




































American Interdisciplinary Journal of Business and 

Economics 
ISSN: 2837-1909| Impact Factor : 6.71 

Volume. 11, Number 3; July-September, 2024; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadijournals.org/index.php/AIJBE| editorial@sadijournals.org 

 

 

American Interdisciplinary Journal of Business and Economics |  

35   https://sadijournals.org/index.php/AIJBE 

  

EMPIRICAL EVALUATION OF OIL PRICE VOLATILITY  

AND STOCK MARKET RETURNS IN NIGERIA  
  

Ikemenogo, Eze Solomon  
Department of Economics, Faculty of Social Sciences, University of Lagos, Akoka, Nigeria  

DOI: https://doi.org/10.5281/zenodo.13236955 

 

Abstract: The study is on empirical evaluation of the impact of oil prices volatility on stock market returns in 

Nigeria from 1986 to 2021. With the understanding that energy runs like a bloodstream of any business of 

which oil is a major source, it becomes imperative to explore the incessant oil price fluctuations and its effects 

on critical indicators of the economy like stock market performance. The objectives include to examine the 

patterns of oil price volatility and stock market returns in Nigeria, analyse the influence of oil price fluctuations 

on Nigeria's economic growth, and investigate the causal relationship between oil price volatility and the stock 

market returns in Nigeria. Arbitrage Pricing Theory (APT) was employed as the framework while the 

methodology adopted the all-share indices (ASI) as the dependent variable, then, oil prices (OPRICE), 

exchange rate (EXCH), inflation rate (INFL), gross domestic growth rate (GDPGR), and interest rate (INT) as 

explanatory variables. Secondary data sourced from UNCTAD, etc. were analysed with aid of EVIEWS 2021. 

The Generalised Autoregressive Conditional Heteroscedasticity (GARCH) and Granger causality were the 

estimation techniques used. The study used unit root test to check the stationarity of the variables, the ARDL 

bound test to check for long-term relationships between the variables, and the ARDL model to estimate both 

short- and long-term relationships between the variables. A normality test revealed that the study's variables 

are all typical. Breuseeh-Gdfrey serial correlation revealed no association between the study's variables. The 

heteroscedasticity test showed there was no outlier’s effect on the output of the result. It was found that inflation 

and exchange rate volatility are positive and statistically significant; inflation and interest rate are equally 

positive related to private consumption during the period under investigation. It found out inflation, oil price, 

exchange rate, and real gross domestic product have positive effect on stock market performance in Nigeria. 

The work concludes oil price is a major determinant of economic growth in Nigeria. It was recommended that 

all brokerage firms and investment advisors need to conduct periodic research on macroeconomic environment 

and advise their clients accordingly on the best counters to invest in owing to the various influences by 

macroeconomic environment on the stock market performance. 

Keywords: Oil, Prices, Volatility, Stock, Market, Returns  

 

 

 



Ikemenogo, Eze Solomon (2024)  

  

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INTRODUCTION  

1.1 Background of the study  

Oil plays a significant role in the global economy, hence there has been a lot of research done to understand the 

financial and economic effects of fluctuations in oil prices. There is controversy about the relationship between 

the oil price and the stock market returns, making it an unpredictable factor in economic growth (Ndlovu, 2019). 

Oil is a major source of energy globally and a crucial element of Nigeria's economy, significantly influencing its 

economic and political landscape. Although Nigeria’s oil industry dates to the early 20th century, it was not until 

after the Nigerian Civil War (1967-1970) that oil began to dominate the economic sphere. The discovery of crude 

oil has had both positive and negative impacts on Nigeria's economy. The oil sector contributes approximately 

90% of Nigeria’s total revenue. It generates employment opportunities, boosts foreign exchange reserves, and 

supplies energy to various industries and commerce. Nigeria joined the Organization of Petroleum Exporting 

Countries (OPEC) in 1971 and established the Nigerian National Petroleum Corporation (NNPC) in 1977, a state-

controlled entity involved in both the upstream and downstream sectors (Blair, 1976), following the discovery of 

crude oil by Shell D’Arcy Petroleum, pioneer production commenced in 1958 from an oil field in Oloibiri, 

Eastern Niger Delta. 

On the negative side, oil exploitation has led to significant environmental degradation in surrounding 

communities. This has caused a loss of livelihood and other economic and social challenges. Despite NNPC’s 

efforts to maximize capacity and improve petroleum product distribution, inefficiencies persist, leading to 

inconsistent supply and allocation issues. Crude oil prices have experienced significant volatility over time. 

According to Olayungbo and Ojeyinka (2021), the first major global oil price shock occurred from 1973 to 1974, 

when prices surged from $3 to $12 per barrel due to the Arabian embargo. The second shock occurred between 

1978 and 1979 during the Iranian Revolution, with prices rising from $12 to $18 per barrel. The third shock took 

place during the Iraq-Iran conflict (19801990), which saw oil prices increase from $28 to $40 per barrel. The 

2008-2009 global financial crisis caused oil prices to plummet from $100 to $47 per barrel. 

The Nigerian Stock Exchange (NSE), established in 1960 and renamed in 1977, is a critical institution in Nigeria's 

capital market. It has branches in major cities, with its head office in Lagos and another office in Abuja (Akigbo, 

1996). The NSE opened in 1961 with 19 listed equities. Today, it lists 328 securities with a total market value of 

approximately N28.26 trillion as of January 2020 (Akigbo, 1996). The NSE and the Securities & Exchange 

Commission (SEC), which enforces the Investments & Securities Decree 1999, regulate transactions on the 

Exchange. The deregulation of Nigeria's capital market in 1993 and the repeal of foreign participation restrictions 

in 1995 allowed foreigners to engage as operators and investors. Since 1987, the NSE has been a part of the 

Reuters Electronic Contributor System, facilitating global distribution of stock market data, trade statistics, and 

company news.  

The primary objectives of the NSE include developing a system for capital formation, offering efficient 

resource allocation, providing unique financing options, maintaining market discipline, and broadening share 

ownership (Akigbo, 1996). The influence of oil price volatility on the capital market is significant. Oil price 

fluctuations impact the stock market by reflecting the market's expectations of future profitability (Akigbo, 

2014). The stock market absorbs the current and expected future impacts of oil price shocks, which are reflected 

in stock prices and returns. With oil production and exportation playing a crucial role in driving economic 

growth and development, the volatility of oil prices has been a subject of great importance in the country's 



Ikemenogo, Eze Solomon (2024)  

  

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financial landscape (Wang et al, 2022). Over the period from 1990 to 2022, Nigeria experienced notable 

fluctuations in oil prices, which, in turn, had a profound impact on the performance of its stock market. 

The effect of oil price volatility on the economy is complex and unsettled, with no consensus on the relationship 

between financial variables and oil prices. Researchers like Salisu and Oloko (2015), Babatunde et al. (2013), 

and Fowowe (2013) argue that there is a direct relationship between oil prices and stock market performance. 

This study aims to link these variables, examining the impact of oil price volatility on stock market returns using 

disaggregated data. The Nigerian Stock Exchange serves as a catalyst for mobilizing and utilizing private and 

public savings for productive uses (Akigbo, 1996). Fluctuations in oil prices can pose threats to the stock market, 

given the oil industry's critical role in providing foreign exchange and total revenue for Nigeria's socio-political 

and economic wellbeing. 

1.2 Statement of the Problem  

Oil discovery in Nigeria has driven economic growth and infrastructural development but has also caused 

significant environmental harm, particularly water pollution that has devastated aquatic life and stripped local 

fishermen of their livelihood. As a mono-economy heavily reliant on oil, Nigeria's macroeconomic stability is 

highly sensitive to fluctuations in oil demand and supply. The 1970s oil boom led to the neglect of non-oil 

revenues, an expanding public sector, and poor financial discipline, exposing Nigeria to oil price volatility and 

financial instability. Despite the efforts by various governments to revive agriculture and diversify the economy 

which include initiatives like Operation Feed the Nation (OFN), Green Revolution, and National Economic 

Empowerment and Development Strategy (NEEDS), etc., the economy remains vulnerable. Nigeria's open 

economy is highly susceptible to crises, which significantly impact its volatile stock market, posing challenges 

for investors and financial analysts. The Nigerian stock market experiences high volatility due to risks and price 

shocks. Risk-averse investors often avoid the market due to the uncertainty and volatility in expected returns 

(Ashamu et al., 2017). High volatility increases unfavourable market premiums, and investors demand higher 

returns on investments (Atoi, 2014). Oil price fluctuations induce large variations in stock market returns, making 

oil prices a significant concern for scholars. Most research focuses on developed economies, with limited studies 

on developing economies like Nigeria, despite its significant stock exchange.  

1.3 Objectives of the Study  

The broad objective of this research study is to examine the causality between oil price volatility and stock market 

returns. To achieve the broad objectives, it is pertinent to streamline the specific objectives which are to:  i) 

Determine effect of oil price fluctuation on the economic growth in Nigeria. ii) Establish the nexus trend between 

oil price volatility and stock market in Nigeria. iii) Determine the impact of oil price volatility on stock market 

returns in Nigeria.  

