measuring liquidity risk in a banking management … · 2017-04-28 · hence liquidity risk ratios...

ELK ASIA PACIFIC JOURNAL OF FINANCE AND RISK MANAGEMENT

SSN 2349-2325 (Online); DOI: 10.16962/EAPJFRM/issn. 2349-2325/2015; Volume 8 Issue 2 (2017)

www.elkjournals.com

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MEASURING LIQUIDITY RISK IN A BANKING MANAGEMENT FRAMEWORK

Dr. Raygani Pathi

M. Com., M.Phil., Ph.D

Head Department of Commerce,

Wesley Degree College Co-Ed. Sec-bad

INDIA

[email protected]

ABSTRACT

Liquidity risk in banking has been attributed to transactions deposits and their potential to spark runs or panics.

During the early “liquidity phase” of the financial crisis that began in 2007, many banks – despite adequate

capital levels – still experienced difficulties because they did not manage their liquidity in a prudent manner. The

crisis drove home the importance of liquidity to the proper functioning of financial markets and the banking

sector. Hence, the paper endeavoured to study an overview picture of liquidity risk management in commercial

banks, measure the magnitude of liquidity risk in SBI AND ICICI banks and finally the hypothesis is tested to

analyse the relationship between CAR as per Basel I norms with liquidity risk ratios using regression model. The

result of study suggests that there is a strong relationship between CAR (BASEL I) and liquidity risk ratios and

hence liquidity risk ratios can be used as a proxy to measure liquidity risk inorder to effectively manage the

liquidity risk in the Indian Scheduled Commercial Banking sector (SCBs).

Key words: Banks, Basel I, Capital Adequacy Ratio (CAR), Liquidity risk, Indian Scheduled Commercial

banking sector.

INTRODUCTION

Banks have grown from being a financial

intermediary into a risk intermediary at

present. In the process of financial

intermediation, banks are exposed to severe

competition and hence are compelled to

encounter various types of risks viz., credit

risk, market risk, and operational risk.

Credit risk is the potential that a bank

borrower or counterparty will fail to meet

its obligations in accordance with agreed

terms. Market risk is the risk of incurring

losses on account of movements in market

prices on all positions held by the banks.

Operational Risks may be defined as the

risk of loss resulting from inadequate or

failed internal process, people and systems

or because of external events. Liquidity risk

of banks arises from funding of long term

assets (advances) by short term sources

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(deposits). Liquidity risk consists of

Funding Risk, Time Risk, Call Risk and

Market Liquidity Risk. Funding risk is the

need to replace net out flows due to

unanticipated withdrawal/non renewal of

deposit. It is the risk of inability to obtain

funds to meet cash flow obligations. Time

risk is the need to compensate for non-

receipt of expected inflows of funds, i.e.

performing assets turning into Non-

Performing Assets. Call risk happens on

account of crystalisation of contingent

liabilities and inability to undertake

profitable business opportunities when

desired. Market Liquidity Risk arises when

a firm is unable to conclude a large

transaction in a particular instrument

anything near the current market prices.

REVIEW OF LITERATURE

Ongore and Kusa (2013), in their article,

studied the determinants of financial

performance of commercial banks in

Kenya. In their study, one of the bank

specific factors considered is liquidity

management.The objective of the study was

to fill in the gap left by scanty studies on

the moderating effect of ownership

structure on bank performance. The authors

used linear multiple regression model and

generalized Least Square on panel data to

estimate the parameters.

Bhavin U. Pandya & Kalpesh P.

Prajapati (2013), in their research article

concluded that the Indian Banking Industry

requires a combination of new

technologies, better processes of credit and

risk appraisal, treasury management,

product diversification, internal control and

external regulations. There is a need for

bank employees to have sufficient

understanding of the Basel II accord in

order to guide the banking growth rate in

the positive direction and lack of

understanding affects the banks negatively

as these are the basis for any banking sector.

