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VOLATILITY OF JAKARTA ISLAMIC INDEX

Hendri Tanjung1

 
Abstract: Volatility of Jakarta Islamic Index. This study investigates the 
volatility of Jakarta Islamic Index (JII) in Jakarta Stock Exchange.  The method 
that used in this research is used a simple statistical analysis. The normality 
of JII return is analyzed to answer whether the return of JII follows normal 
distribution.  By using data of Jakarta Islamic Index from 2nd March 2009 
to 30th October 2013 (1122 daily data), it is found that the distribution of 
return of JII is not normal, even the 5 sigma occurred.  This means the return 
of Jakarta Islamic Index is much volatile than the theory predicted.  This will 
make too much gain or loss in one day in the economy.  

Keywords: Volatility, normal distribution, Jakarta Islamic Index

Abstrak: Volatilitas Jakarta Islamic Indeks. Penelitian ini melakukan 
investigasi terhadap volatilitas Jakarta Islamic Index (JII) pada Jakarta Stock 
Exchange. Teknik analisis yang dipergunakan dalam penelitian ini hanya 
analisis statistic sederhana. Kenormalan distribusi dari tingkat return pada 
JII dianalisis untuk menjawab apakah return nya menyebar normal atau 
tidak.  Dengan menggunakan data Jakarta Islamic Index dari  2 Maret  2009  
sampai 30 Oktober 2013 (1122 data harian), ditemukan bahwa distribusi dari 
return JII tidak menyebar normal, bahkan 5 sigma terjadi.   Penelitian ini 
menyimpulkan  bahwa return dari Jakarta Islamic Index sangat berfluktuasi, 
melebihi apa yang teori jelaskan.  Implikasinya adalah, akan diperoleh 
keutungan yang sangat tinggi dan kerugian yang sangat besar pada suatu hari, 
dalam ekonomi.

Kata Kunci: Volatilitas, Distribusi Normal, Jakarta Islamic Indeks

 1 Diterima: 15 Maret 2014, direvisi: 15 April 2014, disetujui: 28 April 2014
Universitas Ibn Khaldun, Jl. Sholeh Iskandar, Km. 2, Bogor. Email: hendri.tanjung@gmail.com



Al-Iqtishad: Vol. VI No. 2, Juli 2014 208

INTRODUCTION

There is a book title “When Genius Failed” by Roger Lowenstein (2001) 
that tells the story of two prominent economists. One of them is the Nobel Prize 
winner for his contributions in the field of financial econometrics. They both ran 
hedge funds company which is named ‘Long Term Capital Management’ (LTCM).   
All people were vying to invest money through the LTCM because of the one of 
founding fathers was a Nobel Prize winner. They believed that the company would 
not collapse.  How could LTCM collapsed while his partner is a champion in that 
field? That was the belief in the mind of the investors at the time.

LTCM used highly sophisticated calculations by assuming that the return has 
a normal distribution. With this calculation, investors could calculate the probability 
to get certain benefits. For four years, LTCM showed very good performance with 
high margins. All investors were stunned. However, one day, after four years dazzling 
Wall Street as a huge moneymaker 100 billion U.S. dollars, everything was over.

Because LTCM managed huge money, so when they collapsed, the stock 
market collapsed as well. To avoid the breakdown of all aspects of the economy 
due to loss of capital markets, the U.S. Federal Reserve had stepped in to help by 
pumping money into LTCM.

As a response to this event, Lowenstein writes:
On account of a crisis at LTCM, Mc Donnough had summoned—“invited”, in 
the Fed restrained idiom—The head of every major Wall Street Bank.  For the 
first time, The chiefs of Bankers trust, Bear Stearns, Chase Manhattan, Goldman 
Sachs, J.P. Morgan, Lehman Brothers, Merrill Lynch, Morgan Stanley Dean 
Witter, and Salomon Smith Barney, gathered under the oil portraits in the Fed’s 
tenth floor boardroom—not to bail out a Latin American Nation but to consider 
a rescue of one of their own.

The question that arises is of course, why did these two geniuses fail? Is it true 
as they assumed that the return of stock market follows the normal distribution? The 
problem statements are: (1) Is the distribution of return on the JII normal or not?; 
(2) What is the volatility of the returns of JII? 