1.4 Research Questions   

In line with the research objectives, the study aims to provide the answers to the following research questions.  i. 

i. Does oil price volatility have an impact on stock market returns?  

ii. What is the effect of oil price fluctuation on the economic growth in Nigeria?  

iii. Is there any nexus between oil price fluctuations and economic growth in Nigeria?      

1.5 Research Hypothesis  

To carry out this study, the following hypothesis must be tested:  

H01.  Oil price volatility has no significant impact on stock market returns.  



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H02. There is no significant effect of oil price volatility on stock market returns.   

H03. There is no significant nexus of oil price fluctuation and economic growth.  

1.6 Scope of the Study  

This research study focused on oil price volatility effect and stock market returns in Nigeria from 1986- 

2021. The study starts in 1986, a year prior to 1987 because there was a world stock market crash called ‘black 

Monday’ with worldwide losses of about US$1.71trillion and this significantly affected the world’s market, and 

it ends with 2021 because of the difficulty in finding accurate and verified data. The study will not cover other 

African countries who may be facing similar challenges. But the findings of this study can be used as a guide.  

LITERATURE REVIEW  

2.1 Conceptual Review 

Oil price changes affect numerous economic variables such as interest rates, investment decisions, economic 

growth, investors' confidence etc. these variables have been documented to affect both the stock market and 

exchange rate market (Hamilton, 1983; Amano & Van Norden, 1995). Again, oil prices are expressed in US 

dollars in the international market; hence, the dollar exchange rate may affect the price perceived by oil producing 

nations (Roubaud & Arouri, 2018). This study reviews the connection between these variables (Oil price, 

exchange rate and stock market returns) using a bivariate and multivariate approach. To some economists, there 

is a positive correlation between crude oil price and stock market performance (Cong, Weiy, Jiao & Fan, 2008; 

Boyer & Filion, 2007; Sadorsky, 2001). 

 According to Babatunde (2013) while the initial effects were contained due to low levels of exposure to complex 

financial instruments, the large swings in oil prices, combined with the resulting depreciation of the naira and 

drop in investor confidence led to growing pressures about the oil price volatility which was supported with 

exposure of oil subsidy frauds by  importers of fuel products, who had a high foreign currency obligations owing 

to the high fuel prices in 2008, the subsequent falling oil prices and devaluations of naira added to the shocks 

experienced in the Nigerian stock market. 

2.1.1 Oil Price and Exchange Rate 

Theories generally posit that crude oil prices and exchange rates are positively correlated in oil-exporting 

countries, with higher crude oil prices leading to currency appreciation and vice versa. Crude oil price shocks 

affect exchange rates through two primary channels: the terms of trade channel and the wealth effect channel. 

The terms of trade channel suggests that a negative shock to the terms of trade drives down the price of non-

tradable goods in the domestic economy, causing the real exchange rate in oil-exporting economies to depreciate 

and vice versa. The wealth effect channel indicates that a drop in crude oil prices results in losses for oil exporters 

but gains for oil importers, shifting current account balances and leading to portfolio reallocations between oil 

trading companies (Akigbo, 2014).  

A negative oil price shock transfers wealth from oil exporters to oil importers, or higher oil prices lead to higher 

production costs and inflation, which have contractionary effects on the economy and trade balances. To restore 

or improve trade balances, the exchange rate must adjust. The impact of oil prices on exchange rates can vary 

between advanced and emerging market economies. Hamilton (2009) notes that oil price shocks respond directly 

to economic or geopolitical events and economic downturns (demand-side shocks). Supply-side shocks are driven 

by disruptions in oil production, such as the Iranian invasion of the U.S. embassy in 1978, the Iraq invasion of 



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Kuwait in 1980, the Arab Spring in 2000, and the Iranian attack on Saudi oil fields in 2019. These events disrupted 

oil production without corresponding reductions in demand, driving up prices. Demand-side shocks, on the other 

hand, are influenced by global economic movements. For example, the economic growth in China and other 

developing economies significantly increased oil demand without a matching supply increase, leading to high oil 

prices. Conversely, during the 2007-2009 global financial crisis, a dramatic reduction in oil demand led to a 

collapse in oil prices.  

Kilian (2009) identifies three types of oil price shocks: supply-side shocks, aggregate demand shocks, and 

precautionary demand shocks. Geopolitical unrest often triggers precautionary demand shocks, causing 

uncertainty about future oil availability and driving up prices. For instance, geopolitical events lead economic 

agents to expect shortages in oil supply, which results in high oil prices. According to Salisu and Oloko (2015), 

crude oil price shocks affect exchange rates through the terms of trade and wealth effect channels. A negative oil 

price shock transforms wealth from oil exporters to importers, leading to higher production costs and inflation, 

which adversely affects trade balances. To improve trade balance, the exchange rate must adjust. The relationship 

between exchange rates and oil prices can differ between advanced and emerging market economies. 

2.1.2 Exchange Rate and Stock Market Returns  

Aruori (2011) emphasized that the relationship between the movement of exchange rate and stock returns could 

be explained with several perspectives. The flow-oriented model of exchange rate behaviour posits that 

depreciation in the exchange rate for instance would lead to improved trade balance as exports become cheaper. 

This would result in upward shift in aggregate demand (AD), hence overall expansion in real gross domestic 

product with the attendant positive effect on stock market performance. The stock-oriented model, on the other 

hand, emphasizes the role of capital accounts in the determination of a country's exchange rate. In this theory, 

the exchange rate equates the demand and supply of financial assets (stock and bonds). Thus, expectation of 

future exchange rates affects the current price of financial markets (Aruori. 2011). From another perspective 

Brown & Yucel, (2002) aligning with the arbitrage price theory argued that a rise in real interest rate will reduce 

the present value (PV) of the future cash flow which consequently make stock returns to fall. As the real interest 

rate rises, capital flows increase, causing the domestic currency to appreciate and fall in stock returns, they argued.  

2.1.3 Oil Price and Stock Price  

Oil price may impact stock performance through several channels such as uncertainty, fiscal, output and stock 

variation channels (Degiannakis, Filis & Arora, 2018). Oil price is susceptible to high volatility due to supply 

shocks and therefore, the risk of uncertainties occasioned by oil price volatility usually affect investors' portfolio, 

particularly, portfolio managers seeking to make optimal portfolio allocations (Arouri, 2011); cited in Salisu and 

Oloko (2015). Also, Uncertainty channels views explain that rising crude oil prices heightens uncertainty in the 

real economy (firms and households) due to its effect on inflation, consumption, and output (Brown & Yucel, 

2002). For a firm, it tends to reduce the demand for irreversible investment and consequently, expected cash flow 

declines. On the household, increased uncertainty, resulting from higher cost of crude oil also increases the 

households' ability to save rather than consume. (Brown & Yucel, 2002).  

Given the above, the value of postponing investment and consumption decisions rises and hence, economic 

growth and stock market returns stifles. Oil prices also impact stock performance through a more direct channel— 

that is, the stock variation channel. The nexus suggests that stock returns are impacted by factors that can alter 

expected cash flows and discount rates. However, this depends on whether the firm is an oil user or oil producer. 



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Given that oil is a major production factor, any increase in oil price will result in an increased production cost 

(assuming a case of absence of substitution effect between production factors). This leads to reduced profit levels 

and future cash flows. For the oil producer, increase in crude oil prices results in increased profit margins and 

thus, increased future cash flows (Basher & Sadorsky, 2006). Furthermore, higher interest rate response by the 

monetary authority to an inflationary pressure from the rising oil prices also affect the discount rate; an important 

factor in stock price formulation (Basher, Haug & Sardosky, 2012). 

2.2 Theoretical Review  

Theory-based framework from various ideas have been used in the past to support analyses of the impact of oil 

price volatility on stock markets. As a result, the theoretical foundation for this study comes from the Arbitrage 

Pricing Theory (APT).  

2.2.1 Arbitrage Pricing Theory (APT)  

The Arbitrage Pricing Theory is a multi-factor asset pricing model which assumes that an asset's returns may be 

forecasted using a linear relationship between the assets expected return and several other macroeconomic 

variables that influence risk. Stephen Ross, an American economist, created this idea in 1976. The APT provides 

a multi-factor pricing model for securitized assets to analysts and investors. The APT provides analysts and 

investors with a multi-factor pricing model for securities that is based on the link between the projected return of 

a financial asset and its risk characteristics. The goal of APT is to determine the fair market price of a security 

that has been temporarily mispriced. APT is a more flexible and complicated alternative to the Capital Asset 

Pricing Model (CAPM). The theory allows investors and analysts to tailor their studies to their specific needs. 

