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Journal of Economic StudiesEmerald Article: OIL PRICE SHOCKS AND
STOCK MARKET BEHAVIOUR IN NIGERIA
Musibau Adetunji Babatunde, Olayinka Adenikinju, Adeola
Adenikinju
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Article Title Page
[Article title]: OIL PRICE SHOCKS AND STOCK MARKET BEHAVIOUR IN
NIGERIA
Author Details (please list these in the order they should
appear in the published article)
Author 1 Name: Musibau Adetunji BABATUNDEDepartment:
EconomicsUniversity/Institution: University of IbadanTown/City:
IbadanState (US only): Oyo StateCountry: Nigeria
Author 2 Name: Olayinka ADENIKINJU
Department: Department of EconomicsUniversity/Institution:
University of IbadanTown/City: IbadanState (US only): Oyo
StateCountry: Nigeria
Author 3 Name: Adeola F. ADENIKINJUDepartment:
EconomicsUniversity/Institution: Bowen UniversityTown/City:
IwoState (US only): Osun StateCountry: Nigeria
Author 4 Name:Department:University/Institution:Town/City:State
(US only):Country:
NOTE: affiliations should appear as the following: Department
(if applicable); Institution; City; State (US only); Country.No
further information or detail should be included
Corresponding author: [Name] Musibau Adetunji
BabatundeCorresponding Authors Email: [email protected]
Please check this box if you do not wish your email address to
be published
Acknowledgments (if applicable):
Biographical Details (if applicable):
[Author 1 bio]
[Author 2 bio]
[Author 3 bio]
[Author 4 bio]
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Type footer information here
Type header information here
Structured Abstract:
Purpose of this paper: The purpose of this study is to
investigate the interactive relationships between oil
price shocks and the Nigeria stock
market.Design/methodology/approach: The paper applied the
multivariate vector auto-regression that employed the
generalized impulse response function and the forecast variance
decomposition error.Findings: Empirical evidence reveals that stock
market returns exhibit insignificant positive response to oil
price shocks but reverts to negative effects after a period of
time depending on the nature of the oil price
shocks. The results are similar even with the inclusion of other
variables. Also, the asymmetric effect of oil
price shocks on the Nigerian stock returns indices is not
supported by statistical evidences.
What is original/value of paper?: This is the first study to
examine the dynamic linkages between stock market
behaviour and oil price shocks in Nigeria.Keywords: Oil Price
Shock, Stock Market, VAR, Impulse Response, Nigeria.
Article Classification: Research paper
JEL classifications: G10, Q40
For internal production use only
Running Heads:
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OIL PRICE SHOCKS AND STOCK MARKET BEHAVIOUR IN
NIGERIA
AbstractThe purpose of this study is to investigate the
interactive relationships between oil price shocks
and Nigeria stock market. It applied the multivariate vector
auto-regression that employed the
generalized impulse response function and the forecast variance
decomposition error. Empirical
evidence reveals that stock market returns exhibit insignificant
positive response to oil price
shocks but reverts to negative effects after a period of time
depending on the nature of the oil
price shocks. The results are similar even with the inclusion of
other variables. Also, theasymmetric effect of oil price shocks on
the Nigerian stock returns indices is not supported by
statistical evidences.
Keywords: Oil Price Shock, Stock Market, VAR, Impulse Response,
Nigeria.
JEL Classification: G10, Q40
I IntroductionThe aim of this study is to analyze the
relationships between oil price shocks and stock
market behavior in Nigeria. The choice of Nigeria is informed by
some factors. First, Nigeria is a
major supplier of oil in world energy markets and her stock
market is likely to be susceptible to
changes in oil prices. Second, a sizable amount of the
literature has concentrated their attentionon oil importing
countries and ignored these issues for oil exporting countries.
When the oil price
increases, an income transfer occurs from oil importing
countries to oil exporting countries. For
example, asset prices and stock prices in particular will be
affected by the price of oil, through
the cash flow of oil related firms in an oil exporting country.
Asset prices may then influence
consumption through a wealth channel and investments through the
Tobin Q effect and,
moreover, increase a firms ability to fund operations (credit
channel).
Third, most of the studies have also focused on Asia, gulf
cooperating countries
((Hammoudeh and Aleisa, 2004; Arouri and Fouquau, 2009), United
States (Kling, 1985;
Sadorsky, 1999), Australia (Faff and Brailsford, 1999), Greece
(Papapetrou, 2001) Malaysia
(Ibrahim and Aziz, 2003), Thailand (Valadkhani and Chancharat,
2008), Mexico, and Norway
(Hammoudeh and Li, 2004), Turkey (Eryigit, 2009) United Kingdom
(El-Sharif et al., 2005)andCanada (Sadorsky, 2001) among others
leaving a glaring gap for countries in sub-Saharan
Africa, and Nigeria in particular. In addition, the relationship
between oil price shocks and stock
market performance has also remained unresolved in the
literature. For example, while Jones and
Kaul (1996), Sadorsky (1999) and Ciner (2001) report a
significant negative connection between
oil price shocks and stock market returns, Chen et al. (1986)
Huang et al. (1996), and Gjerde and
Saettem (1999) established that there is a positive association.
An increase in the price of oil will
likely translates into a decline in stock market performance.
More expensive fuel translates into
higher transportation, production, and heating costs, which can
put a drag on corporate earnings.
Rising fuel prices can also stir up concerns about inflation and
curtail consumers discretionary
spending. The increasing price of oil causes the costs of
production to also increase. To preserve
a profit margin for an industry, the price of an item will have
to be increased to cover highercosts. When prices are increased,
sales decrease. People will be less inclined to buy some
article
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if it costs more. When sales drop off due to higher prices,
profits decrease. When profits drop,
the price people will be willing to pay to invest in a business
in the form of stocks and bonds willalso drop. Thus, when oil
prices go up, stock goes down.
The converse of decreasing stock prices with increases in the
price of oil is that as oil
prices decline, stocks will tend to increase in value. This is
because lower oil prices mean a
decrease in costs associated with production and distribution of
goods, which means prices on
goods can drop and sales can increase. Increased sales lead to
higher profits for a business and
more attractive stock ownership. Thus, when oil prices go down,
stock goes up. Investigation of
the nature of this relationship in Nigeria is part of the focus
of this study. Thus, we attempt to
undertake what is, to the best of our knowledge, the first study
of the dynamic linkages between
stock market behaviour and oil price shocks in Nigeria. The
sequence of this paper is clear.
