International Journal of Islamic Economics and Finance (IJIEF)
Vol. 6(1), January 2023, pages 105-132
Indonesian Stocks’ Volatility during COVID-19 Waves: Comparison
between IHSG and ISSI
Muhammad Syauqy Alghifary1, Dzuliyati Kadji2, Iffah Hafizah3
Corresponding email: alghifary.oqy@student.ub.ac.id
Article History
Received: May 30th, 2022
Revised: October 3rd, 2022
November 14th, 2022
Accepted: December 9th, 2022
Abstract
This study aims to compare Islamic and conventional stocks’ performance amid a crisis.
The performance was measured by analyzing the volatility of the Indonesian Sharia
Stock Index (ISSI) and the Composite Stock Price Index (IHSG) during the COVID-19
pandemic. Based on the results of the different tests using the paired t-test and
Wilcoxon rank test methods, it was uncovered that the ISSI and IHSG experienced
significant changes before and after discovering the first case of COVID-19 in Indonesia.
Significant changes in both values were also found when the Delta variance spread.
Meanwhile, when the third wave occurred due to the presence of the Omicron variant,
ISSI and IHSG could move more stable and did not experience significant shocks. Then,
the estimation results of the GARCH model conclude that both Islamic and conventional
stocks have an immense volatility power with an identical value of 0.94 or close to 1.
The volatility is also significantly influenced by the previous volatility and the squared
error, representing other previous events outside the model. Moreover, the volatility in
Islamic and conventional stocks is not much different, even though both stocks have
different characters in the debt and income ratio. Fundamental factors also cause this
high volatility in the form of shocks in several macroeconomic variables, including the
rupiah exchange rate, gold prices, and world oil prices. Besides, the contagion effect that
occurred during the COVID-19 crisis also contributed to the spread of systemic risk in
global stock indexes on stock volatility in Indonesia.
Keywords: Islamic Stocks, Conventional Stocks, Volatility, COVID-19 Pandemic, GARCH
Model
JEL Classification: E22, E44, G11, G17
Type of paper: Research Paper
@ IJIEF 2023 published by Universitas Muhammadiyah Yogyakarta, Indonesia
DOI:
https://doi.org/10.18196/ijief.v6i1.14838
Web:
https://journal.umy.ac.id/index.php/ijief/article/view/14838
Citation:
Alghifary, M. S., Kadji, D., & Hafizah, I. (2023). Indonesian stocks’ Volatility during COVID-19 waves:
Comparison between IHSG and ISSI. International Journal of Islamic Economics and Finance (IJIEF),
6(1), 105-132. DOI: https://doi.org/10.18196/ijief.v6i1.14838.
1,2 Brawijaya University, Indonesia
3 Hasanudin University, Indonesia
mailto:alghifary.oqy@student.ub.ac.id
https://doi.org/10.18196/ijief.v6i1.
https://doi.org/10.18196/ijief.v6i1.
https://crossmark.crossref.org/dialog/?doi=10.18196/ijief.v6i1.14838&domain=pdf
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 98
I. Introduction
The growth of stock investors has experienced a rapidly increasing trend over
the last five years. As of September 2021, the number of stock investors has
reached 2.8 million SID (Single Investor Identification) number. This amount
covers 45.26% of the total capital market investors with a SID registered in the
Indonesian Central Securities Depository (KSEI). Compared to the previous
year, the number of stock investors throughout 2021 has grown by 72.69%.
This number is greater than the total growth of investors in the capital market,
which reached 61.86% (Puspitasari, 2021).
Figure 1. Number of Stock Investors on the Indonesia Stock Exchange
Source: Financial Services Authority (OJK) (2021)
Interestingly, Indonesia's stock investment trend experienced rapid growth
during the COVID-19 pandemic. Based on Figure 1, the number of stock
investors more than doubled during the pandemic compared to the previous
1.08 million SID in 2019. In this regard, millennials are the most dominating
age group, with 59.23% of the total investors in the capital market (Sidik,
2021). Based on a survey conducted by the Katadata Insight Center of 806
stock investors, 41.3% of millennials stated that they had just started investing
in stock in the last two years, particularly when the COVID-19 pandemic case
was first identified in Indonesia (Siringoringo, 2021).
However, it is undeniable that the presence of COVID-19 has also affected the
Indonesian economy since it first appeared in March 2020. Not to mention the
capital market, the pandemic crisis caused the Composite Stock Price Index
(IHSG) to reach its lowest point of decline in the last decade. The incident
occurred on March 24, 2020, when the IHSG value fell 37% from the beginning
of the year to 3,937. A drastic reduction followed this decline in stock market
capitalization reaching IDR 1,907 trillion (Tamara, 2020). The impact of the
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COVID-19 pandemic has also caused Indonesia to fall into a recession in the
third quarter of 2020 due to negative economic growth for two consecutive
quarters. The second quarter recorded economic growth of -5.32%, the lowest
growth rate since 1998. Meanwhile, the third-quarter economic growth
reached -3.49% (Fauzia, 2020). The negative economic growth extended until
the first quarter of 2021, at the level of -0.74%.
The crisis consequently required the Indonesian government to adjust
macroeconomic policies to maintain domestic economic stability. One of the
implemented policies is to change the benchmark interest rate set by the
central bank. Figure 2 shows that Bank Indonesia has made six changes to the
benchmark interest rate or BI 7-Day Reverse Repo Rate during the last two
years, from the initial 5.0% in early 2020 to 3.5% in February 2021. This level
is the lowest in the history of applying the benchmark interest rate in
monetary policy (Elena, 2021).
Figure 2. The Trend of BI 7-Day Reverse Repo Rate
Source: Bank Indonesia (2021)
Changes in these macroeconomic variables certainly impact capital market
development since one of the macroeconomic variables affecting investment
is interest rates (Mankiw, 2009). Therefore, changes in the benchmark interest
rate set by the central bank will determine the public interest in investing,
including in the capital market.
Apparently, several researchers have attempted to investigate the impacts of
the COVID-19 pandemic crisis on stock markets from varied points of view.