The purpose of this study are: (1) To analyze whether the distribution of 
return on the JII normal or not. (2) To show the volatility of the returns of Jakarta 
Islamic Index (JII). At the end of this study, there will be recommendation regarding 
the findings.  

LITERATURE REVIEW

There was at least fifteen historical episodes of stock market crashes in the 



Hendri Tanjung: Volatility of Jakarta Islamic Index  209

United States over the last one hundred years, and Mishkin and White (2002) argue 
that financial instability is the key problem facing monetary policy makers.  Some 
authors showed that instability of capital market is a common phenomenon found 
everywhere.  Igbatayo (2011) found that instability in financial market (banks and 
capital market) occured in Nigeria.  In the capital market, equity prices in the past 
couple of years, have fallen sharply, with the All-share index at the Nigerian Stock 
Exchange down by 33 percent at the end of December 2009, from levels recorded 
in December, 2008.  Butt (2010) found the instability of return of stock market in 
Pakistan.  He argued there is relationship between risk and return in Karachi Stock 
Exchange 100 index.  

Edwards, Biscarri and de Gracia (2003) analyzed the cycles of the stock 
markets in four Latin American (Argentina, Brazil, Chile and Mexico) and two Asian 
countries (South Korea and Thailand), and compared their characteristics. They found 
that cycles in emerging countries tend to have shorter duration, larger amplitude 
and volatility than in developed countries. Some authors related the stock market 
with the political development and economic growth.  Asteriou and Siriopoulos 
(2003) examined empirically the relationship between stock market development, 
political instability and economic growth in Greece.  Using time series data, the 
results indicated that there was a strong negative relationship between uncertain 
socio-political conditions and the general index of Athens Stock Exchange (ASE) 
and supported the theoretical hypothesis that uncertain socio-political conditions 
affect economic growth negatively, is true for Greek case.  Konzelmann et.al.(2010) 
argued that at the heart of the crisis was a breakdown in governance that has it roots 
in the co-evolution of political and economic developments and of economic theory 
and policy since 1929 stock market crash  and the Great Depression that followed.  

Some authors argued that the cause of volatility in stock market is not 
political uncertainty, but other factors.  Voth (2001) found that the majority of 
events increasing political uncertainty had little or no effect on the value of German 
assets and the volatility of returns on them.  Instead, it was inflation (and the fear 
of it) that is largely responsible for most of the variability in asset returns.  Panetta 
(2002) argued that the relation between stock returns and the macroeconomic 
factors was found to be unstable in Italia. Miller and Ratti (2009) found negative 
relationship between the world price of crude oil and international stock markets in 
the last decade.  This may suggested the presence of several stock market bubbles.    

Yusof and Madjid (2007) found that interest rate volatility affected the 
conventional stock market volatility but not the Islamic stock market volatility in 
Malaysia. This highlights the tenet of Islamic principles that the interest rate is not 
a significant variable in explaining stock market volatility. Their finding provides 



Al-Iqtishad: Vol. VI No. 2, Juli 2014 210

further support that stabilizing interest rate would have insignificant impact on the 
volatility of the Islamic stock markets.

There are at least eight models to explain the volatility in stock market.  
They are overreaction model, adverse impact of related laws, increasing linkages 
model, transmission of volatility model, adverse impact of derivatives’ model, 
adverse impact of related markets’ model, impact of volume volatility model, and 
econophysics’ model of stock market crises.  Shabri (2002) explored the causes of 
stock market prices and high price volatility in several countries.  He found that 
very high volatility which indicated by more than 50% change occured in several 
countries, i.e. Amsterdam, Athens, Belgia, Italia, Spain, Swiss, Mexiko, Argentina, 
Korea, malaysia, Thailand and Turkey.  

We may summarize that volatility of stock market occured everywhere in the 
world.  Some researchers tried to explain the roots, the causes and the relations of 
stock market volatility with some other factors.  In this study, we focus on analyzing 
the magnitude of  the volatility of Jakarta Islamic Index and the consequence of that 
into economy.  

METHODS

The data are collected from Jakarta Stock Exchange (JSX), Indonesia.  The 
data are the return of Jakarta Islamic Index (JII) from  2nd march 2009 to 30th 
september 2013.  This study analyzes 1122 daily data of return of JII.

