36

Oil price shocks and Nigeria’s stock market:

what have we learned from crude oil
market shocks?
Ekpeno L. Effiong
Lecturer, Department of Economics, University of Uyo, P.M.B. 1017, Uyo, Nigeria. Email:
ekpenoeffiong@uniuyo.edu.ng; ekpenol@gmail.com

Abstract
Oil price shocks do not only originate from the supply-side of the crude oil market but may also be
demand driven. The impact of oil price shocks on stock market activities may be different depending
on its origin (i.e. demand and supply shocks). This paper provides the first examination of the impact
of the origin of oil price shocks on Nigeria’s stock market for the period 1995:1–2011:12. Oil price
shocks is decomposed into oil supply shocks, aggregate demand shocks and oil-specific demand
shocks using a structural vector auto-regression model, and their impacts on stock market prices
were analysed using impulse response and variance decomposition analysis. The impulse response
results show that stock market’s response to oil supply shocks is insignificantly negative but signifi-
cantly positive to aggregate demand and oil-specific demand shocks. The cumulative effects of the
oil price shocks account for about 47 per cent of the variation in stock prices in the long term. These
results suggest that the origin of oil price shocks is crucial for understanding the volatility in
Nigeria’s stock market. Future policy direction should focus on diversifying the economy to reduce
its vulnerability to oil price fluctuations while addressing the inefficiencies in the stock market.

1. Introduction
Since the seminal work of Hamilton (1983), a large literature has investigated the impact
of oil price shocks on the macroeconomy for both oil-importing and oil-exporting coun-
tries.1 Oil price shocks may have significant effect on economic activities of both oil-
importing and oil-exporting countries. Based on the classic supply-side model, oil price
shocks may slow down economic growth and stimulates inflation for oil-importing coun-
tries while increasing the oil revenues of oil-exporting countries through the wealth trans-
fer effects. Despite the fact that the oil price–macroeconomy relationship has been
extensively studied, another strand of the literature that is gradually growing identifies the
impact of oil price shocks on the financial markets, particularly the stock market.

JEL Classification: C32, G12, Q43

© 2014 Organization of the Petroleum Exporting Countries. Published by John Wiley & Sons Ltd, 9600 Garsington

Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
 Oil price shocks and Nigeria’s stock market 37

Oil price shocks can be transmitted to the stock markets through its impact on stock
prices. Stock markets are known to possess informational efficiency and, in turn, stock
prices are reasonably expected to reflect all current and available information including oil
price shocks. Theoretically, oil price shocks affect stock market prices through their effect
on expected earnings (Huang et al., 1996). An increasing literature has investigated the
impact of oil price shocks on stock market prices (or returns) with mixed results.2 The con-
ventional wisdom in the literature is that oil price shocks should lead to stock market
decline. However, such impact may differ between oil-importing countries and oil-
exporting countries depending on the relative importance of oil to their macroeconomy
(see Park and Ratti, 2008; Bjørnland, 2009).
Recent development in the crude oil market such as wide volatility of oil prices and the
increased demand for oil from emerging economies (such as China and India) has led to
the recognition that the impact of oil price shocks may depend on its origin (or source).
Contrary to the implicit assumption in standard macroeconomic models that unexpected
oil price hikes were exclusively due to exogenous supply shocks in the crude oil market,
researchers such as Hamilton (2009a, b) and Kilian (2009) have shown that oil prices may
also be demand driven. Thus, oil price shocks has been decomposed by Kilian (2009) into
shocks driven by demand (aggregate demand and oil-specific demand shocks) and supply
(oil supply shocks) and are likely to have different impact on macroeconomic variables.
The impact of oil price shocks based on its origin has been extended to the analysis on
stock markets for only developed oil-importing and oil-exporting countries and emerging
economies (see Aspergis and Miller, 2009; Kilian and Park, 2009; Basher et al., 2012;
Wang et al., 2013). However, a considerable gap exists in the literature for developing oil-
exporting countries like Nigeria, which is yet to be explored.
This paper provides the first examination of the impact of oil price shocks on stock
market activities in Nigeria, an oil-exporting country, with particular reference to the
origin of oil price shocks using monthly data from 1995 to 2011. As an emerging stock
market and in the light of the 2008 global financial crisis that bolstered a cyclical downturn
in oil prices and stock market activities, understanding the dynamic interaction between
crude oil market shocks and stock market activities is of relevance to policymakers, finan-
cial experts and investors in the Nigerian economy. Following Kilian and Park (2009), a
structural vector auto-regression (VAR) model is employed to decompose oil price shocks
into oil supply shocks, aggregate demand shocks and oil-specific (or precautionary)
demand shocks, and their impacts are examined on Nigeria’s stock market prices. The
direction, timing and duration of stock market prices response to each oil price shocks is
analysed using impulse response functions, and their relative importance is analysed by
means of variance decomposition.
The remainder of the paper is organised as follows. Section 2 provides a review of the
empirical and theoretical literature on the relationship between oil price shocks and stock

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
38 Ekpeno L. Effiong

market. Section 3 describes the data and the methodology used for this study. Section 4
reports the empirical results, whereas the conclusion is contained in section 5.

2. Oil price shocks and stock market activities

2.1. Review of literature
Since the oil price shocks of the 1970s, an extant literature identifying the relationship
between oil prices and several macroeconomic variables such as real gross domestic
product growth rates, inflation, employment, exchange rates, current account and trade
balances, etc., exist with different econometric methodologies and oil price specifications
being employed (recent examples are Cologni and Manera, 2008; Kilian, 2008, 2009;
Kilian et al., 2009; Peersman and van Robays, 2012). Another recent strand of the litera-
ture focuses on the impact of oil price shocks on the financial markets especially on asset
prices (such as stock prices). While oil price changes is crucial for understanding the fluc-
tuations in stock market prices, the literature suggests that there is no consensus among
researchers and economists on its impact. In actual fact, the evidence is mixed with par-
ticular focus on developed and emerging markets economies.
Jones and Kaul (1996) provide evidence of a negative relationship by using a standard
cash flows and dividend valuation model to investigate the reaction of stock markets in
advanced countries (United States, Canada, Japan and UK) to oil price shocks. They found
that the stock markets of United States and Canada reaction was completely determined by
the impacts of oil price shocks on the cash flows, whereas the outcome for Japan and UK
was not strong and indecisive. Huang et al. (1996) applied a VAR model to examine the
link between oil returns and some US industries stock prices. They found a significant
impact of oil prices on some individual oil companies stock returns but found no evidence
of any relationship between oil prices and aggregate stock market indices such as the
S&P500. Sadorsky (1999) showed that oil price changes significantly affected stock
returns using an unrestricted VAR model with Generalized Autoregressive Conditional
Heteroskedasticity effects on US monthly data. Within an enlarged market model of
several industry returns in the Australian stock market, Faff and Brailsford (1999) found
that oil prices had an effect on stock prices with positive sensitivities in the oil and gas
industry and negative sensitivities in the papermaking, packing and transportation indus-
try. Papapetrou (2001) examined the dynamic relationship between oil prices and several
macroeconomic indicators such as real stocks prices, interest rates, real economic activity
and employment in Greece. His evidence shows that oil price changes had significant
effect on economic activity and employment and also explained stock price movement.
Sadorsky (2001) used a multifactor market model that considers the presence of several
risk premiums and identifies factors such as exchange rates, interest rates with oil prices

