ABSTRACT
Understanding the stock return volatility in emerging markets especially in policy formulation and investment decision making has been extensively explored in the financial literature. This research investigated the presence and pattern of volatility clustering, leverage effects, risk of stock returns and news arrival in emerging markets of West African sub region with evidence from Abidjan, Ghana and Nigeria. The study made use of un-aggregated data which covered the period from December 1, 2011 to January 31, 2019. Descriptive statistics was used to analyze the data while GARCH (1,1), GJR-GARCH (1,1), TGARCH (1,1) and GED GARCH models were used to test the stated hypotheses. The results provided evidence to show that volatility clustering, leverage effects, persistent volatility and non-normality of stock return distributions characterized the return series across the selected markets and that trading volume (news arrival) significantly influenced the stock return volatility in West African emerging markets. The results also affirmed that yesterday’s volatility has greater influence in explaining today’s volatility and a significant risk premium was prevalent across the selected emerging markets. In comparative analysis, the study observed that stock return in Nigerian stock market is more volatile with higher risk premium than that of Ghana and Abidjan. The outcome of this study is of immense use to financial professionals, investors, market regulators and the government. The study recommended enhanced policies, quality trading instruments, robust capital markets, and stricter regulatory surveillance to checkmate the stylized facts of stock return volatility. The individual state governments within the sub region should control the bad news (insecurity, inflation and political unrest) which help to increase the fear index of investors, and influence investment decisions.
TABLE OF CONTENTS
Title Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgements v
Table of Contents vi
List of Tables vii
List of Figures viii
Abstract ix
CHAPTER 1:
INTRODUCTION
1.1
Background to the Study 1
1.2
Statement of the Problem
9
1.3
Objectives of the Study 11
1.4
Research Questions 13
1.5
Hypotheses
14
1.6
Significance of the Study 15
1.7
Scope of the Study 17
1.8
Limitations of the Study
17
1.9
Operational Definition of
Terms 18
CHAPTER 2: REVIEW OF RELATED LITERATURE
2.1 Conceptual Review 20
2.1.1 Concept
of stock market and the economy 20
2.1.2
Conceptual framework
23
2.1.3 Price
discovery and volatility transmission processes 24
2.1.4 Volatility
of financial instruments 27
2.1.5 Why
stock return volatility really matters 28
2.1.6 Volatility
of the stock market prices and returns 29
2.1.7 Why
stock market volatility changes over time 31
2.1.8 Stock
return and its determinants in emerging markets 33
2.1.9 Emerging market liberalization and volatility 35
2.1.10 Volatility
impacts in market returns and equity 36
2.1.11 Asymmetric
volatility in equity market 37
2.1.12 Time- varying volatility modeling; the
conditional variance exposition 38 2.1.13 Impacts of leverage effects on stock return volatility 41
2.1.14 The skewness and kurtosis of stock
return 42
2.2 Theoretical
Review
44
2.2.1 The
theory of market phases 44
2.2.2 Technical
analysis approach 45
2.2.3 The
modern portfolio theory (MPT) of
investment 47
2.2.4 Efficient
frontier theory 47
2.2.5 Conditional
volatility models 49
2.2.6 Theoretical
framework 51
2.2.6.1 The Efficient market hypothesis
(EMH)
51
2.3 Empirical
Review of Literature 54
2.3.1 The presence of arch effect, volatility
clustering and persistence of volatility
54
2.3.2 The
leverage effects and asymmetry
69
2.3.3
The leptokurtosis/ stock return distribution
76
2.3.4 Influence of trading volume on stock return
volatility 80
2.4 Summary
of Literature Review 88
2.5 Gap in Past Studies 113
CHAPTER 3:
RESEARCH METHODOLOGY
3.1 Research
Design 115
3.2 Areas
of Study 116
3.3
Nature and Sources of Data
116
3.4 Model
Specification 117
3.5 Description and Measurement of Research
Variables 122
3.6 Techniques of the Analysis 123
3.6.1 Descriptive
statistics 123
3.6.2 Inferential
statistics 125
3.7 Model
Justification 125
3.8 Decision
Rule 126
3.9 Apriori Expectations 128
CHAPTER 4: DATA PRESENTATION, ANALYSIS AND DISCUSSION
4.1 Data Presentation 129
4.2 Data Trend 129
4.3 Unit Root Test 133
4.4 Test of Descriptive Statistics 135
4.4.1 Summary of descriptive statistics 138
4.5 Data Transformation 139
4.6 Test of Hypotheses 145
4.6.1 Test of hypothesis 1: 145
