The impact of religious practice on stock returns and volatility
Osamah Al-Khazali*
Department of Finance
School of Business and Administration
American University of Sharjah
Sharjah, United Arab Emirates
[email protected]
Elie Bouri
USEK Business School
Holy Spirit University of Kaslik
Jounieh, Lebanon
[email protected]
Taisier Zoubi
Department of Accounting
School of Business and Administration
American University of Sharjah
Sharjah, United Arab Emirates
[email protected]
*Corresponding author
JEL classification: C32; G12; G15
Keywords: Religious practice; Ramadan effect; Islamic calendar anomaly; Stock return; Stock
volatility
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The impact of religious practice on stock returns and volatility
From a psychological and behavioral perspective, this paper examines whether religious practice
can, through its influence on investors' moods and emotions, affect the behavior of the stock
markets and investors in 15 Islamic countries over the period December 31, 2005 to December 31,
2015 and over four sub-periods (before and after both the global financial crisis and the Arab
spring). The results indicate that volatility decreases during the month of Ramadan and is
significantly different from the volatility in the other 11 months of the Islamic calendar year in
most Muslim countries. We also observe that changes in stock returns and volatility during the
month of Ramadan are due to religious practice and not due to the global financial crisis or the
Arab spring. The findings are important in understanding the role of religious practice on stock
market behavior and are of great interest to investors and market regulators.
1
The impact of religious practice on stock returns and volatility
1.
Introduction
Previous studies that examined the impact of investor sentiment on stock prices were
motivated by theories that focus on the effects of emotions, moods, and feelings on people’s
judgments and decision making (Subrahmanyam, 2007). One of the main theories in this regard is
that of behavioral finance, which argues that the actions and performance of people are influenced
by how they feel (Elliot and Echols, 1976) and that investors’ financial decisions are not fully
rational. For instance, a number of studies have shown that social moods can influence the
judgments made by investors and corporate managers and that the level and nature of business
activity follow social moods rather than lead them (Nofsinger, 2005). In this sense, the stock
market can be regarded as a direct index of social moods, as it reflects the combined level of
optimism or pessimism in society at a given time (Prechter, 1985, 1999; Green, 2004).
Accordingly, numerous studies have examined the impact of moods and emotions on stock returns,
risk, and decision making of individuals (Wright and Bower, 1992; Bagozzi et al., 1999;
Hirshleifer and Shumway, 2003; Loewenstein et al., 2001; Edmans et al., 2007; and Chang et al.,
2012).
Nevertheless, it seems clear that investors, practitioners, and decision makers operating in
different religious and social environments will display different behaviors. Employees’
activities—including their hobbies, religious practices, sports activities, and others—most likely
influence their decision making at work (Hilary and Hui, 2009). In fact, religion has been part of
economic thought for a long time. For example, Smith (1776) suggests that participation in religion
could be viewed as a rational action by which individuals enhance the value of their human capital
2
(Anderson, 1988). Later, Weber (1905) reports that the Protestant ethic was at the core of the
economic development of capitalism. Modern economic theory has revisited the analysis of
religions at the microeconomic and macroeconomic levels. At the microeconomic level,
Iannaccone (1998) states that religion has been linked to a large range of social decisions. At the
macroeconomic level, Barro and McCleary (2003) find that macroeconomic development and
church attendance are negatively correlated. Stulz and Williamson (2003) find that religion
explains variation in creditor rights and the enforcement level. Other studies (i.e., Brown and
Taylor, 2010; Hong and Kacperczyk, 2009; Hilary and Hui, 2009) recognize the effect of religion,
social interactions, and social norms on investment decisions of individuals as well as corporations.
Recently, Canepa and Ibnrubbian (2014) show that religious beliefs affect portfolio choices of
investors in Saudi Arabia.
However, few prior studies have empirically examined the impact of religious beliefs on
investment decisions in the stock market (see, among others, Stulz and Williamson, 2003; Frieder
and Subrahmanyam, 2004; Hilary and Hui, 2009; Kumar et al., 2011; Bialkowski et al., 2012,
2013; Canepa and Ibnrubbian, 2014).
Different countries and societies follow their own calendar based on their religion and
culture. For example, Jewish societies follow the Hebrew calendar, Chinese follow the lunisolar
calendar, Indians follow the Saka calendar, Christians follow the Gregorian calendar, and Muslims
follow the Hijri calendar, which is based on the lunar calendar. There are 12 months in the Hijri
calendar. There are approximately 29.53 days in a lunar month. Ramadan is one of the months of
the Hijri calendar.
From a psychological and behavioral perspective, we examine whether a religious practice
can, through its influence on investors’ moods and emotions, affect the behavior of the stock
3
markets and investors in Islamic countries. In this paper, we focus on the most celebrated religious
tradition in the world: the holy month of Ramadan, which is practiced by more than 1.6 billion
people. Therefore, it would be very surprising if changes in the general moods and emotions of
investors did not have a significant impact on stock markets in Muslim countries during the holy
month of Ramadan (one of the five pillars of Islam).1 One should know that Muslims increase their
religious practice during the holy month of Ramadan.2 Muslims believe that the blessed month of
Ramadan will generate something valuable for both an individual and for society. During
Ramadan Muslims can experience a whole series of emotions and moods. Ramadan brings
happiness and greater satisfaction to Muslims around the world. This most likely will lead to
optimistic behavior, which in turn will affect Muslims’ investment decision making. The month
of Ramadan is a period of fasting, sacrifice, giving, and devotion, with the hope that these qualities
will stay with individuals throughout the year. Indeed, the essence of fasting in Ramadan is
spiritual. Nevertheless, this holy month also offers a number of benefits for both the mind and
health. As mentioned in the Qur’an (the Muslim holy book), fasting can promote both the physical
health and the mental well-being of individuals (Saleh et al., 2005). This leads people to be less
tense and anxious and that may also induce mild states of euphoria during Ramadan (Daradkeh,
1992; Knerr and Pearl, 2008). Rosen and Wu (2004) argue that investors in good health are willing
to invest in more risky portfolios. The joy derived from fasting during the month of Ramadan could
influence investor behavior and thus have a positive effect on equity markets in Islamic countries.
Muslim investors may also increase their demand on equities as a result of good health. Based on
the previous discussion, we would expect a significant increase in the price of equities during the
month of Ramadan.
1
2
The five pillars of Islam are: faith, prayer, charity, fasting, and pilgrimage to Mecca.
Qur’an (Muslim holy book) describes the month of Ramadan as “better than a thousand months”.
4
A few studies have examined the behavior of stock markets in Islamic countries during the
Islamic year (lunar calendar) (Seyyed et al., 2005; Al-Hajieh et al., 2011; Bialkowski et al., 2012;
Al-Khazali, 2014).3 Those studies have investigated whether stock returns in Islamic countries
show a seasonal trend during a certain month(s) of the Islamic calendar. They have examined a
moving calendar anomaly based on the Islamic lunar calendar which predominantly marks the
religious activities and holidays in Muslim countries. Results have been mixed. Some have
reported higher stock returns and/or lower volatility during the month of Ramadan compared to
the other months. This Ramadan effect refers to significantly higher stock returns during the ninth
month of the Islamic calendar. In addition, the impact of political instability in the Middle East
due to the Arab spring and the global financial crisis (GFC) on stock returns and volatility during
the month of Ramadan was neglected in previous studies. Given the importance of GFC and Arab
Spring and their effect on the stock markets of the Islamic countries, there is a need for a rigorous
research that examines whether the effects of GFC and the Arab spring events overshadow the
effect of Ramadan on stock returns and volatility during Ramadan and other Islamic months.
Furthermore, as the moods and emotions of the Muslims who observe Ramadan may change over
the three sub-periods of Ramadan (the first 10 days, the second 10 days, and the last 10 days), we
assess whether there are some differences in the stock returns and volatility among the three subperiods.
This paper attempts to contribute to, and extend, the current literature by trying to answer
the following questions:
The Arabic months in the Islamic calendar are: Muḥarram (1), Ṣafar (2), Rabi I (3), Rabi II (4), Jumada I (5),
Jumada II (6), Rajab (7), Shaʿban (8), Ramaḍan (9), Shawwal (10), Dhu al-Qaʿda (11), and Dhu al-Ḥijja (12). The
numbers in parentheses indicate the order of each month in the Islamic year.
3
5
1. Are investors in Islamic countries affected by religious practice during the month of
Ramadan due to psychological behavior, moods, and emotions?
2. Are there differences in stock returns and volatility across the first, second, and last 10 days
of the holy month of Ramadan?
3. Has the global financial crisis (GFC) affected the stock returns and volatility during the
holy month of Ramadan?
4. Have the Arab uprisings in the Arab world, i.e., the Arab spring, affected the stock returns
and volatility during the holy month of Ramadan?
