idiosyncratic and market-related factors. It indicates that all the factors 

are significant to the liquidity ratio. Based on the values in the table, all independent variables are statistically significant, 

as the p-value is less than 0.05. 

 

 



Kapur et al., Indian Journal of Finance and Banking 10(1) (2022), 61-74 

 

72 

Regression Analysis for All Factors 

Table 19. Summary table for all factors 

 
  Coefficients Standard 

Error 

T-stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 

Intercept 0 #N/A #N/A #N/A #N/A #N/A #N/A #N/A 

Deposit growth -57.98 86.79 -0.66 0.04 -431.4 315.47 -431.44 315.47 

NPL -0.89 1.73 -0.51 0.04 -8.34 6.55 -8.34 6.55 

CAR 2.78 2.79 0.99 0.04 -9.25 14.81 -9.25 14.81 

ROA 12.22 11.98 1.01 0.04 -39.35 63.79 -39.35 63.79 

Unemployment 129.56 194.20 0.66 0.037 -706.0 965.15 -706.0 965.15 

Inflation -649.7 703.41 -0.92 0.04 -3676. 2376.8 -3676.3 2376.8 

GDP -76.10 108.47 -0.70 0.02 -542.8 390.62 -542.8 390.62 

Average oil 

prices 

0.05 0.148 0.35 0.01 -0.58 0.69 -0.58 0.69 

 

RESULTS 

This research evaluated the impact and statistical significance of the bank-specific and market-related factors on the liquidity 

of banks in the UAE. The test was conducted using a linear regression model that tested the significance of each independent 

variable on the dependent variable.  

It has clearly been shown that the bank-specific factors impact the liquidity of banks in the UAE. Additionally, 

while the data on the market-related factors was insufficient, it still managed to evidence that these factors have a statistically 

significant effect on the dependent variable. The diagnostic summary of the regression analysis for both the idiosyncratic 

and market-related factors is given in Table 20. 

 

Summary Table Containing the Regression Analysis Results 

Table 20. Summary Test data for all variables 

 
Test Name (whether test conducted or not) Idiosyncratic Factor Market-related Factor 

Missing data test Yes Yes 

Test of normality Yes Yes 

Homoscedasticity test Yes Yes 

Test of linearity Yes No* 

Multicollinearity test Yes Yes 

Error-term test Yes No 

Residual independence test Yes No 

Outlier test Yes No 

Regression model Yes Yes 

* Analysis not conducted due to the inadequacy of available data. 

As the significance has now been tested, the next step is to rank these factors according to their order of its impact—from highest to lowest—using the 
MURAME approach.  

 

 
 

Author Contributions: Conceptualization, M.K., A.B. and K.M.; Methodology, M.K.; Software, M.K.; Validation, M.K., A.B. and K.M.; Formal 

Analysis, M.K.; Investigation, M.K.; Resources, M.K.; Data Curation, M.K.; Writing – Original Draft Preparation, M.K.; Writing – Review & Editing, 
M.K., A.B. and K.M.; Visualization, M.K.; Supervision, A.B. and K.M.; Project Administration, M.K.; Funding Acquisition, M.K.. Authors have read and 

agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 
groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.      

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