Section II highlights the trend of discussion in the literature.
Section III discusses the data and
methodological issues while a discussion of the main empirical
findings is provided in sectionIV. Section V concludes.
II Review of Related Studies
Although changes in the price of crude oil are often considered
an important factor for
understanding fluctuations in stock prices, there is no
consensus about the relation between stock
prices and the price of oil among economists. While some of the
studies have established a
positive link between oil price changes and stock market, other
studies have considered the
impact as being weak, conditional or non-existing.
By way of illustration, Kling (1985) investigated the
relationship between crude oil pricechanges and stock market
activity between 1973 and 1982 in the US and found that crude oil
price
changes affect the future stock prices in the industries which
use oil as input factors. Jones and Kaul(1996) tested the
rationality of stocks prices by constructing cash-flow dividend
valuation model
between 1947 and 1991 and found a negative significant effect of
oil shocks on the stock market.Sadorsky (1999) investigated the
dynamic interaction between oil price and other economic
variables
including stock returns using an unrestricted VAR with US data.
The study found that oil pricechanges and oil price volatility have
a significantly negative impact on real stock returns. The
study
also found that industrial production and interest rates
responded positively to real stock returns
shocks. Sadorsky, however, found that in periods subsequent to
1986, oil price shocks have
significant effect on real stock returns. Above all, the study
showed that oil price movements
explain a larger portion of the forecast error variance in real
stock returns than interest rates.
Faff and Brailsford (1999) studied the sensitivity of oil price
factor and the Australian
stock market during the period of 1983 to 1996. They highlighted
that there is significantpositive oil price sensitivity in the Oil
and Gas and Diversified Resources industries. In addition,
they showed that negative oil price sensitivity is higher in
industries with a relatively high
proportion of their costs devoted to oil-based inputs such as
transportation industries. However,
their predicted negative sensitivity may be due to the fact that
the companies may have passed on
higher fuel costs to their customers by increasing prices of
their goods and services.
Similarly, Papapetrou (2001) studied the dynamic relationship
among the oil price, real stockprices, interest rates, real
economic activity and employment with data from Greece. The study
found
that an oil price shock has an immediate negative impact on the
stock market as well as industrial
production and employment. That is, a positive oil price shock
depresses real stock returns. However,
contrary to the literature, Papapetrou showed that stock returns
do not rationally signal (or lead)
changes in real activity and employment in his analysis since
growth in industrial production andemployment respond negatively to
real stock returns. Also in another study, Hondroyiannis and
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Papapetrou (2001) using multivariate vector autoregressive model
(VAR analysis) and monthly
time-series data to test the dynamic relations between
macroeconomic variables and stock returnsin Greece found a positive
oil price shock depresses real stock return.
Nandha and Faff (2008) conducted a study that examined the
impact of oil price changes
on 35 industry sectors based on the standard FTSE Global
Classification System. They found
that oil price changes have a negative impact on equity returns
from all industries, with the
exception of mining, and oil and gas. They were of the opinion
that the broad oil price impacted
across industries, because crude oil has a huge array of
by-products, which find applications
from aviation fuel through to shampoo and shoes. Moreover,
higher oil prices might have an
impact on interest rates and discourage consumer confidence,
creating indirect channels for
reflecting higher oil prices into equity prices. Their analysis
demonstrated that oil price increases
and decreases have a symmetric impact on the equity markets.
Jahan-Parvar and Mohammadi
(2009) studied the potential loss of competitiveness due to
higher oil prices through the monetarychannel in a group of six oil
producing countries. The Dynamic Simultaneous Equations was
applied to Vector Autoregressive Moving Average model with
exogenous variables. Mixed
evidence was found of loss of competitiveness due to high oil
prices in their sample.
However, some other set of studies have found a positive
relationship between crude oil
prices and stock markets. For example, Al-Mudhaf and Goodwin
(1993) in a firm-specific study
examined the returns from 29 oil companies listed on the New
York Stock Exchange. Their
findings suggested a positive impact of oil price shocks on ex
post returns for firms with
significant assets in domestic oil production. Huang, et al
(1996), however, found no negative
relationship between stock returns and changes in the price of
oil futures. Gjerde and Saettem(1999) investigated the relationship
between stock returns and macroeconomic factors by using a
multivariate vector autoregression (VAR) in Norway. Oil price
shocks have a positive impact on thestock market while interest
rates changes affect the stock market negatively. Surprisingly,
the
relationship between stock returns and domestic activity is
different from those of big economies
such as the US and Japan, where stock markets rationally signal
changes in real activity. However, in
Norway changes in real stock returns do not have a significant
influence on domestic economic
activity, while industrial production significantly affects real
stock returns. This means that
Norwegian stock market respond inaccurately to economic news
from the real sector.
Ibrahim and Aziz (2003) analyzed dynamic linkages between stock
prices and four
macroeconomic variables for the case of Malaysia using
cointegration and vector autoregression.
Empirical results suggest the presence of a long-run
relationship between these variables and the
stock prices and substantial short-run interactions among them.
They documented positive short-
run and long-run relationships between the stock prices and two
macroeconomic variables. Theexchange rate, however, is negatively
associated with the stock prices. They also noted the
disappearance of the immediate positive liquidity effects of the
money supply shocks and
unstable interactions between the stock prices and the exchange
rate over time.
Valadkhani and Chancharat (2008) investigated the existence of
cointegration andcausality between the stock market price indices
of Thailand and its major trading partners
(Australia, Hong Kong, Indonesia, Japan, Korea, Malaysia, the
Philippines, Singapore, Taiwan,
the UK and the USA), using monthly data spanning December 1987
to December 2005. Based
on the empirical results obtained from these two residual-based
cointegration tests, potential
long-run benefits exist from diversifying the investment
portfolios internationally to reduce the
associated systematic risks across countries. However, in the
short-run, three unidirectional
Granger causalities run from the stock returns of Hong Kong, the
Philippines and the UK tothose of Thailand, pair-wise. Furthermore,
there are two unidirectional causalities running from
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the stock returns of Thailand to those of Indonesia and the USA.
Empirical evidence was also
found of bidirectional Granger causality, suggesting that the
stock returns of Thailand and threeof its neighbouring countries
(Malaysia, Singapore and Taiwan) are interrelated.
Sadorsky (2001) chose Canadian companies as an example. Using
the stock market
index, energy price, interest rates, and exchange rates as
explanatory variables, he found the rise
of the stock market index and oil price had a positive effect on
oil companies returns, while the
rise of interest rates and exchange rates had a negative effect.
