Dimas Kusuma
Dimas Bagus Wiranata Kusuma
Resident Economist
Email: d.kusuma@grandestrategy.com
Resident Economist
Email: d.kusuma@grandestrategy.com
ABSTRACT
Background
– Financial
liberalization and development has been taking place and major interesting
phenomena in many developing as well as developed countries. One of most
impetus factor to liberalize and integrate their markets is the presumption
that such development in financial sector can push up in promoting growth.
Subsequently, the turning of economic growth actually can be transmitted
through financial intermediation or bank based financing. The present paper is
attempting to explore several problems that may be closely related to above
topic and expected to capable of generating accommodated conclusions at the
end. The present paper would employ some econometric approaches by running
several variables as economic indicators. In general, this paper is going to
evolve several possible variables which are envisaged significantly effect in
accommodating the financial deepening in regards on crisis, particularly
currency crises. Inflation (INF), Real Effective Exchange Rate (REER), ratio
money supply towards reserve (M2/Res), and Real Credit Growth (RKG) are
correspondingly utilized as predictors in looking on the impact of financial
deepening on currency crises occurrence.
The
present paper ends with some concluded statements as follows (1) financial
deepening would lead to a higher incidence of growth inhibiting currency crises
due to it might be excessive. The paper comes up with the suggestion that
surely the link between finance and growth is seemingly more complex than the
single relationships suggest. Therefore, it would appear that financial
deepening should be completed by developing associated legal and regulatory
institutions so that policies for financial reform and regulation could be
welldesigned and no longer positively impact to financial stability in
financial system.
Key Words: Financial Stability, Currency Crises,
Financial Deepening, and Mitigating Risks
JEL:
E44, F15, G01
INTRODUCTION
“What improves the
circumstances of the greater part can never be regarded as inconveniency to the
whole. No society can surely be flourishing and happy, of which the far greater
part of the members are poor and miserable.”
Adam Smith (The Wealth of
Nations, Book
I Chapter VIII, p.96, Para. 36.)
The
global financial has triggered in every aspect of monetary, financial economics
and policy. The current issue now, financial liberalization and development
have been taking place and major interesting phenomena in many developing as
well as developed countries. One of most impetus factor to liberalize and
integrate their markets is the presumption that such development in financial
sector can push up in promoting growth. Subsequently, the turning of economic
growth actually can be transmitted through financial intermediation or bank
based financing. Walter Bagehot (1873) and Joseph A. Schumpeter (1912)
emphasize the crux of banking role in economic growth.
Indonesia,
which is currently categorized as emerging economies, has been transforming its
economy in order to maintain a sound economic growth. Therefore, during
stabilization period (19691996), Indonesia did several reforms and structural
changes. In 1983, credit reform began in which the artificial restrictions on
the allocation of bank credit and the state bank loans were eliminated.
Moreover, financial reform was taken one step further in October 1988 with what
referred to as Pacto 88. Under this regulation, restrictions on the operations
of foreign banks were eased; the procedures for establishing branch banks were
simplified. Pakto 88 also reduced the special privileges and responsibilities
of the stateowned financial institutions and narrowed the differential tax
treatment affecting various financial instruments. On that sense, the financial
deepening was starting to establish by further controlling bank credit in the
light of the surge in capital inflows. In brief, before financial crises in
1997, Indonesia had liberalized its economy to expand financial extension for
finally fostering economic growth.
TABLE 1.1
Source:
Bank Indonesia (Various Sources)
However,
since economic was overheating, a credit boom had triggered an excessive
depreciation of the rupiah. The economy shrank 13,8% during 1998 and annual
rate of inflation reached a very high, 77,6% in 1998 (year on year basis).
Generally, speaking the process of financial deepening during stabilization and
liberalization had ceased since financial crises toke place. Nevertheless, some
economists suggest that financial deepening basically has a positive effect on
growth if not done to excess (Rousseau, 2007).
Rapid
and excessive deepening, as manifested in a credit boom, can be problematic
even in the most developed markets because it can both weaken the banking
system and bring inflationary pressures. We had experience in financial crises
turmoil in 1997/1998 when Indonesia suffered economic crises. At that time,
banking system failed to safeguard economic system and also incapable of
providing sufficient liquidity needed to take away from crises. It implies to
enhancing capital outflows and reserve was depleted tremendously to keep
buffering the economy. The excessive credit growth is the outcome of financial
liberalization and officially has been inspired by Washington consensus that
may have altered the basic structural relationship between finance and growth.
Many countries have competed to apply and accelerate financial deepening as the
benefits may be got from it. However, such condition has led many countries,
unfortunately, to liberalize its economy as precondition attaching with
financial deepening without developing the associated legal and regulatory
institutions. As a consequence, the impact of financial deepening on growth
would become smaller and remain a premature financial development. That
condition, on contrary, will spur a higher inflation rate and interest rate and
finally vulnerable to financial crises. Figure below clearly shows the secular