The objective of the study is to find out the

awareness level, as well as the perception

among bank employees about the Basel-II

norms, and also examines the efforts made

by them for implementing it in their banks.

Ravi Kant & S.C. Jain (2013), in their

article concluded that the Capital

Conservation Buffer (2.5%) stipulated by

Basel III is simply a top up, over and above

the stipulated capital levels of 8%. The

study observed that on one hand, the recoup

of capital conservation buffer would be

difficult once it gets depleted and on the

other, the banks would find it attractive to

further boost up the credit growth in order

to reduce the impact of additional capital

requirements. The other adverse impacts of

discretionary buffers would be upsetting




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the growth plans of the industry, caution

among investors and effect on bank’s asset

quality. On the contrary, the release of

discretionary buffers is only leverage

enhancing enabling factor and by itself does

not amount to increase in cash flows and

liquidity for credit growth. And, it would

not positively impact the banking

profitability either.

Ibe (2013), studies the impact of liquidity

management on profitability on banks in

Nigeria. The work was necessitated by the

need to find solution to liquidity

management problem in Nigerian banking

industry. Three banks were randomly

selected to represent the entire banking

industry in Nigeria.Theproxies for liquidity

management include cash and short term

fund, bank balances and treasury bills and

certificates, while profit after tax was the

proxy for profitability.

Olagunju et al.(2011), in their study

concluded that for the success of operations

and survival, commercial banks in Nigeria

should not compromise efficient and

effective liquidity management and that

both illiquidity and excess liquidity are

"financial diseases" that can easily erode

the profit base of a bank as they affect

bank's attempt to attain high profitability-

level.

Raghavan R.S (2008), in his article

focused on Basel II accord, its implication

in banking sector and challenges for the

banks on implementation of Basel II norms.

The study concluded that Basel II principles

should be viewed more from the angle of

fine tuning one’s risk management

capabilities through constant mind

searching rather than as regulatory

guidelines to be complied with.

OBJECTIVES OF THE STUDY

The objectives of the present study is

1. To measure the magnitude of liquidity

risk in SBI and ICICI bank.

HYPOTHESIS

H0: There is no significant relationship

between CAR as per Basel I norms and

liquidity risk ratios of SBI and ICICI

Bank.

METHODOLOGY OF THE STUDY

The present study is emphirical and

exploratory in nature. The study is based on

secondary data. The required data are

collected from RBI reports, annual reports

of banks, articles from journals, M.Phil.

Dissertations and Ph.D. theses.

For the purpose of the study two banks are

selected i.e., one from public sector bank

and another from private sector bank.

Accordingly, State Bank of India (SBI)




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selected from public sector which is the

largest state-owned bank and ICICI Bank

selected from private sector which is the

largest private sector bank in India and

second largest bank in India. These banks

are selected for the study, because RBI

designated SBI and ICICI Bank as

Domestic Systemically Important Banks

(D-SIBs) due to their size, cross-

jurisdictional activities and complexity. In

the light of the RBI and BASEL norms,

various liquidity risk ratios for the SBI and

ICICI banks have been studied and

analyzed. To measure the magnitude of

liquidity risk the following ratios are used:

1. Ratio of Core Deposit to Total Assets

(CD/TA)

2. Ratio of Total Loans to Total Deposits

(TL/TD)

3. Ratio of Time Deposit to Total Deposits

(TMD/TD)

4. Ratio of Liquid Assets to Total Assets

(LA/TA)

5. Ratio of Prime Asset to Total Assets

(PA/TA)

6. Ratio of Short-Term Liabilities to Liquid

Assets (STL/LA)

7. Ratio of Market Liabilities to Total

Assets (MKL/TA)

8. Ratio of Short-Term Liabilities to Total

Assets (STL/TA)

To study the impact of the liquidity risk

ratios on Capital Adequacy Ratio (CAR) as

per Basel I norms, multiple regression

analysis is employed.