This paper is complementary to a strand of literature which examines the impact of Islamic
calendar effects on the behavior of stock returns and volatility using GARCH-based models. While
the methodological framework adopted in this paper is in the broad line of Bialkowski et al. (2013),
our approach differs in two ways. First, the conditional means of return series are more properly
modeled by capturing many of the salient features of the data, such as the predictability associated
with lagged returns, the effect of worldwide stock price movements, and the effect of days of the
week. Second, we select the best specifications for the conditional variance of return series that
account for conditional heteroscedasticity, asymmetric response to positive and negative news,
and non-normality in return. In regard to the latter, three different distributions of the GARCH
model (normal, student-t, and GED) are examined and the appropriate one is selected. Compared
to prior studies, the above-mentioned differentiation in the methodology is important in the sense
of avoiding biased estimates of return and volatility coefficients caused by a shift of focus from
the unpredictable component of returns. Misspecifications in the GARCH-based modeling can
potentially lead to biased estimates of the levels of return and volatility during the holy month of
Ramadan, and, eventually, to erroneous investment conclusions.
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This paper contributes to a small but growing literature on Ramadan effect on stock
markets in countries which observe the holy month of Ramadan. To the best of our knowledge,
this paper is the first to examine the effect of Ramadan during the GFC and Arab spring periods.
Prior studies that examine the effect of Ramadan ended their study periods at about the same time
the GFC was beginning and before the start of the Arab spring. In contrast, our study covers the
entire periods of the GFC and Arab spring. Furthermore, while there is a limited literature on
Ramadan effect on stock markets in general, there is no research on the effect of partitioning
Ramadan into three sub-periods (the first 10 days, the second 10 days, and the last 10 days) on
stock markets. This paper is the first to examine the effect of partitioning Ramadan into three
different sub-periods on the return and volatility of the stock markets in Muslim countries.
Our main results indicate that the Ramadan mean return is positive and higher than nonRamadan mean returns in most of the Islamic countries, confirming the view that investors are
subject to feelings and emotions and that their financial decisions are not fully rational. Also, it is
found that the returns for the extended period (Ramadan + seven days) are positive. We provide
evidence that the volatility of returns during Ramadan and the extended period of Ramadan is
lower than for the non-Ramadan period for most of the Islamic stock markets under study. We also
show that the impact of Ramadan increases on the days with higher worship intensity. The findings
suggest that the first 10 days of Ramadan have lower changes in returns and volatility than the last
two periods (second and third ten days of Ramadan). The returns of the stock market did not
decline and the volatility of the returns did not increase during either the Arab spring or the GFC.
The findings of this paper should be of interest to both regulators and participants in the
financial markets of Islamic countries in the Middle East, the Far East, and elsewhere. They may
have useful implications for trading strategies and investment decisions which investors may seek
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to implement for monthly prices; investors can time their trading, thereby earning abnormal
returns, confirming the belief among Muslim investors that through good actions they will be
rewarded twice as much in the month of Ramadan as they normally would.
The remainder of this paper is organized as follows. Section 2 discusses several features of
the holy month of Ramadan, while Section 3 offers a review of the relevant literature. Section 4
describes the data and methodology, while Section 5 presents and discusses the results. Finally,
Section 6 provides a summary and conclusion.
2.
Features of the holy month of Ramadan
Fasting during the holy month of Ramadan is one of the five pillars of Islam4. It is one of
the most celebrated religious rituals in the world. During the month of Ramadan, Muslims fast
from dawn until sunset and gifts, mainly in the form of food, are shared with the poor. The 12
Islamic months derived from the lunar cycle are separated by the appearance of the new moon.
Each month in the Islamic calendar averages between 29 and 30 days. During the holy month of
Ramadan, Muslims become more socially active and spiritually oriented. Ramadan for Muslims
is not just the month of fasting; it is the month of values, happiness, and blessings. It is the only
month that is mentioned by name in the Qur’an. The Prophet Muhammad—peace be upon him—
called Ramadan ‘a blessed and a great month’. Ramadan has many features: spiritual and moral as
well as historical and cultural. Ramadan is called by many names, including, but not limited to,
month of the Qur’an, month of patience, month of prayers and remembrance of Allah, month of
repentance and seeking Allah’s forgiveness, month of charity and generosity, month of kindness
and good relations, and month of Du’a (asking God for gaudiness and forgiveness).
4
The five pillars of Islam consist of shahada, prayer, zakat, fasting during Ramadan, and hajj.
8
Furthermore, the month of Ramadan is perceived as consisting of three parts, equal in
length but different in promised rewards, characteristics, and intensity of worship. The Prophet
Muhammad—peace be upon him—says that “the first part (1-10 days) of Ramadan brings God’s
Mercy, the middle (11-20 days) … brings God’s forgiveness and the last part (21-30 days) …
brings freeing from hellfire”. Fasting during Ramadan creates a space for emotional healing to
begin. One of the benefits of fasting is the effects it has on our emotional statuses. One will likely
feel more emotional during, and perhaps right after, a fast. In addition, the month of Ramadan
brings special joy to believers. The Prophet—peace be upon him—said, “The person who fasts has
two joys: when he makes Iftar he feels happy and when he shall meet his Lord he shall be happy
with his fast”. Muslims who fast will feel very happy when they break fasting at sunset (Iftar).
They appreciate the food and drink that Allah has provided for them. They thank him and recognize
his blessings and bounties. Those who fast will also have a special happiness when they shall meet
their Lord. He shall greet them with greetings of peace, grant them special honor, and grace them
with His mercy and kindness. Thus, during the month of Ramadan individuals enjoy positive
emotions and optimistic moods.
The financial markets in the Islamic countries around the globe experience noticeable
changes in their trading activities (with reduced working hours) and greater religious practice of
the market participants during the fasting month of Ramadan. Most Islamic countries use both the
Gregorian and the Islamic lunar calendars. The Islamic calendar predominantly marks religious
activities and holidays, whereas the Gregorian calendar is used by businesses and governments.
During Ramadan the economic activities in general tend to slow down, with reduced working
hours in all sectors. Despite the fast, however, grocery sales go up during the month of Ramadan.
Similarly, electricity consumption is reported to rise as a result of an increase in late-night socio-
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religious activities and shopping. Trading in securities may decline, as some Muslims consider
speculative trading a form of gambling, which is prohibited by Islam. Similarly, the use of
leveraging (margin trading) or trading in interest-based securities may decline during the month
of Ramadan in view of the strict prohibition against the use of interest or riba.
3.
Literature review
Previous studies acknowledge that religious holidays (i.e., Christmas and Good Fridays)
can influence stock returns. For example, Lakonishok and Smidt (1988), Ariel (1990), and Cadsby
and Ratner (1992) report significantly higher stock returns for pre-holiday trading days than other
periods. Similarly, Frieder and Subrahmanyam (2004) examine the impact of the Jewish holy days
of Rosh Hashanah and Yom Kippur and the Catholic Irish one of St. Patrick’s Day on stock returns
and trading volume. Their results show significantly higher stock returns and trading volume on
the days immediately preceding these holidays. Yuan and Gupta (2014) examine stock returns
during the days preceding the Chinese lunar New Year for the major Asian stock markets for the
period 1999–2012. The results of this study indicate higher stock returns in the trading days prior
to the Chinese New Year holiday. Salaber (2007) examines the returns of sin stocks (publicly
traded companies involved in the tobacco, alcohol, and gaming industries) of 18 European
countries for the period 1975–2006. The results of this study show that sin stock returns are
significantly higher than the stock returns of non-sin stocks. They also indicate that stock returns
are influenced by religion and legal environment. Recently, Canepa and Ibnrubbian (2014) report
that religious beliefs have a major impact on stock portfolio selections of investors in Saudi Arabia.
Decisions about complex and uncertain matters are particularly influenced by emotions
such as those experienced by Muslims during the holy month of Ramadan. The Ramadan effect
refers to significantly higher stock returns during the ninth month of the Islamic calendar. The
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finance literature contains mixed evidence on the existence of the Ramadan effect in stock markets
of Muslim countries. Unlike the fixed calendar events—such as the January effect and the day-ofthe-week effect, which have been extensively examined—the effect of moving calendar events
such as Ramadan on risk and return have not received much attention. Wong et al. (1990) report a
negative Eid ul-Fitr (an Islamic three-day festival after the holy month of Ramadan) effect and
Chinese New Year effect on the Malaysian stock market. Chan et al. (1996) find an Islamic New
Year effect on the Kuala Lumpur stock exchange (KLSE) in Malaysia. Their results confirm the
importance of cultural influences on stock returns, and none of the Islamic months, except for
Rabi-ul-Awal, is significantly different from zero. In Pakistan, Husain (1998) shows a significant
drop in stock market volatility during Ramadan, even though there was no significant change in
the mean return. Using GARCH specifications, Seyyed et al. (2005) find no significant change in
Ramadan mean returns in the Saudi stock market. However, the authors document a systematic
pattern of decline in volatility during Ramadan, implying a predictable variation in the market
price of risk. An examination of trading data shows that this anomaly appears to be consistent with
a decline in trading activity during Ramadan. Contrary to other studies, Bialkowski et al. (2012)
investigate stock returns during Ramadan for 14 Muslim countries over the period 1989–2007.