This study does not use sophisticated model to explain the volatility of 
return of stock market  such as overreaction model, adverse impact of related 
laws, increasing linkages model, transmission of volatility model, adverse impact 
of derivatives’ model, adverse impact of related markets’ model, impact of volume 
volatility model, and econophysics’ model of stock market crises.  Instead, this study 
uses simple statistical analysis, i.e., Time series Plot to see the fluctuation of data, 
Histogram to see the pattern of distribution, and QQ plot to analyze the normality 
of return of Jakarta Islamic Index (JII) data.  QQ plot is a probability plot, which 
is a graphical method for comparing two probability distributions by plotting their 
quantiles againts each other. 

 
DISCUSSION

It is important to see the daily data of Jakarta Islamic Index (JII) in Jakarta 
Stock Exchange (JSX) and try to find out if the return is normal or not.  People 
think that it is normal, even though really it is not.  



Hendri Tanjung: Volatility of Jakarta Islamic Index  211

First of all, the time series plot of the return for all data will be seen.  The data 
consist of 1122 observations, from 2nd March 2009 to 30th September 2013.  The 
time series plot can be seen in Figure 1.

  

112010088967846725604483362241121

10,00%

5,00%

0,00%

-5,00%

-10,00%

Index

re
tu

rn

Time Series Plot of return

Figure 1. Time Series Plot of JII return from 2nd March 2009 
to 30th September 2013 (1122 data)

From figure 1, it could be seen that minimum return is almost -10% and 
maximum is almost 10%.  This shows that in one day, there is probability to gain 
almost 10 percent and to loss almost 10 percent.  If we see the data comprehensively, 
we could split the data into four parts based on its volatility.  The first part is from 1st 
observation to 300th observation (300 data).  The second part is from 301th to 600th 
observation (300 data).  The third part is from 601th to 900th observation (300 data) 
and the last part is from 901th to 1122th observation (222 data).   In the next sub-
chapter, we will discuss the first part, the first 2 parts, the first 3 parts, and all parts 
separately.  But before analysis of the four parts be done, it is important to check for 
100 observations which is approximately 4 months observations.
100 observations

Consider 100 observations of Jakarta Islamic Index (JII).  Those are data 
from 2nd   March 2009 to  28th July 2009 (see Appendix).  Every day the price of 
stock fluctuates.  

Every minutes the price changes.  On 2nd March 2009, the maximum price is 
214.042, the minimum price is 208.330 and at the end of the day, the price of these 
stock is 209.418 (closing price).     

There is 0.26 % gain from 2nd to 3rd March 2009.  0.26% is obtained by 
dividing closing price on 3rd March which is 209.959 with closing price on 2nd 



Al-Iqtishad: Vol. VI No. 2, Juli 2014 212

March 2009 which is 209.418.  The result is 100.26%.  This means 0.26% increase 
within one day and it is gain.  If we had stocks on 2nd March and we sell it on 3rd 
March (one day later), we will gain 0.26%.  On the other hand,   there is 1.67 % loss 
from 16th to 17th March 2009.  1.67% is obtained by dividing closing price on 17th 
March which is 215.049 with closing price on 16th March 2009 which is 218.707.  
The result is 98.33%.  This means 1.67% decrease within one day and it is loss.  

To assess whether those 100 observations follow normal distribution, Q-Q 
Plot is used.  Actually, people usually assess normality by using 68%, 95% and 
99,7% rules.  If data follows normal distribution; by using 68% rule we could say 
that 68% of the data between -1 std and +1 std, by using 95% rule we could say 
that 95% of the data between -2 std and +2 std and by using 99,7% rule we could 
say that 99,7% of the data between -3 std and +3 std .  Std stands for Standard 
Deviation.  This rule is just checking normality with 3 points, but with Q-Q plot, 
we assess all points of the data.  It is just a slight generalization.  

First of all, we look at the histogram of the returns. The result is like Figure 2.

6,00%4,00%2,00%0,00%-2,00%-4,00%

20

15

10

5

0

return 100

Fr
eq

ue
nc

y

Mean 0,005839
StDev 0,02065
N 100

Histogram of return 100
Normal 

Figure 2. Histogram of return of JII (100 observations) from 2nd march to 28th July 2009

In figure 2, that the distribution is likely normal.  Next, Q-Q plot is used to 
assess the normality of the data.  The ‘Q’ in Q-Q plot stands for Quintile-Quintile.  
Quintile is also called percentile.  The Q-Q plot of this data is as Figure 3.  



