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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:
H01: There is no significant relationship
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
H01: There is no significant relationship
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
observed from table 5 that the regression
coefficient is -0.4635, which means there is
a negative relationship between CAR (as
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
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.
Liquid Assets to Total Assets: It is also
observed from table 5 that the regression
coefficient is -0.7120, which means there is
a negative relationship between CAR (as
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
observed from table 5 that the regression
coefficient is -0.3110, which means there is
a negative relationship between CAR (as
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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shows that the overall regression is
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
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.
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
shows that the overall regression is
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 relationship between CAR and
the ratio, though the F-test in ANOVA
shows that the overall regression is
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
observed from table 8 that the regression
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.
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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
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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
Page 13
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)
………………………………………………………………………………………………………
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
Source: Computed from the data
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
Source: Computed from the data
Regression Statistics
Multiple R 0.9697
R Square 0.9403
Adjusted R Square 0.8449
Standard Error 1.0280
Observations 14
Page 14
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)
………………………………………………………………………………………………………
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