Hammoudeh and Li (2004) have
suggested that the oil price increases have had positive impacts
on the US, Mexico, and Norway
oil and transportation industries stock yields, while Boyer and
Filion (2007) have found out the
similar impact for the Canadian oil and gas stock returns. This
result was supported for the UK
by El-Sharif et al. (2005) who also argued that the sensitivity
of the stock yields of the non-oil
and gas sectors to oil prices is weak. A similar conclusion was
given by Osmundsen et al. (2007)
including the oil and gas companies.Eryigit (2009) also found
that the price changes of oil orenergy affect emerging economies
markets more than developed markets. He had studied the
impact of oil prices changes in both US Dollars and Turkish Lira
on sub-sector indices in
Istanbul Stock Exchange. He found that oil price changes have
statistically significant positive
effects on trading and services, consumer products, industrial
products, manufacturing and
financial sector covering insurance but do not have significant
impact on transportation and other
financial sectors.
On the contrary, there are also arguments suggesting that the
oil prices have no impact on
asset pricing. For example, Chen et al. (1986) suggested that
there is no evidence indicating that
the oil prices risk is priced by the stock markets. Huang et.al
(1996) applied a vector
autoregression (VAR) approach to daily oil future prices and
daily US stock returns for period
1979 to 1990 and found no evidence of relationship between oil
future prices and the S&P 500.Sadorsky (1999) stated that there
is no evidence indicating that the shocks caused by the
volatility of the oil prices have an asymmetric impact on the
economy. Maghyereh (2004) studiedthe dynamic relationship between
oil price shocks and stock market returns for 22 emerging
economies. Results from the variance decomposition analysis show
very weak evidence that oil price
shocks affect stock market returns in emerging economies. He
concluded that, inconsistent with
previous empirical studies in developed economies, stock markets
in the emerging economies are
inefficient in the transmission of new information of the oil
market, and stock market returns in those
countries do not rationally signal changes in crude oil price.
Further, Hammoudeh and Aleisa
(2004) consider five oil-exporting countries, Bahrain, Kuwait,
Saudi Arabia, and the UAE. In
their study only the Saudi Arabian stock market exhibits some
dependence on oil prices; the
smaller Gulf stock markets are apparently invariant to oil price
changes.Nevertheless, there is also argument in the literature that
the characteristics of the
economy in each country matter for the kind of association that
is found between oil prices and
stock markets. For example, based on the analysis to the US and
13 European countries, Park
and Ratti (2008) found that the impacts of oil price shocks on
oil-importing countries stock
market are negative while the impact on oil-exporting countries
stock market are positive. With
regard to the impacts of the oil prices on the stock indices,
Billmeier and Massa (2009)
conducted an empirical study covering 17 countries and selected
these countries from among the
oil exporter Middle Eastern countries and the oil importer
Central Asian countries. In their study,
they indicated that the increase in the oil prices had positive
impact on the stock yields of the
exporter countries but negative impact on the stocks of the
importer countries.
In addition, some studies have shown that oil price shocks
influenced various industriesstock price differently. A common held
view is that oil price shocks are beneficial for oil
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companies upstream, yet has an adverse effect on companies
downstream and other industries.
For example, Huang et al. (1996)s research, based on correlative
coefficient method and a VARmodel, used the S&P 500 index, 12
US industries stock price indices, and three oil company
stock prices. They found crude oil future returns had
significant abilities to explain oil
companies stock returns, which could be seen as their lead
index, but had little effect on the
total market. Faff and Brailsford (1999) used an enlarged market
model to research several
industry returns in the Australian stock market. They found that
oil price had an effect on stock
prices, and the oil and gas industry and diverse resources
industry had positive sensitivities,
while papermaking, packing, and transportation industry had
negative sensitivities. Choe (2002)
studied how oil price shocks on another perspective i.e. oil
price impact on large and small firms
between the periods of 1950 to 2000. The companies were divided
into sizes depending on the
market capitalization of the companies. The empirical results
showed that the oil price shocks in
general affect the stock returns of large firms more than the
smaller firms. The asymmetricanalyses also showed that positive oil
price shocks have more significant effect on both large and
small firms than do negative shocks. Thus, in conducting this
research, the market capitalization
of the sample companies is an essential consideration.
Sadorsky (2008) concurred with Choe when he investigated the
empirical relationship
between firm size, oil prices, and stock prices. The empirical
results showed that increases in
firm size or oil prices reduce stock price returns. They found
that changes in oil prices have an
asymmetric effect on stock prices. Increases in oil prices have
a greater effect on stock returns
than decreases in oil prices. It is also the case that when
asymmetric oil price changes are
considered, the effect of firm size shows up most pronounced for
medium-sized firm. He gave an
example of being the middle child in a family. That is, it is
tough being a medium-sized firm.
Medium-sized firms do not enjoy the production efficiency and
financial leverage of large firmsnor do they have the flexibility
and responsiveness of small firms. Thus, medium-sized firms are
more likely to be more adversely affected, in terms of stock
prices, by changes in oil prices.
Taking an overview of these studies, it is obvious that the
literature is inconclusive
regarding the relationship between crude oil prices and stock
market, although the studies
reviewed have tried to generate a clearer understanding of the
relationship between oil prices and
stock market. There is therefore hardly any doubt that a
possible relationship between oil price
movement and stock market could exist. Perhaps, a fundamental
reason why it is difficult to
reach a definitive conclusion regarding the link is the web of
interrelationships that is involved in
establishing the relationship between crude oil prices and stock
market. Oil price movement can
have a significant impact on the stock market, but so can many
factors that are related to the
stock market. Thus, a positive (negative) relationship between
oil price movement and stockmarkets could have well existed but
because the result in some cases depends on the
characteristics of the economy, the industry being considered
and the firm size, the results have
been inconclusive. The suspect may have shot the victim but the
jury may still have insufficient
evidence to indict her. Thus, whether these relationships exist
in Nigeria is the focus of this
paper.
III Data and Methodological Issues
3.1 Data EmployedThis study examines the effect of oil price
shocks on the real stock returns of Nigeria.