PERIOD OF THE STUDY

The period of study covers a seven years

period i.e., from 2006-2007 to 2012-2013,

since, in 2005 the RBI issued the first draft

guidelines on Basel II implementation in

which an initial target date for Basel II

compliance was set for March, 2007 for all

commercial banks, but this deadline was

postponed to March, 2008 for

internationally active banks, and March,

2009 for domestic commercial banks.

DATA ANALYSIS AND

INTERPRETATION

Measuring and Managing Liquidity Risk

Measuring and managing liquidity are

among the most vital activities of

commercial banks. Liquidity management

can reduce the probability of an irreversible

adverse situation developing. When crises

develops, because of a problem elsewhere

at a bank, such as a severe deterioration in

asset quality or the uncovering of fraud, or

where a crisis may result in loss

of confidence in financial institutions, the

time available to a bank to address the

problem will be determined by its liquidity.

A liquidity shortfall at a single big bank can

have system-wide repercussions. For this




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reason, the analysis of liquidity requires

bank managements to measure not only the

liquidity positions of banks on an ongoing

basis but also to examine how funding

requirements are likely to evolve under

crisis scenarios.

It is observed from the table 1 and table 2

that SBI and ICICI bank maintained capital

adequacy ratio (CAR) well above the RBI

and BASEL norms. It is apparent from table

1 and table 2 that during the study period

the ratio of Core Deposits to Total Assets of

SBI and ICICI bank is above the bench

mark 50 percent and both the banks did not

comply with the ideal ratio of Total Loans

to Total Deposits which is between 65 to 75

percent during the study period. The ratio

of Liquid Assets to Total Assets of SBI and

ICICI bank is much below the ideal ratio

which is between 18 to 20 percent in all the

years of study.

Analyse the relationship between CAR

as per Basel I and liquidity risk ratios of

SBI and ICICI using multiple regression

model.

To study the relationship between Capital

Adequacy Ratio (CAR) and liquidity risk

ratios of the banks, Multiple Linear

Regression model is employed which took

the form of:

Y=b0+b1X1+b2X2+b3X3+b4X4+b5X5+b6X6

+b7X7+b8X8+e

Where; Y= Dependent variable; b0=

constant; e = error term

X1,X2, X3, X4, X5, X6, X7, and X8 =

Independent variables

b1, b2, b3, b4, b5, b6, b7, b8, b9 and b10=

Regression coefficient

Multiple Linear Regression model is

employed to study the following

Hypothesis:


between Capital Adequacy Ratio (CAR) as

per Basel I and liquidity risk ratios.

To study the above hypothesis CAR and

liquidity risk ratios of select banks viz., SBI

and ICICI Banks is considered for the study

period 2006-07 to 2012-2013. In this study,

CAR is dependent variable. Every bank is

required to maintain minimum CAR of 9

percent or more as prescribed by RBI. SBI

bank and ICICI bank reported capital

adequacy ratio as per Basel I during the

study period 2006-07 to 2012-13 and CAR

as per Basel II during the period 2007-08

and 2012-2013 as prescribed by RBI. Eight

independent variables (liquidity risk ratios)

considered for this study are as follows:

a. Ratio of Core Deposit to Total Assets

(X1)

b. Ratio of Total Loans to Total Deposits

(X2)




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c. Ratio of Time Deposit to Total Deposits

(X3)

d. Ratio of Liquid Assets to Total Assets

(X4)

e. Ratio of Prime Asset to Total Assets

(X5)

f. Ratio of Short-Term Liabilities to

Liquid Assets (X6)

g. Ratio of Market Liabilities to Total

Assets (X7)

h. Ratio of Short-Term Liabilities to

Total Assets (X8)

ANALYSIS


between Capital Adequacy Ratio (CAR) as

per Basel I and liquidity risk ratios.

The output of the regression in simplified

form is presented in table 3, table 4 and

table 5.