Arbitrage is the practice of the simultaneous purchase and sale of an asset on different exchanges, taking 

advantage of slight pricing discrepancies to lock in a risk-free profit for the trade. APT provides traders with a 

model for calculating an asset's theoretical fair market value. Having determined that value, traders then look for 

slight deviations from the fair market price and trade accordingly. For example, if the APT pricing model 

determines the fair market value of a company's stock to be ₦50, but the market price drops to ₦45, the trader 

will buy the shares in the idea that additional market price action will rapidly "correct" the market price back to 

₦50/share. Thus, this study uses the APT to link crude oil prices and other selected macroeconomic variables 

(such as the exchange rate, inflation rate, and interest rate) to stock market performance in Nigeria from 1981 to 

2019. Its theoretical underpinning is derived from the APT.  

2.2.2 Capital Asset Pricing Model (CAPM)   

The capital asset pricing model was pioneered by notable authors including Sharpe (1964), Umer (1965), Mossin 

(1960). The CAPM is a single factor model, quantifies the expected rates of return of an asset with level of market 

systematic risk. The CAPM has variously been lead among others by Chen (2003) and Kim et al. (a) 2017. It is 

a finance model that establishes a linear relationship between the required return on an investment and risk. 

CAPM evolved to measure this systematic risk. It is widely used throughout finance for pricing risky securities 

and generating expected returns for assets, given the risk of those asset and cost of capital. Algebraically, CAPM 

is presented as   𝑅𝑖 = 𝑅𝑓 +  𝛽𝑖(𝑅𝑚 − 𝑅𝑓)                                                                                          2.1 

𝑊ℎ𝑒𝑟𝑒: 𝑚 = 𝑚𝑎𝑟𝑘𝑒𝑡 𝑝𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜,  𝑅𝑚 = 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑟𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑝𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜,  𝑅𝑓 = 𝑅𝑖𝑠𝑘 𝑓𝑟𝑒𝑒 𝑟𝑒𝑡𝑢𝑟𝑛    

 𝑅𝑖 = 𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝐴𝑠𝑠𝑒𝑡,  

          𝛽𝑖 =  
𝐶𝑂𝑉(𝑅𝑖,𝑅𝑚)

𝛿𝑚
2                                                                                                            2.2 



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Equation 2.2 is called the beta of the asset (𝑖) and (𝑚) is the variance of the market portfolio. For any portfolio 

< = (<1---<n) of ranky assets, its beta can be constructed as a weighted average of individual asset betas as 

follows:  

𝐸(𝑅𝑖) =  𝑅𝑓 +  𝛽𝜆(𝐸(𝑅𝑚) −  𝑅𝑓                               2.3 

𝑊ℎ𝑒𝑟𝑒: 𝐸(𝑅𝑖) = 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑟𝑒𝑡𝑢𝑟𝑛,  𝑅𝑓  = 𝑟𝑖𝑠𝑘 − 𝑓𝑟𝑒𝑒 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 

 𝛽𝜆 = 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦,  𝐸(𝑅𝑚) = 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑟𝑒𝑡𝑢𝑟𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑚𝑎𝑟𝑘𝑒𝑡 

   𝛽𝜆 =  ∑ 𝛼𝑖𝛽𝑖
𝜌
𝑖=1                                2.4 

The Beta value indicates a measure of risk for individual assets/portfolio. It measures the non-diversifiable or 

transferable part of risk known as systematic risk. According to this model, the expected return of an asset 

depends on its stand-alone risk. However, the CAPM has unrealistic assumptions for example, the perfect 

competitive market environment does not hold as forces of demand and supply often guide investors decision 

making as they affect prices of assets. Also, tax liabilities affect the level of investment and the type of asset to 

invest in. The model also assumes that at the risk-free rate, borrowing will be unlimited. However, individual 

investors cannot borrow at the same rate with government and its agencies.  

2.2.3 The Discount Cash Flow Model (DCF)  

The relationship between oil price shocks and stock market return can also be theoretically explored or viewed 

through the discounted cash flow model (equity pricing model) developed by Huang et al (1996). The model has 

been adopted in several literature (Sek, 2015; Basher, 2014; Zakanya and Abdala, 2013; Abeng, 2017; 

Degiannakis, 2017). According to this model, macroeconomic variables including commodity prices can exert a 

significant effect on stock returns of firms. The price of equity at a given point in time is equal to the expected 

present value of the discounted future cash flows as follows: 

𝑃𝑉 =  ∑
𝐸(𝐶𝑡)

(1+𝑟)𝑡
𝑛
𝑡=1                                         2.5 

Where: P = Stock price, C = cash flow, r = Discount rate (interest rate), E (.) = expectation operator. The realized 

stock returns R can be expressed approximately as    𝑅 =
𝑑(𝜌)

𝑃
                                                                   2.6 

Where d(.) is the difference operator. Stock returns, R, are determined by the systematic movements in expected 

cash flows and discount rates, which can be affected by changes in oil prices in several ways. For cash flows, it 

is assumed that oil, together with labour, capital and other inputs, represents import components in the production 

friction of most goods and services. Therefore, changes in the prices of these inputs including oil, affect cash 

flows of firms. i.e. rising oil prices increases production costs leading to dampened cash flows which ultimately 

translates to a reduction in stock prices. It however depends on whether a particular firm is a net producer or net 

consumer of oil. The discounted rate can also be affected by oil price changes. According to Huang et al (1996), 

expected discount rate consists of two components; expected inflation and expected real interest rate. Higher oil 

prices will lead to a negative effect the trade balance in oil importing economics. Imposing upward pressure on 

domestic prices (inflation) which leads to higher discount rate culminating in lower stock returns. As an important 

resource in the economy, oil prices can affect real interest rates. High oil price is expected to raise the rates of 

real interest rates which may likely lead to increases in stock returns.  



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2.3 Empirical Review  

In a study of 22 emerging economies (Nigeria not included), Maghyereh and Al-Kandari (2004) findings implied 

that oil shocks have no significant impact on stock index returns in emerging economies. Agren (2006) argued 

that the stock market's own shocks, which are related to other factors of uncertainty than the oil price, are more 

prominent in explaining stock price movements.   

Similarly, in Ghana, findings by Adjasi (2009) showed that higher volatility in Cocoa prices and interest rates 

increased volatility of the stock prices, whilst higher volatility in gold prices, oil prices, and money supply 

reduced volatility of stock prices. Other studies, however, found the existence of a weak relationship among the 

variables. For instance, Sujit and Kumar (2011) evaluated the dynamic relationship among gold price, oil price, 

exchange rate and stock market returns. The authors used daily data from January 2, 1998, to June 5, 2011, 

constituting 3,485 observations and adopted the vector autoregressive and cointegration techniques. The results 

showed that exchange rate was highly affected by changes in the other variables, while stock market plays a 

minor role in affecting the exchange rate. The study suggested that there is weak long-term relationship among 

the variables. Also, Sahu, Bandopadhyay, and Mondal (2015) investigated the dynamic relationships between oil 

price, exchange rate and the Indian stock market from 1993 – 2013. Results from the Johansen's cointegration 

test and vector error correction model showed that although there is a long run cointegrating relationships between 

crude oil price and Indian stock indices, no sufficient evidence existed to conclude that the direction of the 

relationship in the long run was from oil price to the Sensex. However, the Granger causality test showed that the 

volatility of stock prices in India granger caused the movement in oil price and exchange rate in the short-run. 

The study further showed that the observed relationship between oil price and stock indices was not because of 

exchange rate fluctuations, because the change in exchange rate had no significant impact on oil prices or stock 

prices in India during the study period.   

In Nigeria, attempts have also been made to examine the relationship among oil price, exchange rate and stock 

market returns. The findings from the study by Fowowe (2013) on the dynamic relationship between oil prices 

and stock market returns in Nigeria, using the GARCH-Jump model showed the existence of a negative, but 

insignificant effect of oil prices on stock returns in Nigeria.  

Another study was conducted by Mechri, Ben Hamad, De Peretti and Chart (2018) on the impact of exchange 

rate volatilities on stock markets dynamics in Tunisia and Turkey, using the GARCH estimation method. The 

variables used were stock market price returns, exchange rates, inflation rates, interest rates, gold prices and 

petrol prices index. The results indicated that exchange rate volatility has a significant effect on stock market 

fluctuations.  Alzyoud, Wang and Basso (2018) also examined the dynamics of Canadian oil price and its Impact 

on Exchange Rate and Stock Market performance. The authors adopted the cointegration technique and used 

stock index, exchange rate, and crude oil price as variables in the study. The findings indicated that oil price, 

exchange rate, and their variations had a positive and significant impact on the Canadian stock market returns.  

2.3.1 Trend Analysis in Oil Price and Stock Performance.  

Before 2012, the movement of crude oil prices and the All-Share Index (ASI) in Nigeria was inconsistent. As 

crude oil prices increased, stock behaviour was often bearish, suggesting other factors influenced stock prices. 