4.6.2 Test of hypothesis 2: volatility clustering 147
4.6.3 Test of hypothesis 3: leverage effect 149
4.6.4 Test of hypothesis 4: the leptokurtosis of return distribution 151
4.6.5 Test of hypothesis 5: persistency of volatility
153
4.6.6 Test of hypothesis 6: the trading volume
influence on volatility 155
4.7 Comparison of Hypotheses Results For BRVM,
GSE and NSE
157
4.8 Discussion of Findings
150
CHAPTER 5: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary of Findings 171
5.2 Conclusion 172
5.3 Recommendations 173
5.4 Contributions to Existing Knowledge 175
References 177
Appendices 189
LIST OF TABLES
2.1 Return distribution characteristics 43
2.2 Summary of empirical review of literature 89
4.1 Data trend 130
4.2 Dickey – fuller unit root test with all shares index (ASI) 133
4.3 Phillips Perron unit root test with all shares index (ASI) 134
4.4 Dickey – fuller unit root test with trading volume 134
4.5 Phillip Perron unit root test with trading volume 134
4.6 Descriptive statistics of BRVM 136
4.7 Descriptive statistics of GSE 136
4.8 Descriptive statistics of NSE 136
4.9 Summary of descriptive statistics 138
4.10 GARCH (1,1) estimate for RBRVM (Arch effect) 146
4.11 GARCH (1,1) estimate for RGSE (Arch effect) 146
4.12 GARCH (1,1) estimate for RNSE (Arch effect) 146
4.13 GARCH (1,1) estimate for RBRVM (GARCH effect) 148
4.14 GARCH (1,1) estimate for RGSE (GARCH effect) 148
4.15 GARCH (1,1) estimate for RNSE (GARCH effect) 148
4.16 GJR GARCH (1,1) estimate of (RBRVM) 150
4.17 GJR GARCH(1,1) estimate of (RGSE) 150
4.18 GJR GARCH(1,1) estimate of (RNSE) 150
4.19 GED-GARCH estimate of RBRVM 152
4.20 GED-GARCH estimate of RGSE 152
4.21 GED-GARCH estimate of RNSE 152
4.22 GARCH(1,1) estimate for RBRVM (volatility persistency) 154
4.23 garch(1,1) estimate for RGSE (volatility persistency) 154
4.24 GARCH(1,1) estimate for RNSE (volatility persistency) 154
4.25 TGARCH (1,1) estimate of
RBRVM 156
4.26 TGARCH (1,1) estimate of
RGSE 156
4.27 TGARCH (1,1) estimate of
RNSE 156
4.28 Comparison of hypotheses results for
BRVM, GSE and NSE 158
4.29 Summary of empirical values
161
LIST OF
FIGURES
2.1 The
conceptual framework
24
2.2 The efficient frontier graph 48
2.3 The Arch model concept 50
4.1 Graph of level of BRVM stock exchange
(ASI) 130
4.2 Graph of level of Ghana stock exchange
(ASI) 131
4.3 Graph of level of Nigeria stock exchange
(ASI) 131
4.4 Graph of log level of BRVM trading volume 131
4.5 Graph of log level of Ghana stock exchange
(GSE) trading volume 132
4.6 Graph of log level of Nigeria stock
exchange (NSE) trading volume 132
4.7 Graph of log level and return series of
BRVM (ASI) 140
4.8 Graph of log level and return series of
Ghana stock exchange (GSE) (ASI) 140
4.9 Graph of log level and return series of
Nigeria stock exchange (NSE) (ASI) 140
4.10 Graph of log level and return series of BRVM
trading volume 141
4.11 Graph of log level and return series of
(GSE) trading volume 141
4.12 Graph of log level and return series of
(NSE) trading volume 141
4.13 Histogram of level series of BRVM (ASI) 142
4.14 Histogram
of level series of Ghana stock exchange (ASI) 142
4.15 Histogram of level series of Nigeria stock
exchange (ASI) 143
4.16 Histogram of the return series (BRVM) (ASI) 143
4.17 Histogram of the return series of Ghana
(ASI) 144
4.18 Histogram of the return series of Nigeria
stock exchange (ASI) 144
4.19 Graph of the comparison for BRVM, GSE and
NSE 158
4.20 Residual
graph of first difference of return series of (BRVM) 164
4.21 Residual
graph of first difference of return series of (GES) 164
4.22 Residual
graph of first difference of return series of (NSE) 164
4.23 Distribution
graph of first difference of return series (BRVM) 167
4.24 Distribution graph of first difference of
return series (GSE) 168
4.25 Distribution graph of first difference of
return series (NSE). 168
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND TO THE STUDY
Volatility in
stock markets has been defined as the tendency of an asset price to fluctuate either
up or down. In other words, the degree of unpredictable change in a certain
variable over time is commonly presented as volatility (Agarwnal, 2017). To
describe volatility without a specific applied metric, is the variability of
the random (unforeseen) component of a time series specially used to measure
the specific risk of a single instrument, or portfolio instruments (Acquah,
2014). According to Herbert, Ugwuanyi and Nwaocha (2019), volatility is the
risk associated with the upward and downward swings in the value of an asset.
