slides presentation for 5th international conference, jordan dimas kusuma-
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IN THE NAME OF ALLAH THE
MOST GRACIOUS AND
MOST MERCIFUL
WELCOME TO IIUM GARDEN OF KNOWLEDGE AND VIRTUE
BUILDING AN EARLY WARNING SYSTEM FOR
ISLAMIC BANKING CRISIS IN INDONESIA:
SIGNAL APPROACH MODEL
By
Dimas Bagus Wiranata Kusuma and Abu Asif
Candidate Doctor of Economics,
International Islamic University Malaysia
Paper Presented at “Fifth International Conference on
Islamic Banking and Finance” "RISK MANAGEMENT, REGULATION AND SUPERVISION"
CENTRAL BANK OF JORDAN, AMMAN
6-8TH OCTOBER 2012
OUTLINE OF PRESENTATION
INTRODUCTION
PURPOSES OF STUDY
LITERATURE REVIEW
DATA AND METHODOLOGY
RESEARCH FINDINGS
BACKGROUND
CONCLUSION AND RECOMMENDATION
INTRODUCTION
- Islamic Banking (IB) was able to endure the implication of
global crises
- However, given the presence practices of IB has close
connection to the development in conventional banking and
dominantly practicing debt and trade-based financing, it might
create a triggering factor to instability, later trigger
vulnerability into crisis in Islamic banks.
- In fact, banking crisis is costly and might create systemic risk in
economic system, an implementation of EWS for Islamic
banking crisis is very important and calls for existence.
- Therefore, adopts study conducted by Kibritcioglu (2003)
and Kaminski et al (1998), an extraction signal is utilized to
monitor and predict impeding banking sector problem in
Indonesia.
PURPOSES OF THE STUDY 1. To identifies crises periods, or periods of unusual volatility, based on monthly
information for the period from March, 2004 to June, 2012
2. To evaluates the alternative filtering mechanism which leads to the minimum type
I error by employing four selected leading indicators
SIGNIFICANCE OF THE STUDY
- As far as concerned, the study related with EWS model for
Islamic Banking crises is rarely found.
- The paper investigates the predictive ability of the developed model
namely predicting global financial crises in 2008.
- It undertakes a surveillance mechanism for monitoring banking
system operation by exercising conventional selected
macroeconomic indicators
3. To estimate the out-sample data by using in-sample results estimation in order
to testify the reliability of EWS predictability towards the onset of crises
OVERVIEW OF ISLAMIC BANKING DEVELOPMENT
IN INDONESIA
ITEM 2005 JUNE 2012
Islamic banking
network
3 banks and 304 offices 11 banks and 1.529 offices
Islamic Financing
Contract
Sale-Based Contract 63%, and
Equity-based financing 33%
Sale-Based Contract 58%, and
Equity-based financing 28%
Islamic Financing by
sector
Trade, Restaurants and Hotel
11.24%, Business Services 29.49%,
and Agriculture, Forestry and
Agricultural Facilities 4.5%
Trade, Restaurants and Hotel
32.93%, Business Services 12.44%,
and Agriculture, Forestry and
Agricultural Facilities 8.98%
Islamic Banking
Performance
Assets 36.24%, Financing 32.57%,
Deposit 31.36%
Assets 49.16%, Financing 50.56%,
Deposit 51.79%
Financing based on
Type of Usage
Working Capital 52.4%, Investment
28.1%, and Consumption 19.4%
Working Capital 39.63%,
Investment 20.7%, and
Consumption 42.75%
Financing based on
Purpose
66.94% for SMEs and 33.06%
Non SMEs
69.07% SMEs, and 30.93%
Non-SMEs
LITERATURE REVIEW
No Year Focus Method Data Coverage Results
1* 2004 Banking,
Islamic
Banks
Discriminant
Analysis
Total Income/Total Assets, Investment
Income/Total Income, Total
Income/General and Administration
Expenses, Provisions for Bad Debts
and Investment/Total Assets,
Cash/Total Deposits, Customers
Investment Deposits/Shareholders
Equity, Net Profit before Zakat and/or
Taxes/Total Assets
All explanatory variables are significant
to test the classification accuracy ad
prediction reliability of the model.