From March 2012 to May 2014, the stock market was mostly bullish, with capital market indicators pushing 

positively except for a decline from June to September 2013 due to concerns over the US Federal Reserve's 

adjustment of its quantitative easing policy. This period saw higher and stable crude oil prices and increased 



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economic activities. However, during the recession in Nigeria from September 2014 to April 2017, both the ASI 

and crude oil prices declined significantly (Iyoha, 2017).  

Despite the recession, the Nigerian stock market was listed among the best performing globally. The NSE ASI 

increased by 47.19% and crossed the 38,000 points mark by the end of the year, driven by strong corporate 

earnings from blue-chip companies, increased capital inflow, and portfolio investments (Central Bank of Nigeria 

Economic and Financial Review, December 2020). In 2014, the market started positively, but activities turned 

bearish due to foreign investors' withdrawal, currency risks, and the recovery of developed economies. The 

bearish sentiment worsened in the second half of 2014 due to the global economic recession, which saw crude 

oil prices crash from $110 to $40 per barrel. Attacks on oil installations by militants in the Niger Delta region led 

to a loss of about one million barrels of crude oil exports per day, further impacting the stock market. Other 

macroeconomic factors, such as declining foreign reserves and weak corporate earnings, contributed to the 

market's decline. Investors adopted a 'flight to quality' strategy amidst the uncertainty (Salisu & Oloko, 2015). In 

2015, the bearish trend continued, with the NSE ASI falling by 17.4% to 28,642 points by year-end. The market's 

poor performance was due to political risk, currency volatility, and uncertainty in global crude oil prices. This 

bearish trend persisted into 2016, with the stock market recording a 16.05% loss in January. However, in May 

2016, the market saw a slight improvement with a 0.38% gain, the highest monthly gain that year. By December 

19, 2016, the market recorded a 5.33% gain, but the NSE ASI remained negative on most trading days, ending 

the year with an 18.0% year-to-date loss. In 2017, the market gradually recovered from the economic recession, 

with the NSE ASI index increasing by 42.0%, making it the third best-performing market globally. This 

improvement was partly due to the Central Bank's monetary policies that increased liquidity in the foreign 

exchange market.  

In 2018, as the Nigerian economy continued its recovery, the NSE equities market started strongly, with the ASI 

reaching a ten-year peak of 45,092.83 in January. However, the market began to decline in the second quarter, 

with the ASI falling by 17.81% to 31,430.50 points by year-end. Macroeconomic factors, including political 

risks, oil price volatility, and rising global yields, contributed to the bearish sentiment. In the first half of 2019, 

market sentiments were driven by uncertainty in oil prices and the 2019 general elections. Post-election stability 

dampened the volatility in the equities market. With the approval and implementation of the 2019 budget, positive 

impacts on company earnings and consumer spending were expected to boost market activity in the second half 

of 2019. These periods also experienced higher and stable crude oil prices and increased economic activities 

(Central Bank of Nigeria Economic and Financial Review, December 2020).  

For the purpose, of this study, a simple stock return series is specified as a function of exchange rate and crude 

oil price: 𝐴𝑆𝐼 =  𝑓(𝐸𝑅, 𝑃) where ER is the Bureau-de-Change exchange rate and P is the crude oil price. To 

determine if volatility in these series matters more than the crude oil price and exchange rate return series 

themselves, a variation of this equation is specified such that All-Share Index is a function of volatilities in crude 

oil price and exchange rate: 𝐴𝑆𝐼 = 𝑔(𝑠).  

THEORITICAL FRAMEWORK AND METHODOLOGY  

3.1 Theoretical Framework 

3.1.1 Arbitrage Pricing Theory (APT) 

The study adopted arbitrage pricing theory (APT) as the theoretical framework. This was developed by Stephen 

Ross in 1976. The arbitrage pricing theory is a general theory of asset pricing that holds that the expected returns 



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of a financial asset can be modelled as a linear function of various macroeconomic factors or theoretical market 

indices, where the sensitivity to change in each factor is represented by a factor specific beta coefficient 

(Alexander, Sharpe, and Bailey, 2001). The model’s derived rate of return will then be used to price the financial 

asset correctly; this asset price should be equal to the expected end of period return discounted at the rate implied 

by the model. In an event that the prices diverge then arbitrage actions should bring the price back to its correct 

level. APT assumes that asset returns are related to an unknown number of macroeconomic factors (Alexander, 

Sharpe, and Bailey, 2001). The model attributes the expected return of a capital asset multiple risk factors, and 

in the process measures the risk premiums associated with each of these risk factors. APT addresses the question 

of whether the risk associated with the macroeconomic variable is reflected in the expected market returns.  

According to (Chen, Roll, & Ross, 1986), economic variables have a systematic consequence on stock market 

returns because economic forces affect discount rates, the ability of the firm to generate cash and future dividend 

payments. The core idea of APT is that only a small number of systematic influences affect the long-term average 

returns of securities. 

3.2 Method of Analyses  

GARCH model was employed measure the volatility in exchange rate, oil prices, inflation and interest rate using 

the GARCH as developed by Robert (1982). The approach to estimate volatility in financial markets; we can 

think of heteroskedasticity as time-varying variance (i.e., volatility). Conditional implies a dependence on the 

observations of the immediate past, and autoregressive describes a feedback mechanism that incorporates past 

observations into the present. GARCH then is a mechanism that includes past variances in the explanation of 

future variances. More specifically, GARCH is a time series modelling technique that uses past variances and 

past variance forecasts to forecast future variances. The principal method employed to analyse the time series 

behaviour of the data involves unit root test, co-integration test, normality test, heteroskedasticity, and the 

estimation of an error correction model (ECM). Specifically, we employ unit root test to detect the order of 

integration of the variables using the Dickey Fuller and Augmented Dickey Fuller (ADF) Test by dickey And 

fuller (1979) The unit root test is necessary because research has shown that non-stationary data leads to spurious 

regression. We employ Co- integration test to examine whether there is long-run co-movement in the variable 

using the Engle and granger two stage technique. The ECM measures the short run dynamic adjustments towards 

long run equilibrium. We commence by testing for unit root in the data. The first step is to determine the order 

of integration of the variables before testing for co-integration.   

3.2.1 Model Specification  

To examine the impact of macroeconomic variables on stock return in Nigeria, a model anchored on the theory 

as used by Ray, (2012) is adapted as follows.  

𝐴𝑆𝐼 = 𝑓(𝐶𝑃𝐼, 𝐼𝑃, 𝑀𝑆, 𝐸𝑋𝐶𝐻)                              3.1  

Where ASI represents stock market performance, CPI represents consumer price index, IP represents industrial 

production, MS represents money supply and EXCH represents exchange rate while f represents the functional 

relationship. However, the model is modified to INTR, INFL, OILP, and INV as in equation 3.2.  

𝐴𝑆𝐼 = 𝑓(𝐸𝑋𝐶𝐻, 𝐼𝑁𝑇𝑅, 𝐼𝑁𝐹𝐿, 𝑂𝐼𝐿𝑃, 𝑅𝐺𝐷𝑃)                            3.2  

The reason for the inclusion of oil price as one of the explanatory variables and variable of interest is that. It is 

said that increase in oil price led to an appreciation of the naira as more foreign currencies are generated through 

https://www.investopedia.com/terms/v/volatility.asp
https://www.investopedia.com/terms/v/volatility.asp


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improved oil revenue as further shown by growth in value of oil export as a percentage of total export. However, 

contrary to expectation Nigeria, an oil exporting country still experiences the golden rule- “oil up, stock down” 

which should be applicable to oil importing countries. This may be an indication the country’s failure to translate 

its huge foreign exchange earnings from oil into an improved industrial sector productivity. It also an indirect 

manifestation of the deleterious effect of huge annual foreign exchange expenditure on importation of 

petrol/diesel for energy supply bothering on the inability to locally refine a substantial part of its crude oil and 

the apparent collapse of power supply by the Power Holding Company of Nigeria (PHCN) for domestic and 

industrial use. It is recommended that the most viable solution towards improved economic performance lies in 

refining the Nigerian crude oil locally so that the huge benefits of the naturally endowed oil can be fully realized 

rather than developing the economies of other nations.   

Also, the inclusion of investment as control variable, it is simply because there is a close relation between the 

stock market and investment. That is fluctuations in the stock market can affect investment of firms. The 

relationship between stock market prices and firms’ investment in physical capital is captured by the “q theory 

of investment”, developed by James Tobin (1969). That is, an increase in the prospective return on capital or a 

decrease in the market’s discount rate raises q and thereby increases investment. With a simple form of 

adjustment cost for changing the capital stock, the optimal amount of current investment depends only on the 

current value of q. 

Definition of Variables   

The variables used in the model are defined below: 

i. Dependent Variable ASI = All Shares index  

ii. Independent Variables:  EXCH = Exchange Rate, INTR = Interest Rate, INFL = Inflation, OILP = Oil Price, 

RGDP = Real GDP  

Expressing equation 3.2 in linear form yields equation 3.3.  