The results show that stock returns during Ramadan are significantly higher and less volatile than
during the rest of the year. No significant declines in market liquidity are reported. The results are
consistent with the notion that Ramadan affects investor psychology in a positive manner, thereby
affecting investment decisions. Almudhaf (2012) examines the Islamic calendar seasonal
anomalies in the stock returns of 12 countries and finds evidence supporting a Ramadan effect in
four countries: Jordan, Kuwait, Pakistan, and Turkey. Al-Hajieh et al. (2011) use runs tests and
report significant and positive calendar effects for the whole period of Ramadan in most of the
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countries examined. They argue that the positive effect can be attributed to the generally positive
investor mood. They also find that year-on-year variations in market returns in the first and last
days of Ramadan are statistically significant. Al-Ississ (2010) investigates the effect of the Muslim
holy days of Ramadan and Ashura (the tenth day of Muharram in the Islamic calendar) on the daily
returns and trading volume of 17 Muslim countries from 1988–2009. The results of this study
show a significant decline in the trading volume, significant positive returns during the five holy
days of Ramadan, and significant negative returns during the day of Ashura. Mustafa (2011) uses
OLS technique daily data from Pakistan over the period December 1991–December 2010. The
results indicate significantly higher stock returns and market risk during the month of Ramadan.
Bialkowski et al. (2013) examine the effect of Ramadan on security returns and mutual funds in
Turkey. Their findings show higher stock returns during the month of Ramadan for the Istanbul
Stock Exchange, consistent with the results of previous studies. Ramezani et al. (2013) test
seasonality in lunar months for the Tehran stock exchange total index. Their results indicate
significant and positive stock returns during the month of Ramadan. Using stochastic dominance
analysis, Al-Khazali (2014) investigates the Ramadan effect on the stock returns of 15 Muslim
countries from 1989 to 2012. The results show that the stock markets under study did not
outperform in Ramadan from a wealth perspective. However, the author concludes that risk-averse
investors would prefer investing during the month of Ramadan relative to the other months of the
Islamic calendar. Recently, Halari et al. (2015) examine Islamic calendar anomalies in Pakistani
firms from 1995 to 2011. Their results show very little evidence of a monthly seasonal anomaly in
average returns, but there is evidence of monthly patterns in the volatility of returns. Their finding
suggests that investors can formulate an investment strategy and choose a trading time in order to
outperform on a risk-adjusted basis.
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4.
Data and econometric model
4.1.
Data
This paper uses daily data on stock market indices of 15 Muslim countries from December
31, 2005 to December 31, 2015. Interestingly, the sample period can be divided equally into two
periods of five years each, relating to before and after the Arab spring that started in Tunisia on
December 18, 2010. All data are compiled from DataStream and cover Morgan Stanley Capital
International (MSCI) indices. For the cases where MSCI are not available for the entire period
under study, domestic indices are used instead. Following prior studies, the MSCI World Index is
also considered to be a proxy for world influence (Białkowski et al., 2013; Chau et al., 2014; Bouri,
2015). The analysis is conducted based on daily returns calculated as the first differences of the
natural logarithms of the index multiplied by 100.
The summary statistics of daily returns for the various stock indices are given in Table 1.a.
They illustrate the presence of a negative average return in eight markets and a positive average
return in the remaining seven markets. Indonesia and Bahrain have respectively the lowest and the
highest mean returns, whereas Dubai and Malaysia have respectively the highest and the lowest
standard deviation. Except for Tunisia, all return series are negatively skewed, suggesting that
extreme negative returns are more likely to be seen than extreme positive returns. As for the
kurtosis, excess kurtosis is omnipresent in all the return series and Oman has the highest value of
kurtosis. Jarque–Bera test statistics reject the null hypothesis that residuals are normally distributed
at the 1% significance level. Moreover, Table 1.b presents summary statistics of Ramadan returns
versus non-Ramadan returns for each Islamic country. We observe that Ramadan mean returns are
positive in all Islamic countries under study except Bahrain, Morocco, and Saudi Arabia. NonRamadan mean returns, however, are positive in six countries: Egypt, Indonesia, Malaysia,
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Morocco, Tunisia, and Turkey. Furthermore, Ramadan mean returns are higher than non-Ramadan
mean returns in all countries except Bahrain, Malaysia, and Morocco. However, Ramadan mean
returns are significantly different from non-Ramadan mean returns at 10% and 5% in Abu Dhabi
and Dubai, respectively. Furthermore, the standard deviation of returns in the month of Ramadan
is significantly lower than the standard deviation of returns in non-Ramadan months in all Islamic
countries except Morocco, Qatar, and Turkey. This indicates that returns during the month of
Ramadan are less risky than returns during other months of the Islamic year. In general, risk-averse
investors choose an investment with a higher return for the same level of risk, or one with the
lowest risk for the same level of return. This implies that investors prefer to invest a few days
before or at the beginning of the month of Ramadan. Jarque–Bera test statistics reject the null
hypothesis that residuals are normally distributed at the 1% significance level for both Ramadan
and non-Ramadan returns. The presence of conditional heteroscedasticity in the return series, as
shown in the ARCH-LM test, justifies the use of GARCH volatility models. Finally, testing the
null hypothesis of the existence of a unit root is a necessary prerequisite to ensure reliable estimates
of the GARCH models. Results from the unit root test (Dickey and Fuller, 1981) show that all the
return series are stationary.
[Insert Tables 1.a and 1.b here]
4.2.
Model
To examine the effect of the holy month of Ramadan on the returns and conditional
volatility of the stock market indices under study, this paper follows Bialkowski et al. (2013) and
Chau et al. (2014) and applies a GARCH-based specification that accounts for a time varying in
both the conditional mean and the conditional variance. However, instead of an ad hoc model
selection of the conditional means and variance processes, we estimate the best-fitting univariate
14
model for the return series. In fact, the econometric methodology follows a two-step approach. In
the first step, the most appropriate specifications for both the mean and variance equations are
selected. For the mean equation, the appropriate specification has to address many features in the
stock indices under study, such as potential autocorrelation, day-of-the-week effects, and
worldwide price movements. For the variance equation, the best conditional variance equation has
to account for conditional heteroscedasticity in returns, and the asymmetric impact of good and
bad news on stock returns. In the second step, we test the effects of Ramadan on the returns and
volatility of the 15 stock indices via the inclusion of a dummy variable—representing the month
of Ramadan—in the best conditional mean and conditional variance processes. However, different
definitions of the Ramadan dummy are used in the empirical analysis.
4.2.1. Step one
Prior to testing the Ramadan effect, however, choosing the most appropriate specification
of the GARCH-based model is crucial in order to concentrate only on the unpredictable part of the
return series. As in Chau et al. (2014) and Bouri (2015), such a concentration is done by accounting
for potential anomalies in the data, with the aim of addressing many features in the stock indices
under study, such as potential autocorrelation, day-of-the-week effects, and worldwide price
movements. However, in addition to worldwide price movements, and unlike Bialkowski et al.
(2013), we control for potential autocorrelation and day-of-the-week effects. Such control is
important in the sense of avoiding biased estimates of return and volatility coefficients caused by
a shift of focus from the unpredictable component of returns (Chau et al., 2014; Bouri, 2015).
Therefore, the mean equation considers different autoregressive (AR), moving average (MA), and
exogenous (X) orders, leading to the adoption of an ARMAX structure. The inclusion of an ARMA
in the conditional mean equation accounts for possible nonlinearity, which is in line with the work
15
of Westerhoff and Reitz (2005) and Kyrtsou and Labys (2007), which suggests that overlooking
this characteristic may undermine some of the dynamics of the relationships of the model. The
ARMAX (p,q) model is given by:
𝑅𝑡 = 𝑎0 + 𝑎1 𝑅𝑡−𝑝 + 𝑎2 𝜀𝑡−𝑞 + 𝑎3 𝑅𝑤,𝑡 + 𝑎4 𝑅𝑤,𝑡−1 + 𝑎5 𝐷𝑎𝑦𝑡 + 𝜀𝑡
(1)
where Rt is the daily return on each price series on day t; Rt−p is the lagged daily return on each
price series; εt-q is the lagged residuals; Rw,t is the daily return on the world market index on day t;
and Dayt is a 4×1 vector of dummy variables, such that the first element is one if Dayt is Sunday
and zero if otherwise.