Based on Fama (1981)s hypothesis that measures of economic
activity and inflation have played
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6 | P a g e
a role in the analysis of stock market activity, we also
consider short-term interest rates,
consumer price index (CPI), and industrial sector GDP, which may
influence the relationshipsbetween oil price shocks and stock
market. With respect to oil price, we adopt the Bonny Light
crude oil price as a representative of the oil price. This is
because it represents the dominant
crude oil resource for Nigeria. The real oil price is product of
Bonny Light oil price and
exchange rate (Naira per US Dollar) deflated by the CPI. We
proxy the short-term interest rates
with 3 months deposit rates of commercial banks. On the stock
market indices, we choose the
Nigerian Stock Exchange to calculate the stock returns. One
composite index1 (Nigerian stock
market composite index) and five classification indices
(agriculture, financial, commercial,
manufacturing and services) are used to examine the Nigerian
stock market. The industrial sector
GDP is deflated by the GDP Implicit price deflator.
All the variables were sourced from the Central Bank of Nigeria
Statistical Bulletin and
Annual Report and Statement of Account (2008) edition. We
employed Nigerian StockExchange indices (NSE) to calculate the
stock returns by ln (nse t/nset-1). Thereafter, following
(Papapetrou (2001); Sari and Soytas (2006); Afshar et al. (2008)
and Cong et al (2008) we
compute the real stock returns (rstns) as the difference between
the continuously compounded
return on stock price and the log difference of inflation. All
the variables (except the real stock
returns) are in logarithmic form. We define the logarithm of
interest rate as log (1+r/100). The
sample period runs from the first quarter of 1995 to the fourth
quarter of 2008. The scope of our
study was based on data availability. The variables are measured
as follows:
r: is the log of short-term interest rate
ind: is the log of real industrial GDP;
op: is the log of real oil price;
infl: is the log of consumer price index;rstns: is real stock
returns (Nigeria stock market composite index, agriculture,
commercial,
financial, manufacturing, and services).
3.2 MethodologyWe adopt the vector auto-regressive analysis
(VAR) for this analysis. We choose the
VAR model to do the following analysis because unrestricted VAR
is superior in terms of
forecast variance to a restricted VECM at short horizons when
the restriction is true and the
performances of unrestricted VAR and VECM for orthogonalized
impulse response analysis
over short run are nearly identical. The VAR approach sidesteps
the need for structural modelingby treating every endogenous
variable in the system as a function of the lagged values of all
of
the endogenous variables in the system.
Our basic VAR model will have four variables, log of short
interest rate, real oil price and
industrial GDP, and real stock returns. If we then define a
four-dimensional column vector:
( , , , )t t t t
y r op ind srtns= , the VAR model can be set up as follows:
1 1 2 2 ... , 1, 2,..., (1)t t p t p t y A y A y A y t T = + + +
+ =
p is the lag orders, which is determined by the AIC and SC
information criterion. T is the size of
the sample. A1, A2, Ap and B are parameter matrices; t is a
column vector of errors with the
1Value Index of all Common Stocks Listed by Sector on the
Nigerian Stock Exchange (1984 = 100)
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7 | P a g e
properties of f ( ) 0t
E = for all t,
/( )s tE = if s = t and/( ) 0s tE = if ,s t where is the
covariance matrix. Therefore, t are assumed to be serially
uncorrelated but may becontemporaneously correlated and is assumed
to have nonzero off diagonal elements. The
basic model will be extended to allow for non-linear
transformations of real oil price changes.
Since estimated coefficients from VAR models often appear to be
lacking in statistical
significance due to the inaccuracy of the technique in
estimating standard errors, impulse
response functions and forecasting variance decomposition are
often used to explain the dynamic
effects of the shocks on the endogenous variables. An impulse
response function (IRF) traces
the effect of a one-time shock to one of the innovations on
current and future values of the
endogenous variables. While impulse response functions trace the
effects of a shock to one
endogenous variable on to the other variables in the VAR,
variance decomposition separates the
variation in an endogenous variable into the component shocks to
the VAR. Thus, the variance
decomposition provides information about the relative importance
of each random innovation in
affecting the variables in the VAR.
However, the IRF has been criticized on the basis of being
sensitive to variables ordering.
Hence we adopt the use of the generalized impulse response
function (GIRF) which is insensitive
to variable ordering to analyze the interactive responses
between oil price and stock markets and
obtain the contribution of oil price shocks to the variability
in stock returns. Also, in contrast withimpulse response functions
for structural models, generalized impulse responses do not require
that we
identify any structural shocks. Pesaran and Shin (1998) propose
a more general alternative to the
Choleski decomposition which is unaffected by the ordering of
the variables and which does not
require the orthogonalisation of the reduced form innovations.
The resulting responses are
unique and fully take account of the historical patterns of
correlations observed amongst thedifferent shocks.
Following Warne (2008) we consider a VAR process for somep
dimensional time series
xtgiven by :
1
1,... , (2)k
t t i t i t
i
x D x t T=
= + + =
where Dt is a vector with deterministic variables. The process
xtmay be covariance stationary,
integrated of orderd (and possibly cointegrated), while t is p
dimensional and assumed to be
i.i.d. with zero mean and positive definite covariance matrix .
The h-steps ahead forecast error
forxtis given by:
[ ]1
0
| , (3)h
t h t h t j t h j
j
x E x C
+ + + =
= where It is an information set which includes the history
ofxsup to and including period tas
well as the entire time path forDt. Thep p matrices Cjare given
by C0 =Ipandmin ,
1
1 (4)k j
j i j i
i
C C j=
= so that all Cj matrices can be determined recursively from the
i matrices. Under this scenario, Koop,Pesaran and Potter (1996)
defined the generalized impulse response function by:
[ ] [ ]1 1 1( , , ) | , | , (5)x t t h t t t h tGI h E x E x + +
= = =
where is some known vector. For the VAR process this means
that:
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8 | P a g e
1( , , ) . (6)x t hGI h C =
The choice of is therefore central to determining the time
profile for any generalized impulseresponse function. As an
alternative to shocking all elements of t one may consider just
shocking one element such that jt= j . We may therefore define
the generalized impulse
responses as:
[ ]1 1 1( , , ) | , | , (7)x t t h jt j t t h tGI h E x E x + +
= = Letting j=jj, the standard deviation of jt, and assuming that t
is Gaussian, it follows that :
12| , (8)t jt jj j jjE e
= = where ejis thej:th column ofIp. For the VAR model we then
find that:
12
1( , , ) (9)
x jj t h j jjGI h C e
=
This measures the response in xt+h from a one standard deviation
shock to jt, where the
correlation between jtand itis taken into account. Defining the
diagonalp p matrix as:
1/ 21 1
1/ 21 1
1/ 2
( )
( )
.(10)
.