The established regression equation is,

CAR (Basel I) = 103.5579 - 0.8742X1 -

0.4635X2+0.1699X3 - 0.7120 X4 - 0.3110

X5 - 0.0132 X6 +0.0576X7+0.3127X8

Table 3 shows the model summary of

regression. It is observed form table 3 that

the value of R square is 0.9403, which

means that 94.03 percent variation in

Capital Adequacy Ratio can be explained

by the liquidity risk ratios used in this

model. There is statistically a strong

relationship between the CAR as per Basel

I and liquidity risk ratios because only 6

percent of variations in CAR is

unexplained. Since R square is very high

and close to ‘1’, thus the linear model is a

good fit.

Table 4 shows the results of ANOVA. It is

observed from table 4 that the significance

of F which is 0.011 is the p-value of the F-

test carried out in ANOVA. Since p-value

of the F-test is less than 0.05, hence

regression model is statistically significant.

The regression is statistically significant

indicates that the relationship between

CAR as per Basel I and liquidity risk ratios,

is not an occurrence by chance.

It is also observed from table 5 that the

intercept is 103.5579. The intercept gives

the estimated value of CAR when

independent variables (liquidity risk ratios)

are kept zero.

Core Deposits to Total Assets: It is

observed from table 5 that the regression

coefficient is -0.8742, which means there is

a negative relationship between CAR (as

per Basel I) and the ratio Core Deposits to

Total Assets. In other words it means that,

a unit increase in the ratio will lead to

decrease in the CAR by 0.8742 units with

other independent variables constant. The

p-value being 0.0184, which is less than




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0.05, indicates that there is statistically

significant correlation between CAR and

the ratio. Thus there is statistically

significant negative relationship between

CAR and the ratio. SBI bank and ICICI

bank have to control and monitor this ratio

in order to maintain the CAR at prescribed

levels as per RBI.

Total Loans to Total Deposits: It is also




per Basel I) and the ratio Total Loans to

Total Deposits. This in turn means that a

unit increase in this ratio will lead to a

decrease in CAR by a factor of 0.4635. The

p-value being 0.0625, which is more than

0.05. Thus, there is statistically no

significant relationship between CAR and

the ratio, though the F-test in ANOVA

shows that the overall regression is

significant.

Time Deposits to Total Deposits: It is

evident from table 5 that the regression

coefficient is 0.1699, which means there is

a positive relationship between CAR (as per

Basel I) and the ratio Time Deposits to

Total Deposits. In other words it means

that, as per the data available, if the ratio

increases by one unit, the CAR can be

estimated to increase by 0.1699 units with

other independent variables constant. The

p-value being 0.2886, which is more than





significant.

Liquid Assets to Total Assets: It is also




per Basel I) and the ratio Liquid Assets to

Total Assets. This implies that a unit

increase in this ratio will further lead to a

0.7120 decrease in CAR. The p-value being

0.6225, which is more than 0.05. Thus,

there is statistically no significant

relationship between CAR and the ratio,

though the F-test in ANOVA shows that the

overall regression is significant.

Prime Assets to total Assets: It is also




per Basel I) and the ratio Liquid Assets to

Total Assets. It implies that a unit increase

in this ratio will lead to a 0.3110 decrease

in CAR. The p-value being 0.6509, which

is more than 0.05. Thus, there is statistically

no significant relationship between CAR

and the ratio, though the F-test in ANOVA




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significant.

Short Term Liabilities to Liquid Assets:

It can also be observed from table 5 that the

regression coefficient is -0.0132, which

means there is a negative relationship

between CAR (as per Basel I) and the ratio

Short Term Liabilities to Liquid Assets. It

implies that a unit increase in this ratio will

cause 0.0132 decreases in CAR. The p-

value being 0.6204, which is more than





significant.