increase in inflation rate and its impact to trigger exchange market pressure
during 1997/1998 in Indonesia.
In contrast, not all economists prescribe and stand in
the view that financial deepening negatively affect to financial crises and
financial development. As noted by Singh and Weiss (1998) and Diaz Alejandro
(1985) that the deregulation of repressed financial market can impose risks of
financial collapse and lead to economy recession. The liberalization financial markets
have been occurring in many developing. Country has gained its goal with deepen
and force increase in financial depth. Some experiences suggest that even the
best approach to regulation and supervision will not be able to completely avoid
incidences of financial crisis. An efficient crisis management strategy
requires a lender and fiscal support. In our paper we will explain it, detail
about this problem, how to free from financial crisis and is it any effect from
economy deepening in Indonesia.
TABLE 1.2
Source:
Bank Indonesia (Various Sources)
According
to above description, we can get the idea that the relationship between
financial deepening and crises might be unstable and fluctuating. Therefore, we
would basically investigate further on the relationship between financial
deepening and the onset of currency crises. Later, it also continues to
conceive on establishing of what possible variables that significantly
influences the existing of financial deepening in Indonesia during period of
observation. Lastly, the current paper deliberates on the role of early warning
system of currency crises approach in mitigating and specifically detecting in
such particular period of observations.
The
organization of the present paper is as follows. The next section provides some
literature review on the articles of financial deepening on financial crisis.
Section III describes the data and empirical approach. Section IV presents
results from our VAR and logit analysis. We first look at dynamic responses of
financial deepening to financial crises; we then estimate a VAR model to
investigate what variables affect the process of financial deepening; and
finally by logit model we discern the probability of each variable observed in
triggering to financial crisis. To end, section V concludes the paper with a
summary of the main findings and some concluding remarks.
LITERATURE REVIEW
“Why is financial deepening a good
thing?” this is one of the issues mentioned by the Deputy General Manager of
the BIS, Hervé Hannoun, in the middle of 43^{rd} SEACEN Governors’
Conference 2008. He also mentioned that economist have long insisted on the
need to develop strong and deep financial markets in order to promote economic
growth. This is because of the key importance of having marketbased and
diversified channels of intermediation between ultimate borrowers and
investors.
Walter Bagehot (1873) and Joseph A.
Schumpeter (1912) emphasize the crux of banking role in economic growth. They,
then, highlight circumstances when banks can actively spur innovation and
future growth by identifying and funding productive investments. Thus, the
first prerequisite stage imposed to assure economic growth is that the
availability services provided by financial markets which commensurate with the
need to finance productive and investment projects in economy. Such condition
is capturing a situation of the existence of financial stability in the system.
Generally speaking, the widespread
liberalization of financial markets that have been occurring in many developing
countries has gained its goals to deepen and force increases in financial
depth. In other hand, financial liberalization may lead to a rapid and
excessive financial deepening implementation as the further outcome, and it is
indicated has tendency in leading to the creation of premature financial
development. A premature financial development tends to have a higher risk in
facing and creating any possible shocks which readily would be transmitted
largely to the financial stability if done to excess and it is fundamentally
leads to financial crises. Rousseau and Wachtel (2007) further argue that
excessive financial deepening spread into economy may be a result of financial
liberalization without completing the associated legal and regulatory
institutions which were sufficiently well developed. As a consequence, the
impact of financial deepening on growth would become smaller, and ultimately
create premature financial development which fundamentally leads to financial
crises. Singh and Weisse (1998) and DiazAlejandro (1985) also noted that the
deregulation of repressed financial markets can impose risks of financial
collapse and consequently, lead to economic recessions.
James (2009) tries to relate the impact
of financial liberalization with the innovation and knowledge creation. He
notes that the literature suggest that financial reforms may negatively
influence innovations in several different theoretical settings. It may do so
by weakening the incentives to save and this reducing the domestic resources
available for facilitating invention, by producing more instability and
triggering financial crises that exacerbate economic fluctuations and dampen
knowledge creation, or by enabling the financial sector to expand disproportionately
and become more profitable and thus leading to more talent being attracted from
the technology sector. He also noted from his findings that financial
liberalization relocates talent to the financial system, thus hurting the
technology sector. There is also clear evidence supporting the view that
financial liberalization retards technological development through inducing
financial instability.
In brief, financial deepening will
promote growth as long as it is not excessive and those fundamental variables
are expected would advocate spelling out precisely the route of financial
deepening incurred in the economy whether deteriorate the system or share
advantages to financial stability. James (2009) mentioned that whilst financial
liberalization appears to have a harmful impact on ideas accumulation, this
negative effect can potentially be mitigated through improving the quality of