Regarding the conditional variance process, we consider the standard GARCH of
Bollerslev (1986) and the asymmetric GARCH of Glosten et al. (1993). Bollerslev’s (1986)
specification of the GARCH process is given by:
2
2
𝜎𝑡2 = 𝜔 + 𝛼𝜀𝑡−1
+ 𝛽𝜎𝑡−1
(2)
where  t2 is the conditional variance, 𝜺t-1 is the innovation, α represents the ARCH term which
measures the impact of past innovations on current variance, and β represents the GARCH term
which measures the impact of past variance on current variance. The sum of the ARCH and
GARCH parameters (α + β) measures the degree of persistence of the variance shock. Furthermore,
four constraints have to be respected to ensure stationarity and stability: ω > 0; α ≥ 0; β ≥ 0; and
α + β < 1.
As for the asymmetric GARCH of Glosten et al. (1993), known as the GJR-GARCH process, it is
given by:
2
2
2
𝜎𝑡2 = 𝜔 + 𝛼𝜀𝑡−1
+ 𝛽𝜎𝑡−1
+ 𝛾𝜀𝑡−1
𝐼𝜀<0 (𝜀𝑡−1 )
(3)
where I is a dummy variable that measures the asymmetric response of the conditional variance to
shocks. This dummy variable takes a value of one in response to negative shocks and zero in
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response to positive shocks. In case the asymmetric coefficient of the conditional variance γ is
significantly positive, then a negative shock leads to a rise in the future conditional variance more
than a positive shock of the same magnitude. In addition to the above-mentioned constraints in the
case of the standard GARCH model, the following constraint has to be respected in the case of the
GJR-GARCH process: α +β +0.5γ < 1.
However, a misspecification in fitting a GARCH-type model together with an imprecise
assumption of the error-term distribution may substantially undermine the efficiency of the related
estimators. To prevent such a misspecification, we conduct extensive specification tests for the
most appropriate ARMAX-(GJR)-GARCH process along with its corresponding error distribution
(normal, t-student, or generalized error distributions), instead of an ad hoc selection. Those tests
are particularly important for the analysis, because all the return series under study showed
evidence of non-normality, volatility clustering, and fat-tailedness (Table 1.a and 1.b). Based on
the Schwarz Bayesian information criterion (SIC), we select the order of the ARMA specification
and the type and density of the GARCH formulation (Beine and Laurent, 2003). The SIC is known
for leading to a parsimonious specification. Several diagnostic tests for the residuals and the
squared residuals are conducted to evaluate the goodness of fit of the selected models.
4.2.2. Step two
After selecting the best conditional mean and conditional variance, the next step consists
of testing whether the holy month of Ramadan has increased or decreased the returns/volatility of
stock market indices. To this end, we add a dummy variable into the best conditional mean and
conditional variance processes. For the conditional mean process, the adopted model is:
𝑅𝑡 = 𝑎0 + 𝑎1 𝑅𝑡−𝑝 + 𝑎2 𝜀𝑡−𝑞 + 𝑎3 𝑅𝑤,𝑡 + 𝑎4 𝑅𝑤,𝑡−1 + 𝑎5 𝐷𝑎𝑦𝑡 +𝑎6 𝑅𝑎𝑚𝑎𝑑𝑎𝑛𝑡 + 𝜀𝑡
17
(4)
where 𝑅𝑎𝑚𝑎𝑑𝑎𝑛t is a dummy variable that takes the value of one during the exact days of
Ramadan and the days surrounding the festival (Eid ul-Fitr) that follows it, and zero otherwise. If
the parameter a6 is significant and negative (positive), this indicates a(n) decrease (increase) in the
mean returns during the month of Ramadan as compared to all other months of the Islamic calendar
year.
The adapted model for the GJR-GARCH process is given by:
2
2
2
𝜎𝑡2 = 𝜔 + 𝛼𝜀𝑡−1
+ 𝛽𝜎𝑡−1
+ 𝛾𝜀𝑡−1
𝐼𝜀<0 (𝜀𝑡−1 ) + 𝜑 𝑅𝑎𝑚𝑎𝑑𝑎𝑛𝑡
(5)
If the parameter 𝜑 is significant and negative (positive), this indicates a(n) decrease (increase) in
the conditional volatility during the month of Ramadan as compared to all other months of the
Islamic calendar year.
Given the dummy variable Ramadan only considers the Ramadan effects and disregards
the possibility of post-Ramadan effects, we modified its (above-mentioned) definition in order to
account for the positive post-festivity sentiment. Following Bialkowski et al. (2013), the Ramadan
dummy takes the value of one during the days of the month of Ramadan and the seven days that
follow it.
In addition to testing the Ramadan effect on the returns and the conditional volatility during
the whole month of Ramadan, we also examine this effect in the first 10 days, the second 10 days,
and the last 10 days of Ramadan. Accordingly, three dummy variables are defined to capture the
Ramadan effect in those three time periods. Ramadan 1–10,t is a dummy variable that takes the value
of one if day (t) falls on days 1–10 of Ramadan, and zero otherwise. Ramadan 11–20,t is a dummy
variable that takes the value of one if day (t) falls on days 10–20 of Ramadan, and zero otherwise.
Ramadan 21–30,t is a dummy variable that takes the value of one if day (t) falls on days 21–30 of
Ramadan, and zero otherwise.
18
To test whether the Ramadan effect is different before the Arab spring period as opposed
to the period after the Arab spring, we split the entire sample period into two sub-periods (a preArab spring sub-period covering the period from December 31, 2005 to December 17, 2010, and
a post-Arab spring sub-period spanning from December 18, 2010 to December 31, 2015), and reestimate the models given by equations 4 and 5. We also test whether the Ramadan effect is
different before and after the global financial crisis (GFC). To this end, the entire sample period is
divided into a pre-GFC period (December 31, 2005 to December 31, 2007) and a post-GFC period
(December 31, 2008 to December 31, 2015). It is worth noting that the year 2008 has been
excluded from the analysis. This is simply because during that year the holy month of Ramadan
coincided with the month of September, when the GFC hit its most critical stage with the collapse
of Lehman Brothers.
5.
Empirical findings
5.1.
Mean and variance equations
For each return series, the best univariate model for the mean and variance is selected on
the basis of the SIC method, which is known for leading to a parsimonious specification (Beine
and Laurent, 2003). As shown in Table 2, the best univariate model for Bahrain and Egypt is the
ARMAX(1,1)-GJR-GARCH(1,1), whereas for Jordan the ARMAX(1,1)-GARCH (5,1) model is
the best. For the remaining cases, the ARMAX(1,1) representation of the GARCH(1,1) model is
the best univariate framework. Moving to the estimated coefficients, the results in Table 2 show
that several of the ARMAX structure coefficients are statistically significant. The coefficient on
movements in world stock prices is statistically significant in most cases, suggesting that the stock
market indices under study are influenced by movements in their global counterparties. Few of the
coefficients on the day-of-the-week effects are significant. This result shows numerous Islamic
19
stock markets exhibit a trend toward increased weak-form efficiency and the disappearance of the
day-of-the-week effects. As for autocorrelation coefficients, they are significant in six cases, and
in most of these cases they are positive. As for the conditional variances process, all the coefficients
of the GARCH and ARCH parameters are statistically significant at 1% levels, implying the
presence of strong GARCH and ARCH effects. In all cases, the sum of the ARCH and GARCH
terms is close to one, implying high variance persistence. As for the conditions of stability and
stationarity of the selected models, they are all met. Put differently, the constant, ARCH, and
GARCH coefficients are positive, and the sum of the ARCH and GARCH coefficients is less than
unity, indicating that the conditional variance is stationary over a wide range for all cases.
Regarding the return distributions of the GARCH model, it is worth mentioning that the normal
distribution is the best-fit distribution in all cases except that of Turkey, where the GED
distribution performed better.
To assess the goodness of fit for the selected models, we report the results of diagnostics
tests. As shown in the last two rows of Table 2, and with the exception of Jordan, Kuwait, and
Oman, the results of Box–Pierce and ARCH tests show no evidence of significant autocorrelation
in squared residuals and heteroscedasticity in the residuals at the conventional level of 5%. In those
three cases, the empirical results on the Ramadan effects should therefore be interpreted with
caution.
[Insert Table 2 here]
5.2.
Results of the return and volatility effects of Ramadan
In this section, we focus on the effect of the holy month of Ramadan on the returns and
volatility of the 15 countries under study. To this end, we give particular attention to the coefficient
of the dummy variable Ramadan. The parameter a6 in equation 4 gives an indication of whether
20
there is a change in the stock market return during Ramadan, whereas the parameter 𝜑 in equation
5 focuses on the impact of Ramadan on the stock market volatility.
After observing that stock markets in Islamic countries exhibit a trend toward weak-form
market efficiency, the sign and the size of the average monthly returns are now examined. If
Ramadan affects Islamic investors’ mood positively, then one would expect positive stock returns
during Ramadan. The results presented in Table 2 (under the Ramadan row) show positive returns
for 12 of the 15 countries, with an average return across the 15 markets of 5.69%.