Incorporating political variables,
macroeconomic variables will improve
the model.
2** 2011 Banking
crises
A review and
Bibliography
of EWS
Sharing some observations regarding a
non-exhaustive collection of the Early
Warning literature from 1971 to 2011,
including evolution of the interest in
EWS, methodology spectrum of
studies, and coverage of economic
variables
The study reveals that (1) EWS seem to
increased after mid-1990s, reached its
climax between 2001-2005, and slightly
decreased afterward, (2) in terms of
methodology, binary dependent variable
family seem to have the most popular.
Individually, logit analysis is the first (21
out 124), signal extraction analysis, and
discriminant/factor analysis share the
second place (14 out of 124).
* Al-Osaimy and Bamakhramah (2004) “ An Early Warning system for Islamic Banks
Performance, JKAU : Islamic Econ. 17 (1) :3-14
** Eray, Yucel (2011) “ A Review and Bibliography of Early Warning Models, MPRA Paper No
32893
DATA AND EMPIRICAL FRAMEWORK
• Time Path of the BSF2 Index and Five Phases of
• A Hypothetical Banking Crisis
BENEFITS OF EXTRACTION SIGNAL OR NON-PARAMETRIC APPROACH
Kibritcioglu (2005) Herrera and Garcia (1999)
1. The Banking Sector Fragility (BSF) Index
which a part of this approach, is very useful
to monitor and interpret the instability in
the sector
2. Easily employed within a single-country
framework
3. Can be used to differentiate “normal” (non-
systemic) and “crisis” (systemic) phase.
1. The simplest approach for EWS
2. Can be updated monthly
3. The lowest feasible cost
4. Can be used to aggregate the individual
leading indicators into a composite index,
and then the index is used as a signaling
device.
Time Path of the BSF2 Index and Five Phases of A Hypothetical Banking Crisis
STAGES FOR BUILDING EWS THROUGH SIGNAL APPROACH
• DEFINING SIGNAL
• BUILDING EVALUATION CRITERIA
– % of observation correctly called, - Noise-to-signal-ratio, - % of crises correctly called, - % of false
alarm of total alarms, - % probability of crisis given an alarm, - % probability of crisis given no alarm
• DETERMINING SIGNALING HORIZON
– Kaminski (1997) defines the period a-priori as 24 months, meanwhile Bussiere and Fratzscher (2002)
set for 12 and 18 months. Those separated horizon is set in order to get the optimal time horizon
• DEFINING CRISES AND THRESHOLDS
– Exercises some threshold levels which are applied by Kaminski (1997), Park (2001), Garcia (1999), and
Lestano (2003), namely respectively 3 standard deviation (SD), 1.1 SD, 1.5 SD, and 1.0 SD.
– The crises periods is defined as a period in which Islamic banking sector fragility (IBSF) > μ+m𝝈
(where μ is the sample mean and 𝝈 the standard deviation of the IBSF, and m the threshold levels).
• DEFINING AND CONSTRUCTING ISLAMIC BANKING CRISES INDEX
– This paper proposes IBSF2 (general index) to measure the fragility of banks to crisis, namely:
Table 10. Possible Scenarios of Signals and Crisis
Crisis No Crisis
Signal Issued A B
No Signal Issued C D
IBSF2 = Islamic Banking
Sector Fragility,
DEP=Deposit, DC=Domestic
Financing, , μ= mean, 𝝈=standard deviation
• We differentiate the degree of
crisis into three types of fragility
values (Kibritcioglu, 2005)
• While, an Islamic banking
system is supposed to be in a
medium fragility period, if the
value of the IBSF2 index is
between 0 and -0.5
(-0.5<IBSF2t<0).
• Then, if the value of the IBSF2
index is lower than -0.5, we
assume that the relevant banking
sector is highly fragile to have
systemic crisis (-0.5>IBSF2t).