It is a useful summary measure of the likely effect of a change in return of an
asset value. Stock return volatility can also be used to measure the random
variability of stock returns and standard deviation of daily equity returns
around the mean value, while the market volatility is the return volatility of
the aggregate market portfolio (Okicic, 2015). This implies that reasonable
changes in market volatility would reflect changes in the local or global economic
environment. Therefore, the higher the
volatility of an asset price, the riskier the security. This implies that a
highly volatile asset or security is one that experiences erratic movements,
rapid increases and dramatic falls, hitting new highs and lows in the swing
(Herbert, Ugwuanyi and Nwaocha).
In recent
times, volatility of stock market has been widely discussed as a measure of
risk in finance. It could be said to be the quantum of uncertainty or
risk about the size of changes in the value of a security or firm. This implies
that there is always a chance that investment market will decline and diminish
the value of investment or holdings. The fundamental law of investment is
associated with the uncertainty of the future returns, yet investors
(individual and institutions) have no choice but to forecast the risk and
returns of individual asset or group of assets, (Kannadhasan, 2018). Many
investors use to incorporate their expectations in capital markets while
estimating the risk and returns of individual asset or group of assets.
Investing is about risk taking and like a sharp knife, it cuts both ways. Invention and the economy are balanced on a
knife-edge, implying anxiety about the effect of risk. Both investors and
financial authorities place a lot of emphasis on the level of volatility that
can be used to measure risk and stock market stability (Zhuo and Wing, 2010).
Usually, a percentage change in prices or rate of returns is used to measure
the volatility of a financial market (Yilmaz, 1999). Modeling volatility in
financial markets provides further insight into the data generating process of
the returns (Pam and Zhang, 2006). This explains why many market risk
assessment models use estimate of volatility parameters.
The nexus
between volatility and certain economic fundamentals is still a moot point. The
need to understand the functioning of securities market has warranted
investigations into information propagation process, market volatility, and
information utilization process. The outcome of such investigation is the
development of the Efficient Market Hypothesis as an appropriate paradigm for
examining stock market return behavior. However empirical studies have
shown that volatility of financial sector in general and stock markets in
particular can adversely affect the smooth functioning of the financial system,
allocation of economic resources and have negative impact on economic growth
and development. Change in stock prices mostly reflect information and
the quicker they are in absorbing accurately new information, the more
efficient is the stock market in allocating resources. Increase in stock market
volatility can be attributed to absorption of new information about economic
fundamentals or the expectations about them. This type of volatility that has
no association with societal social cost is not harmful. But if increased
volatility is not explained by fundamental economic factors, there is the
tendency that stocks will be mispriced and this situation will lead to misallocation
of resources (Floros, 2008).
Volatility as the conditional variance is
time–varying and its concept has been used in several financial models
including amongst others, the pricing of options and corporate liabilities
(Black and Scholes, 1973) and portfolio diversification and hedging (Paul,
2006). The understanding of the resources and dynamics of volatility in a stock
market will be useful amongst other factors in the determination of the cost of
capital and in the evaluation of asset allocation decisions (Yilmaz, 1999). The
understanding will also have direct implication on investors, portfolio and
hedging strategies (Paul, 2006). Apart from investors, policy makers rely on
market estimates of volatility as a barometer of the vulnerability of financial
markets.
However, the existence of excessive volatility
in the stock market undermines the usefulness of stock process as a “signal”
about the true intrinsic value of a firm which is a concept that is code to the
paradigm of the information efficiency of markets (Njimante, 2012). Stock
market volatility also has a number of negative implications to any economy,
firm and investor. One of the ways in which it affects the economy is through
its effects on consumer spending (Okicic, 2015). The impact of stock market volatility
on consumer spending has a relationship with any economy’s wealth effect. When
wealth increases, it drives up consumer spending. However, a fall in the prices
of stocks in capital market will weaken consumer confidence and thus drive down
consumer spending. Stock market volatility may also affect business investment
and economic growth directly (Shin, 2005). A rise in stock market volatility
can be interpreted as a rise in risk of equity investment and disrupt firms’
capital structure and thus a shift of funds to less risky assets. This move
could lead to a rise in costly funds to firms and this new firms might bear
this effect as investors will turn to purchase of stock in large well known
firms.