Market Liabilities to Total Assets: As it

is seen from table 5 that the regression

coefficient is 0.0576, which means there is

a positive relationship between CAR (as per

Basel I) and the ratio Market Liabilities to

Total Assets. This implies that a increase in

this ratio will cause 0.0576 increase in

CAR. The p-value being 0.2009, which is

more than 0.05. Thus, there is statistically

no significant relationship between CAR

and the ratio, though the F-test in ANOVA


significant.

Short Term Liabilities to Total Assets: It

is also observed from table 5 that the

regression coefficient is 0.3127, which

means there is a positive relationship

between CAR (as per Basel I) and the ratio

Short Term Liabilities to Total Assets. It

implies that a unit increase in this ratio will

further lead to a 0.3127 increase in CAR.

The p-value being 0.3937, which is more

than 0.05. Thus, there is statistically no




significant.

Hence it is observed from table 5 that from

eight liquidity risk ratios taken in this

model only Core Deposits to Total Assets

ratio is statistically significant and have

negative impact on CAR (as per Basel I).

Hence, the seven ratios are eliminated

while performing regression analysis the

second time for further analysis.

Table 6 shows the model summary of

regression. It is evident form table 6 that the

value of R square is 0.7580., which means

that 75.8 percent variation in Capital

Adequacy Ratio can be explained by the

Core Deposits to Total Assets ratio used in

this model. There is statistically a

significant relationship between the CAR

as per Basel I and liquidity risk ratio




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because only 24 percent of variations in

CAR are unexplained. Since R square is

very high and close to ‘1’, thus, the linear

model is a good fit.

Table 7 shows the results of ANOVA. It is

evident from the table, that the significance

of F which is 0.000 is the p-value of the F-

test carried out in ANOVA. Since p-value

of the F-test is less than 0.05, hence

regression model is statistically significant.

It is evident from table 8 that the Intercept

is 28.9184. The intercept gives the

estimated value of CAR when independent

variable (liquidity risk ratio) is kept zero.

Core Deposits to Total Assets: It is


coefficient is -0.22247, which means there

is a negative relationship between CAR (as

per Basel I) and the ratio Core Deposits to

Total Assets. In other words it means that,

a unit increase in the ratio will lead to

decrease in the CAR by 0.22247 units. The

p-value being 5E-05, which is less than

0.05, indicates that there is statistically

significant correlation between CAR and

the ratio. Thus, there is statistically

significant negative relationship between

CAR and the ratio. The banks have to

control and monitor this ratio in order to

maintain the CAR at prescribed levels as

per RBI.

From table 8, the following regression

equation is arrived at:

Y= (-0.22247) * (Core Deposits to Total

Assets) + 28.9184

Where y represent CAR as per Basel I.

The above regression equation can be

used to predict the actual value of CAR

as per Basel I at any point of time.

From the above regression equation it is

evident that SBI Bank and ICICI Bank

should monitor the ratio Core Deposits

to Total Assets to maintain the capital

adequacy ratio at required levels as per

Basel I.

If y=9, the maximum limit the banks

have to maintain the ratio of Core

Deposits to Total Assets is 89.53% In

case the ratio exceeds 89.53%, CAR

ratio as per Basel I norm will be less than

9%. Therefore the banks ratio should be

89.53% in order to maintain the CAR at

9%.

The ratio of Core Deposits to Total

Assets of SBI bank and ICICI bank is

below 89.53% indicating that the banks

have scope to increase the ratio and

decrease their liquidity risk.