.
( )p p
e e
e e
diag
e e
=
Consequently, we may express the generalized impulse responses
in matrix form as:
11 1( , , ... , ) , (11)x pp t h h hGI h C C B A = = =where
column j is given by 11 1( , , )x tGI h . When is diagonal, then B
=
1/2=
-1, a
diagonal matrix with standard deviations along the diagonal.
In order to determine to asymptotic covariance matrix for an
estimate ofChB we make
the following assumptions. We assumed that Chdepends on
aKdimensional vector RK
and
that Ch
is differentiable with respect to . Relative to the VAR model,
includes the elements of
ior some transformations thereof, but they do not include any
element from or . In case the
VAR model includes cointegration rank restrictions, then does
not include the cointegration
vectors but only the parameters on stationary transformations
ofx. For the VAR model wherext
is cointegrated of order (1,1), this means that only includes
parameters on lagged first
difference ofxtand on the 0 < r < p cointegration
relations/xt1.
Furthermore, assume that we have an estimator of , denoted
by^
, based on a sample of T
observations, which satisfies:^
( ) (0, ), (12)dK
T N
with NK being a K-dimensional Gaussian distribution,d denoting
convergence in
distribution, and being positive semi-definite. Furthermore, let
= vech(), with vech being
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the column stacking operator which only takes the elements on
and below the diagonal. The
estimator of, denoted by^
is assumed to satisfy:^
( 1)2
( ) (0, ), (13)d p pT N +
while^
and^
are asymptotically independent. In case t is Gaussian and, for
example, xt is
cointegrated of order (1,1) these assumptions are all satisfied
as long as there are no restrictions
which involve both and . Furthermore, for such a model:/
2 ( ) (14)p p
D D
+ + =
where is the Kronecker product,Dp is the duplication matrix
(Magnus and Neudecker, 1988),
and / 1 /( )p p p p
D D D D+ = is the Moore-Penrose inverse ofDp. Given our
assumptions it follows that
the asymptotic distribution of the matrix form of the
generalized impulse responses in equation
(7) can be expressed as:
2
^
( ( ) ( ) (0, ) , (15)h
d
h h ApT vec A vec A N
where/ /
/ /
/ / / /
( ) ( ) ( ) ( )(16)
h
h hA p p p h p h
vec C vec C vec B vec BB I B I I C I C
= +
Hence, what remains to be shown is what the matrix with partial
derivatives vec(B)// looks
like. It can therefore be shown that:3 /
/
( ) 1, (17)
( ) 2p p
vecL L
=
whereLp is ap2 p 0-1 matrix defined by:
/
1 1
/
2 2
/
.(18)
.
.
p
p p
e e
e e
L
e e
=
It then follows that the differential of vec(B) satisfies:
3 /1( ) ( ) ( ). (19)2
p p p pvec B I dvec I L L dvec =
Since ( )p
dvec D d = we have that :
3 /
/
( ) 1(20)
2p p p p p
vec BI I L L D
=
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Ifxt is cointegrated of order (1,1) with r cointegration
vectors, denoted by the full rankpr
matrix , we may also define generalized impulse responses for
the cointegration relations. Hence, suppose that we have an
estimator of, denoted by
^
, such that:^
( ) 0, (21)PT
where P denotes convergence in probability. Estimators of, such
as the ML estimatorsuggested by Johansen (1996), typically satisfy
this assumption. Let the cointegrating relations
be defined by zt = /xt. The generalized impulse response
function forzt+h from one standard
deviation shocks to tis then given by:/
11 1( , ,...,z pp t hGI h I A =
It can now be established that an estimator of/Ah satisfies:
2
^ ^/ / /( ( ) ( ) (0, ) (22)
h
dh h p A pp
T vec A vec A N I I
The reason for this result is, of course, that^
is T-consistent whereas^
hA is consistent. Ifxtis
cointegrated of order (1,1), we may rewrite the VAR in VEC form
such that:1
/
1 ,
1
(23)k
t t i t i t t
i
x D x x
=
= + + +where and are full rankp rmatrices (0 < r < p)
(Johansen, 1996). In this case we may
define = vec(1 k1 ) and thep p matrix:/ 1 /( ) , (24)C =
with1
1
k
p i iI
= = .We now find that :
lim , (25)hh
A CB
=
while/lim 0, (26)h
hA
=
Hence, the long-run generalized impulse responses in levels
depend on the long-run impact
matrix C and converge to finite matrix, while the long-run
generalized responses for the
cointegration relations converge to zero. The asymptotic
distribution ofCB is readily determined
from the above results and those regarding the asymptotic
distribution for the ML estimator of C(Paruolo, 1997; Johansen,
1996). Specifically, lettingA=CB then:
2
^
( ( ) ( ) (0, ) , (27)dAp
T vec A vec A N
where/ /
/ /
/ / / /
( ) ( ) ( ) ( )(28)
A p p p p
vec C vec C vec B vec BB I B I I C I C
= + The matrix with partial derivatives vec(B)// is given in
equation 11. Furthermore, it is
readily shown that:
/
/
( )
, (29)( )
vec C
C
= where is anp(k 1) p matrix given by:
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/ 1 /
.
. (30)
.
( ) ( )p
C
C I
=
The generalized impulse responses forzprovide us with a tool to
measure how quickly
the long-run relations converge to their steady state values.
Since the p shocks may result in
/Ahej 0 for different h, we may, for example, choose a
convergence horizon h* based on the
slowest response. The generalized impulse responses are equal to
impulse responses from a
structural VAR when the structural shocks are identified from a
recursive structure and is
diagonal. In all other circumstances will the generalized
impulse responses differ from theimpulse responses of a structural
VAR.
We investigate the time-series characteristics of the data to
test whether these variables
are integrated. The Dickey-Fuller Test with GLS Detrending
(DFGLS) and Ng-Perron tests are
employed. Thereafter, an unrestricted vector auto-regression
(VAR) model is employed to
investigate the complexities of the dynamic connections between
oil price shocks and stock
prices. In addition we carried out non-linear transformations of
the oil price variables. The oil
price is characterized by large price rises and high
volatilityand the apparent asymmetric
response of economic activity to oil price shocks has led
researchers to explore different oil
specifications in order to test the relationships between
variables from different views (Hamilton,
1996, Cong et al., 2008). Adopting the approach of Cong et al.