From the Islamic stock market point of view, the evaluation of the effect of
COVID-19 on Islamic stock markets is crucial for several reasons. First, during
the last decade, the Islamic finance industry has recorded tremendous growth,
which is anticipated to reach 8% average yearly growth by 2025 to $4.95
trillion (Adil, 2022). Second, in recent times, the attractive risk-return
characteristics and ethical issues of Islamic products tend to motivate non-
Muslim investors, particularly ethical investors, to choose Islamic products for
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Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 100
their portfolios. Third, the Islamic stock markets have empirically shown their
outperformance over their conventional counterparts, especially in times of
crisis, even though the portfolio size of the Islamic stock markets is smaller
than the conventional (Al-Khazali et al., 2014).
In this paper, from a portfolio performance perspective, the researchers
extended the current literature to examine whether returns earned by
investors tracking the ISSI (Indonesian Sharia Stock Index) were significantly
different from those of the IHSG (Composite Stock Price Index). Thus, the
researchers conducted a comparative study regarding the volatilities of ISSI
and IHSG during this pandemic crisis to provide a better avenue for investors
to diversify their portfolios by considering the volatility of both stocks.
Several previous studies have analyzed comparing Islamic and conventional
stocks’ performance amid a crisis. Siregar (2020) compared the performance
of LQ45 with JII (Jakarta Islamic Index) at the beginning of the spread of COVID-
19 cases and explained that LQ45 experienced a 1.22% decline in stock prices
on average, while JII, on average, experienced an increase of 0.14%. Globally,
Al-Khazali et al. (2014) found that the Dow Jones sharia index performed
better than the conventional index during the global economic crisis. For this
reason, the current study aims to fill the gap in previous research by using
inferential statistical analysis to compare the performance of the ISSI
(Indonesian Sharia Stock Index) and the IHSG during the COVID-19 pandemic.
It is based on the recommendation of Nurdany et al. (2021), which only
examined ISSI's volatility during the COVID-19 pandemic with GARCH analysis.
Following the problems described, this study aims to compare the ISSI values
before and after the COVID-19 virus spread, compare the IHSG values before
and after the COVID-19 virus spread, and measure the ISSI and volatility IHSG
during the COVID-19 pandemic. This study further attempts to verify the stock
index endurance during the pandemic and indicates that the stock market
volatility may last if the crisis is not over. In addition, fundamental factors also
cause this high volatility in the form of shocks in several macroeconomic
variables, including the rupiah exchange rate, gold prices, and world oil prices.
The results of this research are expected to benefit various interested parties
in the capital market industry in Indonesia. For capital market regulators, this
research can become a reference for evaluating policies implemented to
develop the stock market and sustain macroeconomic stability. The results of
this study can also be deemed to determine the strategic steps to maintain
stock price stability during an economic crisis for stock issuing companies. As
for investors, the research findings can help predict stock prices in the future,
especially in times of crisis, by considering the volatility when choosing
investment products since the stocks have experienced high volatility
throughout the pandemic.
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 101
II. Literature Review
2.1. Volatility
Volatility is a statistical measure used to gauge the movement and distribution
of a security product or market index prices at a specific time (Hayes, 2021).
The greater the volatility of the value of an asset, the greater the risk of
investing in it (Nugroho & Robiyanto, 2021). In this regard, price fluctuations
that occur in a short period have high volatility, whereas if the price movement
is slow, the volatility is low (Mamtha & Srinivasan, 2016). In other words,
volatility can be an indicator to assess financial market stability. Volatility, in
general, can be measured by calculating the variance or standard deviation of
the data set of price movements of an asset.
According to Thampanya et al. (2020), stock price volatility is broadly
influenced by two determinants: fundamental and behavioral factors.
Fundamental factors are derived from conventional financial theory, assuming
that investors follow fundamental financial theories and design investment
strategies based on risk and profit calculations. Meanwhile, behavioral factors
emphasize that investors are ordinary people easily influenced by sentiment
and psychological conditions, so investment decisions are made more based
on good or bad news circulating.
Fundamental factors also consist of indicators that can be measured clearly
and unbiased, such as macroeconomic variables, including inflation rates,
interest rates, exchange rates, and GDP (Francis & Soffer, 1997), as well as
company financial ratios such as ROA (Return on Assets), ROE (Return on
Equity), and cash flow (Chang & Dong, 2006). On the other hand, several
studies have also proven that behavioral factors determine stock volatility
driven by investor sentiment based on their beliefs about future conditions
(Baker & Wurgler, 2007). Based on the theory of capital market behavior,
investors will buy more shares, and asset prices will be pushed above their fair
value when bullish sentiment dominates the market. Meanwhile, when
bearish sentiment dominates, investors will sell or hold their shares, so prices
are dragged below the fundamental value (Shefrin & Statman, 1994).
2.2. Previous Studies
Numerous studies have been conducted on the impacts of the economic crisis
on both conventional and Islamic stocks. Chebbi et al. (2021) researched the
stock liquidity conditions of S&P 500 index companies during the COVID-19
pandemic-induced economic crisis. The S&P 500 is a collection of the 500
largest publicly traded companies in the United States by market
capitalization. The study's findings demonstrated a significant negative
correlation between COVID-19 cases and company liquidity. Those indicate
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 102
that the company's liquidity would decrease if daily COVID-19 cases increased.
Moreover, Li et al. (2021) comprehensively analyzed the relationship between
the COVID-19 pandemic and the stock market in G20 member countries. They
concluded that the volatility linkage between stock markets in G20 member
countries increased significantly during the COVID-19 crisis. This volatility
linkage was primarily transmitted by developed country stock markets,
affecting developing country stock markets.