When stock price variability reaches extreme
levels, the consequences can be adverse. First, if such variability persists,
firms are less able to use their available capital efficiently because of the
need to serve a large percentage of cash-equivalent investments in order to
re-assure lenders and regulators. Secondly, such volatility increases
market-making risks and requires their liquidity services, thereby reducing the
liquidity of the market as a whole. Thirdly, higher volatility discourages
investors from holding stocks for longer periods given that the expected
returns have to be traded off for the higher risk exposure, thus leading to
demand for higher risk premium to leverage volatility risk (Black and Scholes,
1973)
According to Bali and Scott (2017), justified
volatility can lead to efficient price discovery which can be helpful to
investors due to its certain features. Changes in volatility affect equilibrium
prices while valuation of derivative depends upon accuracy of volatility
predictions. Extreme volatility on the other hand is a dangerous signal as it
ruins the smooth working of financial system and has a negative impact on
economic performance
Stock return volatility is central to finance
whether in asset pricing, portfolio selection, risks management, policy making
and financial stability and studies focusing on the relationship between stock
returns and conditional volatility are now a continuum especially as it affects
emerging markets. Stock market returns are critical sustainability factors for
investment decisions. Investors and stock market administrators pay particular
attention to the properties of stock market volatility (stylized facts of
volatility) which include amongst others leptokurtosis, volatility clustering/
pooling, leverage effect and persistence of volatility shocks (Cont, 2001).
Leptokurtosis
implies the tendency for financial assets returns to have distributions that
exhibit fat tails and excess peakedness at the mean while Volatility clustering
is the tendency for volatility in financial markets to appear in bunches,
(Jesmina, 2014). Thus large returns (of either sign) are expected to follow
large returns and small returns (of either sign) are equally expected to be
followed by small returns. The phenomenal movement of returns is almost a
universal feature of asset return series in both developed and emerging markets
and one can attribute such behaviours as a reaction to the information arrival.
Leverage
effect means the tendency for volatility to rise more during price falling days
of stock prices than it does during price rising days of the same magnitude and
persistence of volatility shocks is the tendency for financial markets to
respond to new information with large price movements and the resultant high
volatility environments tend to last for a moment after the initial shock
(Brooks, 2008). This phenomenon is what gives rise to volatility clustering.
Ndwigam
and Muriu (2016) have examined the trend of stock returns in the African stock
exchange markets and discovered a consistently bubbling phenomenon. The
emerging markets in West African States were not left behind in this discovery.
These West African markets which include; Bourse Regionale de Valeurs
Mobilieres (BRVM) in Abidjan, Ghana Stock Exchange and Nigerian Stock Exchange
have been having the challenges in stock market development. Many regulations
have arisen in the past due to the proposition that high volatility in stock
returns adversely affect investors decisions since majority of them are risk
averse. The West African countries in their bid to develop like other sub
regions in Europe, and America need a continuous review of stock market
development which led to the establishment of the Regional Council for Public
Savings and Financial Markets (RCPSFM) with the responsibility of monitoring
and development of West African capital markets which include, Bourse
Regionale de Valeurs Mobilieres (BRVM), which situates
in Abidjan, Ghana stock market and Nigerian stock market.
Bourse
Regionale des Valeurs Mobilieres (BRVM), which covers the stock exchange
activities of Benin, Burkina Faso, Guinea Bossau, Cote d’lvoire, Mali, Niger
and Senegal started operation on September 16, 1998. The operation of the
market is entirely electronic with the central site in Abidjan and branches in
the eight member States. The principles guilding the stock market satisfy the
compliance to international standard and acceptability to the West African
Economic and Monetary Union (WAEMU) socio-economic environment which ensure
equal access to information and network costs are available to both investors
and management at the same time. The market started with two sections for
stocks exchange and a single section for bonds. The two exchange sections are
made up of the: BRVM composite index of all listed securities in the exchange
and BRVM 10 index of the ten most active traded stocks in the exchange. BRVM
activities are regulated by the Le Conseil Regionale de I’Epargne Publique et
des Marches financiers (CREPMF) that has the responsibility of establishing
procedures and policies that guide the operations of the exchange. The exchange
started with 9 listed companies in the BRVM composite section with market
capitalization of 50,000,000 CFA. After period of stagnation due to the civil
conflict in Cote d’Ivoire in the early 2000s, the listed companies had
increased to 45 which included non-Ivorian firms with participants up to 22%.
Before the contagion effect of the global stock crash, BRVM all share index got
up to 240.25 billion CFA in 2008 but declined to less than 130.12 billion CFA
in 2010/2011. From 2012 to 2014, the all share index recovered to 320.67 billion
CFA but declined gain to 141.37billion CAF in January, 2019.
Ghana
stock exchange (GSE) was established in 1989 but started actual trading
activities in 1990 with 11 listed companies. With the listing of Ashanti
Goldfields in 1994, the liquidity of the exchange market had increased and
international dimension and attention was introduced in the market. Trading
days increased from 2-3 days and some banks were listed with the exchange after
independence.
In
2004, the market made an annual return of 144% and became the best world
performing market of the year. After the official listing of Tullow oil in
2011, GSE was reported as the third largest capital market in sub-saharan
Africa after South Africa and Nigeria, which made vibrant investors to invest
in the market and the all shares index improved by 23.81 billion cedis in 2012
with 34 listed companies (Acquah-Sam, 2014).