CONCLUSIONS AND POLICY

IMPLICATIONS

This study attempted to analyse various

liquidity risk related ratios that could be




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useful as an internal risk monitoring tool for

the scheduled commercial banks. Since risk

is contagious, and the SBI bank and ICICI

bank being the largest public sector bank

and private sector bank respectively, handle

large volume of loans; the recent trend in

the increase of the non performing loans in

the commercial banking sector poses a

serious concern. This trend needs better

monitoring and requires necessary

corrective measures. To effectively manage

the liquidity risk in the Indian Scheduled

Commercial Banking sector (SCBs), the

Reserve Bank of India has developed

policies and guidelines in accordance with

the norms set out by Basel Committee on

Banking Supervision. Banks can monitor

various liquidity risk associated ratios by

using their internal data. Hence the Core

Banking System (CBS) that is now being

fully implemented in the Indian Scheduled

Commercial Banks, the data for such ratios

can be easily obtained and liquidity risk can

be monitored effectively and internal

corrective measurements can be taken on a

timely manner to avert any catastrophic

effects. Since the Scheduled Commercial

Banking sector is the driving engine of the

Indian economy and the liquidity risk

associated with this sector is very

significant, the RBI is keen on monitoring

this sector and develop policies and other

corrective measures as necessary.

The study established that liquidity risk

ratios of SBI Bank and ICICI Bank had a

strong impact on their CAR. The study

established that 94.03 percent variations in

Capital Adequacy Ratio as per Basel I can

be explained by the liquidity risk ratios. The

banks have scope to increase the ratio of

core deposits to total assets and decrease

their liquidity risk.The study therefore

concludes that liquidity risk ratios can be

used as a proxy for measuring the

magnitude of liquidity risk in SBI Bank and

ICICI Bank.

REFERENCES

Bhavin U. Pandya & Kalpesh P.

Prajapati, (2013), “Awareness and

Perception of Basel – II Norms across

Indian Banks: An Empirical Study”,

Indian Journal of Finance, pp31-41,

April.

Bibow, J. (2005), “Liquidity preference

theory revisited”, The Levy economics

institute. Working paper No. 427.

Emami, M., Ahmadi, M. &Tabari,

N.A.Y. (2013), “The Effect of Liquidity

Risk on the Performance of

Commercial Banks”, International

Research Journal of Applied and Basic

Sciences, 4 (6), 1624-1631.




………………………………………………………………………………………………………

Ibe, S.O. (2013).” The Impact of

Liquidity Management on the

Profitability of Banks in Nigeria”,

.Journal of Finance and Bank

Management, 1(1), 37-48.

Kumar, M.&Yadav, G.C. (2013),

“Liquidity risk management in bank: a

conceptual framework”, AIMA Journal

of Management & Research,7(2), 2-12.

Lartey, V.C.,Antwi, S. & Boadi, E.K.

(2013), “ The Relationship between

Liquidity and Profitability of Listed

Banks in Ghana”, International Journal

of Business and Social Science,4(3),

48-56.

Olagunju, A., Adeyanju,

O.D.&Olabode, O.S. (2011), “

Liquidity Management and

Commercial Banks Profitability in

Nigeria”, Research Journal of Finance

and Accounting, 2(7),2222-2847.

Ongore, V.O. &Kusa, G.B.

(2013).Determinants of Financial

Performance of Commercial Banks in

Kenya.International Journal of

Economics and Financial Issues, 3(1),

237-252.

Raghavan R.S., “Basel II, the Way

Forward for Banks”, The Chartered

Accountant, April, pp. 1767-1774,

2008.

Ravi Kant & S.C.Jani, (2013), “Critical

Assessment of Capital Buffers Under

Basel III”, Indian Journal of Finance,

April, pp 5-12.