Aside from the crisis phenomenon during the COVID-19 pandemic, there have
previously been numerous studies analyzing stock market conditions during
the economic crisis. Dang & Nguyen (2020) analyzed the relationship between
liquidity risk and stock performance during the 2008-2009 global financial
crisis from 17,493 companies across 41 countries. The study found that stocks
that made more profits before the crisis experienced a more significant price
decline when there was a liquidity shock on global financial markets during a
crisis. In particular cases in Indonesia, Haryanto (2020) examined the
relationship between the number of COVID-19 cases and the IHSG value. Using
the multiple linear regression analysis techniques, the study results concluded
a significant negative effect of the COVID-19 case on the IHSG value. Hence,
every 1% increase in COVID-19 cases would cause a decrease in the value of
the IHSG by 0.03%. Alfira et al. (2021), examining the impact of COVID-19 on
the share price of Islamic banks in Indonesia, also discovered comparable
findings. Their research revealed that the share prices of Bank Rakyat
Indonesia Syariah (BRIS) and State Pension Savings Bank Syariah (BTPS) had
decreased since the first case of COVID-19 was reported in Indonesia.
On the other hand, Mirza et al. (2022) revealed the condition of Islamic stock
mutual funds when the COVID-19 crisis occurred. This study took samples
from six countries: Malaysia, Pakistan, Saudi Arabia, Qatar, Kuwait, and the
United Arab Emirates. Using the Sharpe Ratio, Sortino Ratio, and Jensen's
Alpha measurements, the study results demonstrated that Islamic equity
mutual funds in the six countries could show positive performance amid
economic pressure due to COVID-19. This study also concludes that Islamic
stock mutual funds are investment products with haven properties during a
crisis. Before the emergence of the COVID-19 crisis, Kenourgios et al. (2016)
analyzed the condition of the Islamic stock market during the global financial
crisis in the case of the subprime mortgage and eurozone sovereign debt
crises. This study took the period 2007-2015, which included both crisis
phenomena, and used a sample of Islamic stock indices in European countries,
G7 members, and BRICS (Brazil, Russia, India, China, and South Africa). The
study unveiled that most Islamic stock indices were unaffected by financial
system shocks or transmission risks during the global financial crisis.
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 103
In Indonesia, Muhaimin et al. (2021) scrutinized the movement of shares listed
on JII during the COVID-19 crisis. By using descriptive analysis, the research
results revealed that JII experienced positive performance trends following the
implementation of Large-Scale Social Restrictions (PSBB) in Indonesia in the
April-July 2020 period. According to Nuryani et al. (2021), JII's performance
during the COVID-19 crisis was influenced by exchange rate variables, the SCI
index (Shanghai Composite Index), and the DJIA index (Dow Jones Industrial
Average). Meanwhile, another index, the ISSI, also experienced relatively high
volatility from positive and negative shocks during the COVID-19 crisis
(Nurdany et al., 2021). Also, positive shocks had a more substantial effect than
adverse shocks on the ISSI stock return rate.
From the results of these studies, it can be concluded that the crisis impacts
the volatility of both Islamic and conventional stocks. For an in-depth analysis
of this phenomenon, developing studies also compared the impact of the crisis
on Islamic and conventional stocks. Hasan et al. (2021) compared the
conditions of Islamic and conventional stocks during the COVID-19 crisis. Their
study aimed to assess the impact of COVID-19 on Islamic stock markets and
compared market reactions to comparable conventional stock markets to
understand stock market reactions during crisis periods better. The study used
the Dow Jones index and the FTSE as a sample for January-November 2020,
each of which has a particular index for conventional and Islamic stocks. The
study uncovered that the pandemic caused identical volatility in both stock
market categories. This study also stated that Islamic and conventional stocks
experienced a reasonably strong relationship in their movements during the
COVID-19 crisis.
A study on the performance of Islamic and conventional stocks was also
carried out by Siregar (2020) in Indonesia in the March-July 2020 period, or
when the COVID-19 case first entered Indonesia. The study compared the
passion for conventional stock transactions and Islamic stocks in the capital
market to find out the differences and advantages of these Islamic stocks. The
study employed the LQ45 and JII indexes as a representative sample of
conventional and Islamic stocks. The study results revealed that LQ45 and JII
experienced fluctuations during the crisis. However, this study found that JII
performed better with an average increase in the share price of 0.14%, in
contrast to LQ45, which experienced a decrease in the average share price of
1.22%.
Based on the literature above, it was found that the economic crisis impacted
the occurrence of capital market volatility, including Islamic and conventional
stocks. However, the volatility varies depending on the stock index sampled
and the crisis period analyzed. This volatility can also cause performance
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 104
differences in Islamic and conventional stocks depending on their respective
strengths.
III. Methodology
3.1. Data
This study used time-series data. The time-series data consisted of daily stock
prices listed on the ISSI and IHSG values. Data on the movement of stock prices
could be obtained from the official portal of the Indonesia Stock Exchange.
Because these data were not collected directly by the authors, the data used
were included in the category of secondary data. In this case, the daily stock
price means the ISSI and IHSG values at the closing of the stock exchange on
that day according to the stock exchange operating hours. Then, the value of
all stocks on the Indonesia Stock Exchange is represented by IHSG. Meanwhile,
ISSI signifies only stocks of sharia-compliant companies. Each of their volatility
would be estimated in a different equation.
Daily stock prices were used to compare stock performance before and after
the spread of the COVID-19 virus. This study further compared stock prices
between 30 days before and after the official announcement from the
government regarding the COVID-19 variant. Daily stock price data were also
utilized to measure volatility during a pandemic, but the data should be
transformed to find the daily return measurement of volatility that began in
March 2020 when the first COVID-19 case was found in Indonesia until March
2022 with a total period of 500 days.
3.2. Model Development
Time series data in the financial sector, such as stock prices, are prone to
volatility clustering, which is if there is relatively high data variability at one
time, the same trend will occur in the next period. The distribution of residuals
from stock price data is also often fat tails, and it has a greater tendency for
extreme events to occur in a certain period. Based on these properties, the
GARCH model can explain data variance (Enders, 2004).
Bollerslev (1986) introduced the GARCH model of the simplest equation as
follows:
𝜎𝑡
2 = 𝜔 + 𝛼𝜀𝑡−1
2 + 𝛽𝜎𝑡−1
2
The model is a variance equation, stating that the conditional variance σ at
time t depends not only on the square of the error in the previous period but
also on the conditional variance in the previous period (Gujarati, 2004).