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 39

as determinants of oil and gas stock returns. He found a significant positive relationship
between oil prices and oil and gas stock returns.
More recently, Park and Ratti (2008) examined the oil-stock price nexus for the US and
13 European countries and found that the impact of oil price shocks differs with stock
markets reacting negatively and positively for oil-importing and oil-exporting countries,
respectively. Cong et al. (2008) investigates the interactive relationships between oil price
shocks and Chinese stock market using multivariate VAR and found that oil price shocks
has no statistically significant impact on indices of stock market returns except for manu-
facturing index and some oil companies. Henriques and Sadorsky (2008) used a VAR
model to show that oil price shocks have no significant impact on stock prices of alterna-
tive stock prices. Miller and Ratti (2009) analysed the long-run relationship between world
crude oil price and international stock markets within a cointegration vector error correc-
tion model (VECM) with varied results. Narayan and Narayan (2010) provide evidence of
cointegration between stock prices, oil prices and nominal exchange rates and also found
that oil prices have a positive and significant impact on Vietnam’s stock prices. Fayyad and
Daly (2011) applied the VAR analysis to examine the effects of oil price changes on the
Gulf Cooperation Council (GCC) countries, the UK and US stock markets and found that
the predictive power of oil prices for stock markets increased after a rise in oil prices and
during the periods of the global financial crises. Arouri and Rault (2012) used a panel data
approach based on seemingly unrelated regression systems and Wald tests with Granger
causality to study the sensitivity of stock markets to oil prices for GCC countries. They
found that oil price increases influence stock prices positively except for Saudi Arabia,
which had a bidirectional relationship. Other studies that have contributed to the nexus
between oil price shocks and stock market include Bjørnland (2009), Filis et al. (2011),
and Zhu et al. (2011).
Kilian (2009) recently criticised the conventional approach in the oil-literature,
which treats oil prices as exogenous with respect to the global economy. Existing studies
(such as Barsky and Kilian, 2002, 2004; Kilian, 2008, Hamilton, 2008) argue that same
economic shocks that drives macroeconomic aggregate (and thus stock prices) may like-
wise drive the oil prices, making it impossible to separate reverse causality. Therefore,
aggregate oil price shocks must be decomposed into structural factors to reflect the
endogenous character of such shocks. In other words, oil price shocks should be consid-
ered in terms of its origin. Using recursive structure, Kilian (2009) identifies three oil
shocks: an oil supply shocks, an oil-market specific shocks and a global demand shocks.
As pointed out by Aspergis and Miller (2009), the decomposition of oil price shocks
eliminates not only the deficit of previous studies that considered oil prices exogenous
with respect to other variables that determine economy activities but also the deficiency
of those studies to document the relative importance of such differentiated shocks for the
course of asset prices.

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
40 Ekpeno L. Effiong

Kilian’s approach has been adopted to investigate the impact of oil price shocks on
stock prices. Kilian and Park (2009) investigate the relationship between US stock prices
and oil price shocks using Kilian’s modelling framework. They found that oil demand
shocks depress stock prices due to precautionary demand, whereas oil price shocks driven
by global economic expansion has a positive effect on stock prices, while oil supply shocks
had less impact on stock prices. Aspergis and Miller (2009) modified Kilian’s approach
and used a VAR analysis to analyse the effect of structural oil-market shocks on
stock prices in a multi-country context. They found that oil-market shocks have a minor
impact on international stock markets. Basher et al. (2012) used a structural vector
autoregressive (SVAR) approach to model the dynamics between real oil prices, exchange
rate indices for major currencies, emerging market stock prices, interest rates, global real
economic activity and oil supply. They found that oil price shocks depress stock prices and
also that increases in emerging market stock prices increases oil prices. Wang et al. (2013)
analysed the oil price shock and stock market relationship for some oil-exporting and oil-
importing countries.3 They found that the magnitude, direction and duration depends on
whether the country is a net importer or exporter in the world oil market and whether
changes in oil price are driven by supply or aggregate demand.
With reference to Nigeria, while the oil price–macroeconomy relationship has been
extensively studied (see, e.g. Ayadi, 2005; Olomola and Adejumo, 2006; Akpan, 2009;
Adeniyi et al., 2011; Chuku et al., 2011; Iwayemi and Fowowe, 2011; Chuku, 2012),
the literature on oil price shocks and stock market have received little attention from
researchers. Adebiyi et al. (2010) studied the effects of oil price shocks and exchange
rates on the stock market behaviour of Nigeria using a multivariate VAR analysis. They
found that oil price shocks had an immediate and significant negative impact on real
stock returns and the causation runs from oil price shocks to stock returns. Based on
cointegration and Granger causality tests, Adaramola (2012) found that oil price shocks
has a negative effect on stock market returns while the causation runs from oil price
shocks to stock market returns. Asaolu and Ilo (2012) applied VECM to the oil price and
stock market relationship and found that oil price shocks leads to a fall in stock market
returns. The limitation of these studies like the previous literature before Kilian’s (2009)
seminal work is the reliance on the maintained assumption that oil price shocks are
exogenously determined rather than endogenously based on its underlying source (i.e.
demand and supply shocks).
As a contribution to the literature, this paper investigates the oil price shocks and stock
market relationship for Nigeria taking into consideration the origin of oil price shocks.
Consequently, the impact of the three different types of oil price shocks in the crude oil
market on stock market prices are disentangled within a structural VAR model and their
interaction analysed based on impulse response functions and forecasting variance
decomposition.