𝐴𝑆𝐼 =  𝛽0 + 𝛽1(𝐸𝑋𝐶𝐻)1 +  𝛽2(𝐼𝑁𝑇𝑅)2 +  𝛽3(𝐼𝑁𝐹𝐿)3 +  𝛽4(𝑂𝐼𝐿𝑃)4 + 𝛽5(𝑅𝐺𝐷𝑃)5                         3.3 

Where β0 = constant, Β1 to β5 represents various slope coefficients while EXCH, INTR, INFL, OILP and RGDP 

remain as defined above. Putting the variables in the sane scale of measurement and adding the stochastic 

disturbance term yields equation 3.4.  

𝐿𝐴𝑆𝐼 =  𝛽0 +  𝛽1𝐿(𝐸𝑋𝐶𝐻)1 +  𝛽2𝐿(𝐼𝑁𝑇𝑅)2 +  𝛽3𝐿(𝐼𝑁𝐹𝐿)3 +  𝛽4𝐿(𝑂𝐼𝐿𝑃)4 +  𝛽5𝐿(𝑅𝐺𝐷𝑃)5 +  𝜇        3.4 

Where, L represent the natural log of the variables. This is necessary to avoid large fluctuation in the variables.  

All other variables remain as defined above. On a-priori Β1, β2, β3, β5 > 0 and Β4 > or < 0 Exchange rates 

supposed to have positive relationship with stock market: due to globalization, businesses are affected either 

directly or indirectly by international trade activities. 

 

3.2.2 Data Analysis  

Research has shown that most time series data poses unit root i.e. they are not stationary. Thus, research carried 

out with them is likely to be spurious or non-sense. A test of stationarity in time series data is very important 

because since the 1970s, macroeconomic aggregates in Nigeria have been fluctuating greatly. The consequence 

of using non-stationarity is so grave that well established models are breaking down as they continuously fail 

to predict outcomes. The problem according to Granger and Newbold, (1974) is that regression results on non-



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stationarity series may, most times be “spurious or nonsensical” to the extent that a relationship would be 

accepted as existing between two variables as measured by their co-efficient of determination when in fact no 

relationships exist. Inference from non — stationary time series apart from being spurious, violate the classical 

econometric assumption, thus making the result unreliable for policy making. Also, pre-estimation test such as 

unit root tests and cointegration test were carried out while, post estimation tests such as stability test, normality 

test, auto correlation test etc. were also carried out to establish the consistency and reliability of the models 

adopted in this study. 

 Unit Root/Stationarity Test  

Adopting the Engel — Granger (1987) and Engel and Yule, (1987), we proceed to modelling a framework by 

first testing for stationarity to provide a more definitive answer to the non-stationarity, in each time series, the 

Dickey – Fuller (1979) regression is estimated as follows for unit root.  

∆𝑌𝑡 =  𝜆𝑌𝑡−1 +  𝑉𝑡             3.5  

If λ equals 0, 𝑌𝑡 is non-stationary, as a result 𝑌𝑡 and 𝑋𝑡 are not co-integrated. In order words, if λ is significantly 

different from 0,  𝑌𝑡and 𝑋𝑡 are found integrated individually. Given the inherent weakness of the unit root to 

distinguish between null and the alternative hypotheses, it is desirable that the augmented Dickey — Fuller 

(ADF), 1981 test be applied. To be co-integrated; both 𝑌𝑡 and 𝑋𝑡 must have the same order of integration (Engel 

and Granger, 1987, and Granger, 1986).  The ADF regression is specified as follows:  

∆𝑌𝑡 =  𝛼 + 𝛽1𝑡 + 𝛿𝑋𝑡−1 +  ∑ 𝛽𝑖∆𝑋𝑡−𝑖
𝑚
𝑡=1 +  휀𝑡      3.6 

∆ is the first difference operator, 6.466.486.406.506.526.566.546.426.44 Akai ke Infor mati on Criteri a (top 20 models) is the new random error term, M is the optimum number of lags 

needed to obtain “white noise’. This is approximated when the DW values approaches 2.0 numerically. The 

null hypothesis of non-stationarity is rejected if the estimated ADF statistic is found to be larger in absolute 

term or more negative than its critical values at 1 or 5 percent level of significance. 

 Concept of Co-integration  

Co-integration among the variables is used to determine the existence of a long run equilibrium relationship 

between the variables. The concept of co-integration (Granger, 1986, Mill 1990) creates the link between 

integrated processes and the concept of steady state equilibrium. The idea behind co-integration is that ‘although 

two different series may not themselves be stationary, some linear combination of them may be indeed stationary 

with the generalization to more than two series” (Komolafe, 1996). Economic variables are inherently non-

stationary and thus, could meander without any tendency to return to equilibrium in the long run. Implicit in the 

co-integration theory is the fact that there exists a linear combination of these non-stationary variables that is 

stationary. The traditional approach to the modelling of short-run disequilibrium is the partial adjustment method. 

However, an extension of this in the co-integration technique is the Error correction mechanism (ECM) (Granger 

and Newbold, 1977). The original co-integration relationship is specified as follows:  

𝑌𝑡 =  𝛽0 +  𝛽1𝑋𝑡 + 𝜇𝑡                  3.7   

Analysing the long-run behaviour of 𝑌𝑡 implies investigating co-integrating relationship in (1). If µt is stationary 

then, the 1(1) variables in 𝑋𝑡 may be thought of as capturing the long run component of 𝑌𝑡 while 휀𝑡 captures 

the short run or temporary movements. 

 Error correction technique  

If the 𝑌𝑡  and 𝑋𝑡  are found to be co-integrated, then there must exist an associated Error Correction Model 

(ECM), according to Engel and Granger (1987). The usual ECM may take the following form: 



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      ∆𝑌𝑡 =  𝛼0 +  𝛽1𝑡 ∑ ∆𝑋𝑡−1
𝑚
𝑡=1 + 𝛿𝑖 ∑ ∆𝑌𝑡=1

𝑚
𝑡=1 +  𝛾𝑡휀𝑡−1 +  𝜑              3.8 

Where, ∆ denotes first difference operators, 휀𝑡−1 is the error correction term, m is the number of lags necessary 

to obtain “white noise” and 𝜑𝑡 is another random disturbance term. If /δ/ is significantly different from zero, 

then 𝑌𝑡 and 𝑋𝑡 will have longer run relationship. The (ECM) error correction term (휀𝑡−1) depicts the extent of 

disequilibrium between 𝑌𝑡 and 𝑋𝑡. the ECM, reveals further that the change in 𝑌𝑡 not only depend on lagged 

changes in 𝑋𝑡 but also on its own lagged changes. The estimate of the parameters of the ECM are generally 

consistent and efficient (Hendry and Richard, 1983). Inference about the long run Granger causality can be 

drawn from the ECM model. The presence of co-integration will indicate at least, unidirectional long run 

causality from ∆𝑋𝑡−1, if statistically significant will indicate a short run causality from ∆𝑋𝑡−1 to ∆𝑌𝑡−1. The 

statistically significant non-Zero co-efficient of ∆𝑌𝑡−1will indicate feedback to ∆𝑌𝑡 from its own lagged values. 

It may be noted that even in the absence of co-integration, the error correction model may be estimated to detect 

if there is any short run granger causality. 

3.2.3  Nature and Sources of Data  

The data used in the study are collected from various publications of the Central Bank of Nigeria and (CBN), 

World Development Indicator (WDI), National Bureau of statistic (NBS). Specifically, the data used are time 

series data, which includes the exchange rate (EXCH), interest rate (INTR), inflation (INFL), oil price (OILP) 

and Real GDP(RGDP). 

3.2.4 Criteria for Model Evaluation  

To analyse the model, we employed the economic criteria which is used to measure the sign and size of the 

parameters in the model.  

Statistical Criteria: This includes the T-statistic, F-statistic, and 𝑅2  

Co-efficient of determination (𝑹𝟐)  

Coefficient of determination also known as R square (𝑅2) or goodness of fit tells the proportion of the total 

variable in the dependent variable y that is explained by the regression line or the explanatory variables x. in a 

single / simple regression model, it is the square of the correlation coefficient in a simple regression model. The 

value of the 𝑅2 lies between 0 and 1 i.e. 0< 𝑅2<1 when the 𝑅2 = 0, it means the explanatory variable do not 

explain the dependent variable and when the 𝑅2 = 1, it means the model is best fit. If the 𝑅2 is multiplied by 100, 

then it shows the percentage of total variation in y the dependent variable that is explained by variation / changes 

in x. the closer the 𝑅2 is to one. The stronger is the explanatory power of the estimated regression line, and thus 

the closer are the observation to the line. If 𝑅2 = 0.56 it means that 56% of total variation in y is explained by the 

regression line / changes in x and the other 44 remains unexplained by x. 