Moreover, each IHSG and ISSI has its model and is not united in one equation.
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 105
The result obtained from the GARCH estimation would be compared to
measure the difference between IHSG and ISSI in their volatility.
3.3. Method
Comparative analysis can be done using a different test method: Paired t-test
or Wilcoxon signed-rank test. Paired t-test is carried out if the sample data are
normally distributed. Meanwhile, Wilcoxon signed-rank test is conducted if
the sample data are not normally distributed. Therefore, before performing a
different test, it is necessary to test for normality. In this study, the difference
test was conducted utilizing the software SPSS.
Meanwhile, in measuring the volatility of a variable, the most appropriate
analytical method to use is the GARCH (Generalized Auto Regressive
Conditional Heteroskedasticity) model (Enders, 2004). As the name implies,
this model considers heteroscedasticity elements in different time series.
Several previous studies that analyzed stock volatility also used the GARCH
model, including Aliyev et al. (2020), Azakia et al. (2020), Mhd Ruslan &
Mokhtar (2021), Naik et al. (2020), and Nurdany et al. (2021).
To test using the GARCH model, the data must first go through the stationarity
test process. The stationarity test can be done by using unit roots and
correlograms. If the data are stationary, it can be estimated using the ARMA
(Autoregressive Moving Average) model to obtain the best model from the
mean equation. The selection of the best ARMA model is shown from the
smallest Akaike Information Criterion (AIC) and Schwarz Information Criterion
(SIC) values. Against the ARMA model formed, a heteroscedasticity test was
conducted to identify the model’s volatility element. When it was found that
the existing model was not homoscedastic, the data processing continued to
the GARCH analysis stage. A series of analysis processes would be carried out
utilizing the software EViews.
IV. Results and Analysis
4.1. Comparative Analysis
4.1.1. Results
The presence of the COVID-19 pandemic undoubtedly impacts the movement
of the country's economy, including the stock market. When compared
between 30 days before and after the emergence of the first COVID-19 case
in Indonesia, it can be seen that the movement of ISSI and IHSG values
experienced a negative trend. The lowest value was recorded on March 24,
2020, or 22 days after the entry of the COVID-19 case in Indonesia, where ISSI
touched 115.95 and the IHSG touched 3,937.63.
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The same thing happened when the peak second wave of COVID-19 cases in
Indonesia occurred due to the spread of the Delta variant. The ISSI and IHSG
values decreased when compared between 30 days before and after the Delta
variant’s appearance in Indonesia. At first, the ISSI value was 184.29. This
figure decreased to 171.29 after 30 days since the detection of the first Delta
case. Likewise, the IHSG decreased from 6,356.16 to 5,996.25. Meanwhile,
due to the Omicron variant, a different trend occurred in Indonesia's ISSI and
IHSG values movement during the third wave of COVID-19 cases. The ISSI and
IHSG values increased slightly when compared between 30 days before and
after the emergence of the first Omicron variant case on December 16, 2021.
The ISSI value increased from 186.22 to 188.36. Meanwhile, the IHSG
increased from 6,586.44 to 6,645.51.
Figure 3. ISSI and IHSG Value Before and After the Emergence of the First Case of COVID-19
Source: Indonesian Stock Exchange (2020)
Figure 4. ISSI and IHSG Value Before and After the Emergence of Delta Variant
Source: Indonesian Stock Exchange (2021)
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Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 107
Figure 5. ISSI and IHSG Value Before and After the Emergence of Omicron Variant
Source: Indonesian Stock Exchange (2021)
Based on Figure 3, 4, and 5, it can be clearly seen that the highest volatility of
the ISSI and IHSG value movements occurred during the first wave of COVID-
19 cases in Indonesia. When compared between 30 days before and after the
first case’s appearance, the ISSI value fell to 24.86%, and the IHSG value fell to
25.93%. It was in stark contrast to the second wave, which only fell by 7.05%
and 5.66%, respectively, and the third wave, which actually grew positively by
1.15% on the ISSI and 0.90% for the IHSG.
According to the prior discussion, every wave of COVID-19 has a similar
movement tendency between ISSI and IHSG. However, this tendency does not
adequately explain the comparison between ISSI and IHSG before and after
the COVID-19 case. Given that the COVID-19 virus affected the stock market,
the researchers had to divide the data into two categories: before and after
the COVID-19 variants arrived in Indonesia.
Table 1. Descriptive Statistics of ISSI and IHSG
Mean N Std. Deviation Std. Error Mean
Pair 1 IHSG Pre-First Case 5955.8260 30 194.52154 35.51461
IHSG Post First Case 4720.2557 30 458.89349 83.78211
Pair 2 ISSI Pre-First Case 172.5367 30 6.55064 1.19598
ISSI Post First Case 138.2620 30 12.93999 2.36251
Pair 3 IHSG Pre Delta 6062.2040 30 105.22733 19.21179
IHSG Post Delta 5959.9083 30 113.88358 20.79220
Pair 4 ISSI Pre Delta 178.4510 30 2.55470 .46642
ISSI Post Delta 173.6917 30 2.42699 .44311
Pair 5 IHSG Pre Omicron 6628.9517 30 56.50490 10.31634
IHSG Post Omicron 6623.6017 30 51.85356 9.46712
Pair 6 ISSI Pre Omicron 188.2253 30 1.53414 .28009
ISSI Post Omicron 188.0177 30 1.49000 .27203
Table 1 reveals that each wave of COVID-19 reduced the average ISSI and IHSG
values in 30 days. The first wave had the most significant decline in ISSI and
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1 6 11 16 21 26 31 36 41 46 51 56 61
In
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V
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lu
e
(
p
o
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Period (day)
4,100.00
4,400.00
4,700.00
5,000.00
5,300.00
5,600.00
5,900.00
6,200.00
6,500.00
6,800.00
1 6 1116212631364146515661
In
d
e
x
V
a
lu
e
(
p
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Period (day)
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
IHSG and ISSI
International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 108
IHSG values. Meanwhile, the smallest drop happened in the third wave when
the Omicron variety expanded to Indonesia. When comparing the two types
of stocks, the IHSG's average value dropped more severely than the ISSI during
the first wave of COVID-19. Before the arrival of COVID-19 cases in Indonesia,
the average IHSG value declined by 20.75%, while the average ISSI value
decreased by 19.87%. When the decline in values was compared to the change
in the average value of the two during the Omicron variant wave, it was
discovered that there was a very slight difference. The average IHSG value
decreased by 0.08%, slightly less than the average ISSI value, which fell by
0.11%.