institutions with more effective regulation and supervision.
EMPIRICAL MODEL AND ESTIMATION
PROCEDURES
Data
The
analysis is conducted using quarterly data series for the period 1990 quarter 1
to 2010 quarter 3. We are employing several variables to capture the behavior
of crisis and financial deepening. Ratio broad money (M2) over Reserve
(M2/Res), domestic real credit (RKG), Inflation (INF), and Real effective
exchange Rate (REER) are used to further investigate theirs relationships to
explain the existing crises and the process of financial deepening. All data
are expressed in logarithmic forms, with the exception of inflation, and ratio
M2 over reserve (data used is official reserve minus gold). All data are from
International Financial Statistic and Bank Indonesia data base in various
sources. In addition, to measure crisis, we use the exchange market pressure
(EMP) which are comprised of exchange rate variation and reserve variation. And
financial deepening is measure from the ratio broad money supply over GDP
nominal.
Estimation Procedures
It
is the purpose of the authors to further investigate the directional relation
between the existing financial deepening and its possibility leads to crises.
The definition of crises here is that the occurrence of currency crises. Our
aim is to outline whether in Indonesia the process of financial deepening is
likely leads to a currency crises. Thus, our empirical model is specified as
follows:
Crises_{t}_{ }
_{}
Where,
crises is the currency crises and financial deepening is the ratio of money
supply to GDP. Subsequently, Kaminsky and Reinhart, 1996 used some leading
indicator to monitor the possible movement in the sense of measuring unusual
behavior in the period preceding a crisis. For Indonesia, the study could only
highlight four possible leading indicators to be incorporated into analysis. We
propose such variables, namely ratio money supply to reserve, Domestic Real Credit
Growth, Real Effective Exchange Rate, and Inflation as our leading indicators.
Those selected variables are utilized as departing from study conducted by
Susatyo (2002) which had examined several macroeconomic variables and finally
obtained keenly 4 out of 20 indicators correspondingly related with the
occurrence of currency crises.
Definition of Currency Crises
According
to the study conducted by Kaminsky et. Al, “A crisis is defined as a situation
in which an attack on the currency leads to a sharp depreciation of the
currency, large decline in International reserve of a combination of the two. A
crisis so defined includes both successful and unsuccessful attacks on the
currency.” (Kaminsky et al, 1997, page 15)
Thus,
the currency crisis is more specific defined as the unusual behavior of
Exchange Market Pressure (EMP). In addition, Goldstein, Kaminsky and Reinhart
(2000) further define EMP as weighted average of exchange rate changes (
) and rate of change of the reserve (SR_{t}).
The weighted which are selected encompass two same of component index with
sample volatility. Therefore, EMP is formularized as follows.
EMP_{t }=
Where,
And
The
formula above shows the exchanges the reserve and the exchange rate; each of
them is positively associated with Exchange Market Pressure (EMP). Thus, an
economy justified as crises if EMP exceeds the average plus deviation standard,
let’s say we set the threshold as m. if EMP entails the average index of EMP and
Shows
deviation standard from EMP index, hence formally we may say that currency
crises will occur in a couple period of time. Therefore, the crises can be
defined as
Crises_{t }
_{}
Hypothesis and Research Model
In
order to elaborate the effect of financial deepening to mitigate crises, the
present paper is attempting to present the hypothesis that ultimately can
answer to the question whether financial deepening is harm for economy as a
whole, in particular in Indonesia which is currently developing its economy
through financial based development.
Hypothesis
I:
Financial
deepening has a significant impact to the occurrence of crises, in particular
currency crises.
Hypothesis
II
The
selected macroeconomic variables or dealing indicators such as ratio
M2/Reserve. Real effective ratio rate, inflation and the real credit growth are
considered to be significantly effects the process financial deepening.
Hypothesis
III
The
selected macroeconomic variable or leading Indicators are considered and
identified on the main cause in creating a higher probability of currency
crises occurrence in Indonesia over period of observation.
Model Analysis
To
investigate further toward the mitigation of risk, in particular over crises
and in discerning the impact may be observed as the process of financial
deepening. The present paper would apply two kind different approaches where
each of them is used by looking at previous study as follow:
A. VAR approach
The
analysis used on the present paper is VAR as all variables which are explaining
the financial deepening for sure will involve the causal nexus relationship
among nonstructured variable so that all variable are treated as endogen
variable. According to the Sim (1980), VAR approach is enabled to do need to
differentiate between exogenous and endogenous variables, and its ability to
capture dynamic movement as one or more variable are reached in respond a
movement from other variable.
As
the variable conceived are considered as endogenous variable. Hence we may
write as below expressions:
Or
being expressed in the form of matrices as follows:
=
+
+
Or:
In
addition, if there variable have unit roots, then we can exploit that there may
exist comovement in their behavior and possibilities that they will trend
together toward a long run equilibrium state. Then, using the greater
representation theorem, we may posit the following testing relationships that
constitute a VEC model for financial deepening, as follows:
Where
��Z_{t} contains
the growth rate of the variable. The ��’s
are estimable parameters. �� is
a difference operator, �� is
a vector of impulses which represent the unanticipated movements in Z_{t}
and ᴨ is