Test of individual statistics of the slope coefficient (T. Test)  

This tests the individual significance of the co-efficient, to do this, we test the null hypothesis that bi= 0 against 

the alternative hypothesis that bi. ≠0 in employing the t test, we compare the computed t-value with the value read 

from the student’s t table at given level of significance, (α) and n-k degree of freedom/ if the absolute value of 

the computed t value is greater than the absolute value of the theoretical, t value, we reject the null hypothesis 

(H0) at the given level of significance i.e. if 𝑡𝑐𝑎𝑙>𝑡𝑡𝑎𝑏, reject H0; Accept H1 𝑡𝑐𝑎𝑙<𝑡𝑡𝑎𝑏 , Accept H0, reject H1. 

Alternatively, the rule of thumb can be used, which state that if the 𝑡𝑐𝑎𝑙 is greater than 2 at the 5 percent level of 

significance, we reject the null hypothesis and conclude that the parameter is statistically significant in explaining 

the dependent variables.     



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The F Statistic (Test for overall significance of the model)  

This is used to test for the statistical significance of the entire slope coefficient jointly on the dependent variable 

using a level of significance. The F statistic is done by comparing the 𝐹𝑐𝑎𝑙with the F tabulated. When the 𝐹𝑐𝑎𝑙 

is greater than the 𝐹𝑡𝑎𝑏  we reject the null hypothesis and conclude that the entire variable put together is 

statistically significant in explaining the dependent variable. 

Econometric Criteria 

This includes the test for serial correlation.  

D.W Statistic Durbin – Watson Test  

It is the most popular test for serial/autocorrelation. The Durbin-Watson statistic is used to test the presence of 

serial correlation in a model, to determine whether there is serial correlation in the model, we compare the 

Durbin Watson value from the model to the Durbin-Watson critical value at 5%. If the value of Durbin Watson 

lies between the upper limit and four minus upper limits (i.e. u<dw<4-du), We reject the null hypothesis at the 

5 percent level of significance and conclude that there is no autocorrelation (positive or negative 

autocorrelation) in the model. 

Table 3.2.5: Definitions and Measurement of Variables  

S/N  Variable  Symbol  Measurement  

1  All Shares Index  ASI  Obtained by multiplying the price/share by the no. of shares outstanding  

2  Exchange Rate  EXCH  The price of one country’s currency expressed in another country currency  

3  Inflation Rate  INFL  The rate of inflation reported in CBN Statistical Bulletin  

4  Interest Rate  INTR  An accrued amount that includes principal plus interest  

5  Real  GDP RGDP  Real Gross Domestic product  

6  Oil Prices  OILP  The price of bulk oil, usually quoted in US dollars per barrel   

PRESENTATION AND ANALYSIS OF RESULTS  

4.1 Presentation of Results  

4.1.1 Trend Analysis  

In this section, graphical illustrations of the various variables that were used within the time to see their direction 

were conducted and this helps to show whether they are increasing or not and see their cyclical pattern. Thus, 

a graphical sketch of each of the variable over time was made as shown in figures 4.1 to 4.6.  

Figure 4.1: All-Shares Index  

 
Source: Author’s Analysis using EViews 12 

4

5

6

7

8

9

10

11

12

1990 1995 2000 2005 2010 2015 2020

Log ASI



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Figure 4.1 shows the level of All-Shares Index in Nigeria and the trendline pattern during reviewed period. The 

ASI was fluctuating frequently for the periods, at some points it was above trendline while other times below 

trendline, which makes the capital market unstable. In conclusion, the Nigerian ASI reflects the volatility of 

the country's stock market, influenced by various domestic and global economic factors. It underscores the 

economy's sensitivity to external shocks, like oil price volatility. The data highlights the importance of 

economic diversification to reduce reliance on oil and promote overall economic stability. 

Figure 4.2: Oil Price 

 
Source: Author’s Analysis using EViews 12 

Figure 4.2 shows the trend of oil price and the trend line pattern during reviewed period. Oil prices were stagnant 

for some years and skyrocketed for some years and went back to the initial price before it skyrocketed again. 

This can be because of demand and supply. This invariably affects cost of running business and business 

performance upon which stock market indicators are driven. Nigeria's heavy reliance on oil also made it 

vulnerable to external shocks, highlighting the need for policies that encourage non-oil sectors and other sources 

of energy to reduce the level of dependence on oil. 

Figure 4.3: Exchange Rate 

 
Source: Author’s Analysis using EViews 12 

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1990 1995 2000 2005 2010 2015 2020

Log OPRICE

0

50

100

150

200

250

300

350

400

1990 1995 2000 2005 2010 2015 2020

EXCH



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Figure 4.3 shows exchange rate volatility trend during the reviewed period. From 1986 to 1996, it was 

essentially zigzagging and varied frequently, but it was still somewhat steady. However, starting in 1997, it was 

no longer stable. Between 2000 and 2020, it was extremely volatile. Of course, this can negatively impact 

Nigeria's stock market performance. The data suggests that the Nigerian economy has faced significant volatility 

over the years, influenced by various internal and external factors such as oil prices, volatile exchange rates, 

and high dependence of importation of consumable goods, etc. This implies that, if serious action is not taken, 

this trend may have adverse impact on economic growth and development. 

 

Figure 4.4: GDP Growth Rate 

 
Source: Author’s Analysis using EViews 12 

Figure 4.4 shows the real GDP trend during reviewed period. Overall, Nigeria's GDP growth rate indicates a 

mixed economic performance, with periods of growth and recession. The heavy reliance on oil revenues has 

made the economy susceptible to fluctuations in global oil prices, which significantly influenced growth rates. 

To achieve sustained and stable economic growth, Nigeria has been working towards economic diversification 

and addressing structural challenges. Nonetheless, economic growth is influenced by numerous factors, and 

achieving long-term sustainability requires continuous efforts in various sectors of the economy. 

 

Figure 4.5: Inflation Rate 

 
Source: Author’s Analysis using EViews 12 

-4

0

4

8

12

16

1990 1995 2000 2005 2010 2015 2020

GDPGR

0

10

20

30

40

50

60

70

80

1990 1995 2000 2005 2010 2015 2020

INFL



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Figure 4.5 shows the trend of inflation rate during reviewed period. Interest rate was very high in 1986-1990, 

skyrocketed in 1986 and 1987 as well as 1992 to 1997; but went below trendline from 1997 to 2020. In 

summary, the inflation trend in Nigeria's economy has been characterized by fluctuations over the years. Several 

factors have influenced inflation, including oil price volatility, global economic conditions, monetary and fiscal 

policies, and structural challenges within the economy. To achieve more stable inflation rates, Nigeria needs to 

implement effective monetary and fiscal policies, diversify its economy, address structural issues, and promote 

sustainable economic growth. 

 

Figure 4.6: Interest Rate 

 
Source: Author’s Analysis using EViews 12 

Figure 4.6 shows interest rate trend within reviewed period. From 1986 to 2021, Nigeria's interest rates have 

exhibited a notable fluctuation, reflecting the country's economic situation and monetary policies. The late 

1980s and early 1990s saw high volatility, with interest rates ranging from approximately 0.7% to over 9%, 

while the early 1990s experienced several peaks, reaching around 10.77%. Subsequently, there was a gradual 

decline in rates until the mid-2000s, followed by a period of rising rates from 2006 to 2009 due to inflation and 

global financial challenges. From 2010 to 2016, rates showed a moderate decline, stabilizing in the range of 

6% to 7.5% since then. The effect of interest rates on stock market returns appears insignificant and suggests 

that changes in interest rates may not have a notable impact on the Nigerian stock market. 

4.1.2 Descriptive Statistics 

Table 4.1 presents the statistical properties of the variables under study. The emphasis is on the mean, skewness, 

Jarque-Bera statistics, and its probability for the variables involved. 

 

 

 

 

 

 

 

 

0

2

4

6

8

10

12

1990 1995 2000 2005 2010 2015 2020

INT



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Table 4.1 Descriptive Statistics 

 LOG(ASI) LOG(OPRICE) EXCH INFL INT GDPGR 

 Mean  8.918784  3.593225  126.3965  19.63280  7.089196  4.302662 

 Median  9.899855  3.458244  126.0951  13.18114  7.344341  4.396731 

 Maximum  11.09213  4.928412  359.8946  72.83550  11.06417  15.32916 

 Minimum  4.902307  2.231089  1.754523  5.388008  0.724167 -2.035119 

 Std. Dev.  1.791551  0.716557  107.1580  16.17657  2.011650  3.430461 

 Skewness -0.946079  0.167757  0.739581  1.707700 -1.081472  0.253667 

 Kurtosis  2.552464  1.659073  2.629395  4.592789  4.800631  2.868568 

       

 Jarque-Bera  68.04994  34.39179  41.85480  255.6347  142.5708  4.943913 

 Probability  0.000000  0.000000  0.000000  0.000000  0.000000  0.084420 

       

 Sum  3852.915  1552.273  54603.30  8481.370  3062.533  1858.750 

 Sum Sq. Dev.  1383.362  221.2984  4949100.  112784.7  1744.143  5072.036 

       

 Observations  432  432  432  432  432  432 

Source: Author 

In the study, the means of the variables: All-share index, (ASI), oil price (OPRICE), interest rate (INT), 

exchange rate EXCH, Inflation rate (INFL) and gross domestic product (GDPGR) are the variables employed. 