From
2012 to 2017, the number of listed companies has improved to 42 with
capitalization of 131,633.22 billion cedis. Non resident investors were allowed
to deal in securities and can hold up to a cumulative total of 74% with a
withholding tax of 8% on dividend income.The GSE was stable until 2009 when the
contagion effect of the world financial crises with stock crash strucked. The downward
pressure came upon the market and the all shares index started declined by 11% in
January 2009. Both foreign and local investors started exiting the market which
made some equities in the market to become illiquid. This trend affected the
market to the extent that the all shares index droped below 1,000 billion
cedis, volume below 300,000 and capitalization stood at 20,000 billion Cedis in
January to May 2011. In 2012, the capitalization improved to 55,000 and
66,142.99 in 2018 while the all shares index improved from 1,000.00 in 2011 to 3,000.00
in 2018, but decline to 2500 in 2019 (Seidu, 2011).
According
to Osaze (2007), Nigerian stock exchange was incorporated as a private limited
liability company on 15th September 1960 with authorized share
capital of N10,000.00 but opened for
business on 5th June 1961
with 19 listed securities. In 1977 it was changed to Nigerian stock exchange
with six branches in major cities to meet the aspiration of the users of its
services. In 1988, the function was increased to include merger, acquisition,
privatization and commercialization. It underwent many reforms from 1989 to
2000 and in 2001 the all shares index has crossed the 10,000 point mark from
the 100 of 1984 and began operation as a floorless, electronically driven
exchange with fully automated order-driven screen-based trading system.
The
government of Nigeria among the many measures to encourage foreign investments
into the country has to abolish legislation preventing the flow of foreign capital.
This allowed foreign brokers to enlist as dealers on the exchange. Nigerian
companies are equally allowed for multiple and cross boarder listings on foreign
capital markets.
The
Nigerian Stock Exchange is been regulated by the Securities and Exchange Commission,
which has the mandate of surveillance over the exchange to forestall breaches
of market rules and to deter and detect unfair manipulations and trade mal-practices.
Historically, the Nigerian Stock Exchange reached an all time high of 66,371.20
All Shares Index in March 2008 from 4,792.03 of 1999. When the contagion effect
of the global crash pressured on the exchange, the all shares index dropped to
24,770.52 in 2010 and further dropped to 19,828 in December 2011. The recovery
from the global crash then started and the all shares index increased to 41,000
in 2017 and 2018 but declined gain to 30,400.80 in January 2019.
Many
regulations have arisen in the past due to the proposition that high volatility
in stock returns adversely affect investors decisions since majority of them
are risk averse. The West African countries in their bid to develop like other
sub regions in Europe and America need a continuous review of stock market
development which led to the establishment of the Regional Council for Public
Saving and Financial Market (RCPSFM) with the responsibility of monitoring and
development of West African capital markets. As the volatility of stock market
indices varies from time to time, it is essential to carry out empirical
studies to estimate the conditional volatility variability of the stock market
indices from time to time and compare with their forecasting performances. Besides
efficiency in any given market, it is the volatility prevailing in the market
that influences the return distribution of the market stock. The evaluation of
stock returns volatility parameters in West African countries becomes very
important because a less volatile and efficient financial markets are critical
factors in any economic transformation process.
1.1
STATEMENT
OF THE PROBLEM
The volatility of stock markets has
generated debates and interests among economists, stock market analysts,
investors, government regulatory agents and policy makers. Volatility is
symptomatic of a highly liquid stock market (Goudarzi and Ramananayanan, 2011),
which is a measure of uncertainty possibly from a positive outcome (Poon, 2005).
Increased volatility can be perceived as indicating a rise in financial risk
which can adversely affect investor’s assets and wealth. It is observed that
when stock market exhibit increase in volatility, there is a tendency on the
part of the investors to lose confidence in the market and they tend to exit
such market (Batra, 2004).
Trade liberalization across nations has
opened the diverse avenues for investors to select and manage varieties of
portfolios across the world. The globalization of stock markets has won the
substantial amount of confidence of investors to put their holdings in any
financially lucrative part of the world. The relaxations in investment embargos
in stock markets have not only expanded the investment returns but also became the
source of integration of several stock markets around the world (Sarkar and
Roy, 2016).
The main goal of the study is to explain stock
returns vulnerability in stock markets of West African sub-region. In both
developed and developing economies, stock return volatility is central to
finance whether in asset pricing, portfolio selection, or risk management. Vidanage,
Carmignani and Singh (2017) listed the three major reasons of understanding
and predicting stock return volatility.
Firstly, investors look at the volatility of
assets when taking investment decisions. Secondly, hedging and asset
diversification strategies to a large extent rely on volatility forecasts.