the long run parameter matrix. Then using the saved residuals from the
estimation of the long run equilibrium relationship, we can estimate the error
correcting as:
Above
equation are representing VAR in first differences which are constituted by l_{t1.
}And l_{t1} is the value of the residual which estimates the
deviation from long run equilibrium in period (t1). a_{11}…a_{55}
are constituting the parameter and restriction that all a_{jk}(i)=0 can
be checked using Ftest. In addition a_{FD}, a_{RKG}, a_{REER},
a_{M2/RES}, a_{INF} are the speed of adjustment coefficients
and particular interest in that they have important implication for the dynamic
of the system. Their restriction can be conducted by using ttest. Asymptotic
theory indicator a_{FD} and so on converge to a tdistribution as
sample size increases.
B. Logit approach
To
evaluate the probability of crises as the process of financial deepening takes
place, we attempt to employ logit model which p independent variables and the further study will refer to
completely study conducted by Matthieu Bussiere and Marcel Fraztzscher (2002).
It is then expressed by:
Where
: P_{0} = Probability Normal regime, P_{1}= Probability Crises
Regime, Ln(P_{1}/P_{0}) = log odd, C_{1,2} = Constanta,
β_{1} =
marginal effect of the changes in independent variables X_{i,t1} for
the probability of crises in the crises period relative to probability crises
in tranquil/normal regime (Matthieu Bussiere and Marcel Fraztzscher, 2002)
Hence,
the model specification is obtained as follows:
Crises_{t}
= Ln (P_{1}/P_{0}) =Z = C_{1} + a_{1}M_{2}/ReS_{t}
+a_{2} GKRED_{t} +a_{3}REER_{t} +a_{4}INF_{t}
+E_{t}
Where:
Ln (P_{1}/P_{0}) = log odd ratio which constitutes currency
crises, a_{0} = Constanta of the model, a_{1},a_{2},a_{3},a_{4}
= parameters of coefficient, E_{t} = error term, M_{2}/RES =
ratio M_{2} over reserve, GKRED = the growth of real credit domestic,
REER = real effective exchange rate, and INF = inflation.
Technique of Analysis
Unit
Root Test and Cointegration Test
The
paper will apply the commonly used tool which is called for Augmented Dickey
Fuller (ADF) and PhillipsPerron unit root tests. The estimation procedure
takes the following form:
(ADFTest)
:
(PPTest) :
Where
denotes lag difference of the variable under consideration. M is the
number of lags and
is the error term. Based on the critical
values of respective statistics, if null hypothesis cannot be rejected, then
the time series are nonstationary at the level and therefore need to go
through higher order differentiating process to achieve stationarity and to
find the order of integration.
To test for cointegration, we employ aVARbased approach of Johansen
(1988) and Johansen and Juselius (JJ, 1990). The latter develops two tests
statistic to determine the number of cointegrating vector the trace and the
maximal Eigenvalue (M.E) statistic.
A VARbased approach is classified as a dynamic model since it can
generate variance decomposition and impulse response to further examine short
run dynamic interactions among the variables. Generally, time series data get
stationary in first difference I (1) order of cointegration. Hence, VECM is
utilized to identify the long run behavior of the variables and their short run
relations and the therefore can better reflect the relationship among the
variables. In addition, in a traditional VAR analysis, Luthepohl and Reimers
(1992) show that impulse responses and variance decomposition analysis can be
used to obtain information concerning the interactions among variables. The
impulse response functions permit inferences on the direction of response of a
variable of interest to be a one standard deviation shock in other variables.
Meanwhile, variance decompositions indicate the percentage of a variable’s
forecast error variance attributable to innovations in all variables considered
in the system.
Logit
Model
Logit
model is categorized as cumulative discrete function which is appropriate to
explain dependent variable which constitutes qualitative response and
characterized as dichotomy. Thus, to further comprehend the logit model, below
expression explains as cumulative logistic probabilistic:
Overall,
logit model is nonlinear model, both in parameter and in variable. Thus, we
cannot use OLS to estimate logit parameter. Logit, basically, is specified as
probability function. Hence, mathematically, logit is used to solve odds ratio.
In addition, odd is defined as ratio of events that have happened to events
that have not happened. This can be expressed as follows:
Basically,
if the value of P is small, (1P) is going to close to 1, as consequence the
odd entails to zero. In other words, odd represents an indicator which explains
whether a country is detected to crises or not. In short, the more odd closes
to zero; it means that a country will be small in creating a probability in
occurrence a crisis.
FINDINGS
Before
we proceed, it is imperative to perform a priory analysis of the variables
temporal properties. We subject each time series to the standard augmented
Dickey Fuller (ADF) unit root test. The results indicate that almost all data
series under consideration are integrated of order 1, or I (1). That is, they
are stationary in their first differences. Accordingly, we implement the
PhilipsPerron (PP) test for all variables under consideration. The PP test
confirms the stationarity in I (1) process.
Subsequently, we proceed with a cointegration
test, as suggested by Johansen (1992) and Johansen and Juselius (1990).
Essentially, the test of a VAR based test, treating all variables as
essentially endogenous. In implementing the test, we place emphasis on the
precondition that the error terms need to be serially uncorrelated. As it can
be observed from the table, we find evidence for the presence of a long run
relationship among the variables in all systems we estimate. These findings
provide an important guide for our dynamic modeling in respect to variables
observed shocks in the variation on financial deepening, and creating a higher
probability of crises, the issue that we address.
TABLE 4.1
ADF AND PP UNIT ROOT TESTS
Variable