First, we consider the Ramadan effects in the mean and variance equations. Results from
Table 2 indicate that stock market returns increase during the holy month of Ramadan in all the
countries under study except Indonesia, Malaysia, and Saudi Arabia. The decrease in the return
for Saudi Arabia is almost negligible (0.002). Statistical significance alone is not enough to assess
the “importance” of returns. For example, in a small sample, a result that is statistically
insignificant might be economically important. In evaluating the substantive importance of the
returns during Ramadan, we need to examine the size of the increase in returns. For example, eight
Islamic markets (Abu Dhabi, Egypt, Jordan, Kuwait, Oman, Pakistan, Qatar, and Tunisia) have
stock returns of 5% or more during Ramadan. In an economic sense, these increases represent a
significant increase in the returns of a single month. If we extrapolate this return to the entire year,
this will give us more than a 60% annual return for each market. Similarly, the volatility during
the holy month of Ramadan decreased in 11 countries out of 15. The decrease in volatility is
significant in seven countries out of the 11—Abu Dhabi, Bahrain, Dubai, Egypt, Indonesia, Oman,
and Pakistan—while it increased significantly in Saudi Arabia. Interestingly, the volatility
increases, but not significantly, in Jordan, Malaysia, and Qatar.
21
In summary, we find a fair increase in stock returns with a reduction in volatility during
the holy month of Ramadan in the majority of Islamic countries under study. But the increases in
returns are not statistically significant except in Dubai, implying that the stock returns in most of
the Islamic countries are not significantly different during the month of Ramadan from the other
months of the Islamic calendar. Unlike the impact of Ramadan on daily returns, the effect on
conditional volatility is significant and pronounced in seven countries and decreased by a fair
amount in four countries.
The increase in stock returns and the drop in return volatility during the month of Ramadan
may be due to a change in investor psychology, and this is a result of more religious practice.
During the fasting period, most people in Muslim countries pay more attention to worshiping God
by increasing their Ramadan activities.5 Ramadan is a religious month that affects almost every
aspect of Muslims’ lives. Thus, investors devote more time to religious activities than to the stock
market in the holy month of Ramadan. We conclude that the drop in return volatility during the
month of Ramadan may be due to a change in investor behaviour which is caused by more religious
practice. This may lead to a change in their stock market trading activities. In general, the economic
activities in most of the Islamic countries slow down, with reduced working hours during the
daytime in virtually all sectors. However, grocery sales go up. Similarly, electricity consumption
increases during the night as a result of an increase in late-night socio-religious activities and
shopping. Trading in securities is likely to decline during the month of Ramadan for two reasons:
First, some Muslims consider speculative trading a form of gambling, which is prohibited by
Activities during Ramadan: reciting the Holy Qu’ran, suhoor (meal before dawn), iftar (breaking of the fast at
sunset), taraweeh (optional prayer at night), qiyam (optional late-night prayers, especially in the last 10 nights of
Ramadan), and visits (social gatherings, and sharing food and gifts with neighbors, friends, and the poor).
5
22
Islam; and, second, the use of leveraging (margin trading) or trading in interest-based securities
may decline in view of the strict prohibition against the use of interest or riba (Al-Ississ, 2010).
Having reported positive returns and low volatility in most of the Muslim countries, the
results in Table 2 provide evidence to suggest that the generally positive mood of the population
that exists throughout the period of Ramadan has a positive impact on stock prices. If the social
mood is positive, investors are more likely to have optimistic expectations about future stock
performance. Muslim investors believe that through good actions, they will be rewarded twice as
much in the month of Ramadan as they normally would. As a result, they may expect to gain a
higher return per unit of risk during Ramadan. To the extent that Ramadan generates a positive
mood, there may be an increased tendency to invest in stocks, and the positive mood could cause
investors to be less discriminating and less analytical in relation to their investments.
Apart from Ramadan, there are important Islamic events in the other 11 Islamic months.
For example, at the start of Shawwal, the month following Ramadan, Muslims celebrate the
festival of Eid ul-Fitr, which marks the end of fasting. The behaviour of the people returns to
normal after Eid ul-Fitr. In addition to the effects of the actual fasting days of Ramadan on stock
returns and volatility, which were presented in Table 2 and discussed above, we next consider the
possibility of post-Ramadan effects on the returns and volatility of stock markets. To this end, we
modify the former definition of the Ramadan dummy by making it cover the exact days of the
month of Ramadan and the seven trading days that follow it. This is based on the rationale that the
positive sentiment of stock market participants may extend to the seven days following Ramadan.
Table 3 shows the results of re-estimating the models for the period of Ramadan and the seven
days following Ramadan. The results show that the returns have increased in 13 countries out of
15, with an average return of 7.17% across the 15 markets. As for the volatility, it has decreased
23
in nine countries, but only significantly decreased in four countries: Abu Dhabi, Bahrain, Egypt,
and Oman. The volatility of returns has increased in six countries, but has only increased
significantly in Malaysia and Saudi Arabia. The results imply that during Eid ul-Fitr the
consumption of foods and particularly sweets by people increases and they pay less attention to
stock investments. After Eid ul-Fitr is concluded, people concentrate their efforts on investments
in the stock market, and as a result trading activity increases. Furthermore, purchasing activities in
Muslim society increase before Eid ul-Fitr. Usually, Muslims purchase new clothes, gifts, and food
commodities during the last few days of Ramadan in preparation for the Eid ul-Fitr celebration.
As a result of increased spending during Ramadan, many Muslims may not be able to invest in the
stock market or even to spend time on stock trading. This may result in a decrease in the volatility
of stock market volume and returns.
[Insert Table 3 here]
As we discussed before, the first part (1–10 days) of Ramadan brings God’s Mercy, the
middle part (11–20 days) brings God’s forgiveness, and the last part (21–30 days) brings
emancipation from hellfire. It is expected that the early days of Ramadan are dominated by the
physical impact of fasting rather than the spiritual (Al-Ississ, 2010). This is due to fact that the first
days of Ramadan are the hardest on those fasting, as their bodies have to adjust to a new eating
schedule and food deprivation during the day. Therefore, we present the effect of partitioning
Ramadan into three different periods on the return and volatility dynamics in the stock markets
under study. In fact, an alternative definition of the Ramadan dummy variable is provided and used
to examine the Ramadan effect in the first 10 days, the second 10 days, and the last 10 days of
Ramadan. Table 4 presents the results of the re-estimated models by including three modified
Ramadan dummies in both the mean and variance equations. The results from Table 4 show that
24
in the first 10 days of the holy month of Ramadan, the returns have increased in 12 Islamic
countries, but none of them are statistically significant. On the other hand, the returns have
decreased in three countries, but only significantly in Saudi Arabia. While no significant changes
in the returns have been reported during the second 10 days, during the third period the returns
have significantly increased in Dubai and Saudi Arabia and significantly decreased in Indonesia.
It is worth noting that the average return for all the countries during the last 10 days (8.02%) is
higher than that in the first 10 days (3.52%) and the second 10 days (0.80%).
More significant changes in the volatility are also reported in Table 4. In the first 10 days,
the volatility has decreased in six countries, but only significantly in Abu Dhabi, Bahrain, and
Oman, whereas it increased in Kuwait. In the second 10 days of the holy month of Ramadan, the
volatility has decreased in 12 countries, but only significantly in Bahrain, Dubai, Egypt, Indonesia,
Kuwait, and Oman. There is evidence of an increase in the volatility in three countries, but only
significantly in Qatar. In the last 10 days, volatility has decreased in nine countries, but only
significantly in Abu Dhabi, Bahrain, Egypt, Indonesia, Pakistan, and Qatar. In contrast, the
volatility has increased in six countries, but only significantly in Saudi Arabia.
It is worth noting that the first 10 days of Ramadan have lower changes in returns and
volatility than the last two periods (the second and third 10 days). This is consistent with our
argument that the spiritual aspects of fasting are countered by physical hardship during the earlier
days of the holy month of Ramadan. The impact of Ramadan increases on the days with higher
worship intensity. This is consistent with the above-mentioned findings that the average return for
all the countries is highest (8.02%) in the last 10 days compared to the other two periods of
Ramadan, especially to the second one. Additionally, the third period of Ramadan is perceived as
the most blessed part, during which Muslims increase their worship and experience of faith. The
25
last ten days of Ramadan contain the holiest night in the Islamic calendar—Laylat al-Qader, the
Night of Destiny, when the Qur’an was first revealed to the Prophet Muhammad. This is why we
observed more decline in return volatility in most Muslim countries during the third 10 days.