Normal banking crisis defined as
the IBSF2 does not deviate
significantly from zero. Therefore,
there is no reason to expect a severe
banking sector problem in the short
run, (10.1>IBSF2t<0.1 )
- Selecting Leading Indicators of Crises (Herrera and Garcia, 1999)
- M2/Reserve Growth
- Domestic Credit Growth
- Real Effective Exchange Rate
- Inflation Rate
DATA AND EMPIRICAL FRAMEWORK
DEFINING DEGREE OF CRISES
RESEARCH FINDINGS TIME PATH OF THE IBSF2 INDEX AND THE PHASES IN INDONESIA
MARCH 2004-DEC 2006
• According to above figures, we can interpret
that for initial month in 2004, Islamic banking
in Indonesia were excessively risk taking
• During early seven months in 2004, the direction
of IBSF2 increases significantly above zero, and
expected to fall. In fact, since September 2004,
IBSF2 index suddenly begins to decrease and
implies Islamic bank generally are starting to
risk avoiding
• This index continues to decrease and falls below
-0.5 since July 2005. Therefore, since July 2005
to December 2006, IBSF2 index shows some
indications for the upcoming of banking crises
or systemic banking crises
• Therefore, empirically, this type of index is
capable of providing more information about the
ups and downs in the Islamic banking sector
with respect to certain crisis-years in event-
based studies
RESULTS IN-SAMPLE DATA
Signal-Generating Mechanism and Determining Leading Indicator of Crisis
All indicators show the lowest noise to signal ratio (NTS) which mean showing
the best threshold chosen (Garcia) to minimize type I error (probability of not
anticipating a crisis) and reduce substantially cost of not anticipating the extreme
risk situations. Meanwhile, this paper also finds that 24 month signal horizon is
the best because it would bring the employed indicators to have best ability for
anticipating crises.
No Variable Threshold Signal Horizon Evaluation Criteria
1 M2/Reserve
Growth
Garcia 24 month The highest percentage in terms of
% of observation correctly called
and lowest NTS
2 Domestic
Credit Growth
Garcia 24 Month The lowest NTS
3 Real Effective
Exchange Rate
Garcia 24 Month The lowest NTS, the lowest % false
alarm of total alarm, and highest %
probability of crisis given an alarm
4 Inflation Rate Garcia 24 Month The lowest NTS, highest percentage
of crises correctly called as well as %
probability of crisis given an alarm
RESULTS OUT-SAMPLE DATA
No `Category
REER
(Real Effective
Exchange Rate)
M2/
Reserve
Credit
Growth Inflation
1 NTS (Noise To Signal
Ratio) 0 0 0 0
2
% Of Obs.
Correctly Called
0.8628
0.8633
0.9858
0.7028
3 % Prob. Of Crisis
given No Alarm 0.1330 0.1518 0.1031 0.1522
The out-of sample estimation posit several interesting results
1. All selected leading indicators are able to minimize the type 1 error. It implies
the model outperforms for capturing and explaining the existing crises in Islamic
banking industry
2. All selected variables are able to explain the incoming crises once the signal
issued
3. The model is able to reduce the possibility a crisis occur without signals
CONCLUSION
• The IBSF2 which was developed in this study is able to figure out the
development process of Islamic banking crises in Indonesia over March 2004
to December 2006
• By utilizing signal generating mechanism, The results find that four leading
indicators could be used as best predictor variables for Islamic Banking crisis if
the threshold set is Garcia approach with 24 month signal horizon
• By exercising the out-of-sample period and employing Garcia approach, the
study deduces that all selected leading indicators vindicate the ability for
correctly forecasting the crises occurrence with at least 24 months before the onset
of crises.
RECOMMENDATION
• The call for more leading indicators and more complicated methods or
approaches is highly needed. In the case of conventional banking, for
instance, more macro and micro prudential leading indicators are exercised. In
terms of methodology, the use of logit/probit model, artificial neural network,
and Markov Switching model are empirically encouraged as surveillance
mechanism
THANK YOU FOR YOUR PRECIOUS TIME
MAY ALLAH BLESS US
WITH KNOWLEDGE AND WISDOM
WASSALAM