Level

First Difference


ADF

PP

ADF

PP


EMP

7.981003***

5.716465***

9.205674***

37.86399***

INF

4.408629***

4.591088***

10.58124***

12.13751***

M2GDP

1.864321

1.704157

4.692668***

11.80703

M2RES

1.329521

1.402575

6.579466***

7.275103***

REER

1.214832

1.173666

5.388646***

6.243173***

RKG

0.825091

1.134339

3.709075***

6.411127***

*,**,*** denote significance at
better that 10%, 5%, and 1%
TABLE
4.2
JOHANSENJUSELIUS
COINTEGRATION TEST
Null
Hypothesis

Cointegration
Results

Statistical
Results/Critical values (5%)


Trace

Maximal
Eigen

Trace

Maximal
Eigen


r=0

31.30834

31.30693

12.53

11.44

r=1

0.001418

0.001418

3.84

3.84

The
lag order specified for the test is 1, which we find sufficient to render the
error term serially uncorrelated. The 5% critical values are based on
OsterwaldLenum (1992). Effective number of observation is 77
TABLE
4.3
LONG
RUN COINTEGRATING EQUATION (NORMALIZED ON M2GDP)
Constant

EMP

2.129314

25.53372***
[6.62502]

tStatistic
is in parentheses
***
Significant at 1% significance levels
TABLE
4.4
LONG
RUN COINTEGRATING EQUATION (NORMALIZED ON M2GDP)
Constant

INF

M2RES

REER

RKG

64.56817

184.2111***
[
5.70311]

1.009500***
[3.97680]

5.149189***
[
3.04701]

3.450216***
[
3.15795]

According
to the EMP result, we may say that the financial deepening is affected
negatively by the onset of Exchange Market Pressure. The sign is reasonable
from a theoretical point of view in which the process of financial deepening
would be smaller and insignificant as the financial crisis is taken place. In
addition, inflation will cause positively on financial deepening as the higher
inflation will encourage a higher on interest rate level. As a higher interest
rate established, the marginal propensity to invest might increase and
encourage creditor for saving or investing their funds in financial instrument or
financial institution. Such situation will force financial institution to
transmit in the several investment assets, either categorized as real assets or
financial assets. Therefore, the higher of inflation rate will promote the
financial depth in the economy. And then, the M2/RES seems to negatively affect
the financial deepening in the long run. As noted earlier, the ratio of money
supply and reserve indicates the proportion of money supply might be backed by
reserve in the sake it will not be oversupply and reflect upon the monetary
condition. Generally speaking, we realize that money supply actually as
liquidity assets represents the debt of government to economy. Government is
trying to maintain it volatility to detach from any instability matter. So
that, it must be kept in a proper proportion and precisely commensurate with
economic capacity. However, in other side, money supply looks like an engine of
growth in the economy since many transactions and financial instrument can be
transacted easily by it. In that sense, it is reasonable if it shows negative
sign in affecting financial deepening.
Subsequently,
Real Effective exchange rate represents the exchange rate which adjusts for the
effect of inflation on the nominal value. In other words, REER could be
explained by the theory of purchasing power parity which asserts that in the
long run, exchange rate adjust to different inflation rates among countries so
that the relative purchasing power of various currencies is equalized. In the
context of financial deepening, it would be positively sign as the inflation
and the original exchange rate between Indonesia and let say, United States.
Usually, the inflation rates in developing countries are higher than in
developed countries. Automatically, to keep their real interest rates, the
nominal rates would be higher as well. As the nominal rates higher, the capital
inflows would flow and trickle down to financial and real sectors. On that
reason, the financial markets and financial institutions will take dominant
contribution on accelerating the process of financial deepening. Therefore,
REER will positively affect in financial deepening process in Indonesia. In
terms of Real Credit Growth (RKG) positively affects the financial deepening.
It is reasonable since according to Joseph A Schumpeter (1912) emphasize the
critical importance of the banking system in supporting financial deepening
through fostering economic growth and highlight circumstances when banks can
actively spur innovation and future growth by identifying and funding
productive investment. In other words, real credit growth may have pushed
financial deepening through financial intermediation participating. To some
extent, according to Robert G. King and Levine (1993a), empirically show that the
level financial intermediation is a good predictor of a long run rate of
economic growth, capital accumulation, and productivity improvement. Therefore,
we may say that any extensions on credit would spur accelerating of the
financial deepening process. According to above explanation, empirically the
observed variables all significantly effect on financial deepening process in
Indonesia during period of observation.
Granger Causality Test
The documented cointegration among the variables suggests
only on their long run association and, while it implies causality, does not
reveal unfortunately the directions of causation among them. Thus, in order to
evaluate and observe the causal nexus among the concerned variables, we can
implement the Granger causality test. As noted, with cointegration, the
dynamic causal interactions among the variables should be phrased in a vector
error correction form.
TABLE
4.5
BIVARIATE
GRANGER CAUSALITY RESULTS
Lags: 1


Null Hypothesis:

Obs

FStatistic

Probability

ECM


M2GDP does not Granger Cause EMP

82

0.05178

0.82058

0.355881*** [3.48637]


EMP does not Granger Cause M2GDP

1.59546

0.21026

0.104943
[0.30467]


Basically, the present paper would discern the
relationship and causality between EMP (exchange market pressure) and M2GDP
(financial deepening). Empirically, it shows one cointegration by using
Johansen Juselius test, however according to bivariate causality seems not to
be significant. Nevertheless, we may note the ECM coefficient suggests insignificant
in the long equilibrium. We are expecting that the ECM coefficient results is
to be less than zero as it would adjust upward to restore equilibrium. In that
sense, with the negative coefficient of the error correction term in the M2GDP
equation, it means that the increase on M2GDP (financial deepening) processing
tends to increase the EMP in the long run. In addition, the coefficient
suggests that deviation from the equilibrium path is adjusted by about 35% the
next month through the movements in financial deepening. Indeed, the adjustment
toward the long run relationship is quite high. This implies that, after any
shocks that forces the financial deepening from their long run values it takes
a short quite long for the EMP to return to its equilibrium values.
TABLE 4.6
BIVARIATE GRANGER CAUSALITY RESULTS
Null Hypothesis:

Obs

FStatistic

Probability


M2RES does not Granger Cause M2GDP

82

2.99916

0.08721


M2GDP does not Granger Cause M2RES

0.07708

0.78201


REER does not Granger Cause M2GDP

82

2.89021

0.09305


M2GDP does not Granger Cause REER

2.87365

0.09398


RKG does not Granger Cause M2GDP

82

0.20408

0.65269


M2GDP does not Granger Cause RKG

16.5761

0.00011


INF does not Granger Cause M2GDP

82

1.06898

0.30433


M2GDP does not Granger Cause INF

1.18692

0.27926


REER does not Granger Cause M2RES

82

4.72201

0.03278


M2RES does not Granger Cause REER

0.00137

0.97053


RKG does not Granger Cause M2RES

82

1.16151

0.28443


M2RES does not Granger Cause RKG

0.89225

0.34775


INF does not Granger Cause M2RES

82

0.38913

0.53456


M2RES does not Granger Cause INF

0.07933

0.77894


RKG does not Granger Cause REER

82

3.90339

0.05168


REER does not Granger Cause RKG

3.20912

0.07706


INF does not Granger Cause REER

82

0.39690

0.53051


REER does not Granger Cause INF

0.34220

0.56023


INF does not Granger Cause RKG

82

4.25597

0.04240


RKG does not Granger Cause INF

0.18175

0.67104


According to bivariate granger causality above, we may
note that only M2/Res, and REER which have causality relationship to M2/GDP
(financial deepening). This means that there is a unidirectional causality from
two above variables to M2/GDP and it does seem to confirm on what have
mentioned earlier that they are consistent enough in promoting and
corroborating the process of financial deepening. And the remaining, inflation
(INF) and Real Credit (RKG) are not significant since done to excess. Since
according to Peter Rousseau (2007) the financial crises created as rapid and
excessive deepening, reflected in a credit boom, has weakened the banking
system and brought inflationary pressures so that finally any policies related
with them are not extensively stepping up the pace of financial deepening et
all.
IRF and VDC
Impulse response functions and variance decompositions
capture estimation results of the VAR in the forms that can be easily
interpreted. Essentially, impulse response functions trace temporal responses
of a variable to its own innovation and innovations in other variables in the
system. From the functions, we can observe whether the response financial
deepening is significant or persistent. Meanwhile, variance decompositions
indicate the fraction of a variable’s forecast error variance attributed to shocks
in other variables and, accordingly, provide a natural measure of the of the
relative importance of various shocks to the variable of interest. From
variance decompositions, we can assess REER, INF, RKG, M2/RES, and EMP are more
dominant in accounting for promoting financial deepening (M2/GDP) in the system
of VAR.
FIGURE 4.1
GENERALIZED
IMPULSE RESPONSE FUNCTIONS M2/GDP AND THE OTHER CONCERNED VARIABLES
Above figure presents the generalized impulse response
functions generated from VAR model for the system consisting all variables
observed under consideration. The responses are plotted together with a
2standard deviation band. If the band does not include the zero line, then the
responses are said to be significant. We try to observe the responses of M2/GDP
(financial deepening) as any shocks appear on each variable observed. Above
figure suggests insignificant responses of any shocks come from REER, INF,
M2/RES, and RKG to M2/GDP. In brief, we may testify that no significant
Reponses and acerbated financial depth would be occurred in the short run. It
confirms the earlier findings that financial deepening process in Indonesia
take a quite long time to be in existence. And it might conform to Lucas (1975)
which says the financial deepening in some developing countries, in particular,
is not wellcompleted in terms of the associated legal as well as regulatory
institutions. Thus, such condition will remain a premature financial
development so that it might be vulnerable to financial crises. To that sense,
above concerned variables seem to be not significant as in the short run, there
would be happened an adjustment process in order to maintain the stability and
sustainability in financial deepening process in the long run. However, to gain
the further insight on the relative importance of the concerned variables
innovations to financial deepening process, we compute variance decompositions
up to 30month horizons.
TABLE
4.7
VARIANCE
DECOMPOSITION OF M2GDP
Period