[Insert Table 4 here]
Finally, consideration is given to the possible impact of the Arab spring of 2010 and the
GFC on the effects of the holy month of Ramadan on the market return and volatility. The entire
period is divided into two sub-periods before and after the Arab spring, and equations 4 and 5 are
re-estimated. Results from Table 5 indicate that the return in the pre-Arab spring period has
insignificantly increased in 11 countries, but significantly increased in Dubai only. Similarly, the
return in the post-Arab spring period has insignificantly increased in 11 countries, but significantly
increased in only two countries: Abu Dhabi and Oman. As for the volatility, results indicate that
the return volatility during Ramadan in the pre-Arab spring period has insignificantly decreased in
five countries and significantly decreased in six countries. However, the return volatility during
Ramadan in the post-Arab spring period has significantly decreased in nine countries out of 15.
[Insert Table 5 here]
As for the possible impact of the GFC, the results in Table 6 indicate that the return in
Ramadan during the pre-GFC period has significantly increased only in Bahrain, while it has
significantly increased in Abu Dhabi and Egypt during the post-GFC period. As for the volatility,
the results indicate that the return volatility in the pre-GFC period has decreased in 13 countries,
but only significantly in five countries. Similarly, volatility has decreased in 12 countries, but
seven countries show significant decreases in volatility during the post-GFC period. Surprisingly,
this result contradicts economic intuition, as one would expect an increase in volatility during the
GFC. We infer that the decreases in volatility in Ramadan are due to religious factors.
26
In summary, we can conclude that the effect of Ramadan dominates the effects of the GFC
and the Arab spring. In other words, the positive effect of Ramadan exceeds the negative effect of
either GFC or the Arab spring on the stock market of Islamic countries. This implies that religious
practice has a more pronounced impact on the stock markets than political and/or economic
instability. This finding contradicts the intuitive view that, in times of political and civil unrest,
stock markets often experience increased levels of volatility as the occurrences of major political
events signal a potential shift in policy, which may cause market-wide valuation changes (Karolyi,
2006).
[Insert Table 6 here]
27
6.
Conclusion and summary
In this paper, we investigate whether the most celebrated religious practice in the world—
the Muslim holy month of Ramadan—can, through its influence on investors’ moods and
emotions, affect the behaviour of the stock markets and investors in Islamic countries. While the
essential role played by religion in the stock markets has been investigated in prior studies, this
paper provides richer analyses on this role. To capture the impact of Ramadan on stock return and
volatility, we apply an ARMAX-GARCH framework to daily stock indices of 15 Muslim countries
from December 31, 2005 to December 31, 2015.
The main evidence indicates that, in most of the Islamic countries under study, the
Ramadan mean returns are positive and higher than non-Ramadan mean returns, whereas the
standard deviation of returns in the month of Ramadan is significantly lower than that in nonRamadan months. These findings do not only support the hypothesis that the month of Ramadan
is less risky than the other months of the Islamic year, but also suggest that the general positive
mood of the population that exists throughout the period of Ramadan has a positive impact on
stock returns per unit of risk. This evidence confirms the standpoint that investor behaviour and
sentiment affect stock return and volatility.
To establish the robustness of the above findings, alternative definitions of Ramadan are
considered. First, the analysis is extended to include the seven days post-Ramadan, for which the
results remain qualitatively the same. Second, further analysis has also focused on the days with
higher worship intensity and has shown that the first 10 days of Ramadan have lower changes in
return and volatility than the second and third 10 days. This result is consistent with our argument
that the spiritual aspects of fasting are countered by physical hardship during the earlier days of
the holy month of Ramadan, suggesting that the impact of Ramadan increases on the days with
28
higher worship intensity. Third, the sample period is divided equally into two periods of five years
each, relating to before and after the Arab spring of 2010. Interestingly, we observe that the
increase in return and decrease in volatility during Ramadan is mainly affected by religious
practice and not by political uncertainty. Similar evidence is reported on the impact of the GFC.
Three main policy implications can be drawn. First, the findings of this study may help
investors in understanding the impact of religious practice on stock market behaviour relative to
political and economic events. Second, Ramadan anomalies are important to stock market
participants who are keen to generate excess return. Accordingly, investors would buy shares at
the beginning of (or before) the month of Ramadan and sell them near (or at) the end of the month,
thereby generating profit from the price differences. The Ramadan effect may also allow investors
to generate profit by taking advantages of the three periods of Ramadan (first, second, and third
10 days). Those reported anomalies may lead to an increase in the capital inflows to the stock
markets in Islamic countries and thereby the stock market liquidity. Finally, policymakers in
Islamic countries should consider revisiting current regulations and establishing new rules that
might help in eliminating or reducing the excess return during the holy month of Ramadan.
29
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The Quarterly Review of Economics and Finance, 54(4), pp. 529–537.
33
Table 1.a: Summary statistics of daily returns
Mean
-0.006
-0.086
-0.031
0.008
0.053
-0.025
-0.024
0.022
0.004
-0.013
0.012
-0.002
-0.034
Max
8.250
11.499
12.205
7.314
7.623
4.702
9.200
4.712
5.011
10.749
9.330
9.422
16.400
Min
-8.679
-11.147
-9.620
-11.117
-10.954
-8.855
-10.518
-10.242
-5.884
-10.994
-12.890
-9.359
-11.682
Std. Dev.
1.193
1.221
1.824
1.684
1.399
1.009
1.302
0.815
0.995
1.175
1.473
1.430
1.689
Skewness
Kurtosis
Jarque–Bera
Prob.
ARCH-LM
Prob.
ADF test
Prob.
ABUDHABI
-0.225
11.704
8257
0.000
52.651
0.000
-44.230
0.000
BAHRAIN
-0.564
16.582
20192
0.000
22.276
0.000
-47.970
0.000
DUBAI
-0.140
8.631
3455
0.000
57.898
0.000
-33.218
0.000
EGYPT
-0.701
7.339
2260
0.000
25.851
0.000
-43.343
0.000
INDONESIA
-0.672
10.596
6468
0.000
34.464
0.000
-46.135
0.000
JORDAN
-0.410
9.151
4187
0.000
78.432
0.000
-43.117
0.000
KUWAIT
-0.879
15.534
17413
0.000
60.328
0.000
-48.164
0.000
MALYSIA
-0.961
16.491
20186
0.000
16.619
0.000
-45.372
0.000
MOROCCO
-0.205
6.595
1423
0.000
29.097
0.000
-42.589
0.000
OMAN
-0.845
23.756
47141
0.000
44.143
0.000
-47.041
0.000
PAKISTAN
-0.589
8.419
3343
0.000
70.225
0.000
-30.905
0.000
QATAR
-0.458
11.845
8596
0.000
60.071
0.000
-43.733
0.000
SAUDI
-0.631
14.874
15501
0.000
71.957
0.000
-47.016
0.000
ARABIA
TUNISIA
0.022
9.468
-6.997
0.884
0.150
13.598
12220
0.000
16.543
0.000
-44.762
0.000
TURKEY
0.017
12.723 -10.761
1.798
-0.129
6.465
1312
0.000
21.471
0.000
-50.397
0.000
Note: ADF (Augmented Dickey–Fuller) test with an intercept; ARCH-LM test ARCH-LM (Engle Lagrange multiplier) tests the null hypothesis that there is no
presence of an ARCH process in the residuals up to 10 lags; for each return series, the number of daily observations is 2609.
34
Table 1.b: Summary statistics of daily Ramadan and non-Ramadan returns
Mean
Std.