The data in the preceding table can also be used to compare volatility in the
ISSI and IHSG based on their relative standard deviation values. Both the ISSI
and the IHSG demonstrated an increasing and reducing volatility tendency in
the first and third waves of IHSG volatility increase. In contrast, ISSI volatility
dropped, except for the second wave. When the percentage change was
compared, each wave of COVID-19 consistently delivered more extensive
volatility changes to the IHSG than the ISSI. The IHSG experienced an increase
in volatility of up to 135.91% during the first wave, exceeding the ISSI's growth
of 97.54%. Similarly, the IHSG experienced a volatility change of 8.2 % in the
second and third waves, while the ISSI experienced a volatility change of less
than 5%.
To confirm that the spread of the COVID-19 variant caused the change in
value, a different test should be performed, comparing the IHSG and ISSI value
groups before the spread of the COVID-19 virus and IHSG and ISSI value groups
after the virus variant spread. Before executing the difference test, the data
were assessed to determine their normalcy as a determinant of the different
test methods used. Since the number of samples from each variable exceeded
50, the Kolmogorov-Smirnov test was performed to determine normality
(Cleff, 2014). The following criteria were employed to make decisions in this
test:
The value of sig. >0.05 means the data are normally distributed, and the
comparison test is carried out using the paired t-Test method.
The value of sig. <0.05 indicates that the data are not normally distributed,
and the comparison test is conducted using the Wilcoxon Rank Test
method.
According to Table 2, a significant value of 0.05 was observed in the
IHSG and ISSI variables in the first wave. In the Delta and Omicron
variant waves, the IHSG and ISSI variables showed a significance value
greater than 0.05. As a result, it is possible to conclude that the IHSG
and ISSI data in the first wave were normally distributed. In contrast,
the IHSG and ISSI data in the Delta and Omicron variant waves were
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 109
not. Based on these findings, the Wilcoxon Rank Test was the best
alternative test method for the IHSG and ISSI variables in the first
wave. Meanwhile, the paired t-test method was employed for the
IHSG and ISSI variables on the Delta and Omicron waves.
Table 2. Normality Test Results
IHSG
First Case
ISSI
First Case
IHSG Delta
ISSI
Delta
IHSG
Omicron
ISSI
Omicron
N 60 60 60 60 60 60
Mean 5338.0408 155.3993 6011.0562 176.0713 6626.2767 188.1215
Std. Deviation 714.30593 20.05144 120.32383 3.44412 53.83522 1.50301
Test Statistic .187 .161 .108 .060 .095 .087
Asymp. Sig. .000 .001 .079 .200 .200 .200
Following that, each data group was assessed using a comparative test based
on the method provided by the normality test. Several studies were run to
determine the impact of the COVID-19 variant's spread on changes in the IHSG
and ISSI values. In this regard, if the various tests reveal a substantial
difference, it is determined that changes in the IHSG and ISSI values can occur
due to the COVID-19 variant's spread. The following are the decision criteria
for the various tests utilizing the paired t-Test or Wilcoxon Rank Test methods:
The value of sig. >0.05 indicates no significant difference in the stock index
value before and after the spread of the COVID-19 variant.
The value of sig. <0.05 implies a significant difference in the stock index
value before and after the spread of the COVID-19 variant.
Table 3. Wilcoxon Rank Test Results
IHSG Post First Case - IHSG
Pre-First Case
ISSI Post First Case –
ISSI Pre-First Case
Z -4.782b -4.782b
Asymp. Sig. (2-tailed) .000 .000
Table 4. Paired T-Test Results
Difference t df Sig. (2-tailed)
Pair 3
IHSG Pre Delta –
IHSG Post Delta
102.29567 3.468 29 .002
Pair 4
ISSI Pre Delta –
ISSI Post Delta
4.75933 8.161 29 .000
Pair 5
IHSG Pre Omicron –
IHSG Post Omicron
5.35000 .372 29 .713
Pair 6
ISSI Pre Omicron –
ISSI Post Omicron
.20767 .718 29 .479
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 110
According to Table 3, the value of sig. for IHSG and ISSI in the first wave was
0.05 based on the different test outputs. Similarly, Table 4 revealed the value
of sig. for IHSG and ISSI during the Delta variant wave were both 0.05.
Meanwhile, when the test period was conducted during the Omicron variant
wave, the sig. value of the IHSG and ISSI showed a number greater than 0.05,
indicating that it was insignificant. Based on these results, it is possible to
conclude that significant changes in the IHSG and ISSI values occurred during
the spread of the COVID-19 virus in the first wave and the Delta variant wave.
4.1.2. Analysis
Because the IHSG and ISSI values changed significantly, the two stock indices
were assessed to have weak resistance to the COVID-19 crisis during the first
and Delta variant waves. The findings of this analysis are proportional to the
findings of Hasan et al. (2021) against the Dow Jones and FTSE indexes,
showing that both Islamic and conventional stocks are vulnerable to the
consequences of the COVID-19 issue. Still, it is necessary since the COVID-19
issue prompted Indonesia to enter an economic slump, resulting in numerous
national companies losing income and terminating worker contracts
(Febrianto & Rahadi, 2021).