Thirdly, policies and regulations for market stabilization and prevention of
malpractices associated with excess volatility must be informed by reliable
volatility forecasts. In the first reason, the problem is to which degree of
risk an investor is exposed in an emerging volatile market and how much the
investor is paid for the corresponding risk. Can any portfolio manager just
construct a portfolio blindly without proper understanding of the volatility of
the emerging markets? Therefore, a good knowledge of stock return volatility is
a significant factor in determining the expected returns of a given asset or
portfolio and has subsequent effects on the asset pricing. In the second
reason, policy makers and market regulators rely on market estimates of
volatility as a barometer of the vulnerability of financial market. The
existence of excessive volatility in the stock market undermines the usefulness
of stock price as a signal about the true intrinsic value of a firm (Elie,
2011). The performance of equity market in terms of returns gets better as
volatility tends to decline but extreme volatility on the other hand is a
dangerous signal as it ruins the smooth working of financial system and has a
negative impact on economic performance.
African economies are plagued by economic and
socio-political upheavals, a development that is not only risky for investment
but dents investors’ confidence, and also antithetical to economic development
(Osazevbaru, 2014). The economy of West African States has not been doing well
when compared with other countries in Europe and America sub-regions. To
achieve economic development and stability, the leaders of West African
countries on their fifth meeting in Accra, Ghana opted for a single currency
program (ECO) and gave four criteria to be achieved by each member state for
the commencement of the single currency. The criteria are: (i) single digit
inflation rate at the end of each year, (ii) fiscal deficit of not more than 4%
of the GDP, (iii) central bank deficit financing of not more than 10% of the
previous year’s tax revenue, (iv) gross external reserves that can give import
cover for a minimum of three months (Taylor, 2018).
The above indices of economic development are
not easy to achieve without a well-functioning financial market that will
guarantee the long term funds needed in the economic transformation agenda. To
get the needed long term funds, the fear index of investors in the stock
markets of the region need to be addressed. The foreign investor’s attitude of
‘come and go’ which are inherent in West African stock markets may result to an
increase in stock market volatility and this is a challenge even to policy
makers in the region (Jedran, Chen, Ullah and Mirza 2017)
In another development, the global financial
crisis and the resultant stock market crash which started in USA in 2007/2008
extended to West African sub-region in 2010 through the contagion effects. From
2011, it became very necessary for economic states and sub-regions to evaluate
the speed of recovery from the global stock market crash as to make their stock
markets more viable and attractive for investment.
Therefore, the research problems so far
identified which include: the nature of higher volatility observed in emerging
markets, the continuous debates and sensitivity of stock returns volatility,
evaluation of the rate of recovery from the global stock crash, ‘the come and
go attitudes of foreign investors which militate against stock market
development in West African sub-region and the non- availability of long-term
funds necessary for the actualization of the single currency criteria are the
issues the study tends to address.
1.2
OBJECTIVES
OF THE STUDY
The main objective of this research was to
investigate the volatility clustering, leverage effects, risk of stock returns
volatility and news arrival in the major three emerging markets of West African
sub-region: the Bourse Regionale des Valeurs Mobilieres (BRVM) in Abidjan,Ghana
stock exchange, (GSE) and Nigerian stock exchange (NSE) as to determine the
levels of risk available to stock market investors in the sub region. The
specific objectives of the study were to:
1)
Determine if there is an
ARCH effect in the stock returns of Bourse Regionale des Valeurs Mobilieres
(BRVM), Ghana stock exchange (GSE) and Nigerian stock exchange (NSE) from
December 31, 2011 – January 31, 2019.
2)
Investigate if there is
volatility clustering in the stock exchanges of Bourse Regionale des Valeurs
Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian stock exchange (NSE)
from December 31, 2011 – January 31, 2019.
3)
Ascertain if there is
leverage effect (asymmetry) in stock exchanges of Bourse Regionale des Valeurs
Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian stock exchange (NSE)
from December 31, 2011 – January 31, 2019.
4)
Determine if stock
returns’ distribution in the emerging markets of West Africa sub-region (Bourse
Regionale des Valeurs Mobilieres (BRVM), Ghana stock exchange, (GSE) and Nigerian
stock exchange, (NSE) are leptokurtic (sharply peaked) from December 31, 2011 –
January 31, 2019.
5)
Evaluate if the observed
volatilities in stock exchanges of Bourse Regionale des Valeurs Mobilieres
(BRVM), Ghana stock exchange (GSE) and Nigerian stock exchange (NSE) are
persistent from December 31, 2011 – January 31, 2019.
6)
Examine the influence of
news arrival (trading volume) on the volatilities of stock returns in Bourse Regionale
des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian stock
exchange (NSE) from December 31, 2011 – January 31, 2019.
1.3
RESEARCH
QUESTIONS
Based on the above stated objectives, the study
will seek to provide answers to the following questions:
1)
In what ways does an Arch
effect exist in the volatility of stock returns in West African stock markets
(Bourse Regionale des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and
Nigerian stock exchange (NSE) from December 31, 2011 – January 31, 2019?
