6
The summary of variables’ relationships to the capital ratio will be summarized in Table 2.3 below
Table 2.2 Expected Variables’ Relationships to the Capital Ratio
Variable Relationships to the Capital Ratio
Capital Requirement CR Positive +
Size SIZE Negative -
Risk LRISK
Negative -
Return On Equity ROE Positive + or Negative -
Market Discipline MARKET Positive +
Quality of Capital TIER1 Positive +
3. RESEARCH METHODOLOGY PTER III RESEACH METHODOLOGY
Sampling
The sample of the research will be taken using purposive sampling method. This model is used because researcher intentionally chooses the sample which is
considered appropriate for the study. From the acquired sources, based on commercial banking statistics, the researcher decided to use 4 industries of the
banks as the research samples. Here are the samples used for the research:
Table 3.1 Samples of Research
No Bank Industry
Number of enlisted bank 1.
State-owned bank 4
2. Foreign exchange commercial bank
35 3.
Non-foreign exchange commercial bank
30 4.
Foreign-owned bank 10
Source: Otoritas Jasa Keuangan OJK and Bank Indonesia
7
Data and Data Gathering
Data used in this research are commercial banks statistics from Otoritas Jasa Keuangan OJK in period 2009 to 2013 including: the monthly overall return on
equity of commercial banks, the monthly overall ratio of risk-weighted assets to the total assets of commercial banks, The monthly overall proportion of qualifying
regulatory capital consisting of Tier 1 capital of commercial banks in the year, The monthly overall time demeaned total asset of commercial banks, The monthly
overall ratio of subordinated debt to total liabilities of commercial banks, and individual capital requirement set by Bank Indonesia.
Variable Measurement
In this research, the total risk-based capital ratio is calculated as the ratio of total regulatory capital the sum of eligible Tier 1, Tier 2, and if applicable Tier 3
capital to total risk-weighted assets in the banking and trading book, while The individual capital requirement is measured by using a regulatory bank-specific
capital requirement set by Bank Indonesia. In the analysis, researcher used the return of the minimum capital requirement that the banks hold to be able to
explain the relationship of how changes in the amount of capital requirements will affect capital ratios.
In this research, the indicators of banks’ characteristics will be served by variable: ROE, RISK, SIZE, and MARKET. ROE return on equity is measured
by the ratio of net income to firm’s book equity, as a proxy of the direct opportunity cost of holding excess capital which also used in this research as an
indicator of banks’ capital structure. Variable Risk will be measured as the ratio of risk-weighted assets to total assets. Risk only enters in a lagged form LRISK
referring to the previous regulatory measure of asset risk. The data of risk- weighted asset used from period August 2010 – December 2011 are risk-weighted
assets with operational risks due to the unavailability of the data in the banking statistics. A proxy for bank size will be measured by taking the time-demeaned
value of the log of total assets of the bank. which are the combined value of all assets that a company have. The exposure to market discipline is measured by a
proportion of subordinated debt to total liabilities. Due to the data unavailability of subordinated debt of foreign-owned banks from period January 2010 – July
2011 and from January 2012 – December 2013, the data used during those periods will be the data of one period prior to those missing periods.
In terms of measuring the effect of quality of capital to the banks’ capital ratios, in this research we include capital quality variable Tier 1, or the proportion
of total regulatory capital consisting of Tier 1 equity capital. Tier 1 will act as an indicator of capital quality in this research.
Method of Analysis and Hypothesis Testing
In calculating capital ratio or capital adequacy ratio, we can use the formulated equation below Muljono, 1999:
8
=
i
In this research, researcher will estimate the relationship using generalized method of moments GMM. Generalized method of moments method is
preferable because GMM has made econometric evaluation reliable for studying dynamic asset-pricing models, under more allowance and correlation,
heteroskedasticity realistic assumptions Jagannathan, 2002. GMM put an ease by enabling variables of interest to be serially correlated and conditionally
heteroskedastic. The estimated version of our research model includes these variables and it is formulated as followings:
,
= +
,
+
,
+
,
+ 1
,
+
,
+
,
+
,
ii
where is a potentially serially correlated and heteroskedastic disturbance term.
Except for LRISK, all of the variables are simultaneous. This implies that we model the bank’s choice of risk-based capital ratio as a function of capital ratios
and regulatory risk one month before and current attributes.
In processing the data using EViews 7 software, the method which will be used in the regression of equation 6 is GMM method with a fixed effect for both
of cross-section and period. Observing the difference in the characteristics of each bank classifications, cross-section weights will be used due to the assumption to
the presence of cross section heteroskedasticity Quantitative Micro Software, LLC, 2010.
Hypothesis testing will test the significance of individual hypothesis using t- test. The significance level then can be determined by looking at the number of
probability test that each hypothesis has and comparing them to the confidence level of 95 α = 0.05 which in this case is set to be the parameter.
9
4. DATA ANALYSIS Table 4.1