Explained
by Innovation in


INF

EMP

M2/RES

RKG

REER

M2/GDP


2

0.521865

0.226545

0.220054

0.035604

0.000826

98.99511

4

0.344953

0.418317

1.065429

0.254723

0.001075

97.91550

8

0.946646

0.631135

2.400091

0.957072

0.079351

94.98571

12

1.560786

1.545328

2.505848

1.543311

0.448535

92.39619

16

2.336302

2.696252

2.398632

1.844612

1.103030

89.62117

20

2.984174

3.819896

2.562311

1.879464

1.834115

86.92004

24

3.353417

4.663126

2.965547

1.835831

2.419526

84.76255

28

3.470407

5.180948

3.380435

1.916042

2.767767

83.28440

30

3.470800

5.341316

3.536517

2.036861

2.862287

82.75222

Several interesting results emerge from the variance
decompositions. Reaffirming the results from the impulse response functions,
variations in the M2/GDP (financial deepening) are accounted mainly by its own
shocks and indeed its variations are explained in a negligible portion in
regards to the concerned variables. In the short run, after 30month horizons,
variations in the M2/GDP account for only 25% of the forecast error variance
on variables under consideration. Therefore, we note that there is the relative
irresponsiveness the concerned variables to the process of financial deepening;
perhaps the economy cannot utilize those instruments effectively in the short
run. And also the economy is less capable of spurring financial instruments as
explained before Indonesia’s economic system might be less developed and less
well supported by the legal associated and the sufficient regulatory
institutions. At that reason, the financial deepening is found difficult to
expand even though the concerned variables have been run away.
In sum, there are insignificant short run dynamics that
we observe tend to answer the question on the role of the concerned variables
to affect financial deepening. However, it does not mean that those variables
are not important and relevant to be put into our consideration. Since, in the
short run, the economy might not be reacted much they could be as being
expected in the financial deepening does not reveal too rapid and excessive
deepening, and then hopefully can maintain the stability in banking system, and
avoid inflationary pressures, and move away from financial crises.
Application Logit
Model in Crises Modeling in Indonesia
Logit regression (logit) analysis is a uni/multivariate
technique which allows for estimating the probability that an event occurs or
not, by predicting a binary dependent outcome from a set of independent
variables. Further, by using logit estimation, we can obtain the specific
results on what the concerned variables trigger the currency crises in the form
of probability term. The variables used, namely Inflation (INF), Real credit
Growth (RKG), ratio M2 over Reserve (M2/Res), and real effective exchange rate
(REER) are selected variables which are resulted as empirical research had been
completely done by Susatyo (2000) in which he uses 20 leading indicators.
Subsequently, by extracting signal approach developed by Kaminsky and Reinhart
(1999), he gets those 4 variables. According to signal approach that he did,
those 4 variables are able to show 50% probability in triggering a currency
crisis in Indonesia. Later, the present paper is aiming to utilize those 4
variables but using the parametric approach, namely logit.
The preceding model involves 83 observations, comprised
of 6 observations and 77 observations are grouped as crisis and noncrisis
respectively.
TABLE
4.8
REGRESSION
RESULTS OF LOGIT MODEL
DEPENDENT
VARIABLE: KRISIS
METHOD:
MLBINARY LOGIT
CONVERGENCE ACHIEVED
AFTER 7 ITERATIONS
Variable

Coefficient

Std. Error

zStatistic

Prob.


C

16.41122

13.71625

1.196480

0.2315


M2RES

0.438312

0.187536

2.337217

0.0194


REER

2.841761

1.456331

1.951316

0.0510


RKG

0.661532

1.282463

0.515830

0.6060


INF

9.866552

8.481509

1.163302

0.2447


Mean dependent var

0.072289

S.D. dependent var

0.260540


S.E. of regression

0.257418

Akaike info criterion

0.551720


Sum squared resid

5.168598

Schwarz criterion

0.697433


Log likelihood

17.89637

HannanQuinn criter.

0.610259


Restr. log
likelihood

21.54020

Avg. log likelihood

0.215619


LR statistic (4 df)

7.287661

McFadden Rsquared

0.169164


Probability(LR
stat)

0.121445


Obs with Dep=0

77

Total obs

83


Obs with Dep=1

6


Now, let us interpret the regression results given above
estimation results. Basically, each slope coefficient in this equation is a
partial slope coefficient and measures the change in the estimated logit for
unit change in the value of the given regressor (holding other regressor
constant). Thus, the M2/Res coefficient of 0,438312 means, with other variables
constant, that if M2/Res increases by a unit, on average the estimated logit
increases by about 0,44 units, and then suggesting a positive relationship
between the two. The INF coefficient also suggests a positive relationship.
Conversely, REER and RKG show negative relationships between the two. In terms
of significance, M2/Res and REER seem to be significant at 10% level. However,
together all the regressors have an insignificant impact on the crisis, as the
LR statistic is 7,28, whose p value
is about 0,12, which is not significant at 10% level.
Nevertheless, the aim of logit is not to signify whether
the regressors are significant or not, but instead of showing for estimating
the probability that an event occurs or not. Thus, as noted previously, a more
meaningful interpretation is in terms of odds, which are obtained by taking the
antilog of the various slope coefficients.
The above results show that
intercept coefficient is 16,41. This means
With
other variables held constant. Thus, the value of
or the probability of occurrence the crisis,
held other variables constant is that
= 0,99999. In other words, the probability of
crisis occurrence with other variables constant is that 99,99%. That seems to
be higher and indirectly indicate the crises are dominantly caused by other
variables out of the concerned variables above, for example the contagion
factor. In addition, the slope of M2/Res is 0,438312. Therefore, the value of
probability in occurring crisis if M2/Res tends to fluctuate is that
=
0,607856. Thus, the probability of occurring crises as variables M2/Res is
fluctuating is 60,7856%. The further analysis could be, as the present has
mentioned in previous chapter, that
P _{krisis} =
, P _{normal}
=
.
=
.
, therefore,
= 60,7856%.
It
obviously shows the risk of crises when M2/Res is fluctuating. In that sense,
the M2/Res fluctuation will expound 60,78 times compared as normal condition it
does.
Now,
we look at the inflation (INF) variable which the slope is 9,866552. That means
the probability of crises will be occurred as the inflation is fluctuating is
that
or
equal with 99.99%. and in terms of REER, we note the slope is 2,841761. Thus,
the probability of occurring crises as it is fluctuating, is that,
or
equal with 0,05511. This means any shocks or fluctuations in REER variable will
force the probability of crises for about 5,511%. Lastly, we compute the
probability of crises as real credit (RKG) is fluctuating. Using the antilog of
the RKG coefficient, we will get
or equal with 0,3404. It means any shocks or
fluctuations in credit variable will push up the probability of crises for
34,04%
TABLE
4.9
THE
SUMMARY OF LOGIT MODEL ESTIMATIONS
Variable