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 41

2.2. Theory
Theoretically, an asset price in a financial market is determined by its expected discounted
cash flows (Huang et al., 1996). For stock prices, it equals the discounted values of
expected future cash flows whose realised stock returns is given as:

d ( E ( c )) d ( E ( r ))
R= − (1)
E (c ) E (r )

where c is the cash flow stream; r is the discount rate; E(·) is the expectation operator; d(·)
is the differentiation operator; and R is the stock market returns computed as log (SPt /
SPt–1) * 100. From equation (1), stock market prices (or returns) are influenced systemati-
cally by two factors, namely, the expected cash flows and discount rates. Hence, any exter-
nal influences (such as oil price shocks) can have significant effect on the stock prices by
influencing its determinants.
The transmission mechanism through which oil prices impacts on stock prices follows
from two main channels of supply and demand effects. Oil is a major energy source and
input in the production process. An increase in the price of oil raises the cost of production,
which in turn, reduces productivity and profits likewise the stock prices. Also, the transfer
of oil prices to final consumers of goods and services in form of higher prices causes a
reduction in their real balances, consumption spending, the final demand and profits. Thus,
the impact of an oil price increase on the stock prices should be negative. However, the
effects of such increase may differ for stock markets in oil-exporting and oil-importing
countries.
As oil price shocks feed indirectly through macroeconomic indicators, researchers
such as Bjørnland (2009) argue that for oil-exporting countries, oil price increase impacts
positively in their economy through higher incomes and wealth effects. Consequently,
consumption and investment spending will stimulate productivity and the stock market
will be affected positively. For oil-importing countries, the converse holds. Oil price
increase will lead to higher cost of production that is transferred in form of higher prices to
final consumers. By implication, consumer spending and final demand will fall forcing a
reduction in production and therefore a decrease in stock prices.
Oil prices may also affect stock prices indirectly via the discount rate. As the
expected inflation rate and the expected real interest are components of the expected dis-
count rate, both may depend on expected oil price. For oil-importing countries, Huang
et al. (1996) argues that oil price increase will affect the balance of payments putting
downward pressure on the country’s foreign exchange rate and an upward pressure on
the expected inflation rate. A higher expected inflation rate affects the discount rate posi-
tively and lowers the stock prices. Furthermore, oil price influences the real interest rate
as a higher oil price relative to the general price level leads to an increase in the real

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
42 Ekpeno L. Effiong

interest rate (Huang et al., 1996). This reduces stock prices following increased hurdle
rate on corporate investments.

3. Data and methodology

3.1. Data description
This study examines the effects of oil price shocks based on its underlying source (i.e.,
demand and supply shocks) on the stock market prices in Nigeria, an oil-exporting
country. The response of stock market to oil price shocks depends in part on whether the
country is an oil exporter or oil importer. Monthly data is collected during the period
January 1995 to December 2011 on global oil production, global economic activity, oil
prices and stock market prices.4 In the spirit of Kilian and Park (2009), the oil demand and
supply shocks are identified by using the global oil production in levels, the global real
economic activity and real oil prices. Oil supply shocks capture unpredictable innovations
in global oil production levels. Global oil production data over the sample period is avail-
able from the US Energy Information Agency database, which defines global oil supply as
crude oil including lease condensate.
Recent hike in oil prices has been linked to strong and increasing global demand for
crude oil particularly from emerging market economies such as China and India. Follow-
ing Kilian (2009), the effects of global demand on oil price shocks is accounted for by
including an index of global real economic activity as a proxy for global demand for indus-
trial commodities. The index is based on dry cargo single voyage ocean freight rates of dif-
ferent commodities: grain, oilseeds, coal, iron ore, fertiliser and scrap metal. It captures
the shifts in the demand for global industrial commodities in the world market. The index
is deflated by the US consumer price index (CPI) and is linearly detrended to remove the
effects of technological advances in ship-building and other long-term trends in the
demand for sea-transport. Therefore, the detrended index captures the cyclical variations
in ocean freight rates. Kilian (2009) provides a detailed discussion on the construction of
the index, which is also available for download from his homepage.5
Oil prices are measured in dollars per barrel using the spot prices for the Nigerian
Bonny Light crude oil as a proxy for world oil prices. Oil prices are deflated by the US CPI
to reflect real oil prices. For robust checks, oil prices are orthogonalise for the effect of
exchange rate fluctuations by converting these prices from the US dollar into the domestic
currency that we refer to as the domestic oil price and then deflated by the domestic price
index for inflation-adjusted real values. By this approach, two proxies for real oil prices—
world and domestic—are used in this study. The stock market prices are measured by using
the Nigerian Stock Exchange (NSE) All-Share index (ASI). For modelling purpose, stock
market prices is expressed as the logarithm of the ASI after deflating it for real values by
the domestic CPI.

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 43

OILPROD GEA
76,000 3

2
72,000
1

68,000 0

-1
64,000
-2

60,000 -3
96 98 00 02 04 06 08 10 96 98 00 02 04 06 08 10

OILP NASI
160 70,000

60,000
120
50,000

40,000
80
30,000

20,000
40
10,000

0 0
96 98 00 02 04 06 08 10 96 98 00 02 04 06 08 10

Figure 1 Time series plots of selected variables.

The data for Nigeria’s Bonny Light crude oil prices are available from OPEC 2012
Annual Statistical Bulletin, whereas US and Nigeria CPIs are available from the IMF
International Financial Statistics database. Nominal exchange rates and the NSE ASI are
available from CBN Statistical Bulletin. Figure 1 shows the time series plot of the selected
variables: global oil production (OILPROD), the logarithm of real global economic activ-
ity (GEA), Nigeria Bonny Light crude oil prices (OILP) and the NSE All-Share index
(NASI). From Fig. 1, the effect of the 2008 financial crisis is clearly evident as all variables
exhibit a significant drop during this period with some recovery afterward.

3.2. The structural VAR model

Building on Kilian and Park (2009), the effects of the demand and supply components of
the oil price shocks on stock market can be disentangled using the SVAR models. The
SVAR methodology offers a combination of time series analysis and economic theory in
determining the dynamic responses of macroeconomic variables to independent shocks

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
44 Ekpeno L. Effiong

(Effiong, 2013). By capturing the dynamic relationship between variables of interest

within a linear model framework, a set of restrictions based on economic theory can be
imposed to decompose innovations to the variables into mutually orthogonal shocks with
structural interpretation. Once the shocks are identified, the dynamic effects on all the vari-
ables in the model can be measured while controlling for other exogenous influences on
the variables. With this approach, disentangling the different sources of oil price shocks
and quantifying their dynamic effects on stock prices become a possibility.
The standard structural VAR model representation is given as:

p
Ayt = c + ∑ Ai yt −i + ε t (2)
i =1

where εt denotes the vector of serially and mutually uncorrelated structural shocks;
yt = [oilprodt, geat, roilpt, sopt] is a vector of variables where oilprodt is the logarithm of
global oil production; geat is Kilian’s (2009) index for global real economic activity; roilpt
is the logarithm of real oil prices (for both World and Nigerian price); and sopt is the loga-
rithm of the real stock market price index. The structural shocks, et, is derived by imposing
an exclusion restrictions on A0−1 such that et = A0−1ε t . Kilian (2009) decomposes oil price
fluctuations into three structural shocks: ‘oil-supply shocks’ to capture global supply of
crude oil; ‘aggregate demand shocks’ capturing innovations to global economic activity
due to increased global demand for all industrial commodities; and ‘oil-specific demand
shocks’ to capture precautionary demand for crude oil that reflects concerned about future
shortfalls in oil supply. Based on the underlying sources of oil price shocks, their effect on
stock prices specified in a recursively identified structural model form as given below:

⎛ e1oilprod
t ⎞ ⎡ a11 0 0 0 ⎤ ⎛ ε1oilt supply shocks ⎞
⎜ e gea ⎟ ⎢ a21 a22 0 0 ⎥ ⎜ ε 2aggregate demand shocks ⎟
et ≡ ⎜ roilp2t
⎟=⎢ ⎥⎜ t ⎟ (3)
⎜ e3t ⎟ ⎢ a31 a32 a33 0 ⎥ ⎜ ε 3oilt − specific shocks ⎟
⎜⎝ sop ⎟⎠ ⎢ a ⎥
a44 ⎦ ⎜⎝ ε 4other ⎟⎠
⎣ 41
stock price shocks
e4 t a42 a43 t

The last structural equation depicts the stock market block with one structural shock to
capture the innovations to stock market returns not driven by the crude oil market shocks
but other stock market related factors. This is referred to as the other stock prices shocks.
The error decomposition structure in equation (3) is based on three assumptions of
exclusion restrictions (see Kilian, 2009; Kilian and Park, 2009). Firstly, oil supply shocks
are exogenously determined and therefore do not respond to the other shocks—aggregate
demand shocks, oil-specific demand shocks and other stock price shocks. Such shocks are
driven by oil production disruptions caused by military and political conflicts or adjust-
ment in the production quota as determined by OPEC’s monopoly of the crude oil markets.

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 45

Secondly, the global real economic activity respond immediately to oil supply shocks
while it reacts sluggishly (more than a month) to other oil-specific shocks. Disruptions in
crude oil supply has significant impacts on global economic activity (see e.g., Hamilton,
1983; Kilian, 2009), whereas it lags behind to oil price changes as discussed by Kilian
(2009). As in Kilian and Park (2009), changes in stock prices in any country cannot affect
global economic activity in the short term. Lastly, shocks to oil prices (oil-specific shocks)
that are not driven by oil supply and aggregate demand shocks are explained by the precau-
tionary motives for oil demand, which is induced by uncertainty in the availability of
future supply of crude oil. Shocks to oil prices do not respond to stock market price inno-
vations within a 1-month period. In addition, the stock market responds to all oil demand
and supply shocks and ‘other stock price shocks’ induced by stock market related factors,
such as changes in interest rate and exchange rates as discussed in Bjørnland (2009) and
Basher et al. (2012).

4. Empirical results
4.1. Unit roots and cointegration test
The first step in the empirical analysis is to confirm the integrational properties of the
selected variables through unit roots test. For this purpose, the null hypothesis of non-
stationary variables is tested against the alternative hypothesis of stationary variables
using the Dickey–Fuller generalised least squares test (DF-GLS test) of Elliott et al.
(1996) with a constant and time trend included in the regression. The DF-GLS test is more
powerful than the standard augmented Dickey–Fuller test because it applies the well-
known Dickey–Fuller τ-test to locally demeaned or demeaned and detrended series. The
optimal lag length is selected using the modified Akaike criterion as recommended by Ng
and Perron (2001). Table 1 presents the unit roots test results for the variables. The results
show that all variables are non-stationary as the null hypothesis of a unit root cannot be
rejected at conventional levels. Rather, all variables become stationary after first differenc-
ing, which means that they are I(1).
To test for the long-run relationship among the variables, the multivariate
cointegration technique of Johansen and Juselius (1990) is employed. Johansen (1988)
suggest two tests, namely, the trace and maximum eigenvalue test statistic. The trace test
examines the null hypothesis that the number of cointegrating vectors in the system, r, is
less than or equal to r0 where r0 < p and p is the number of variables in the system, whereas
the alternative hypothesis is that the impact matrix is of a full rank. The maximum
eigenvalue test examines the null hypothesis that there are r0 cointegrating vectors versus
the alternative of r0 + 1 cointegrating vectors. The results are presented in Table 2 for both
measures of the logarithm of world and domestic real oil prices. The trace statistic indi-

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
46 Ekpeno L. Effiong

Table 1 Unit-roots test

DF-GLS Test Statistic

Variables Levels First Differences

oilprod −1.9709 −3.1472**
gea −2.4993 −9.4583***
wroilp −1.3542 −14.1064***
nroilp −2.0571 −5.0622***
sop −1.5763 −3.3377**
Note: oilprod is the log of global oil production, gea is the log real global economic activity, wroilp
is log of real world oil prices, nroilp is the log real domestic oil price, sop is the log of real stock price
index. The optimal lag length is determined through the Modified Akaike Information Criterion
(MAIC) with the following critical values: 1% = −3.46, 5% = −2.93, 10% = −2.63. *** and ** indi-
cate 1% and 5% significance levels, respectively.

Table 2 Cointegration test

H0 HA Eigenvalue λTrace 95% λmax 95%

With world real oil price (lags = 4)
r=0 r=1 0.1378 46.2067 47.8561 29.5174 27.5843
r≤1 r=2 0.0619 16.6892 29.7970 12.7284 21.1316
r≤2 r=3 0.0191 3.9608 15.4947 3.8568 14.2646
r≤3 r=4 0.0005 0.1040 3.8414 0.1040 3.8414
With domestic real oil price (lags = 4)
r=0 r=1 0.1321 44.8579 47.8561 28.2013 27.5843
r≤1 r=2 0.0550 16.656 29.7970 11.2666 21.1316
r≤2 r=3 0.0253 5.3900 15.4947 5.1080 14.2646
r≤3 r=4 0.0014 0.2820 3.8414 0.2820 3.8414
Note: r indicates the number of cointegrating vector. Critical values are from MacKinnon et al.
(1999) P-values. Bold values indicate significance of the test statistic at 5% level.

cates absence of a long-run relationship, whereas the maximum eigenvalue statistic shows
evidence of long-run relationship with at most one cointegrating vector that suggest that
the variables are jointly determined.
With conflicting evidence from both Johansen’s cointegration test statistics, the
cointegration relationship between the variables using different lag length and determinis-
tic trend assumption are further re-examined. The evidence is rather mixed depending on
the selected lag length and deterministic trend assumption used.6

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 47

Given the findings of unit roots and cointegration tests, the next challenge is whether to
estimate the structural VAR in levels (i.e. with variables in non-stationary form), first dif-
ferenced (i.e. with variables in stationary form) or in a VAR that imposes cointegration
(i.e. in an error correction model). A considerable literature on this issue suggests that it is
still desirable to estimate the structural VAR in levels even if the variables have unit roots.
Basher et al. (2012) argues that a VAR specified in first differences assumes that the vari-
ables are not cointegrated because the error-correction terms are not included. In the pres-
ence of cointegration, a model in first differences is misspecified. On the other hand, the
problem with cointegration is that it often indicates too many or few cointegrating vectors
that leads to misspecification. As shown by Sims et al. (1990), the estimated coefficients of
a VAR are consistent, and the asymptotic distribution of individual estimated parameters is
standard (i.e. the asymptotic distribution applies) when variables have unit roots and there
are some variables that form a cointegrating relations. The impulse response functions of
the VAR model in levels are also consistent estimators of their true impulse response func-
tions both in the short and medium run except in the long run.7 Hence, a VAR model in
levels is specified without imposing unit roots and cointegration.