Hendri Tanjung: Volatility of Jakarta Islamic Index  213

10,00%5,00%0,00%-5,00%

99,9

99

95
90

80
70
60
50
40
30
20

10

5

1

0,1

return 100

Pe
rc

en
t

Mean 0,005839
StDev 0,02065
N 100
AD 0,223
P-Value 0,822

Probability Plot of return 100
Normal - 95% CI

Figure 3.  Probability plot of JII Return (100 observations) 
from 2nd march to 28th July 2009

Figure 3 show that the data seems normal distribution.  There are only two 
observations outside the limit line.  The data almost forms 45 degree line.  Anderson 
Darling test is 0.223 with p value 0.822.  This means that null hypothesis which 
mentions that the distribution is normal could not be rejected.  This shows that the 
distribution of return of JII for 100 data is normal.  
The first year data (300 Observations)

100 observations are only 4 months data.  Now, we want to know the 
distribution of one year data, which is approximately 300 observations. Time series 
plot for 300 observations can be seen at Figure 4.

3002702402101801501209060301

10,00%

7,50%

5,00%

2,50%

0,00%

-2,50%

-5,00%

Index

re
tu

rn
 3

00

Time Series Plot of return 300

Figure 4. Time Series Plot of JII Return (300 Observations) 
from 2 March 2009 to 26 May 2010



Al-Iqtishad: Vol. VI No. 2, Juli 2014 214

Figure 4 explains that most of the volatility of return vary from -5% to 
5%, although the gain/loss of some observations are bigger than 5%.  To see the 
distribution, histogram is performed.  The histogram of 300 observations is captured 
as Figure 5.
Figure 5 shows that the return of 300 observations is slightly not normal.  We can 
see that 5 sigma occurs.  5 sigma is 8.94%.  This is calculated by multiplying 5 with 
standard deviation (0,01788).  The gain more than 8 persen happens in the period 
of one year from 2nd march 2009 to 26th May 2010.  The gain from 25th to 26th may 
2010 is 9,15%.  This is the evidence that the distribution slightly not normal.  If 
the data is normal, the probability of 5 sigma is 3 out of 10,000,000.  But in this 
data, the probability is 1 out of 300.  To investigate the normality of distribution, 
Anderson Darling test and QQ Plot are used.  Figure 6 shows the result.  

8,00%6,00%4,00%2,00%0,00%-2,00%-4,00%

90

80

70

60

50

40

30

20

10

0

return 300

Fr
eq

ue
nc

y

Mean 0,002540
StDev 0,01788
N 300

Histogram of return 300
Normal 

Figure 5. Histogram of return of JII (300 observations) 
from 2 march 2009 to 26 May 2010

From the figure 6, it could be concluded that the distribution of 300 
observations is not normal.  Anderson darling test is 1,369 with P-Value < 0.005.  
This means that the null hypothesis is rejected.  The null hypothesis is the distribution 
is normal.  When the null hypothesis is rejected, it means that the distribution is 
not normal.  

Now, The result show normality in small sample (100 observations) and non 
normality in larger sample (300 observations).  This confirms that the volatility in 
large sample is bigger than small sample.  



Hendri Tanjung: Volatility of Jakarta Islamic Index  215

10,00%7,50%5,00%2,50%0,00%-2,50%-5,00%

99,9

99

95
90

80
70
60
50
40
30
20

10

5

1

0,1

return 300

Pe
rc

en
t

Mean 0,002540
StDev 0,01788
N 300
AD 1,369
P-Value <0,005

Probability Plot of return 300
Normal - 95% CI

Figure 6. Probability Plot of return of JII (300 observations) 
from 2nd march 2009 to 26th May 2010

The first 2 years data (600 observations)

300 observations are only one year data.  Now, we want to know the 
distribution of two years data, which are approximately 600 observations. Time 
series plot for 600 observations can be seen at Figure 7.