Websites

www.bankreport.rbi.org.in

www.icicibank.com

www.sbi.co.in


http://www.sbi.co.in/



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LIST OF TABLES

Table 1 SBI - CAR (Basel I) and Liquidity Risk Ratios

(in percentage)

Source: Calculated from the compiled data (RBI reports 2006-07 to 2012-13)

Table 2 ICICI BANK - CAR (BASEL I) AND LIQUIDITY RISK RATIOS

(in percentage)

YEAR CAR CD/TA TL/TD TMD/TD LA/TA PA/TA STL/LA MKL/TA STL/TA

2007 11.69 66.88 84.97 78.22 10.77 9.8 146.68 14.87 15.8

2008 13.96 61.14 92.3 73.91 9.52 8.85 175.29 16.42 16.68

2009 15.92 57.57 99.98 71.3 7.9 7.15 215.22 24.56 17

2010 19.14 55.59 89.7 58.1 10.7 10.48 223.6 25.93 23.92

2011 17.63 55.54 95.91 54.94 8.39 7.02 308.23 26.97 25.87

2012 16.26 53.94 99.1 56.55 7.65 6.392 316.25 29.59 24.19

2013 16.09 54.51 99.19 58.11 7.72 6.27 187.77 27.08 14.49

Source: Calculated from the compiled data (RBI reports 2006-07 to 2012-13)

YEAR CAR CD/TA TL/TD TMD/TD LA/TA PA/TA STL/LA MKL/TA STL/TA

2007 12.34 76.87 77.46 51.52 9.17 6.06 445.29 76.4 40.84

2008 13.54 74.48 77.55 53.04 9.35 7.94 402.46 76.67 37.63

2009 12.97 76.94 73.11 58.36 10.83 8.52 314.08 80.51 34

2010 12 76.33 78.58 52.74 9.13 8.57 417.05 107.1 38.07

2011 10.69 76.32 81.03 50.58 10.04 8.86 393.27 97.31 39.49

2012 12.05 78.15 83.13 55.2 7.28 6.2 502.36 130.71 36.55

2013 11.22 76.79 86.94 55.18 7.33 6.36 486.63 147.35 35.64




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TABLE 3 REGRESSION MODEL GOODNESS OF FIT

Source: Computed from the data

TABLE 4 ANOVA

DF SS MS F Significance F

Regression 8 83.3093 10.4136 9.8535 0.0110

Residual 5 5.2842 1.0568

Total 13 88.5935


TABLE 5 REGRESSION COEFFICIENTS (BASEL I CAR AND LQUIDITY RISK

RATIOS)

Coefficients

Standard

Error t Stat P-value

Intercept 103.5579 31.8548 3.2509 0.0226

Core Deposits to Total Assets (X1) -0.8742 0.2543 -3.4379 0.0184

Total Loans to Total Deposits (X2) -0.4635 0.1941 -2.3877 0.0625

Time Deposits to Total Deposits (X3) 0.1699 0.1432 1.1866 0.2886

Liquid Assets to Total Assets (X4) -0.7120 1.3585 -0.5241 0.6225

Prime Assets to Total Assets (X5) -0.3110 0.6470 -0.4807 0.6509

Short Term Liabilities to Liquid Assets (X6) -0.0132 0.0250 -0.5274 0.6204

Market Liabilities to Total Assets (X7) 0.0576 0.0391 1.4721 0.2009

Short Term Liabilities to Total Assets (X8) 0.3127 0.3353 0.9327 0.3937


Regression Statistics

Multiple R 0.9697

R Square 0.9403

Adjusted R Square 0.8449

Standard Error 1.0280

Observations 14




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Table 6 REGRESSION MODEL GOODNESS OF FIT

Regression Statistics

Multiple R 0.8706

R Square 0.7580

Adjusted R Square 0.7379

Standard Error 1.3364

Observations 14

Table 7 ANOVA

ANOVA

Df SS MS F Significance F

Regression 1 67.1606 67.1606 37.602 5.08246E-05

Residual 12 21.4329 1.7860

Total 13 88.5935

Table 8 REGRESSION COEFFICIENT (BASEL I CAR AND CD/TA)

Coefficients Standard Error t Stat P-value

Intercept 28.9184 2.4646 11.73308 6E-08

CD/TA -0.22247 0.03628 -6.13207 5E-05

Source: Computed from data


measuring liquidity risk in a banking management … · 2017-04-28 · hence liquidity risk ratios...

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