4.2. Impact of oil price shocks on Nigerian stock market

The structural VAR model in equation (3) is estimated using four (4) lags. Akaike Infor-
mation Criterion selects a lag length of three (3) and one additional lag is added for unit
roots following the approach of Toda and Yamamoto (1995). The impact of oil price
shocks on stock prices is analysed using both the impulse response functions and forecast
variance decomposition. The impulse response analysis helps to assess the direction, mag-
nitude, timing and duration of a one-time demand and supply shocks in the crude oil
market, whereas the forecast variance decomposition decomposes the forecast error vari-
ances and estimates the relative importance of various structural shocks. The analysis uses
the two types of real oil prices for robustness checks.

4.2.1. Stock market response with world real oil price

Depending on the underlying source of the oil price shocks, Kilian and Park (2009) show
that the response of the stock prices may differ substantially. Figure 2 shows the impulse
response functions of the Nigerian stock market prices simulated by analytic method to
each of the three demand and supply shocks in the crude oil market. The dashed lines indi-
cate the two standard error confidence intervals. A positive oil supply shocks that reflect
the discovery of new oil fields, better extraction technologies or a possible decline in
OPEC’s control over oil supply causes a transitory decline in stock prices within the first 3
months and afterward revert back to zero. The response pattern of stock prices following
an oil supply shock can be linked to the differences between short-term and long-term
price elasticity of oil demand. In the short term, the price elasticity crude oil demand tends

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
48 Ekpeno L. Effiong

Oil supply shock Aggregate demand shock

.12 .12

.08 .08

.04 .04

.00 .00

-.04 -.04
2 4 6 8 10 12 14 2 4 6 8 10 12 14

Oil-specific demand shock Other stock prices shock

.12 .12

.08 .08

.04 .04

.00 .00

-.04 -.04
2 4 6 8 10 12 14 2 4 6 8 10 12 14

Figure 2 Responses of real stock prices to structural shocks (with world real oil prices).

to zero, whereas it is much higher in the long term (Hamilton, 2009a; Wang et al., 2013).
Hence, a decrease in oil price following an increase in oil supply does not induce an
increase in oil demand in the short term and leads to less profits and stock market declines
in an oil-exporting country. However, the converse will hold if the oil price shock becomes
persistent over a long period of time triggering higher oil demand in oil-importing coun-
tries. However, the response of the stock prices to an oil supply shock is statistically insig-
nificant at 5 per cent level. The result conforms to Wang et al. (2013) for oil-exporting
countries and implies that oil supply is far less important for understanding stock market
price behaviour. The reason being that oil supply has become relatively stable due to stag-
nating oil production and weak exogenous political events that in turn may lead to an insig-
nificant response of oil prices to supply shocks (Hamilton, 2009a; Wang et al., 2013).
The impact of an aggregate demand shock driven by increased global real economic
activity causes a positive delayed response in stock prices within the first 2 months after

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 49

which it increases persistently up to the 7th month and is statistically significant at

5 per cent level before it starts declining. Such pattern indicates the existence of informa-
tional inefficiencies in the stock market and a sharp contrast to the efficient market hypoth-
esis. As in Kilian and Park (2009), an unexpected increase in the global demand for
industrial commodities has a positive effect on oil prices and stock prices. The expansion
in global economic activity induces higher demand for oil and subsequently higher oil
prices. For an oil-exporting country, an increase in oil price results in a wealth transfer
from oil-importing countries that stimulates short-term economic activity and also drives
up stock prices.
The impact of oil-specific demand shocks is positively associated with higher stock
market prices. The impulse response indicate that the stock market price react immediately
to oil price increase driven by the precautionary demand for oil due to uncertainty in the
availability of future supply of crude oil. The effect is statistically significant and persistent
within the first 10 months before flattening out. Like the aggregate demand shocks, oil-
specific demand shocks induces higher oil prices (see Kilian, 2009; Kilian and Park,
2009), which for oil-exporting countries implies higher revenues for stimulating eco-
nomic activity in the short term that in turn drives up stock prices. Our findings for both
aggregate demand and oil-specific demand shocks are consistent with Wang et al. (2013)
for oil-exporting countries but in contrast to Kilian and Park (2009) and Basher et al.
(2012) for oil-importing countries in the case of oil-specific demand shocks. Specifically,
Wang et al. (2013) show that the positive impact of both demand shocks on stock market
returns are stronger and more persistent in oil-exporting countries.
Lastly, the response of stock market price to ‘other stock-prices shock’ induced by
stock market related factors is significantly positive. The response is delayed within the
first 2 months, followed by an increase up to the 6th month before a gradual decline. Again,
this pattern reflects the inefficiencies within the Nigerian stock market. In a well-
functioning market, one would expect the market to react immediately to new information.

4.2.2. Stock market response with domestic real oil price

The response of stock market prices to the structural crude oil markets using the country-
specific (Nigerian) oil price measure to adjust for the movement in exchange rate (Naira
per US dollar) is depicted in Fig. 3. The impulse response of stock market prices is similar
to those in Fig. 2.
The responses of the stock market prices to the three types of crude oil market
shocks—oil supply, aggregate demand and oil-specific demand shocks—and the ‘other
stock price shock’ are similar to those obtained when the world oil prices is used as a proxy
for real oil prices and therefore carry the same interpretation. Overall, the impulse
response functions show considerable robustness with respect to the two oil price meas-
ures and equally demonstrate the reliability of our specified model. In the following analy-

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
50 Ekpeno L. Effiong

Oil supply shock Aggregate demand shock

.12 .12

.08 .08

.04 .04

.00 .00

-.04 -.04

-.08 -.08
2 4 6 8 10 12 14 2 4 6 8 10 12 14

Oil-specific demand shock Other stock price shock

.12 .12

.08 .08

.04 .04

.00 .00

-.04 -.04

-.08 -.08
2 4 6 8 10 12 14 2 4 6 8 10 12 14

Figure 3 Responses of real stock prices to structural shocks (with domestic real oil prices).

ses, we use only the world real oil price only. As pointed out by Park and Ratti (2008), stock
markets anticipate oil price shocks to have a global impact, which is best captured by the
world oil price rather than the country-specific prices that reflect movements in exchange
rates.

4.2.3. Variance decomposition of structural shocks

The relative contributions of the three oil-market shocks plus the other-stock price shocks
to the variations in the stock market prices is captured using the variance decomposition
method. Table 3 displays the forecast error variance decomposition results for the struc-
tural VAR model in equation (3). The numbers reported indicate the percentage of the fore-
cast error of the four shocks at different time horizons from 1 month (short term) to 15
months (long term).