Table 4.1 shows that, apart from ASI and INT, other variables are positively skewed. The ASI, OPRICE, EXCH 

and GPGR are said to be platykurtic because they are less than 3, while INFL and INT are leptokurtic because 

they are greater than 3. The Jarque-Bera statistics for all the variables are all significant at 5 percent level. This 

implies that the variables of this model are not normally distributed. 

4.1.3: The Pairwise Correlation Matrix 

Table 4.2 moves further from descriptive statistics to examine the degree of correlation of the variables 

employed. 

Table 4.2: Correlation Matrix 

 LOG(ASI) LOG(OPRICE) EXCH INFL INT GDPGR 

LOG(ASI)  1.000000      

LOG(OPRICE)  0.773029  1.000000     

EXCH  0.748965  0.646874  1.000000    

INFL -0.530124 -0.484548 -0.419091  1.000000   

INT  0.535660  0.289268  0.324882 -0.138592  1.000000  

GDPGR  0.178544  0.229906 -0.068930 -0.358411 -0.081446  1.000000 

Source: Author  

The explanation here is based on dependent variable and explanatory variables alone. The correlation matrix 

as shown in table 4.2, shows the degree of relationship among the variables used. The ASI and OPRICE have 

a positive and strong relationship together at 0.773029. The ASI and EXCH also have strong and positive 

relations together. The INFL rate as expected has a negative but average relationship with ASI. The INT rate 

has a positive but moderate relationship with ASI. Finally, GDPGR has a positive but a very weak relationship 

with ASI. 

4.2 Analysis and Interpretation of Results 

4.2.1 Stationary Test 



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The study considered the unit root before estimating cointegration equation to choose appropriate econometric 

estimation to be used. Testing the stationarity of economic time series is critical since typical econometric 

approaches assume that the time series are stationary when they are not. As a result, traditional statistical tests 

are likely to be ineffective, and not co-integrated, the ordinary least squares (OLS) estimate of regressions in 

the presence of non-stationary variables produce misleading regressions (Granger and Newbold, 1974). 

Table 4.3: The Results of Unit Roots Test 

Variables ADF P-value (Level) ADF P-value (1st Difference) Remark 

Log (ASI) -1.447325 0.5595 -8.829806 0.0000 I (1) 

Log OPRICE) -2.328190 0.1636 -13.85884 0.0000 I (1) 

EXCH -0,014796 0.9558 -3.825417 0.0029 I (1) 

GDPGR -3.244426 0.0242 -3.349441 0.0134 I (0) 

INFL -4.085014 0.0011 -4.117710 0.0000 I (0) 

INT -4.441870 0.0003 -4.374314 0.0004 I (0) 

Source: Author’s computation from EViews 12 

NB: I (1) Stationarity of the variables at first difference, *Unit root hypotheses are tested at 1%, **Unit root 

hypotheses are tested at 5% and ***Unit root hypotheses are tested at 10% 

The stationarity of the variables was evaluated using the Augmented Dickey-Fuller (ADF) test, which involved 

unit root tests to examine the trends of all variables. The outcomes of the unit root tests are presented in Table 

4.3. According to the results from the Augmented Dickey-Fuller test, GDP growth rate, inflation rate and 

interest rate exhibited stationarity at the level. However, the all-share index, oil price and exchange rate 

stationarity at the 5 percent significance level after undergoing first-order differencing.  

Because of the objectives of this study emphasizing on volatility of oil price on stock market returns, this study 

adopts the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model. 

The trend analysis also showed us that there is volatility clustering of the series in virtually all the variables 

employed for this study. 

Table 4.4: Estimation of GARCH MODEL  

 

Dependent Variable: LOG(ASI)

Method: ML ARCH - Normal distribution (Marquardt / EViews legacy)

Date: 07/27/23   Time: 20:42

Sample (adjusted): 1986M02 2021M12

Included observations: 431 after adjustments

Convergence achieved after 51 iterations

Presample variance: backcast (parameter = 0.7)

GARCH = C(8) + C(9)*RESID(-1)^2 + C(10)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob.  

C -0.010566 0.013897 -0.760309 0.4471

LOG(OPRICE) 0.011365 0.005524 2.057201 0.0397

EXCH -0.000176 3.01E-05 -5.839223 0.0000

INFL 0.000284 0.000158 1.800160 0.0718

INT -0.000789 0.001344 -0.586612 0.5575

GDPGR 0.002072 0.000781 2.651872 0.0080

LOG(ASI(-1)) 0.999486 0.002535 394.3313 0.0000

Variance Equation

C 0.000271 7.17E-05 3.775402 0.0002

RESID(-1)^2 0.440670 0.090984 4.843394 0.0000

GARCH(-1) 0.581494 0.054501 10.66942 0.0000

R-squared 0.998726     Mean dependent var 8.928103

Adjusted R-squared 0.998708     S.D. dependent var 1.783120

S.E. of regression 0.064091     Akaike info criterion -2.951745

Sum squared resid 1.741669     Schwarz criterion -2.857404

Log likelihood 646.1010     Hannan-Quinn criter. -2.914496

Durbin-Watson stat 1.634520



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The estimation output as shown in table 4.4, shows the results of the regression for the variables used to capture 

oil price volatility and stock market returns in Nigeria. The lagged value of all-share indices has a positive and 

significant impact on the current value of ASI. The response of the current value of ASI to the previous value 

of ASI is 0.999486 with a probability value of 0.0000 meaning that it is significant at 1 percent level. The 

economic implication is that a 1 percent increase in ASI (-1) may lead to 0.999486 percent increase in the 

current value of ASI or stock market returns in Nigeria. The oil price also exhibits a positive and significant 

effect on all-share indices in Nigeria. The coefficient of response of ASI to OPRICE is 0.011365 with a 

probability of 0.0397 revealing that it is significant at 5 percent level. By implication, a 1 percent increase in 

the oil price leads to 0.011365 increase in all-share indices in Nigeria. The exchange rate (EXCH) has a negative 

and significant impact on all-share indices. The response of ASI to EXCH is -0.000176 with a probability value 

of 0.0000 showing that it is significant at 1 percent level. The economic intuition is that a 1 percent increase in 

EXCH will lead to 0.000176 decrease in all-share index or stock market returns in Nigeria. The inflation rate 

(INFL) has a positive and significant on all-share indices or stock market returns in Nigeria. The response of 

ASI to INFL is 0.000284 with a probability of 0.0718 meaning that it is significant at 10 percent level. This 

implies that a 1 percent increase in INFL will lead to a 0.000248 percent increase in stock market returns or all-

share index. The interest rate has a negative but insignificant effect on all-share index or stock market returns 

in Nigeria. The response of ASI to INT is -0.000789 with a probability of 0.5575. The economic intuition is 

that a one percent increase in INT may not have any significant impact on stock market returns or ASI in 

Nigeria. 

The gross domestic product growth rate (GDPGR) has a positive and significant impact on all-share index or 

stock market returns in Nigeria. The response of ASI to GDPGR is 0.002072 with a probability of 0.0080 which 

shows that it is significant at 5 percent level. The economic implication is that a 1 percent increase in GDPGR 

will lead to 0.002072 percent increase in ASI of stock market returns in Nigeria. 

The estimated variance equation is as follows: 

ℎ̂𝑡 =
0.000271

(3.775402)
+

0.581494ℎ̂𝑡−1

(10.66942)
+

0.440670�̂�2
𝑡−1

(4.843394)
 

The coefficient of constant variance term, the ARCH and GARCH parameters are positive and statistically 

significant at 1 percent level. This gives the result of the GARCH model. The time-varying volatility includes 

a constant (0.000271) plus its past ( 0.581494ℎ̂𝑡−1 ) and a component which depends on past errors 

0.440670�̂�2
𝑡−1. 

These findings clearly established the presence of time-varying conditional volatility of returns of the stock. 

This, result also indicates that the persistence of volatility shocks, as presented by the sum of ARCH and 

GARCH parameters (𝑏1 + 𝜃1), is very large. It denotes that the effect of today’s shock remains in the forecast 

of variance for many periods in the future. 

 

 

 

 

 

 



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Table 4.5: Granger Causality 

Pairwise Granger Causality Tests 

Date: 07/27/23   Time: 06:10 

Sample: 1986M01 2021M12 

Lags: 2   

    
     Null Hypothesis: Obs F-Statistic Prob.  