600540480420360300240180120601

10,00%

7,50%

5,00%

2,50%

0,00%

-2,50%

-5,00%

Index

re
tu

rn
 6

00

Time Series Plot of return 600

Figure 7.  Time Series Plot of JII Return (600 observations) from 2nd march 2009 
to 12th august 2011

Figure 7 explains the volatility of return vary from -5% to 5%, although the 
gain/loss of some observations are bigger than 5%.  To see the distribution, histogram 
is performed.  The histogram of 600 observations is captured in figure 8.



Al-Iqtishad: Vol. VI No. 2, Juli 2014 216

8,00%6,00%4,00%2,00%0,00%-2,00%-4,00%

120

100

80

60

40

20

0

return 600

Fr
eq

ue
nc

y

Mean 0,001688
StDev 0,01549
N 600

Histogram of return 600
Normal 

Figure 8. Histogram of JII return (600 observations) from 2nd march 2009 
to 12th  August 2011

Figure 8 shows that the return of 600 observations is not normal.  Even, 
that 6 sigma occurs.  6 sigma is 9.29%.  This is calculated by multiplying 6 with 
standard deviation (0,01549).  The gain more than 9 persen happens in the period 
of two year from 2nd march 2009, i.e. at 26th May 2010. This is the evidence that the 
distribution is not normal.  If the data is normal, the probability of 6 sigma is 1 out 
of 1,000,000,000.  But in this data, the probability is 1 out of 300. To investigate 
the normality of distribution, Anderson Darling test and QQ Plot are used.  Figure 
9 shows the result.  

10,00%7,50%5,00%2,50%0,00%-2,50%-5,00%

99,99

99

95

80

50

20

5

1

0,01

return 600

Pe
rc

en
t

Mean 0,001688
StDev 0,01549
N 600
AD 3,506
P-Value <0,005

Probability Plot of return 600
Normal - 95% CI

Figure 9. Probability Plot of JII return (600 observations) from 2nd March 2009 
to 12th August 2011



Hendri Tanjung: Volatility of Jakarta Islamic Index  217

From figure 9 it could be concluded that the distribution of 600 observations 
is not normal.  Anderson Darling test is 3,506 with P-Value < 0.005.  This means 
that the null hypothesis is rejected.  The null hypothesis is the distribution is normal.  
When the null hypothesis is rejected, it means that the distribution is not normal.  

Now, the result shows normality in small sample (100 observations) and non 
normality in larger sample (300 observations and 600 observations).  This confirms 
that the volatility in large sample is bigger than small sample.  

The first 3 years data

600 observations are only two year data.  Now, we want to know the 
distribution of three years data, which is approximately 900 observations. Time 
series plot for 900 observations can be seen at Figure 10.

900810720630540450360270180901

10,00%

5,00%

0,00%

-5,00%

-10,00%

Index

re
tu

rn
 9

00

Time Series Plot of return 900

Figure 10. Time Series Plot of JII Return (900 observations) 
from 2nd March 2009 to 30th October 2012

Figure 10 explains most of the volatility of return vary from -5% to 5%, 
although the gain/loss of some observations are bigger than 5%.  To see the 
distribution, histogram is performed.  The histogram of 900 observations is captured 
in figure 11.



Al-Iqtishad: Vol. VI No. 2, Juli 2014 218

7,50%5,00%2,50%0,00%-2,50%-5,00%-7,50%

160

140

120

100

80

60

40

20

0

return 900

Fr
eq

ue
nc

y

Mean 0,001321
StDev 0,01522
N 900

Histogram of return 900
Normal 

Figure 11. Histogram of JII Return (900 observations) 
from 2nd March 2009 to 30th October 2012

Figure 11 shows that the return of 900 observations is not normal.  Even, 
that 6 sigma occurs.  6 sigma is 9.13%.  This is calculated by multiplying 6 with 
standard deviation (0,01522).  The gain more than 9.13% happens in the period of 
three year from 2nd march 2009, i.e. at 26th may 2010.  This is the evidence that the 
distribution is not normal.  If the data is normal, the probability of 6 sigma is 1 out 
of 1,000,000,000.  But in this data, the probability is 1 out of 900. To investigate 
the normality of distribution, Anderson Darling (AD) test and QQ Plot are used.  
Figure12 shows the result.  