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 51

Table 3 Variance decomposition of structural shocks

Month OSS ADS OSDS OSPS

1 0.3937 7.6019 0.0077 91.9966
2 0.7151 8.1389 0.2646 90.8812
3 1.0095 11.8235 1.3221 85.8447
4 0.6925 18.4952 1.7995 79.0126
5 0.5058 23.9536 2.3408 73.1996
6 0.3962 27.8574 3.1627 68.5836
7 0.3428 30.4290 4.1552 65.0727
8 0.3069 32.1904 5.1805 62.3220
9 0.2800 33.4885 6.1470 60.0842
10 0.2594 34.4256 7.0367 58.2781
11 0.2430 35.0564 7.8616 56.8388
12 0.2287 35.4424 8.6331 55.6956
13 0.2154 35.6484 9.3534 54.7825
14 0.2037 35.7287 10.0217 54.0456
15 0.1941 35.7197 10.6402 53.4459
Note: OSS is the oil supply shock, ADS is aggregate demand shock, OSDS is oil-specific demand
shock and OSPS is other stock price shock.

In the short term, the cumulative effect of the three oil price shocks accounts for only
8 per cent of the variation in the Nigerian real stock market prices. However, the effects of
oil price shocks increases as the time horizon increases. In the long term, approximately
47 per cent of the variability in real stock market prices is accounted for by the three oil
price shocks. Aggregate demand shocks accounts for approximately 36 per cent, whereas
oil-specific shocks and oil supply shocks account for about 11 per cent and 0.2 per cent,
respectively. This is consistent with the impulse response findings in the previous sub-
sections in which the stock market prices responded to both aggregate and oil-specific
demand shocks in a significantly positive manner. The explanatory power of the oil price
shocks to variations in stock market prices suggest that crude oil market shocks are impor-
tant fundamental for the Nigerian stock market. These findings are understandable given
the importance of crude oil to the Nigerian economy.
Our variance decomposition result can be compared with those of Kilian and Park
(2009), Aspergis and Miller (2009) and Wang et al. (2013). Kilian and Park (2009) report
that the three structural oil market shocks have smaller effect on the US stock market in the
short run and a significantly higher effect in the long run with 11 per cent at 12 months and
over 22 per cent at infinity. Aspergis and Miller (2009) show that the three oil price shocks
explain around 11 per cent after 12 months, and this remain unchanged through 60

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
52 Ekpeno L. Effiong

months. On the other hand, Wang et al. (2013) find that the contributions of the oil price
shocks are larger in the long term than in the short term with over 20–30 per cent of the
variations in stock market returns.8 They found the explanatory power of the oil price
shocks much stronger for oil-exporting countries than for oil-importing countries. Over
the 12-month time horizon, the contributions of the three oil price shocks as indicated in
Table 3 is higher than those obtained by Kilian and Park (2009) and Aspergis and Miller
(2009) for oil-importing countries but within close range for oil-exporting countries in
Wang et al. (2013).
In sum, the impact of oil price shocks on Nigeria’s stock market differs depending on
the source of the oil price shocks. The stock market responds negatively to an oil price
shocks that originate from the supply-side, whereas it responds positively if the shock
originates from the demand-side. The significant effect of the demand shocks justifies the
growing importance of the demand component of oil price shocks over the supply shocks
in the last decade. Based on historical decomposition of oil price shocks by Kilian (2009),
sustained increase in oil prices since 1999 has been driven mainly by demand shocks and
in particular aggregate demand shocks stemming from increased demand from emerging
economies with an example being the oil price shock during the 2008 global financial
crisis.

4.3. Robustness analysis

Although the impulse response functions of the stock market prices based on the two oil
price measures provides evidence of the robustness of our results, further robustness
checks is conducted by using an alternative specification of the specified structural model
in equation (3). A two-stage procedure is employed similar to Aspergis and Miller (2009).
Firstly, Kilian’s (2009) three-variable structural VAR model is used to decompose oil price
shocks into supply shocks, aggregate demand shocks and oil-specific demand shocks.
Next, the impact of the structural oil shocks on real stock market prices is examined using
a VAR model framework that accounts for possible endogeneity problems. The resulting
vector is denoted as zt = [ε tOSS , ε tADS , ε tOSD , sopt ] where ε tOSS , ε tADS , ε tOSD denotes oil supply
shocks, aggregate demand shocks and oil-specific demand shocks, respectively.9
Figure 4 illustrates the impact of the structural oil shocks on real stock market prices.
The impulse response of the real stock market prices are similar to those obtained using the
structural VAR model in equation (3). The major difference being the oil supply shock that
depresses the stock market prices in the first 3 months and latter increases up to the 5th
month before flattening out. Again, its impact is statistically insignificant. The stock
market price reacts in a significantly positive manner to oil-specific demand shocks but
with a delay to aggregate demand shocks and shocks to itself.
The forecast error variance decomposition of the shocks is presented in Table 4 for
15 months’ time horizon. The variance decomposition is slightly different from the

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 53

Response of sop to oil supply shock Response of sop to aggregate demand shock
.12 .12

.08 .08

.04 .04

.00 .00

-.04 -.04
2 4 6 8 10 12 14 2 4 6 8 10 12 14

Response of sop to oil-specific demand shock Response of sop to sop shock

.12 .12

.08 .08

.04 .04

.00 .00

-.04 -.04
2 4 6 8 10 12 14 2 4 6 8 10 12 14

Figure 4 Responses of real stock prices to structural oil shocks.

earlier result of the variation in stock market prices over the same time horizon. The oil-
specific demand shocks have an average of 3 per cent compared with 5 per cent in
Table 3, but it is higher than oil supply shocks contribution. The aggregate demand
shocks remain the dominant oil shocks that account for a higher variation in stock
market prices. In the short term, the three structural oil shocks accounts for about
11 per cent of the variation in stock prices, which is higher than 8 per cent in the earlier
decomposition, whereas in the long term, their cumulative effect is approximately
4 per cent less than the previous result in Table 3. Despite the slight differences in the
individual contributions of each structural oil market shocks to the variation of the stock
market prices, their cumulative contribution is consistent with our earlier results and
reaffirms the importance of crude oil market shocks to understanding the variability of
the stock prices in the Nigerian stock market.