Skewness Kurtosis Jarque–Bera
P
T-test
P
F-test
P
Ramadan
0.117
1.113
-0.275
10.035
500.108
0.000
-1.790*
0.074
1.160*
0.067
0.000
Non-Ramadan
-0.018
1.200
-0.217
11.828
7708.343
0.000
Ramadan
-0.091
0.975
-1.145
9.693
502.599
BAHRAIN
0.087
0.930
1.624***
0.000
0.000
Non-Ramadan
-0.086
1.244
-0.533
16.616
18405.850
0.000
Ramadan
0.203
1.637
0.241
10.715
600.147
DUBAI
-2.297** 0.022
1.263***
0.009
0.000
Non-Ramadan
-0.054
1.840
-0.159
8.468
2960.138
0.000
Ramadan
0.043
1.412
-0.672
6.725
157.508
EGYPT
-0.404
0.686
1.465***
0.000
0.000
Non-Ramadan
0.004
1.710
-0.698
7.296
2013.014
0.000
Ramadan
0.063
1.224
-0.695
7.987
269.224
INDONESIA
-0.144
0.885
1.337***
0.001
0.000
Non-Ramadan
0.052
1.415
-0.668
10.679
5994.240
0.000
Ramadan
0.026
0.897
0.293
6.795
148.084
JORDAN
-0.930
0.352
1.290***
0.005
0.000
Non-Ramadan
-0.031
1.020
-0.455
9.241
3924.128
0.000
Ramadan
0.031
1.088
-1.857
17.034
2116.608
KUWAIT
-0.818
0.413
1.474***
0.000
0.000
Non-Ramadan
-0.030
1.321
-0.817
15.322
15243.470
0.000
Ramadan
0.018
0.669
0.113
7.999
251.501
MALYSIA
0.101
0.919
1.528***
0.000
0.000
Non-Ramadan
0.023
0.828
-1.014
16.676
18858.820
0.000
Ramadan
-0.062
0.996
0.025
7.242
180.766
MOROCCO
1.086
0.278
0.995
0.470
0.000
Non-Ramadan
0.011
0.995
-0.228
6.539
1256.627
0.000
Ramadan
0.066
0.968
-0.328
13.117
1032.229
OMAN
-1.308
0.191
1.517***
0.000
0.000
Non-Ramadan
-0.021
1.193
-0.863
23.966
43663.140
0.000
Ramadan
0.176
1.104
0.090
5.792
78.657
PAKISTAN
-2.339** 0.019
1.856***
0.000
0.000
Non-Ramadan
-0.005
1.505
-0.597
8.318
2931.257
0.000
Ramadan
0.078
1.498
-0.101
14.486
1325.250
QATAR
-0.877
0.381
0.900
0.128
0.000
Non-Ramadan
-0.010
1.422
-0.502
11.501
7230.656
0.000
Ramadan
-0.034
1.289
-1.244
12.759
1018.672
SAUDI
0.003
0.997
1.788***
0.000
ARABIA
0.000
Non-Ramadan
-0.034
1.724
-0.600
14.671
13582.870
0.000
Ramadan
0.074
0.772
-0.416
6.327
118.162
TUNISIA
-1.075
0.282
1.341***
0.001
0.000
Non-Ramadan
0.017
0.895
0.191
13.942
11828.460
0.000
Ramadan
0.090
1.860
0.955
12.924
1025.696
TURKEY
-0.640
0.522
0.927
0.205
0.000
Non-Ramadan
0.010
1.792
-0.253
5.682
734.927
The significant results are in bold. *, **, and *** denote that the T-test and F-test are significant at 10%, 5%, and 1%, respectively. The three-month US T-bill
rate is proxy for the risk-free rate.
ABU
DHABI
35
Table 2: Ramadan effect on the return and volatility of stock markets
Abu Dhabi
Bahrain
Dubai
Egypt
Indonesia
Jordan
Malaysia
Morocco
-0.011
-0.001
-0.115
0.036
0.131
-0.011
-0.016
0.064
0.034
-0.080
0.093
-0.001
World
0.087
0.024
0.257
0.227
0.322
0.029
0.063
0.143
0.022
0.097
0.011
World(-1)
0.158
0.096
0.050
0.016
0.173
0.385
0.058
D1
-0.028
-0.316
0.209
-0.007
0.358
-0.050
0.075
0.059
0.070
0.077
0.237
-0.020
0.057
0.038
0.144
0.078
0.117
-0.214
-0.030
-0.064
-0.098
0.057
-0.021
0.121
-0.088
-0.028
0.017
0.125
-0.164
0.092
D2
0.052
-0.078
-0.049
D3
0.032
-0.069
0.100
-0.066
0.042
-0.020
0.006
-0.028
D4
0.054
AR(1)
0.424
0.031
0.298
0.041
0.014
-0.664
0.135
-0.140
-0.098
0.340
0.731
-0.010
-0.288
MA(1)
-0.286
-0.252
0.624
0.298
-0.783
0.110
Variance equation
C
0.028
0.096
0.074
0.266
0.050
ARCH
0.157
0.061
0.137
0.073
0.150
GARCH
0.841
-
0.835
0.717
0.088
0.847
-
-0.022
-
-0.049
-
5.253
4.585
Mean equation
C
RAMADAN
Asymmetric term
RAMADAN
GED Parameter
Model diagnostics
Q-squared (10)
Kuwait
Oman
Pakistan
Qatar
Saudi
Tunisia
Turkey
0.010
0.051
0.009
0.137
0.251
0.011
0.724
0.165
0.104
0.124
-0.006
0.058
0.106
0.020
-0.006
-0.036
-0.013
0.097
0.075
-0.213
-0.058
-0.026
-0.068
-0.048
0.044
-0.014
0.126
-0.027
0.070
0.128
0.040
-0.057
-0.053
0.116
-0.061
0.052
0.148
-0.088
0.025
0.257
0.071
0.711
0.664
0.208
0.820
0.254
-0.349
0.284
-0.234
0.001
-0.640
-0.554
-0.105
-0.781
-0.188
0.291
0.001
0.019
0.011
0.059
0.033
0.068
0.013
0.025
0.035
0.056
0.106
0.072
0.109
0.150
0.168
0.210
0.156
0.139
0.184
0.058
0.978
-
0.918
-
0.873
-
0.795
-
0.834
-
0.793
-
0.857
-
0.864
-
0.810
0.213
0.827
-
0.920
-
-0.031
-
-0.115
-
-0.033
-
0.002
-0.008
0.001
-0.023
-
-
-
-0.037
-
0.004
-
-0.023
-
-
0.020
-
7.460
5.276
15.522
52.763
113.780
9.041
13.713
21.819
6.611
15.506
2.333
0.024
-0.055
-0.004
-0.033
-
1.375
16.692
6.472
ARCH (10)
0.507
0.441 0.758 0.528
1.444
0.888
1.381
0.658
1.552 0.234
1.634
0.654
4.898
10.379
2.063
Notes: AR and MA represent respectively autoregressive and moving averages variables; d1, d2, d3, and d4 represent the day-of-the-week effect; Ramadan is a
dummy variable that takes the value of one during the exact days of Ramadan and zero otherwise; the Box–Pierce Q-squared statistic tests the null hypothesis of
no autocorrelation up to order 10 for squared values; ARCH-LM statistic tests the null hypothesis of no conditional heteroscedasticity up to 10 lags; statistically
significant results at 5% level are reported in bold.
36
Table 3: Ramadan effect on the return and volatility of stock markets—first alternative definition
Abu
Dhabi
Bahrain
Dubai
-0.013
-0.003
-0.111
World
0.088
0.023
World(-1)
0.157
RAMADAN+7
Egypt
Indonesia
Jordan
0.031
0.129
-0.014
0.258
0.228
0.322
0.051
0.175
0.387
0.101
0.023
0.157
D1
-0.030
-0.317
D2
0.047
-0.078
D3
0.026
D4
Kuwait
Malaysia
Morocco
Oman
Pakistan
Qatar
Saudi
Tunisia
Turkey
-0.019
0.064
0.020
-0.085
0.096
-0.004
0.001
0.045
0.005
0.029
0.063
0.144
0.020
0.097
0.013
0.138
0.252
0.011
0.726
0.356
0.075
0.072
0.237
0.057
0.145
-
0.165
0.124
-
0.106
0.062
-0.042
0.079
0.093
-0.020
0.066
0.116
0.097
0.110
0.100
0.093
0.041
-0.014
-0.165
-0.210
-0.064
-0.099
-0.090
0.022
0.053
-0.222
-0.007
-0.040
-0.014
0.097
0.123
0.094
-0.031
-0.021
0.122
-0.027
-0.038
0.073
-0.062
-0.027
-0.068
-0.049
0.045
-0.070
0.098
-0.063
0.048
-0.020
0.004
-0.028
-0.010
0.123
-0.032
0.068
0.129
-0.089
0.040
0.047
0.030
0.296
0.137
-0.100
0.041
0.013
-0.058
-0.043
0.110
-0.066
0.050
0.145
0.024
0.023
AR(1)
0.430
0.341
-0.655
-0.124
0.734
-0.014
-0.283
0.256
0.068
0.706
0.681
0.201
0.815
0.250
-0.339
MA(1)
-0.292
-0.252
0.615
0.283
-0.786
0.113
0.279
-0.235
0.000
-0.637
-0.573
-0.098
-0.777
-0.185
0.281
C
0.028
0.096
0.069
0.264
0.045
0.001
0.017
0.009
0.062
0.030
0.065
0.013
0.025
0.034
0.056
ARCH
0.158
0.063
0.136
0.073
0.146
0.106
0.070
0.107
0.152
0.164
0.214
0.157
0.141
0.184
0.058
GARCH
0.841
0.833
0.849
0.720
0.831
0.978
0.921
0.876
0.792
0.837
0.792
0.856
0.862
0.810
0.920
-
0.089
-
0.211
-
-
-
-
-
-
-
-
-
-0.054
-
RAMADAN +7
-0.019
-0.038
-0.007
-0.098
0.007
0.001
0.002
0.006
-0.023
-0.007
-0.013
0.003
0.018
-0.001
-0.021
GED parameter
-
-
-
-
-
-
-
-
1.091
-
-
-
-
-
5.349
4.441
7.273
5.367
15.262
52.560
111.510
9.248
13.667
21.670
15.390
2.318
16.632
6.395
Mean equation
C
Variance equation
Asymmetric term
Model diagnostics
Q-squared (10)
6.260
ARCH (10)
0.516
0.428 0.737
0.538
1.430
0.908
1.383
0.622
1.541
0.233
1.628
0.647
4.881
10.196
2.054
Notes: AR and MA represent respectively autoregressive and moving averages variables; d1, d2, d3, and d4 represent the day-of-the-week effect; Ramadan is a
dummy variable that takes the value of one during the days of the month of Ramadan and the seven days that follow it, and zero otherwise; the Box–Pierce Qsquared statistic tests the null hypothesis of no autocorrelation up to order 10 for squared values; ARCH-LM statistic tests the null hypothesis of no conditional
heteroscedasticity up to 10 lags; statistically significant results at 5% level are reported in bold.