2)
To what extent doWest
African emerging markets (Bourse Regionale des Valeurs Mobilieres (BRVM), Ghana
stock exchange (GSE) and Nigerian stock exchange (NSE)) exhibit the character
of volatility clustering from December 31, 2011 – January 31, 2019?
3)
In what manner does leverage
effect (asymmetry) exist in the emerging markets of West Africa (Bourse
Regionale des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian
stock exchange (NSE) from December 31, 2011 – January 31, 2019?
4)
To what extent are the
stock returns distribution in emerging markets of West African sub-region (Bourse
Regionale des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian
stock exchange (NSE)) Leptokurtic (sharply peaked from December 31, 2011 –
January 31, 2019?
5)
To what degree does
persistence of volatility exist in West African sub-region stock markets
(Bourse Regionale des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and
Nigerian stock exchange (NSE) from December 31, 2011 – January 31, 2019?
6)
How does news arrival
(trading volume) in emerging stock markets of West African sub region (Bourse
Regionale des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian
stock exchange (NSE) influence the stock returns volatility from December 31,
2011 – January 31, 2019?
1.4
HYPOTHESES
To achieve the objectives and obtain answers to
the research questions, the following hypotheses are formulated and stated in
null form:
H01: There is
no significant ARCH effect in the stock returns of Bourse Regionale des Valeurs
Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian stock exchange (NSE)
from December 31, 2011 – January 31, 2019.
H02:
Significant
volatility clustering does not exist in the stock exchanges of Bourse Regionale
des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian stock
exchange (NSE) from December 31, 2011 – January 31, 2019.
H03: There is
no significant leverage effect (asymmetry) in the stock exchanges of Bourse
Regionale des Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and
Nigerian stock exchange (NSE) from December 31, 2011 – January 31, 2019.
H04:
The
stock returns distribution in the emerging markets of Bourse Regionale des
Valeurs Mobilieres (BRVM), Ghana stock exchange (GSE) and Nigerian stock
exchange (NSE) are not leptokurtic (sharply peaked) from December 31, 2011 –
January 31, 2019.
H05:
The observed
stock returns volatilities in Bourse Regionale des Valeurs Mobilieres (BRVM), Ghana
stock exchange (GSE) and Nigerian
stock exchange (NSE) are not persistent from December 31, 2011 – January 31, 2019.
H06: The news
arrival (trading volume) in Bourse Regionale des Valeurs Mobilieres (BRVM), Ghana
stock exchange (GSE) and Nigerian stock exchange (NSE) do not significantly
influence stock returns volatility from December 31, 2011 – January 31, 2019.
1.5
SIGNIFICANCE
OF THE STUDY
This work is an empirical investigation of the
existence of volatility clustering, the leverage effects, the behaviour of the
risk of stock returns and the influence of news arrival (trading volume) on
stock returns volatility in emerging markets of West African sub-region. In
both developed and developing economies, stock returns volatility is central to
financial stability whether in assets pricing, portfolio selection, or risk
management. Stock market volatility is a critical sustainable factor for
investment decision-making. Both
existing and intending Investors in West African emerging markets are
obviously interested in the stylized facts of stock returns volatility because
higher volatility in stock market could mean huge loses or gains, hence greater
uncertainty. In a highly volatile stock market, risk of equity investment is
high and Investors find it difficult to hold securities on long-term bases
which in turn hinder the availability of long term funds needed for state
economic development. The understanding of the volatility in a stock market
will be useful in determining a firms cost of capital and help in the
allocation of resources. Therefore, the study will be significant to the
following groups:
i.
Investors:
The study will provide the needed information for better investment decisions. The information from
this study will enble investors to know the extent of risk available in each
stock market and arm them them for better investment decisions. From the study
the investors will be in a position to access opportunities for profit
maximization during good news and cost reduction during bad news. The study
will also give investors the choice of investment among the selected stock
markets for better portfolio selection and risk management.
ii.
Firms:
The study will provide the platform for determination of cost of capital, information
for better risk management and proper evaluation of asset allocation decisions
which will help to improve the net worth of the Firms. The Firms will now know
the level of impact of financial leverage and be in a position to access their
debt equity ratio.
iii.
The
government and policy makers: Volatility
estimates of stock market will serve as the barosmeter of the economy and the
vulnerability of the financial market. It will as well open up the areas of
policies that will checkmate the variables (insecurity, high inflation,
political impas, corruption) that are responsible for bad news in the system.
iv.
Market
regulators: The study will provide evidence that
will score or assist the regulatory authorities (Security and exchange
commissions, Central banks) in formulating policies, rules and regulations to
checkmate investors’ expectations, activities and actitudes for better stock market stability and growth.
It will provide the required understanding of risk level existing in the
individual stock markets and provide the basis for policy formulations that
will checkmate the stylized stock return volatility.
v.
