Analysis on Comparative Study
between Islamic and
Conventional Stock Market
in Malaysia
By
Rininta Nurrachmi
Introduction
Literature Review
Model & Methodology
The Findings
Conclusion
Introduction
825 Shariah compliant securities, 166 Islamic unit trust funds from total of 597 Bursa Malaysia joint forces with FTSE group in index measurement Global Financial Crisis FBM KLCI - 13.67% FBM Hijr - 13.77% Q1, 2008 FBM Emas - 15.99% Bersih Rally 2.0 in July 2011 FBM KLCI - 6.56% FBM Hijr - 6.77% August 2011 FBM Emas - 7.44% Previous studies were conducted during the global financial crisis
11. To Explore the Existence of
Cointegration and Direction of
Causality
2. To Compare the Volatility during the period 2007-
2012
Literature
Review
March 1960 – Malaysian Stock Market
April 1999 – Shariah Index KLSE
26 June 2006 – Bursa Malaysia joint forces with FTSE Group
21 May 2007 – FBM Hijrah Index
22 January 2007 – FBM Emas Shariah Index
October 2012 – FBM Small Cap Shariah Index
Malaysian Stock Market
1. Tabak&Lima(2001) Stock Market in Latin America & the US (Indices for countries stock market benchmark)
2. Cho & Ogwang (2006) Stock Market in Canada (TSX Composite Index vs TSX Venture Composite Index
3. Ramona&Razvan(2009)Stock Market in Romania (BET vs RASDAQ-C)
4. Albaity&Mudor (2012) DJINA (Dow Jones Industrial Average) vs FBM KLCI
No cointegration & Unidirectional SR
Causality
The Relationship in Conventional Stock Market
The market is efficient & there is different characteristic of listed company
1. Albaity & Ahmad (2008) Stock Market in Malaysia (KLCI vs KLSI). Data in 1999 - 2005
2. Hengchao&Hamid (2011) Stock Market in US, Japan, China,Malaysia, Indonesia. Data in 2007-2010
3. Chapakia&Sanrego (2007) Stock Market in Malaysia (Shariah index, Composite index, 3-months Treasury bill rate. Data in 1999 - 2003
There is existence cointegration & bidirectional (feedback) causality
No cointegration
1. Hakim & Rashidian (2002) Stock Market in the US (DJIMI, the Wilshire 5000, and the three-month T-Bill ). Data in 2001-2002
2. Beik & Wardhana (2011) Stock Market in Indonesia, Malaysia and the US. Data in 2006-2008
The Relationship between Islamic & Conventional Stock Market
Volatility Level between Islamic & Conventional Stock Market
1. Yusuf – Majid (2007) Stock Market in Malaysia (RHBII vs KLCI). Data in 1992-2000
2. Sukmana – Kholid (2009) Stock Market in Indonesia (JICC vs JAKISL). Data in 2001-2009
3. Akhtar, et al (2012) Stock Market in 9 Islamic and 37 non-Islamic countries. Data in 2007-2010
4. Chiadmi – Ghaiti (2012) Standard and Poor 500 Indices. Data in 2006-2011
Islamic stock market was less volatile compare to its counterpart