Parameter

Odd Ratio

Probabilities

Intercept

16.41122 (13.71625)

0,9999

0.2315

RKG

0.661532 (1.282463)

0,3404

0.6060

INF

9.866552 (8.481509)

0,9999

0.2447

M2/RES

0.438312 (0.187536)

0,60785

0.0194**

REER

2.841761 (1.456331)

0,05511

0.0510***

Source: Eviews 4
Note: ** and *** are the level of
significance at 5% and 10% respectively. The parentheses show the standard
errors
CONCLUSIONS
The
present paper analyzes three issues in looking at the relations between
financial deepening and the probability of occurrence of crises, in particular,
currency crises in the case of Indonesia. By also employing several variables
under consideration, the present paper is able to identify empirically on what
variables importantly effect to financial deepening and theirs relation to
crises. Initially, we ensure whether data used are stationer and integrated,
and we find that our data stationer in first difference I(1) by using ADF and
PP tests. Then, according to Johansen and Juselius test, the data happen to be found
evidence for the presence of a long run relationship among the variables. And
then by establishing the long run cointegrating equation, we suggest that the
financial deepening is affected negatively by the onset of Exchange Market
Pressure (EMP). The sign is reasonable from a theoretical point of view in
which the process of financial deepening would be smaller and insignificant as
the financial crisis is taken place. The other findings related according to the
empirical research as follows:
a. Inflation
will cause positively on financial deepening as the higher inflation will
encourage a higher on interest rate level
b. The
M2/RES seems to negatively affect the financial deepening in the long run
c. REER
will positively affect in financial deepening process in Indonesia
d. Real
Credit Growth (RKG) positively affects the financial deepening
Therefore,
according to long run cointegrating equation, empirically the observed variables
all significantly effect on financial deepening process in Indonesia during
period of observation.
The
causality test suggests finding evidence that there is independence causality
from EMP and M2GDP. However, the M2GDP coefficient shows negatively significant
in the long run. It means that the increase on M2GDP (financial deepening)
processing tends to increase the EMP in the long run. In addition, the
coefficient suggests that deviation from the equilibrium path is adjusted by
about 35% the next month through the movements in financial deepening. Indeed,
the adjustment toward the long run relationship is quite high. This implies
that, after any shocks that forces the financial deepening from their long run
values it takes a short quite long for the EMP to return to its equilibrium
values. Subsequently, the other variables show in respect to M2/GDP (financial
deepening), is that, M2/Res and REER have a unidirectional causality to M2/GDP,
and conversely, INF and RKG is not significant in causal directional
relationship to financial deepening (M2/GDP).
Meanwhile,
the IRF suggests that above concerned variables (REER, INF, RKG, M2/RES) seem
to be not significant as in the short run, there would be happened an
adjustment process in order to maintain the stability and sustainability in
financial deepening process in the long run. The variance decomposition
confirms the earlier findings that suggest in the short run, after 30month
horizons, variations in the M2/GDP account for only 25% of the forecast error
variance on variables under consideration. Therefore, we note that there is the
relative irresponsiveness the concerned variables to the process of financial
deepening; perhaps the economy cannot utilize those instruments effectively in
the short run.
According
to logit results, we get empirical evidence that only M2/Res and REER
significantly effect on crises. However, the aim of logit is not to signify
whether the regressors are significant or not, but instead of showing for
estimating the probability that an event occurs or not. Thus, as noted
previously, a more meaningful interpretation is in terms of odds, which are obtained
by taking the antilog of the various slope coefficients. The odds show the
probability given for a crisis occurrence as one of concerned variables is
fluctuating. The M2/RES and INF seems to show a significant probability since
their probability are above 50%, meanwhile the remaining, RKG and REER are
below 50%. However, the concerned variables tend to fluctuate and effect to a
higher probability crises occurrence as shocks imposed to them.
Finally,
the present paper contains the recommendations in regard to financial deepening
process, is that, financial deepening does not reveal an excessive growth and
liberalization as precondition of fueling financial deepening should be
attributed with sufficient on the associated legal and regulatory institutions
so that financial development produced is not premature and does not lead to
financial crises which exacerbate and deteriorate the economy as well as all
aspects of human beings. Therefore, the synergic and harmonious policies need
to be well developed to promote financial deepening which can positively
support the economic growth and stability within the country as well as
international economy.
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