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
54 Ekpeno L. Effiong

Table 4 Variance decomposition of real stock prices

Month εOSS εADS εOSD sop

1 0.2574 10.2169 0.1801 89.3455
2 0.2884 10.0768 0.1432 89.4914
3 0.3973 12.9703 0.8012 85.8310
4 0.2627 19.2583 1.1225 79.3563
5 0.4239 24.1708 2.6400 72.7652
6 0.5908 27.4498 3.5670 68.3922
7 0.7445 29.8951 4.0053 65.3548
8 0.9104 31.5163 4.1695 63.4036
9 1.0827 32.9623 4.1801 61.7747
10 1.2286 34.0779 4.1608 60.5325
11 1.3481 34.9078 4.1268 59.6171
12 1.4391 35.5600 4.0964 58.9043
13 1.5178 36.0751 4.0590 58.3480
14 1.5849 36.4998 4.0261 57.8890
15 1.6421 36.8544 4.0010 57.5024

5. Concluding remarks
The origin of oil price shocks has become an integral part of the literature analysing the
impact of oil price shocks on macroeconomic activities and stock markets in particular.
Until the separate works of Hamilton (2009a, b) and Kilian (2009), oil price shocks was
implicitly treated as being exclusively an exogenous supply shocks in the crude oil market.
However, it is now recognised that oil price shocks could also be demand driven. In all, oil
price fluctuations depend on its underlying source of demand and supply shocks in the
crude oil market. This paper provides the first examination of the impact of oil price shocks
on Nigeria’s stock market, with particular reference to the origin of oil price shocks (i.e.
demand and supply shocks). A structural VAR model is employed to decompose oil price
shocks into oil supply shocks, aggregate demand shocks and oil-specific (or precaution-
ary) demand shocks and their impact investigated on stock market prices by means of
impulse response functions and variance decomposition for the period between 1995:1
and 2011:12.
The findings of the oil price shocks and stock market relationship can be summarised
as follows. Firstly, the impulse response analysis show that depending on the origin of oil
price shocks, the stock market respond to each demand and supply shocks differently as
earlier suggested by Kilian and Park (2009). Oil supply shocks leads to an insignificantly
negative response in stock market prices, which can be attributed to differences in the

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 55

short-term and long-term price elasticity of demand and the relative stability of oil supply
in recent years. Aggregate demand shocks and oil-specific demand shocks driven respec-
tively by expansion in global economic activities and precautionary demand for oil due to
uncertainty in future oil supplies are positively and significantly associated with higher
stock market prices. As both shocks are positively linked with higher oil prices that
through the wealth transfer effects offers oil-exporting countries increased oil revenues,
short-term economic activity can be stimulated thus pushing stock prices upward. On the
other hand, stock market-related shocks also affect stock prices positively. However, the
response pattern of both demand shocks and stock market-related shocks are in contrast to
the efficient market hypothesis thus providing insights to possible inefficiencies in the
Nigerian stock market.
Secondly, the variance decomposition analysis shows the relative contribution of each
oil price shocks to the variation in stock market prices. Cumulatively, oil price shocks
account for approximately 47 per cent of the variation in the stock market prices in the
long term. The relative importance of both demand and supply shocks tends to increase as
the time horizon lengthens. In specific terms, aggregate demand shocks has the highest
contribution to oil price shocks followed by the oil-specific demand shocks while that of
oil supply shocks is marginal. This justifies the growing importance of the demand-side of
the crude oil market shocks. Thirdly, both results of the impulse response and variance
decomposition analysis are robust using a domestic real oil price measure that
orthogonalise for exchange rate fluctuation and an alternative estimation approach.
From an economic standpoint, the results suggest that there is a relationship between
oil price shocks and the Nigerian stock market given the crucial importance of the oil
sector to the Nigerian economy and should therefore be of interest to the major partici-
pants within the market. As volatility reflects the arrival of new information, information
related to oil price fluctuations (depending on its origin) are likely to affect the volatility of
the Nigerian stock market. For the investors and financial experts, keeping close watch on
the international oil market is of paramount importance, as ‘news’ from one market may
impact on other markets through varied interdependencies. Such information are useful in
building accurate asset pricing models, risk management and future forecasting stock
market price movement in other to make optimal portfolio allocation decisions. For the
policymakers, particular attention should focus on the likely impact of oil price volatility
on the Nigerian economy and the stock market as such volatilities are subject to the
vagaries in the international oil market. Given the over-reliance of the economy on the oil-
sector, diversification of the economy should be assigned top priority by government to
reduce its vulnerability to oil price fluctuations especially during a decline. In addition,
addressing the inefficiencies and bottlenecks in the Nigerian stock market should be of
great importance so that its prices will reflect accurate and current information. The events
of the 2008 global financial crisis should serve as a lesson in this regard.

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
56 Ekpeno L. Effiong

Notes
1. See Bjørnland (2009) for a review.
2. See the section on literature review.
3. Nigeria as an oil-exporting country was not included in the analysis.
4. The sample period is selected based on data availability.
5. http://www.personal.umich.edu/~lkilian/.
6. The results are not reported to conserve space and are available on request from author.
7. In the long run, Phillips (1998) show that the standard impulse responses do not converge to
their true values with a probability of one when unit roots or near-unit roots are present and the
lead time of the impulse response function is a fixed fraction of the sample size.
8. The short and long term was defined as 1 and 12 months, respectively.
9. Kilian (2009) argues that the structural oil shocks are mutually correlated and can be treated as
being predetermined. Therefore, their dynamic effects can be examined on any
macroeconomic variables using regression method.

References
Adaramola, A.O., 2012. Oil price shocks and stock market behaviour: the Nigerian experience.
Journal of Economics 3, 1, 19–24.
Adebiyi, M.A., Adenuga, A.O., Abeng, M.O. and Omanukwue, P.N., 2010. Oil price shocks,
exchange rate and stock market behaviour: empirical evidence from Nigeria. URL
http://africametrics.org/documents/conference09/papers/Adebiyi_Adenuga_Abeng
_Omanukwue.pdf [accessed 1 December 2012].
Adeniyi, O., Oyinlola, A. and Omisakin, O., 2011. Oil price shocks and economic growth in
Nigeria: are thresholds important? OPEC Energy Review 35, 4, 308–333.
Akpan, E.O., 2009. Oil price shocks and Nigeria’s macroeconomy. A Paper Presented at the
Annual Conference of CSAE Conference, Economic Development in Africa, 22–24 March,
Oxford.
Arouri, M.E.H. and Rault, C., 2012. Oil prices and stock markets in GCC countries: empirical
evidence from panel analysis. International Journal of Finance and Economics 17, 3,
242–253.
Asaolu, T.O. and Ilo, B.M., 2012. The Nigerian stock market and oil prices: a cointegration
analysis. Kuwait Chapter of Arabian Journal of Business and Management Review 1, 5, 39–54.
Aspergis, N. and Miller, S.M., 2009. Do structural oil-market shocks affect stock prices? Energy
Economics 31, 569–575.
Ayadi, O.F., 2005. Oil price fluctuations and the Nigerian economy. OPEC Review 29, 3, 199–217.
Barsky, R.B. and Kilian, L., 2002. Do we really know that oil caused the great stagflation? A
monetary alternative. In Bernanke, B.S. and Rogoff, K. (eds) NBER Macroeconomics Annual
2001, MIT Press, Cambridge, MA, pp. 137–183.
Barsky, R.B. and Kilian, L., 2004. Oil and the macroeconomy since the 1970s. Journal of
Economic Perspectives 18, 115–134.