Interestingly, a different stock market reaction occurred during the third wave
caused by the Omicron variant. Based on the various tests above, there was
no significant difference in the IHSG and ISSI values before and after
Indonesia's spread of the Omicron variant. It could happen because of
differences in investor behavior in responding to events that occurred
(Thampanya et al., 2020). According to the Indonesia Stock Exchange (IDX)
monthly report from March 2020, when the COVID-19 virus first invaded
Indonesia, net trade by worldwide investors was negative, specifically -3.49
billion share units. When the Delta version began to spread in May 2021, the
IDX stated that net trading by worldwide investors was similarly negative at -
1.64 billion share units. Meanwhile, worldwide investor mood continued to
recover in December 2021, as demonstrated by a positive net trade of 5.69
billion shares.
The restrictive policies enforced by the local government may impact the
behavior of stock investors. Of course, it can also affect stock price swings. At
first, banning community activities led to panic selling among stock investors.
Regarding COVID-19 prevention policies, the Indonesian government imposed
Large-Scale Social Restrictions (PSBB) at the outbreak's start in reaction to the
virus's increasingly widespread dissemination (Debora, 2020). When the Delta
variety became more widespread, the authorities promptly imposed an
Emergency Community Activity Restriction (PPKM), followed by PPKM Level 4
in the Java-Bali region, for more than a month (Bardan, 2021). These two
policies severely restricted people's activities, causing economic activity to
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 111
suffer. Meanwhile, as the Omicron form spread, the government merely
applied PPKM level 3 with less stringent limitations, which was only valid for
one month (Waseso, 2022). The presence of relaxation policies during the
wave of the Omicron variant undoubtedly provides an opportunity for the
economy's wheels to turn more steadily.
The influence on stock prices can also be attributed to COVID-19 instances.
Haryanto (2020) and Khalid et al. (2021) discovered that the number of COVID-
19 models substantially impacted stock value and volatility. Based on the
Indonesian COVID-19 Handling Task Force report, the number of active COVID-
19 cases in March 2020 reached a very high level, accounting for 85.80% of all
positive cases recorded. The number of active cases was still at 5.6% as of May
2021, and the positive monthly rate was 10.7 %, which was greater than the
WHO (World Health Organization) standard. Meanwhile, active cases declined
dramatically to 0.1% in December 2021, with a positive rate of only 0.11 %.
Based on these findings, it is highly probable that the occurrence of the
Omicron variety in December 2021 did not result in significant changes to the
IHSG and ISSI values.
4.2. Volatility Analysis
4.2.1. Results
Most earlier studies employed stock return data as the observed variable in
the GARCH model (Azakia et al., 2020; Irfan et al., 2021; Mhd Ruslan &
Mokhtar, 2021; Nurdany et al., 2021). To calculate the return value of each
stock index, the stock price data must be transformed into a natural logarithm
using a first-order differential equation (Aliyev et al., 2020). Adopting this
transformation could make it easier for researchers to measure changes in a
stock's value and rate of return.
Figure 6. The Volatility of ISSI and IHSG Return
The data transformation results were then exhibited in a graph to show the
volatility. The ISSI and IHSG return exhibited identical volatility in the graph
Alghifary, Kadji, & Hafizah | Indonesian stocks’ Volatility during COVID-19 waves: Comparison between
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 112
above. Significant changes would follow changes in the high rate of return.
This condition is referred to as volatility clustering, one of the characteristics
of heteroscedastic data that must be examined using the GARCH model
(Enders, 2004). As a result, the two stock indexes under consideration, i.e., ISSI
and IHSG also displayed volatility clustering.
Besides being heteroscedastic, the GARCH model also requires that the data
to be analyzed must be stationary. To ensure this, the stationarity test was
carried out using the Augmented Dickey-Fuller (ADF) method to identify the
presence of a unit root in the observed data. The basis for decision-making in
the stationarity test is as follows:
A probability value of >0.05 indicates that the data contains a unit root
and is not stationary.
A probability value of <0.05 indicates that the data does not contain a unit
root and is stationary.
Table 5. Stationarity Test Results
ISSI IHSG
t-Statistic Prob.* t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -17.56089 0.0000 -17.03850 0.0000
Test critical values: 1% level -3.976629 -3.976629
5% level -3.418889 -3.418889
10% level -3.131986 -3.131986
Based on Table 5, the resultant probability value at the level was <0.05.
Similarly, the statistical value of the ADF test, -17.56 for ISSI and -17.04 for
IHSG, was less than the value of the corresponding critical areas. Therefore,
the researchers could conclude that the ISSI and IHSG data were stationary
since they lacked a unit root. The ADF test results, which revealed that the
data were stationary at the level, were then used to create ACF
(Autocorrelation Function) and PACF (Partial Autocorrelation Function) plots
based on the correlogram graph. Afterward, ACF and PACF charts were used
in ARMA modeling to determine the correct order.
The PACF plot was used to determine the AR (Autoregressive) order, while the
ACF plot was employed to determine the MA (Moving Average) order.
Significant ACF and PACF values were determined based on the lag with a plot
exceeding the boundary line. Based on Table 6 and 7, both returns showed
ACF and PACF plots exceeding the limit at the lag third. Therefore, the
tentative models that could be used were ARMA (3.0), ARMA (0.3), and ARMA
(3.3). Each model was then estimated to get the coefficient of determination
(R2), AIC, and SIC. The best ARMA model chosen was the one with the largest
R2 and the smallest AIC and SIC values.