Academics:
The study will contribute to knowledge and serve as useful source of reference
material in future research works of related topics. The study will also
provide empirical evidence that will enhance the knowledge on the risk of stock
returns in emerging markets and will as well encourage other researchers that
will aim at either sustain or debunk the findings.
1.6
SCOPE
OF THE STUDY
The study is based on West African sub-region and
covers the period from December 1, 2011 to January 31, 2019. It focuses on the
empirical investigation of the volatility clustering, leverage effects, risk of
stock returns and influence of news arrival (trading volume) in West African
emerging markets.
The
study covers the activities of the selected stock exchange markets of Bourse
Regionale des Valeurs Mobilieres (BRVM) in Abidjan, Ghana stock exchange (GSE)
and Nigerian stock exchange (NSE) in West African sub region within the study
period. Bourse Regionale des Valeurs Mobilieres (BRVM) is a regional exchange
market that covers stock exchange activities of Benin, Bukina Faso, Guinea
Bissau, Coted’lvoire, Mali, Niger, Senegal and Togo. This period of study is
very significant because first, it is a post global financial crises period in
which speedy financial recovery in the stock markets of every country and
sub-region is required and evaluated. Secondly, it covers the period that is
very close to vision 2020 for common currency bid of ECOWAS as to access the
readiness of the individual countries in achieving a less volatile stock market
which will guarrantee the long term funds needed for the achievement of the
common currency criteria. Thirdly, this period also covers 2017-2019, which has
been witnessing substantial increase in news arrival due to increase in
insecurity (Terrorism, Herdsmen attacks) and political unrest. In specific
terms the study used the daily all share index and daily trading volume of the
selected stock exchange markets (BRVM, GSE and NSE) in West African sub-region
within the stated period of investigation.
1.8 LIMITATIONS OF THE STUDY
The
study cuts across the borders of West African States. It is possible for a
study of this scope to have some challenging limitations especially on the
sources of the data, collection of the data and the needed soft-wares to run the
data. The researcher is determined in the course of the study and the
determination is considered enough to overcome the challenges. The cost to
cover the selected countries of West African sub-region for the study is also a
challenge which the researcher did overcome.
1.9 OPERATIONAL DEFINITION OF TERMS
1. ARCH effect: Any time series which exhibits conditional
heteroscedasticity or autocorrelations in the squared residuals/ errors is said
to have an Arch (Autoregressive conditional heteroscedastic) effects.
2. Asymmetry: Asymmetry is the absence of symmetry. It refers
to a situation or condition of two things not equal in size, magnitude,
knowledge or comparable measure.
3. Asymmetric effect; Asymmetric effect refers to
a situation where the change in volatility reactions are not equal when the
signs and magnitude of the change are put into consideration
4. Autoregressive: This is a stochastic process
used in statistical calculations in which current and future values are
estimated based on a weighted sum of past values.
5. Conditional
variance: When we consider a time–varying return
distribution, we must refer to the conditional mean variance and covariance.
This is to say that the mean variance and covariance are conditional on
currently available information.
6. GARCH
effect: This is the random process that allows the
conditional variance of a variable to be dependent upon the previous lags, the
squared residual from the mean equation and the present news about the
volatility from the previous period.
7. Heavy tails: The unconditional distribution of returns
display with positive exces kurtosis.
8. Heteroscedasticity:
A collection of random variables, where
sub-populations have different variability from others. The presence of
heteroscedasticity can invalidate statistical test of significance that assume
that the modeling errors are uncorrelated and normally distributed and that
their variances do not vary with the effects being modeled.
9. Information
efficiency: This contends that the prices of securities
fully reflect all available information so that investors buying securities in
an efficient market should expect to obtain an equilibrium rate of return.
10. Leverage
effect: This refers to the well-established
relationship between stock returns of both implied and realized volatility. It
implies that if a company is leveraged, its volatility should increase as the
stock prices moves lower and closer to the level of debt.
11. News
arrival: This is the component of the return
distribution that is assumed to be directed by a latent trading volume change.
12. Persistence
in volatility: Volatility persistence means that volatility
today tells you something not only about volatility for today and tomorrow but
also tells you about volatility in many days to come.
13. Volatility:
Stock market volatility refers to the potential
for a given stock to experience a drastic decrease or increase in value within
a predetermined period of time. In other words, volatility refers to the amount
of uncertainty or risk about the magnitude of changes in a security’s value.
14. Volatility
clustering: Volatility clustering refers to the observation
that ‘large stock price changes tend to be followed by larger price changes of
either signs and small stock price changes also tend to be followed by small
price changes’. In quantitative terms, it implies that whole returns themselves
are uncorrelated.
15. Volatility
risk: This is the risk of a change in price of a
security or portfolio as a result of unpredictable changes in the volatility of
a risk factor. It is usually applied to portfolios of derivatives, where the
volatility of its underlying asset is a major influence on the price.
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