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries
 Oil price shocks and Nigeria’s stock market 57

Basher, S.A., Haug, A.A. and Sadorsky, P., 2012. Oil prices, exchange rates and emerging stock
markets. Energy Economics 34, 227–240.
Bjørnland, C.H., 2009. Oil price shocks and stock market booms in an oil exporting country.
Scottish Journal of Political Economy 2, 5, 232–254.
Chuku, C.A., 2012. Linear and asymmetric impacts of oil price shocks in an oil-importing and
-exporting economy: the case of Nigeria. OPEC Energy Review 36, 4, 413–443.
Chuku, C.A., Akpan, U.F., Sam, N.R. and Effiong, E.L., 2011. Oil price shocks and the dynamics
of current account balances in Nigeria. OPEC Energy Review 35, 2, 119–139.
Cologni, A. and Manera, M., 2008. Oil prices, inflation and interest rates in a structural
cointegrated VAR model for the G-7 countries. Energy Economics 30, 856–888.
Cong, R.-G., Wei, Y.-M., Jiao, J.-L. and Fan, Y., 2008. Relationships between oil price shocks and
stock market: an empirical analysis from China. Energy Policy 36, 3544–3553.
Effiong, E.L., 2013. Exchange Rate Dynamics and Monetary Fundamentals: A Cointegrated SVAR
Approach for Nigeria. Mimeograph, Department of Economics, University of Uyo, Uyo.
Elliott, G., Rothenberg, T.J. and Stock, J.H., 1996. Efficient tests for an autoregressive unit root.
Econometrica 64, 813–836.
Faff, R.W. and Brailsford, T.J., 1999. Oil price risk and the Australian stock market. Journal of
Energy Finance and Development 4, 69–87.
Fayyad, A. and Daly, K., 2011. The impact of oil price shocks on stock market returns: comparing
GCC countries with the UK and USA. Emerging Markets Review 12, 61–78.
Filis, G., Degiannakis, S. and Floros, C., 2011. Dynamic correlation between stock market and oil
prices: the case of oil-importing and oil-exporting countries. International Review of Financial
Analysis 20, 152–164.
Hamilton, J.D., 1983. Oil and the Macroeconomy since the World War II. The Journal of Political
Economy 91, 2, 228–248.
Hamilton, J.D., 2008. Oil and the macroeconomy. In: Durlauf, S. and Blume, L. (eds) The New
Palgrave Dictionary of Economics, 2nd edn, Macmillan, London.
Hamilton, J.D., 2009a. Understanding crude oil prices. Energy Journal 30, 179–206.
Hamilton, J.D., 2009b. Causes and consequences of the oil shock of 2007-08. Brookings papers on
Economic Activity, Spring 2009, 215–261.
Henriques, I. and Sadorsky, P., 2008. Oil prices and the stock prices of alternative energy
companies. Energy Economics 30, 998–1010.
Huang, R.D., Masulis, R.W. and Stoll, H.R., 1996. Energy shocks and financial markets. Journal
Futures Markets 16, 1–27.
Iwayemi, A. and Fowowe, B., 2011. Impact of oil price shocks on selected macroeconomic
variables in Nigeria. Energy Policy 39, 603–612.
Johansen, S., 1988. Statistical analysis of cointegrating vectors. Journal of Economic Dynamics
and Control 12, 231–254.
Johansen, S. and Juselius, K., 1990. Maximum likelihood estimation and inference on
cointegration—with an application to the demand for money. Oxford Bulletin of Economics
and Statistics 52, 169–210.
Jones, C. and Kaul, G., 1996. Oil and the stock markets. Journal of Finance 51, 463–491.

© 2014 Organization of the Petroleum Exporting Countries OPEC Energy Review March 2014
58 Ekpeno L. Effiong

Kilian, L., 2008. The economic effects of energy price shocks. Journal of Economic Literature 46,
871–909.
Kilian, L., 2009. Not all oil price shocks are alike: disentangling demand and supply shocks in the
crude oil market. American Economic Review 99, 1053–1069.
Kilian, L. and Park, C., 2009. The impact of oil price shocks on the U.S. stock market.
International Economic Review 50, 1267–1287.
Kilian, L., Rebucci, A. and Spatafora, N., 2009. Oil shocks and external balances. Journal of
International Economics 77, 181–194.
MacKinnon, J.G., Haug, A.A. and Michelis, L., 1999. Numerical distribution functions of
likelihood ratio tests for cointegration. Journal of Applied Econometrics 14, 563–577.
Miller, J.I. and Ratti, R.A., 2009. Crude oil and stock markets: stability, instability, and bubbles.
Energy Economics 31, 559–568.
Narayan, P.K. and Narayan, S., 2010. Modelling the impact of oil prices on Vietnam’s stock prices.
Applied Energy 87, 356–361.
Ng, S. and Perron, P., 2001. Lag length selection and the construction of unit root tests with good
size and power. Econometrica 69, 1519–1554.
Olomola, P.A. and Adejumo, A.W., 2006. Oil price shocks and macroeconomic activities in
Nigeria. International Research Journal of Finance and Economics 3, 28–34.
Papapetrou, E., 2001. Oil price shocks, stock markets, economic activity and employment in
Greece. Energy Economics 23, 511–532.
Park, J. and Ratti, R.A., 2008. Oil price shocks and stock markets in the US and 13 European
countries. Energy Economics 30, 2587–2608.
Peersman, G. and van Robays, I., 2012. Cross-country differences in the effects of oil shocks.
Energy Economics 34, 1532–1547.
Phillips, P.C.B., 1998. Impulse response and forecast error variance asymptotics in nonstationary
VARs. Journal of Econometrics 83, 21–56.
Sadorsky, P., 1999. Oil price shocks and stock market activity. Energy Economics 21, 449–469.
Sadorsky, P., 2001. Risk factors in stock returns of Canadian oil and gas companies. Energy
Economics 23, 17–28.
Sims, C.A., Stock, J.H. and Watson, M.W., 1990. Inference in linear time series models with some
unit roots. Econometrica 58, 113–144.
Toda, H.Y. and Yamamoto, T., 1995. Statistical inference in vector autoregressions with possibly
integrated process. Journal of Econometrics 66, 225–250.
Wang, Y., Wu, C. and Yang, L., 2013. Oil price shocks and stock market activities: evidence from
oil-importing and oil-exporting countries. Journal of Comparative Economics 41, 4,
1220–1239.
Zhu, H., Li, S. and Yu, K., 2011. Crude oil shocks and stock markets: a panel threshold
cointegration approach. Energy Economics 33, 987–994.

OPEC Energy Review March 2014 © 2014 Organization of the Petroleum Exporting Countries