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Table 6. Correlogram of ISSI Return
Autocorrelation Partial Correlation AC PAC Q-Stat Prob
1 0.000 0.000 6.E-07 0.999
2 -0.096 -0.096 4.6031 0.100
3 0.192 0.194 23.171 0.000
4 0.024 0.012 23.472 0.000
5 0.042 0.082 24.349 0.000
6 0.007 -0.030 24.374 0.000
7 -0.049 -0.046 25.592 0.001
8 0.017 -0.009 25.736 0.001
9 -0.154 -0.172 37.806 0.000
10 -0.082 -0.065 41.228 0.000
Table 7. Correlogram of IHSG Return
Autocorrelation Partial Correlation AC PAC Q-Stat Prob
1 0.018 0.018 0.1613 0.688
2 -0.075 -0.076 3.0124 0.222
3 0.183 0.187 19.899 0.000
4 -0.004 -0.020 19.907 0.001
5 0.068 0.102 22.245 0.000
6 0.034 -0.009 22.827 0.001
7 -0.055 -0.039 24.383 0.001
8 0.034 0.009 24.972 0.002
9 -0.140 -0.163 35.035 0.000
10 -0.093 -0.071 39.462 0.000
Table 8. Summary of ARMA Modelling for ISSI and IHSG Returns
Stock Model R2 AIC SIC
ISSI
ARMA (3, 0) 0.0336 -5.8771 -5.8517
ARMA (0, 3) 0.0329 -5.8764 -5.8510
ARMA (3, 3) 0.0322 -5.8737 -5.8398
IHSG
ARMA (3, 0) 0.0301 -5.8469 -5.8216
ARMA (0, 3) 0.0279 -5.8446 -5.8193
ARMA (3, 3) 0.0282 -5.8429 -5.8091
Based on Table 8, it can be seen that the best model for returns was ARMA
(3,0). This model demonstrated that the return value heavily influenced the
return on ISSI and IHSG in the most recent period over the preceding three
periods. The chosen ARMA model was used as the mean equation in the
GARCH model analysis. To identify the presence of the GARCH effect as an
element of volatility in the model, a heteroscedasticity test using the ARCH-
LM (Lagrange Multiplier) method was performed on the mean equation
formed from the ARMA model. The basis for decision-making in the
heteroscedasticity test is as follows:
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A probability value of >0.05 implies that the data does not contain intense
volatility and is homoscedastic, so it does not need to be estimated using
the GARCH model.
A probability value of <0.05 implies that the data contains intense volatility
and is heteroscedastic, so it needs to be estimated using the GARCH model.
Table 9. Heteroskedasticity Test Results
Indicator ISSI IHSG
F-statistic 57.42780 43.70854
Obs*R-squared 51.67620 40.33075
Prob. F 0.0000 0.0000
Prob. Chi-Square 0.0000 0.0000
Table 9 shows the results in the form of a probability value lower than 0.05 for
both ISSI and IHSG data. Based on this parameter, the ISSI and IHSG return
data were both heteroscedastic and volatile. Thus, the next step was to
estimate how much volatility happened based on the observed data using the
GARCH model. The GARCH model estimation generally yields two types of
equations: the mean and the variance equations. According to the ARMA
model, the mean equation signifies how much stock returns from the prior
period influence the current average stock return.
Table 10. GARCH Model Estimation
Indicator
ISSI IHSG
Coefficient Probability Coefficient Probability
Mean Equation
C 0.000717 0.1135 0.000860 0.0608
AR(3) 0.101435 0.0368 0.118476 0.0123
Variance Equation
C 4.80E-06 0.0228 5.05E-06 0.0133
RESID(-1)^2 0.059841 0.0219 0.064508 0.0239
GARCH(-1) 0.884831 0.0000 0.877207 0.0000
Meanwhile, the variance equation explains how much the volatility
persistence of the stock index is determined by the volatility and the squared
error in the previous period. The estimation in Table 10 was carried out using
the GARCH (1.1) model. All independent variables in the predicted output had
a significant effect since their probability values were less than 0.05. The
constant value was also greater than zero, and the outcome of the sum of the
coefficients of the independent variable was one. In other words, this model
is thought to help evaluate the volatility of the ISSI and IHSG.
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4.2.2. Robustness Test
However, before assessing the model, the ARCH-LM test was performed to
guarantee that the GARCH model was free of heteroscedastic features.
According to Table 11, the heteroscedasticity test using the ARCH-LM
approach yielded a probability value greater than 0.05. This value suggests
that the ISSI and IHSG return data processed using the GARCH model had been
free from heteroscedasticity. These results strengthened the feasibility of the
model to be used in analyzing ISSI and IHSG returns.
Table 11. Heteroskedasticity Test Result for GARCH Model
Indicator ISSI IHSG
F-statistic 2.071596 1.782812
Obs*R-squared 2.071297 1.783595
Prob. F 0.1507 0.1824
Prob. Chi-Square 0.1501 0.1817
4.2.3. Analysis
Table 10 show the estimated output of the GARCH model formed in analyzing
the volatility of the ISSI and IHSG, the resulting equation for the volatility of
the ISSI is as follows:
𝜎𝑡
2 = 4,80 × 10−6 + 0,0598𝜀𝑡−1
2 + 0,8848𝜎𝑡−1
2
Meanwhile, the equation formed for the volatility of the IHSG is as follows:
𝜎𝑡
2 = 5,05 × 10−6 + 0,0645𝜀𝑡−1
2 + 0,8772𝜎𝑡−1
2
The above equation is a form of the variance equation that describes the
factors determining how much stock volatility occurs.
The sum of the coefficients α+β becomes a measure of the volatility
persistence in each stock index investigated (Campbell et al., 2012). The
greater the sum, the more volatility, and the longer it can last. According to
the equation, the volatility persistence of the ISSI stock index was in the region
of 0.94. The volatility equation for the IHSG stock index also yielded the same
result. Since the resulting value was so close to one, the researchers could
conclude that ISSI and IHSG were both highly volatile during the COVID-19
pandemic in Indonesia.
Furthermore, the probability value of each independent variable was less than
0.05, indicating the strength of high volatility. This value suggests that the
previous period's volatility (σ2) and squared error (ε2) considerably affected
the next period's volatility. The coefficient value on variable ε2 also describes
the impact of occurrences outside the model. In this study, variable ε2 in the
IHSG equation had a coefficient of 0.0645, more significant than the
coefficient ε2 of 0.0598 in the ISSI equation. It demonstrates that external
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 116
factors outside the model had a more significant impact on IHSG return
volatility. In this case, domestic macroeconomic factors or global stock index
movements are examples of the events under consideration.
The estimated output of the GARCH model also yielded the same results as
the output of the comparative tests, indicating that the COVID-19 problem had
a proportional effect on ISSI and IHSG during the pandemic period, which
encompasses three waves. These findings are consistent with Hasan et al.