1. Amanina – Safiih (2010) Stock Market in Malaysia (KLSI vs index of financial sector, consumer sector, the construction sector, the trade/service sector and plantation sector). Data in 1990-2010
2. Ibnrubbian (2012) Stock Market in Saudi Arabia. Index from Banking, Industrial, Cement, Service and Agricultural sector. Data in 2002-2008
3. Romli et, al (2012) Stock Market in Malaysia (FBM Hijrah & FBM Emas Shariah vs FBM KLCI) . Data in 2007-2010
Islamic stock market was more volatile compare to its counterpart
Theoretical Framework
Cointegration & Causality Direction
Vector Autoregressive (VAR)
There is presence of cointegration &
bidirectional
There is absence of cointegration &
unidirectional SR Causality
Volatility Comparison
ARCH/GARCH (1,1)
Islamic Stock market has
lower volatility
Islamic stock market has
higher volatility
Model
and
Methodology
𝐿𝑛(𝐶)𝑡= 𝛼0 + 𝛼1𝐿𝑛(𝐶)𝑡−1 + 𝛼2𝐿𝑛(𝐻)𝑡−1 + 𝛼3𝐿𝑛(𝐸)𝑡−1 + 𝜀𝑡𝐶
𝐿𝑛(𝐻)𝑡= 𝛽0 + 𝛽1𝐿𝑛(𝐶)𝑡 + 𝛽2𝐿𝑛(𝐶)𝑡−1 + 𝛽3𝐿𝑛(𝐻)𝑡−1 + 𝛽2𝐿𝑛(𝐸)𝑡−1 + 𝜀𝑡
𝐻 𝐿𝑛(𝐸)𝑡= 𝛾0 + 𝛾1𝐿𝑛(𝐶)𝑡 + 𝛾2𝐿𝑛(𝐻)𝑡 + 𝛾3𝐿𝑛(𝐶)𝑡−1 + 𝛾4𝐿𝑛(𝐻)𝑡−1 + 𝛾5𝐿𝑛(𝐸)𝑡−1 + 𝜀𝑡
𝐸
𝑳𝒏(𝑪)𝒕= 𝜷𝟎 + 𝜷𝟏𝑳𝒏(𝑯)𝒕 + 𝜷𝟐𝑳𝒏(𝑬)𝒕 + 𝜺𝒕
Time Series Data from 5 June 2007 to 28 December 2012 (Daily Index , N= 1376)
Islamic Stock Market FBM Hijrah , FBM Emas SI Conventional Stock Market FBM KLCI
Methodology
Unit Root
Augmented Dickey
Fuller (ADF)
Philip Perron
(PP)
Cointegration Test
Johansen (variable
more than 2)
Causality Test
Short Run Granger
Causality test
Volatility Measurement
Descriptive Analysis
ARCH LM test
ARCH – GARCH
(1,1)
The Findings - 1 The existence of cointegration
and causality direction
ADF PP
Variables No Trend Trend No Trend Trend
(A)Level
Ln(Composite) -0.585669 -1.765736 -0.546347 -1.785986
Ln(Hijrah) -0.673868 -1.552335 -0.649713 -1.504275
Ln(Emas) -0.675609 -1.701642 -0.698035 -1.684282
(B) First Difference
Ln(Composite) -19.81999*** -19.86281*** -33.20673*** -33.21401***
Ln(Hijrah) -33.06682*** -33.08833*** -33.05329*** -33.05729***
Ln(Emas) -32.78544*** -32.82133*** -32.88004*** -32.90799***
Note : *** 1% sig level
All indices contain unit root at level The Variable is stationary at first difference with 1% significance level
Null
Hypothesis
Trace Max Eigenvalue Trace Max Eigenvalue
CR CV (10%) CR CV (10%) CR CV (5%) CR
CV
(5%)
𝑟 = 0 22.29727 27.066 16.539 18.892 22.29727 29.797 16.539 21.131
𝑟 ≤ 1 5.758269 13.428 4.928501 12.296 5.758269 15.494 4.928501 14.26
𝑟 ≤ 2 0.829768 2.7055 0.829768 2.7055 0.829768 3.8414 0.829768 3.8414
Trace statistic < Critical values Maximum Eigenvalue statistic < Critical values There is no cointegration in the variables or there is no long run relationship between Islamic stock market with its counterpart
Note : CR = Cointegration Rank Test, CV = Critical Value The lag order specified is 1 based on Akaike Information Criteria
<
<
<
<
<
<
<
<
<
<
<
<
Null Hypothesis F-Statistic Probability