(2008) and Afshar (2008), we
define two non-linear transformations as follows:
top+ : real oil price increases
top+ = max(0; opt)
In this case, we separate oil price changes into positive and
negative changes in a belief that oil
price increases may have a significant effect on the stock
market even though this might not
occur for oil price decreases.nt
tOP : net oil price increases
nt
tOP : 1max(0, log( ) max(log( ),..., log( ))n
t t t t nNOP p p p =
It is defined as the quarterly percentage change of real oil
price in log level from the past
n quarters if that is positive and zero otherwise. This
transformation of oil price is proposed byHamilton (Hamilton, 1996;
Cong et al., 2008; Afshar, 2008), who argues that if one wants
a
measure of how unsettling an increase in the price of oil is
likely to be for the spending decisions
of consumers and firms, it seems more appropriate to compare the
current price of oil with where
it has been over the previous months rather than the previous
month alone. Hamilton thus
proposes to use the difference between the log oil price in
month tand its maximum value over
the previous n months. If the difference is negative, no oil
shock is said to have occurred. With
this variable, we can check the relationship between oil price
increases and stock market
activities. When n= 1, nttOP is just top
+ . In addition, we also construct an indicator of oil price
volatility. Volatility is found by calculating the standard
deviation of change in crude oil price.
Finally we examine the asymmetric effect of oil price
shocks.
A great deal of literature has researched the asymmetric effects
of oil price shocks since
Mork (1989) found that oil price increases had a greater
influence on a countrys macroeconomy
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than oil price decreases did, (Jones and Leiby, 1996; Sadorsky,
1999; Kilian and Park (2007);
Cong et al., 2008). First, we separate op t into positive and
negative oil price changes as follows:max(0, ),t top op
+ = min(0, ),t top op =
Thereafter, we construct a five-variable VAR (r, op+ , op -,
ind, srtns) model. A Wald coefficient
test is used to examine whether the coefficients of positive and
negative oil price shocks in the
VAR are significantly different. Another six-variable VAR (r,
op+, op_, ind, infl, srtns) model is
also constructed to examine the robustness of the results. In
the equations for real stock returns:
0 1 2 3 4 5
1 1 1 1 1
(31)P P P P P
t i t i i t i i t i i t i i t i t
i i i i i
srtns r op op ind srtns + = = = = =
= + + + + + +
0 1 2 3 4 5 6
1 1 1 1 1 1
inf (32)q q q q q q
t i t i i t i i t i i t i i t i i t i t
i i i i i i
srtns r op op ip srtns + = = = = = =
= + + + + + + +
The null hypothesis are H0: 2 3 0,i i = i=1,p (or H0: 2 3 0,i i
= i=1,,q), where op+
and
op- are positive and negative oil price shocks; p and q are lag
orders, which are determined based
on Akaike Information Criterion or Schwarz criterion.
Variance decomposition decomposes the forecasting variances by
various variables
shocks. We can use it to estimate the importance of various
structural shocks. If the fourth
variable in the basic VAR model ( , , , )t t t t
r op ind srtns= ,srtnst, can be written as:
( )4
(0) (1)
4 4 4 1
1
... , 1,2,..., 4 (33)t t j jt j jt j
srnts y c c t =
= = + + = ( )
4
q
jc is the fourth row and jth line element of Cq, which can
berepresented as( )
4
q t qj
jt
srtnsc
+= .
It shows srtnst+qs response to a shock of yjt in the condition
that other variables keep constant.
However, if it can be assumed that there are no series
correlations among j, the variances are:
( )2
(0) (1) (3) ( ) 2
4 4 1 4 2 4 ,
0
... ( ) 1, 2,..., 4 (34)q
j jt j jt j jt j jj
q
E c c c c j
=
+ + + = =
Additionally, if there are not correlations among disturbances
in the same period, the variance of
srtnst is the sum of four variances above:4
( ) 2
1
1 0
var( ) { ( ) }, 1,2,..., (35)qt j jj
j q
srtns c t T
= =
= =
Because the variance of srtns can be decomposed into four
irrelative effects, we can define the
criterion as follows to measure the contribution of various
disturbances to the variance of srtns t:( ) 2 ( ) 2
4 40 0
44( ) ( ) 2
41 0
( ) ( )(36)
var( ) ( )
q q
j jj j jjq q
j q
j jjj q
c cRVC
srtns c
= =
= =
= =
The relative variance contribution (RVC) measures the effect of
the j th variable on srtnst. This
study decomposes real stock returns shock to four parts related
to every functions disturbance,
which can be used to know the relative importance of various
shocks in the model.
IV. Impact of Oil Price Shock on Nigerian Stock Market
4.1 Unit Roots
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The unit root results are presented in Tables 1 and 2. The null
hypothesis of the two tests
(Dickey-Fuller Test with GLS Detrending (DFGLS) and Ng-Perron)
is that the series has a unitroot. In Tables 1 and 2, the null
hypothesis that real stock returns have unit root are not
rejected
at the conventional test sizes for the DF-GLS and NP test.
Therefore, the real stock returns series
are non-stationary. Also, in the Tables, the null hypotheses of
real oil price, industrial sector
GDP, inflation rate (log difference of consumer price index) and
interest rate have a unit root are
not rejected at conventional test sizes. Therefore, we accept
that real stock returns, interest rate,
real oil price, inflation, industrial GDP and the real stock
returns (Nigerian stock market
composite index, agriculture index, financial index, commercial
index, manufacturing index and
services index) are I (1) processes.
Table 1: Dickey-Fuller Test with GLS Detrending (DFGLS) unit
root test results
Variables Constant (Model 1) Constant and Linear Trend(Model
2)
Levels First Difference Levels First Difference
Nigeria Stock Exchange All Sectors Composite
Index -1.238929 -8.553418* -2.726429 -8.69942*
Agriculture Index (agr) -1.422322 -8.66607* -2.190633
-8.858295*
Commercial Index (com) -1.440459 -8.408759* -2.350795
-8.581181*
Financial Index (fin) -1.531391 -8.08443* -2.48099
-8.064706*
Manufacturing Index (man) -1.312085 -8.671879* -2.06325
-8.812128*
Services Index (ser) -1.283163 -8.808441* -2.79352
-8.959719*
Real Oil Price (op) -0.897353 -4.537954* -1.799748
-5.029518*
Consumer Price Index -0.383388 -2.972423* -1.441877
-4.235674*
Industrial Sector GDP (ind) 1.076871 -4.098378* -1.718114
-9.302853*
Interest Rate (r) -1.513308 -2.94467* -1.87857 -4.166013*
Asymptotic Critical Values:
1% -2.60849 -2.60849 -3.751 -3.751
5% -1.946996 -1.946996 -3.174 -3.174
10% -1.612934 -1.612934 -2.875 -2.875
Note: The Null Hypothesis is the presence of unit root. Model 1
includes a constant, Model 2 includes a constantand a linear time
trend . *,**,***, significant at1%, 5%, and 10% respectively. Lag
length selected based on
schwarz information criterion (SIC). The
Elliott-Rothenberg-Stock DF-GLS test statistics are reported.