(2021), who discovered that Islamic and conventional equities exhibited
identical volatility and a strong association during the COVID-19 crisis. This
type of effect is typical because the COVID-19 pandemic is not just a financial
sector crisis but a multifaceted catastrophe that shocks different social areas
of people's lives (Saputra & Ariutama, 2021). Therefore, while ISSI and IHSG
have distinct personalities, their impact is not much different.
Fundamental reasons, such as volatility in macroeconomic variables, can also
contribute to the persistence of volatility in Indonesian stocks (Thampanya et
al., 2020). Based on Nugroho & Robiyanto's (2021) research, the fundamental
factors that also experienced volatility during the COVID-19 pandemic
included the rupiah exchange rate and world gold price. The volatility in both
variables significantly influenced Indonesia's stock exchange market. Another
variable that also became the attention was world oil volatility, which
increased in the middle of the COVID-19 crisis (Bourghelle et al., 2021).
Syebastian et al. (2021) also mentioned a significant correlation between the
world oil price and stock volatility in Indonesia. The COVID-19 crisis caused
volatility in various domestic and global economic indicators. As a high-risk
return investment asset, the stock is undoubtedly easily influenced by
volatility, which occurs in other instruments.
Macro-economy variables cause it, but stock market volatility in a country is
also caused by volatility in other countries' stock markets. This influence is
called a contagion effect, a theory explaining that a crisis occurring in a region
or country can spread its effect to another country on a domestic or
international scale (Dornbusch et al., 2000). During the crisis of COVID-19, the
systemic risk resulting from the contagion effect experienced an increase in
the financial sector (Louati et al., 2022). Based on the research by Kamaludin
et al. (2021), the capital market condition in ASEAN-5 countries had a solid
correlation to the Dow Jones index movement in the middle of the pandemic
era. Referring to contagion theory, it is widespread if stocks in Indonesia
experienced intense volatility because of Dow Jones index volatility.
The identical volatility between IHSG and ISSI clarified that Islamic rules
application in ISSI stocks does not ensure stronger endurance during the crisis.
It was caused by the substantial impact of COVID-19 in many aspects, affecting
either the real or financial sector. IHSG and ISSI values also reached their
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 117
lowest and highest values in a decade because of this pandemic. It confirms
the presence of great volatility in Indonesian stocks during COVID-19. The
great volatility also indices that Indonesian investors are sensitive to financial
news, especially during the crisis. Based on supply and demand law, investors’
preference as customers is strongly correlated with the stock price.
V. Conclusion and Recommendation
5.1. Conclusion
The stock market experienced high volatility and uncertainty due to the
pandemic. In less than a month, the COVID-19 crisis resulted in the drop of
IHSG value to its lowest point in the last decade. Both Islamic and conventional
stocks experienced similar volatility during the COVID-19 pandemic. Based on
the different test results using the paired t-test and Wilcoxon rank test
methods, it was concluded that the ISSI and IHSG experienced significant
changes before and after discovering the first case of COVID-19. Significant
changes in both values were also found when the Delta variance spread. In
contrast, when the third wave occurred due to the presence of the Omicron
variant, ISSI and IHSG did not experience significant shocks.
This condition might happen because the community's immunity has been
developed, and the government has been able to implement adaptive policies
to prevent virus transmission. The policy was then relaxed during the spread
of the Omicron variant, where the government allowed various community
activities in public spaces. During the first wave of cases and the Delta variant
wave, the number of active cases and the positivity rate were still above the
WHO standard. Meanwhile, when the Omicron variant was found, the active
cases and the positivity rate approached 0, below the WHO standard.
The volatility in ISSI and IHSG is also proven by detecting heteroscedasticity
elements in stock return data. Based on the heteroscedasticity test results, it
was found that both stock indices had a heteroscedastic return value and
experienced high volatility. By applying the GARCH model in the analysis, the
strength of stock volatility could be measured along with the factors
influencing it. The estimation results of the GARCH model conclude that both
Islamic and conventional stocks had an immense volatility power with an
identical value of 0.94 or close to 1. The current volatility is also significantly
influenced by the previous volatility and the squared error representing other
previous events outside the model.
Moreover, the volatility in Islamic and conventional stocks was not much
different, even though both stocks had different characters in the debt and
income ratio. Fundamental factors also caused this high volatility in the form
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International Journal of Islamic Economics and Finance (IJIEF), 6(1), 105-132 │ 118
of shocks in several macroeconomic variables, including the rupiah exchange
rate, gold prices, and world oil prices. In addition, the contagion effect that
occurred during the COVID-19 crisis also contributed to the spread of systemic
risk in global stock indexes on stock volatility in Indonesia.
Furthermore, the identical result between IHSG and ISSI estimation was
caused by the pandemic's significant effect on either the real or financial
sector. It implies that ISSI is not more stable than IHSG to face the crisis, and
vice versa. Besides, Islamic rules application in ISSI stocks does not ensure
more vital endurance during the crisis. The effect of the crisis also cannot be
denied by Indonesian stocks, so it influences the investors’ preference, which
is sensitive to financial news.
5.2. Recommendation
This study indicates that the volatility in the stock market may last as long as
the crisis is not over. Therefore, investors are suggested to pay attention to
the volatility that occurred in the previous days to predict stock prices in the
future. Since the stocks have experienced high volatility throughout the
pandemic, stock issuing companies should adapt quickly and prepare
alternative strategies to maintain stock price stability amid a crisis. Likewise,
government agencies are highly encouraged to maintain macroeconomic
stability, including exchange rates, inflation, and interest rates.
This research, nonetheless, has several limitations on the information
presented. The use of ISSI and IHSG as variables representing sharia and
conventional stocks was not enough to reveal the differences in character
between the two stocks, considering that the IHSG includes all issuers listed
on the Indonesia Stock Exchange, both sharia and conventional stocks. So far,
no index has included conventional stocks only. Therefore, it is recommended
for further researchers who want to compare the performance of Islamic and
conventional stocks to classify between the sharia and conventional stocks
specifically.
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