10,00%5,00%0,00%-5,00%-10,00%

99,99

99

95

80

50

20

5

1

0,01

return 900

Pe
rc

en
t

Mean 0,001321
StDev 0,01522
N 900
AD 5,754
P-Value <0,005

Probability Plot of return 900
Normal - 95% CI

Figure 12. Probability Plot of JII Return (900 observations) 
from 2nd March 2009 to 30th October 2012



Hendri Tanjung: Volatility of Jakarta Islamic Index  219

From figure 12 it could be concluded that the distribution of 900 observations 
is not normal.  Anderson Darling (AD) test is 5,754 with P-Value < 0.005.  This 
means that the null hypothesis is rejected.  The null hypothesis is the distribution is 
normal.  When the null hypothesis is rejected, it means that the distribution is not 
normal.  

Now, the result shows normality in small sample (100 observations) and 
non normality in larger sample (300 observations, 600 observations and 900 
observations).  This confirms that the volatility in large sample is bigger than small 
sample.  
All data (1122 Observations)

900 observations are only three year data.  Now, we want to know the 
distribution of all data, which are 1122 observations. Time series plot for 1122 
observations can be seen at Figure 1.

Figure 1 explains the volatility of return vary from -5% to 5%, although 
the gain/loss of some observations are bigger than 5%.  To see the distribution, 
histogram is performed.  The histogram of all observations is captured as figure 13.

7,50%5,00%2,50%0,00%-2,50%-5,00%-7,50%

200

150

100

50

0

return

Fr
eq

ue
nc

y

Mean 0,001037
StDev 0,01552
N 1122

Histogram of return
Normal 

Figure 13. Histogram of JII return (1122 observations) from 2nd 
March 2009 to 30th Sept 2013

Figure 13 shows that the return of all observations is not normal.  In this case, 
5 sigma occurs.  5 sigma is 7.76%.  This is calculated by multiplying 5 with standard 
deviation (0,01552).  The gain more than 7,76% persen happens in the period of 
three and half year from 2nd march 2009, i.e., at 26th May 2010.  Even, 6 sigma 
occurs in period of all data.  6 sigma is 9.31%.  This is calculated by multiplying 6 
with standard deviation (0,01552).  The gain more than 9.31% persen happens in 



Al-Iqtishad: Vol. VI No. 2, Juli 2014 220

the period of three and half year from 2nd march 2009, i.e., at 22nd september 2011 
which bear loss 9,43% .  This is the evidence that the distribution is not normal.  To 
investigate the normality of distribution, Anderson Darling (AD) test and QQ Plot 
are used.  Figure 14 shows the result.  

From figure 14 it could be concluded that the distribution of all observations 
is not normal.  Anderson Darling (AD) test is 8,538 with P-Value < 0.005.  This 
means that the null hypothesis is rejected.  The null hypothesis is the distribution is 
normal.  When the null hypothesis is rejected, it means that the distribution is not 
normal.  

Now, the result shows normality in small sample (100 observations) and non 
normality in larger sample (300 observations, 600 observations, 900 observations 
and 1122 observations).  This confirms that the volatility in large sample is bigger 
than small sample.  

10,00%5,00%0,00%-5,00%-10,00%

99,99

99

95

80

50

20

5

1

0,01

return

Pe
rc

en
t

Mean 0,001037
StDev 0,01552
N 1122
AD 8,538
P-Value <0,005

Probability Plot of return
Normal - 95% CI

Figure 14.  Probability Plot of JII Return (1122 observations) from 2nd 
March 2009 to 30th Sept 2013

CONCLUSION

The data of small sample which is 100 observations shows that the distribution 
of the return of Jakarta Islamic Index is normal. The data of larger sample which is 
300 observations shows that the distribution of the return of Jakarta Islamic Index is 
not normal, even 5 sigmas occur. The data of larger sample which is 600 observations 
shows that the distribution of the return of Jakarta Islamic Index is not normal, even 
6 sigmas occur.



Hendri Tanjung: Volatility of Jakarta Islamic Index  221

The data of larger sample which is 900 observations shows that the distribution 
of the return of Jakarta Islamic Index is not normal, even 6 sigmas occur. The data of 
larger sample which is 1122 observations shows that the distribution of the return 
of Jakarta Islamic Index is not normal, even 6 sigmas occur. The volatility of Jakarta 
Islamic Index varies from time to time. The volatility of larger sample is bigger than 
small sample.  

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