37
Table 4: Ramadan effect on the return and volatility of stock markets—second alternative definition
Abu
Dhabi
Bahrain
Dubai
Egypt
Indonesia
Jordan
Kuwait
Malaysia
Morocco
Oman
Pakistan
Qatar
Saudi
Tunisia
Turkey
Mean equation
C
0.023
0.000
-0.110
0.038
0.131
-0.011
-0.014
0.064
0.024
-0.078
0.042
0.001
0.010
0.042
0.009
RWORLD
0.086
0.024
0.255
0.228
0.322
0.028
0.061
0.144
0.020
0.099
0.004
0.138
0.249
0.012
0.724
RWORLD(-1)
0.161
0.050
0.166
0.385
0.362
0.074
0.071
0.238
0.053
0.146
-
0.166
0.120
-
0.108
RAMADAN 1-10
0.103
0.124
0.064
0.059
0.071
-0.015
0.090
0.003
-0.077
0.070
0.068
0.045
-0.226
0.043
0.107
RAMADAN 11-20
0.070
-0.077
0.078
-0.054
-0.013
0.103
-0.015
-0.002
0.027
0.101
0.170
0.071
-0.055
0.019
-0.002
RAMADAN 21-30
0.102
0.022
0.364
0.171
-0.224
0.092
0.134
-0.059
0.073
0.039
-0.040
0.132
0.320
0.107
-0.030
D1
-0.044
-0.317
-0.021
-0.168
-0.196
-0.063
-0.105
-0.088
0.029
0.052
-0.137
-0.013
-0.034
-0.012
0.100
D2
0.000
-0.081
0.122
0.089
-0.016
-0.020
0.120
-0.028
-0.039
0.067
0.009
-0.030
-0.067
-0.049
0.043
D3
-0.020
-0.074
0.087
-0.070
0.039
-0.019
0.011
-0.029
-0.004
0.123
0.034
0.071
0.129
-0.089
0.041
D4
0.035
0.031
0.307
0.135
-0.104
0.041
0.012
-0.057
-0.036
0.121
0.017
0.056
0.145
0.026
0.024
AR(1)
0.070
0.346
-0.677
-0.141
0.724
-0.016
-0.276
0.257
0.067
0.707
0.156
0.210
0.746
0.219
-0.322
MA(1)
0.072
-0.259
0.637
0.299
-0.780
0.115
0.272
-0.234
0.003
-0.638
-0.034
-0.108
-0.706
-0.151
0.263
C
0.059
0.095
0.069
0.266
0.072
0.001
0.018
0.011
0.051
0.032
0.090
0.013
0.025
0.039
0.058
ARCH
0.233
0.059
0.134
0.071
0.179
0.106
0.070
0.109
0.130
0.167
0.306
0.154
0.144
0.167
0.058
GARCH
0.756
0.836
0.852
0.718
0.787
0.978
0.921
0.873
0.820
0.835
0.655
0.858
0.860
0.795
0.919
-
0.089
-
0.217
-
-
-
-
-
-
-
-
-
-
-
RAMADAN 1-10
-0.074
-0.024
-0.058
-0.042
0.006
0.004
0.061
0.000
0.080
-0.038
-0.071
0.014
0.001
0.015
0.030
RAMADAN 11-20
-0.021
-0.065
-0.107
-0.140
-0.053
-0.001
-0.076
0.003
-0.063
-0.027
0.042
0.041
-0.036
-0.021
-0.080
RAMADAN 21-30
-0.061
-0.053
0.057
-0.167
-0.088
0.003
0.016
0.001
-0.039
0.008
-0.238
-0.035
0.074
-0.001
-0.034
-
-
-
-
-
-
-
-
1.100
-
-
-
-
-
1.374
Q-squared (10)
5.076
4.641
7.837
5.220
10.604
53.432
109.300
9.019
15.101
22.347
13.187
15.484
2.517
15.940
6.175
ARCH (10)
0.482
0.447
0.796
0.523
1.018
4.957
10.023
0.886
1.497
2.118
1.298
1.548
0.253
1.570
0.623
Variance equation
Asymmetric term
GED parameter
Model diagnostics
Notes: AR and MA represent respectively autoregressive and moving averages variables; d 1, d2, d3, and d4 represent the day-of-the-week effect; Ramadan 1–10,t is a dummy variable that takes the value of
one if day (t) falls on days 1–10 of Ramadan, and zero otherwise; Ramadan 11–20,t is a dummy variable that takes the value of one if day (t) falls on days 10–20 of Ramadan, and zero otherwise; Ramadan
21–30,t is a dummy variable that takes the value of one if day (t) falls on days 21–30 of Ramadan, and zero otherwise; the Box–Pierce Q-squared statistic tests the null hypothesis of no autocorrelation up to
order 10 for squared values; ARCH-LM statistic tests the null hypothesis of no conditional heteroscedasticity up to 10 lags; statistically significant results at 5% level are reported in bold.
38
Table 5: Ramadan effect on the return and volatility of stock markets—before and after the Arab spring
Abu Dhabi
Bahrain
Dubai
0.045
0.072
0.209
-0.056
-0.066
0.128
-0.018
Egypt
Indonesia
Jordan
Kuwait
Malaysia
Morocco
Oman
Pakistan
Qatar
0.058
-0.050
0.059
0.077
-0.020
0.038
0.078
0.117
0.104
-0.031
-0.115
-0.033
0.002
-0.008
0.001
-0.023
-0.023
-0.037
-0.032
0.147
0.091
-0.041
0.011
0.002
-0.051
0.093
0.099
0.000
-0.072
-0.195
-0.042
-0.074
-0.027
0.007
-0.027
-0.035
Saudi
Tunisia
Turkey
-0.006
0.058
0.020
0.004
0.020
-0.004
-0.033
0.048
0.110
0.117
0.045
0.013
-0.054
-0.013
0.004
0.001
-0.039
Before the Arab spring
Mean equation
RAMADAN
Variance equation
RAMADAN
After the Arab spring
Mean equation
RAMADAN
Variance equation
RAMADAN
Notes: The period before the Arab spring spans from December 31, 2005 to December 17, 2010; the period after the Arab spring spans from December 18, 2010 to December 31,
2015; Ramadan is a dummy variable that takes the value of one during the exact days of Ramadan and zero otherwise. Except for the cases of Jordan, Kuwait, and Oman,
unreported results of model diagnostics show no evidence of significant autocorrelation in squared residuals and heteroscedasticity in the residuals at the conventional level of 5%.
39
Table 6: Ramadan effect on the return and volatility of stock markets—before and after the GFC
Abu
Dhabi
Bahrain
Egypt
Dubai
Indonesia
Jordan
Kuwait
Malaysia
Morocco
Oman
Pakistan
Qatar
Saudi
Tunisia
Turkey
Before the GFC
Mean equation
RAMADAN
0.059
0.239
0.158
-0.101
-0.091
0.171
0.209
-0.124
-0.019
0.300
0.404
-0.005
-0.006
-0.017
0.063
Variance equation
RAMADAN
-0.059
-0.120
-0.048
-0.020
-0.161
-0.715
-0.069
0.000
-0.054
-0.029
-0.085
-0.152
0.020
-0.151
-0.017
0.117
0.018
0.217
0.081
-0.022
0.035
0.034
-0.007
0.060
0.050
0.050
0.106
0.043
0.058
-0.024
Variance equation
RAMADAN
-0.015
-0.027
-0.045
-0.174
-0.020
0.000
-0.017
0.007
-0.028
-0.039
-0.011
-0.004
0.021
-0.004
-0.042
After the GFC
Mean equation
RAMADAN
Notes: The period before the GFC spans from December 31, 2005 to December 31, 2007; the period after the GFC spans from December 31, 2008 to December 31, 2015; Ramadan
is a dummy variable that takes the value of one during the exact days of Ramadan and zero otherwise. Except for the cases of Jordan, Kuwait, and Oman, unreported results of model
diagnostics show no evidence of significant autocorrelation in squared residuals and heteroscedasticity in the residuals at the conventional level of 5%.
40