Table 2: Ng-Perron unit root test resultsVariables Constant
(Model 1) Constant and Linear Trend
(Model 2)
Levels (MZa) First Difference(MZa)
Levels(MZa)
First Difference(MZa)
Nigeria Stock Exchange All Sectors Composite
Index -5.1063 -25.6485* -12.9532 -25.5565*
Agriculture Index (agr) -4.2325 -25.534* -14.1289 -25.405*
Commercial Index (com) -1.4182 -25.8764* -13.4553 -25.6914*
Financial Index (fin) -5.5677 -25.7403* -10.9635 -26.0131*
Manufacturing Index (man) -5.5131 -25.5934* -13.5351
-25.4581*
Services Index (ser) -4.3570 -25.398* -13.2647 -25.2678*
Real Oil Price (op) -1.6391 - -24.8346* -6.47055 -24.404*
Consumer Price Index -0.6983 -24.2928** -2.62394 -24.9324*
Industrial Sector GDP (ind) 0.3755 -24.8856* -8.7995
-24.5265*
Interest Rate (r) -16.635 -24.4062* -13.3778 -24.122**Asymptotic
Critical Values:
1% -13.8 -13.8 -23.8 -23.8
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5% -8.1 -8.1 -17.3 -17.3
10% -5.7 -5.7 -14.2 -14.2
Note: The Null Hypothesis is the presence of unit root. Model 1
includes a constant, Model 2 includes a constant
and a linear time trend . *,**,***, significant at1%, 5%, and
10% respectively. Ng-Perron test statistics are
reported. Spectral GLS-detrended Auto Regressive based on
Schwarz Information Criterion (SIC).
4.2 Real oil price shock
In this section, we assess the impact of the real oil price
shock on real stock returns in
Nigeria considering the movement of exchange rate (Naira per
U.S. Dollar). Following Cong et
al (2008), the variables in the VAR model are placed in the
following order: log of short-term
interest rate; log of real oil price; log of industrial sector
GDP and real stock returns (stock
market composite index, agriculture, financial, manufacturing,
services). This ordering assumes
that interest rate shocks are independent of contemporaneous
disturbances to the other variables.
With this order of variables, shocks to the interest rate, oil
price, and industrial production havepossible contemporary effect
on real stock returns, but not the other way around. As
highlighted
in the last section, we adopt the generalized impulse response
method which does not depend on
the variables orders in VARmodel.
Figure 1 shows the generalized impulseresponse function curves
simulated by analyticmethod, based on the VAR(r, op, ind, srtns)
model. For the stock market indices, the impacts of
the oil price shock are not statistically significant at the 1%,
5% and10% level. As revealed in the
figure, the real oil price shock exhibit positive responses only
in the first five periods of the
shock and thereafter exhibit negative responses. All the
orthogonalized impulse responses revert
to zero after the fifth period which means the impact of oil
price shocks is transitory.
Two non-linear transformations of the real oil price shocks
(op+, NOPI) revealed a
similar result compared to the real oil price shocks. The shocks
from op+ have statistically
insignificant positive impact on stock returns in Nigeria. The
positive effect however reverts
towards zero in the tenth period. This means that the impact of
oil price shocks on the Nigerian
stock market returns in Nigeria is also transitory.
Consequently, non-linear measures of the real
oil price shocks yield similar cases of statistically
insignificant impacts just like the linear real oil
price shocks.As space is limited to accommodate all the impulse
response graphs, all the results
are summarized in Table 3.2 The results in Table 3 show that no
responses of stock returns are
statistically significant despite having a positive
relationship.
2The impulse response graphs are available from the authors on
request.
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|Page
-.10
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Innovation
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holesky
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Innovation
Fig.1.re
aloilpriceshocks:OrthogonalizedimpulseresponsefunctionofrealstockreturnstolinearoilpriceshocksinVAR(r,op,ind,srtns).
Notes:Figuresare:firstrowNigeriaStock
Market
CompositeIndex(NSMCI),AgricultureIndex(agr),Co
mmerceIndex(agr),secondrowFinancialIndex(fin),ManufacturingIndex(man),ServicesIndex(serv).Thehorizontalaxisistheperiod.The
verticala
xisistheexplanationlevelofdependentvariablestoindependentvariables.Inthemodel,we
fixtheperiodsat10quarters.
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Table 3: Statistically insignificant impulse responses of real
stock returns to real oil price shocks
NSMCI Agricultural
Index
Commercial
Index
Financial
Index
Manufacturing
Index
Services
Index
Shock to op+ p p p p p p
Shock to NOPI p p p p p PNote: p(n) indicates positive
(negative) statistically insignificant orthogonalized impulse
response to oil price
shocks. The positive impact on the stock returns reverts to zero
towards the tenth-period.
4.3 Alternative VAR specificationIn order to test for the
robustness of the VAR results, other variables are included in
the
VAR model such as inflation (log difference of consumer price
index). A five-variable VAR (r,
op, ind, infl, srtns) model is built for testing the impact of
oil price shocks and their non-linear
transformations (t
op+ and NOPIt). Results are presented in Table 4. There is no
observable
difference between Table 3 and Table 4. The statistically
insignificant positive impact of oil
price shock (op) on the stock returns reverts to zero in the
fifth-period. This is similar to the
result reported in the basic model. In the shock to op+ in the
second row of Table 4, the
insignificant positive impact on the stock returns is close to
zero in the tenth-period. In addition,
the insignificant positive impact of NOPI on the stock returns
reverts to zero in the seventh-
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period. We can therefore conclude that the results from the
basic VAR model are although
positive not significant even when some other variables are
included.
Table 4: Statistically insignificant impulse responses of real
stock returns to real oil price shocks
when Inflation is included in VAR model
All
Sectors
Agricultural Commercial Financial Manufacturing Services
op p p p p p p
op+ p p p p p p
NOPI p p p p p PNotes: p(n) indicates positive (negative)
statistically insignificant orthogonalized impulse response to oil
price
shocks.