LNHIJRAH does not Granger Cause LNCOMPOSITE 3.81989 0.05085
LNCOMPOSITE does not Granger Cause LNHIJRAH 4.37959 0.03656**
LNEMAS does not Granger Cause LNCOMPOSITE 3.44695 0.06358
LNCOMPOSITE does not Granger Cause LNEMAS 4.56083 0.03289**
LNEMAS does not Granger Cause LNHIJRAH 3.80930 0.05117
LNHIJRAH does not Granger Cause LNEMAS 2.84118 0.09210
FBM KLCI
FBM Hijrah Index
FBM Emas
Shariah Index
Note : ** 5% sig level
In the short run granger causality, the statistics show that FBM KLCI
causes FBM Hijrah Index and FBM Emas
Shariah Index
Unidirectional SR Causality
The Findings - 2 Volatility Comparison
-10
-8
-6
-4
-2
0
2
4
6
2007 2008 2009 2010 2011 2012
RCOMPOSITE
-12
-8
-4
0
4
8
2007 2008 2009 2010 2011 2012
RHIJRAH
-12
-8
-4
0
4
8
2007 2008 2009 2010 2011 2012
REMAS
FBM KLCI (Returns) FBM Hijrah Index (Return)
FBM Emas Shariah Index
(Return)
Mean 0.018999 0.025062 0.018516
Median 0.053322 0.057426 0.056589
Maximum 4.350636 4.641286 4.158901
Minimum -9.496810 -10.49478 -10.70320
Std. Dev. 0.863988 0.946926 0.906991
Skewness -1.080658 -1.069366 -1.385491
Kurtosis 16.04194 17.10215 19.64100
Observation 1376 1376 1376
ARCH Test:
F-statistic 37.57417 Prob. F(1,1325) 0.000000
Obs*R-squared 36.59319 Prob. Chi-Square(1) 0.000000
ARCH Test:
F-statistic 43.13461 Prob. F(1,1325) 0.000000
Obs*R-squared 41.83772 Prob. Chi-Square(1) 0.000000
ARCH Test:
F-statistic 36.59806 Prob. F(1,1325) 0.000000
Obs*R-squared 35.66811 Prob. Chi-Square(1) 0.000000
A Simple AR (1) Model and Testing for ARCH (1) effect for FBM Emas Shariah Index
A Simple AR (1) Model and Testing for ARCH (1) effect for FBM Hijrah Index
A Simple AR (1) Model and Testing for ARCH (1) effect for FBM KLCI
Obs*R-Square > 0.05
There is Heteroscedacity in
the variables
ARCH Test:
F-statistic 11.83546 Prob. F(6,1362) 0.000000
Obs*R-squared 67.84064 Prob. Chi-Square(6) 0.000000
ARCH Test:
F-statistic 10.87894 Prob. F(6,1362) 0.000000
Obs*R-squared 62.60862 Prob. Chi-Square(6) 0.000000
ARCH Test:
F-statistic 8.530979 Prob. F(6,1362) 0.000000
Obs*R-squared 49.58545 Prob. Chi-Square(6) 0.000000
Testing for ARCH (6) effects on FBM Emas Shariah Index
Testing for ARCH (6) effects on FBM Hijrah Index
Testing for ARCH (6) effects on FBM KLCI
Obs*R-Square > 0.05
There is Heteroscedacity in
the variables
Variable C ARCH
(𝜶)
GARCH
(𝜷) ( 𝜶 + 𝜷)
FBM KLCI 0.010709 0.13918 0.855041 0.994221
FBM Hijrah Index 0.007442 0.101616 0.895314 0.99693
FBM Emas Shariah Index 0.011712 0.147977 0.85006 0.998037
All variables have high volatility and FBM Emas Shariah Index has the highest volatility
Conclusion
No Cointegration & There is
unidirectional SR Causality
1st Objective 2nd Objective
Islamic stock market is more volatile compare to its
counterpart
Conclusion
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