4.4 Asymmetric effects of oil price shocksThe Chi-square test
results employed to investigate the asymmetric effects of whether
the
coefficients of the insignificant positive and negative oil
price shocks in the VAR are
significantly different is presented in Table 5. These are
obtained by carrying out a Wald test on
the coefficients of positive and negative oil price shocks. In
all the cases, the null hypothesis of
symmetry cannot be rejected in the two VAR (r,top+ ,
top , ind, srtns) and (r,
top+ ,
top , ind, infl,
srtns) models. In summary, the asymmetric effect of oil price
shocks on the real stock returns in
Nigeria is not supported by statistical evidences. This means
that there is a symmetrical effect of
statistically insignificant positive and negative oil price
shocks on the Nigerian stock market
returns.
Table 5: A Wald test for the asymmetric impact of real oil price
shocks on real stock returnsChi-square test results (r, op, ind,
inf, real
stock returns) H0: 2 3i i = Chi-square test results (r, op, ind,
inf, real
stock returns) H0: 2 3i i =
All Sectors 0.031313 0.676682
Agricultural 0.414376 1.421165
Commercial 0.041038 0.762771
Financial 0.005415 0.370752
Manufacturing 0.053859 0.741733
Services 0.010945 0.579623
Note: *, **, ***, denote statistical significance at 1%, 5%, 10%
levels.
4.5 Effects of oil price volatilityIn this section, we assess
whether the uncertainty caused by the oil price volatility have
an impact on the Nigerian stock market. First, we replace the
oil price shocks with oil price
volatility to construct a four-variable VAR (r, vol, ind, srtns)
model. Thereafter, we include oil
price volatility variable together with the real oil price
shocks to construct a five-variable VAR
(r, vol, op, ind, srtns) model. Finally, inflation is also added
to the model to examine the
robustness of the VAR result (r, vol, op, ind, infl, srtns)
model. The generalized impulse
response results of real stock returns are summarized in Table
6. We found that oil price
volatility have a statistically insignificant negative impact on
the stock returns in Nigeria. The
insignificant negative impact of oil price volatility on the
entire stock returns manifest in the fifthperiod except for
financial index which manifest in the third period.When oil price
shocks and
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inflation are considered, the response of the stock indices
exhibit a similar pattern. So the results
are robust.
Table 6: Orthogonalized impulse response results of real stock
returns to oil price volatility in
different VAR modelsAll Sectors Agric Com Fin Man Ser
Sign of statistically significant effect on real stock
returns
of vol in VAR (r, vol, ind, strns)n n n n n n
Sign of statistically significant effect on real stock returnsof
vol in VAR (r, vol, op, ind, strns)
n n n n n n
Sign of statistically significant effect on real stock
returns
of vol in VAR (r, vol, op, ind, inf, strns)n n n n n n
Notes: p(n) indicates positive (negative) statistically
insignificant orthogonalized impulse response to oil price
shocks.
4.6 A comparison of oil price and interest rate shocksFinally,
we assess the relative importance of oil price and interest rate
shocks to real
stock returns. Forecasting variance decomposition is used to
obtain how much of the
unanticipated change of real stock returns can be explained by
the variables over a 10-period
horizon. Results are shown in Table 7. The real oil price is
used in the VAR (r, op, ind, srtns)
model to get robust results. Following, Cong et al. (2008), we
compare the relative importance of
oil price shocks and interest rate shocks using the
definition:
percentage explained by op
percentage explained by riR =
The contribution of the real oil price shock to the real stock
returns over the 10 periodranges from 9.77% for agriculture index
to 12.39% for the financial index in column 3 of Table .
The contribution of interest rate also ranges from 0.521% for
financial index to 1.910% for the
agriculture index. The calculated Ri ranges from 5.13 for
Agriculture to 31.79 for the financial
index in column 4 of Table 7. The results of Ri in column 4 of
Table 7 showed that oil price
shocks can explain much more than interest rate in the stock
market returns in Nigeria which is
an indication that oil price shocks represents a significant
source of volatility to returns. The
findings from this study are similar to Sadorsky (1999) and Cong
et al. (2008). They found that
the contribution of oil price shock was greater than that of
interest rates on real stock returns, and
that the relative importance of oil price shocks and interest
rates varies across different indices.
Table 7: Variance decomposition of forecasting error in real
stock returns due to oil price shocks and interest rateshocks
Percentage of variation in real stock returns which can be
explained
by oil price shocks or interest rate shocks
Due to r Due to op Ri
Nig. Stock Exchange Composite Index 0.968615 12.39616
12.79782
Agriculture Index 1.910489 9.791635 5.125198
Commercial Index 0.959224 11.81903 12.32145
Financial Index 0.521286 16.57537 31.79707
Manufacturing Index 1.479874 9.779657 6.608439
Services Index 1.203963 10.68775 8.877142
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V Concluding Summary
In this paper, we focus on the relationships between the oil
price and the Nigerian stockmarket between 1995 and 2008 using
quarterly series. Empirical evidence reveals that stock
market returns exhibit positive but insignificant response to
oil price shocks but reverts to
negative effects after a period of time depending on the nature
of the oil price shocks. The results
are similar when other variables are included. Positive but
statistically insignificant responses.
Also, the asymmetric effect of oil price shocks on the Nigerian
stock returns indices is not
supported by statistical evidences. Volatility of oil prices
depresses the returns to the stock
market indices. Finally, the real oil price shocks explains much
more than interest rates in stock
market returns indicating that oil price shocks are a
significant source of volatility in the
Nigerian stock market returns. The relative importance of oil
price shocks and interest rates
however varies across different indices in the Nigerian stock
market.
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Appendix A1: Sectoral Composition of SectorsCategory
Components
Agriculture
Financial Banking
Managed Funds
Insurance
Other Financial Institutions
Real Estate Investment Trust
Manufacturing Breweries
Building Materials
Chemical & Paints
Food, Beverages &Tobacco
Industrial and Domestic ProductsPackaging
Healthcare
Textiles
Commercial Automobile & Tyres
Conglomerates
Commercial Services
Computer & Office Equipments
Footwear
Machinery (Marketing)
Petroleum (Marketing)
Services Construction
Engineering Technology
Airlines
Publishing
Hotel and Tourism
Real Estate
Maritime
Leasing
Road Transportation
Aviation
Information, Communication & Telecommunication
Media
Mortgage