    
     LOG(OPRICE) does not Granger Cause LOG(ASI)  430  0.65586 0.5195 

 LOG(ASI) does not Granger Cause LOG(OPRICE)  9.24011 0.0001 

    
     EXCH does not Granger Cause LOG(ASI)  430  7.54490 0.0006 

 LOG(ASI) does not Granger Cause EXCH  1.27020 0.2818 

    
     INFL does not Granger Cause LOG(ASI)  430  2.37528 0.0942 

 LOG(ASI) does not Granger Cause INFL  4.71790 0.0094 

    
     INT does not Granger Cause LOG(ASI)  430  4.63126 0.0102 

 LOG(ASI) does not Granger Cause INT  4.67642 0.0098 

    
     GDPGR does not Granger Cause LOG(ASI)  430  0.59304 0.5531 

 LOG(ASI) does not Granger Cause GDPGR  0.30950 0.7340 

    
     EXCH does not Granger Cause LOG(OPRICE)  430  4.86593 0.0081 

 LOG(OPRICE) does not Granger Cause EXCH  1.94615 0.1441 

    
    Based on the results of the Granger causality test conducted as shown in table 4.5, the following conclusions 

can be drawn. At a 5% level of significance, there is evidence of a unidirectional causal relationship between 

oil price (OPRICE) and stock market returns proxy by all-share index. However, the reverse relationship does 

not hold true, suggesting that changes in ASI granger causes Oil price in Nigeria. On the other hand, exchange 

rate granger causes ASI, but ASI does not granger cause EXCH. This is also an example of a unidirectional 

relationship. Additionally, there is a bi-directional causal relationship between inflation rate (INFL) and real 

ASI in Nigeria. INFL granger causes stock market returns (ASI) while stock market returns also granger causes 

INFL. The interest rate INT also has a bi-directional causality with all-share index or ASI. That is INF granger 

causes all-share indices while ASI. In conclusion, based on these findings, it can be stated that there is a 

significant causal relationship between oil price volatility and stock market returns in Nigeria. 

 

 

 

 

 



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4.2.2 Statistic and Post- Diagnostic Results 

Table: 4.6 GARCH Models and Diagnostic Tests 

 Normal Dist. Student t’s GED 

Significant Coefficients ALL ALL ALL 

ARCH Significant? YES YES YES 

GARCH Significant? YES YES YES 

Log Likelihood 646.1010 667.0322 664.3149 

Adjusted R-squared 0.998708 0.998767 0.998756 

Schwartz IC -2.857404 -2.940460 -2.927848 

Heteroscedasticity NO NO NO 

Autocorrelation NO NO NO 

Source: Author’s Computation (2023) from EViews 9 

From a statistical standpoint as shown in table 4.6, the R-squared value of 0.998308 indicates that 

approximately 99.8 percent of the variation in the stock market is accounted for by the explanatory variables 

used in the model. The remaining 0.02 percent is attributed to the error terms. This suggests that our model fits 

the data well. The F-statistic (82.40192) indicates that the combined effect of the explanatory variables is 

statistically significant, implying a linear relationship among the variables. The Durbin-Watson statistic of 

1.626636, which is approximately 2, indicates that there is no significant autocorrelation in the residuals 

according to the rule of thumb. Additionally, a diagnostic test was performed on the residuals of the model, 

revealing that they exhibit no significant serial correlation. However, it is worth noting that the residuals are 

not normally distributed, although they exhibit constant variances (heteroskedasticity). 

4.3: Discussion of Results    

The above Table 4.5 discusses the results according to the objectives earlier stated in the chapter one. This result 

provides answer to objective one and three: Objective one: the impact of oil price volatility on stock market 

returns in Nigeria. To achieve this stated objective, our result from ARDL model regression provides profound 

empirical answer to the objective. Thus, oil price (OILP), is positive and statistically significant at 5% level of 

significance. This indicates that a unit increase in oil prices will eventually lead to 0.295814 increase in stock 

market performance in the short run, at the same time, it was equally observed from the outcome that oil price is 

also positive and statistically significant in the long run. It equally suggests that one percent increase in oil price 

will bring about 0.047514 increase in stock market performance in Nigeria during the period under investigation. 

This result also corroborates with the study by Alamgir and Amin, (2021) who examined the nexus between oil 

price and stock market, and it was found out that, there is positive relationship between the oil price and stock 

market index, and the response of the stock market index to positive.  

Second objective is to also determine effect of oil price fluctuation on the economic growth in Nigeria. To achieve 

the objective two, we investigate the coefficient and p-value from above Table 4.5, however, it was revealed from 

the outcome that the real gross domestic product is positively and statistically significant at 5% level of 

significance. This suggests that both short and long run, it was observed that a unit rise in real gross domestic 

product (which is economic growth) will bring about 2.235216 and 2.358845 increase in oil price in both short 

and long run respectively. Thus, this study’s outcome undoubtedly corroborates with the study by Erdem, 



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Gozbasi, Ilgun and Nazlioglu, (2010) who investigated stock market and economic growth nexus in emerging 

markets, with findings that there is a close relationship between stock market performance and economic growth 

in the long-run and that stock market performance is an impetus for economic growth in the short run.  

The third objective is to ascertain the nexus between oil price volatility and stock market returns: To achieve this, 

the outcome of granger causality test result from the Table 4.5 will salvage this. Thus, the granger causality 

estimate indicates that oil price (OILP) does granger caused all shares index (ASI), while OILP does not granger 

caused ASI. We therefore accept the hypothesis that oil price does granger caused all shares performance, and 

we fail to accept the hypothesis that ASI does granger caused OILP. This suggests that there is a unidirectional 

relationship that exist between stock market performance and economic growth in Nigeria. This equally suggest 

that an increase in oil price will amount to a rise in all-shares performance in Nigeria. The result above also, 

reveals the inflation (INF) does granger caused stock market performance (ASI), whereas stock market 

performance does not granger caused inflation. Owing to this fact, we fail to accept the hypothesis that inflation 

does granger caused stock market performance, and accept the hypothesis that ASI does not granger caused INF. 

This indicates that, there is a unidirectional found for ASI and INF in Nigeria during the period under 

investigation. The result equally, shows that the interest rate (INTR) does granger caused ASI, while ASI does 

not granger caused INTR. On this note, we fail to accept the hypothesis that INTR does granger caused ASI, and 

accept the hypothesis that ASI does not granger caused INTR. This implies that, there is a unidirectional found 

for INTR and ASI in Nigeria over the period under review.  

Above result indicates that real gross domestic product (RGDP) does granger caused ASI, while ASI does not 

granger caused RGDP. On this note, we fail to accept the hypothesis that RGDP does granger caused ASI and 

accept the hypothesis that ASI does not granger caused RGDP. This implies that, there is a unidirectional found 

for RGDP and ASI in Nigeria during the period investigation. The study further suggests that there is 

unidirectional causality which occurring from exchange rate (EXR) and stock market performance (ASI) which 

does not granger caused ASI does not granger caused EXR. On this note, we fail to accept the hypothesis that 

EXR does granger caused ASI and accept the hypothesis that ASI not granger caused EXR. This indicates that, 

there is a unidirectional found between EXR and ASI during the investigation of this research in Nigeria.  

5.1 Summary of Findings 

The primary objective of this study was to examine the influence of oil price volatility on Nigeria stock market 

returns using time series data spanning from 1986 to 2021. The all-share index (ASI), oil price (OPRICE), 

exchange rate (EXCH), inflation rate (INFL), interest rate (INT), real GDP growth rate (GDPGR) are the 

variables used to capture this topic efficiently. The unit roots were conducted through Augmented Dickey-Fuller 

(ADF) and then used Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Granger 

causality. The estimation output provides valuable insights into the relationship between various economic 

factors and stock market returns in Nigeria. The findings are outlined in this section based on the research 

objectives guiding the study. i) To determine effect of oil price fluctuation on the economic growth in Nigeria. 

The study findings revealed that the oil price volatility studied have a varying effect on the stock market returns.  

The estimation output reveals the complex interplay of economic factors on Nigerian stock market returns. The 

lagged ASI indicates strong autocorrelation, suggesting past performance has a significant influence on current 

returns. Oil prices, exchange rates, inflation, interest rates, and GDP growth rate all have significant effects on 

stock market returns. Inflation influences investor behavior and expectations, while interest rates may not have a 



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significant impact. GDP growth rate positively correlates with stock market performance, leading to increased 

investor confidence and higher returns. Time-varying conditional volatility indicates that stock market returns in 

Nigeria are subject to changing levels over time. The Granger causality test reveals a unidirectional causal 

relationship between oil price and stock market returns (ASI), with changes in ASI granger causing oil price. The 

inflation rate causes real ASI, while interest rate has ab bi-directional causality with the all-share index. The R-

squared value of 0.998308 indicates that explanatory variables account for 99.8% of stock market variation, with 

0.020% due to error terms. Risk management strategies are needed to navigate the dynamic nature of the Nigerian 

stock market. 

5.2 Conclusion: The findings from this study provide valuable insights into the intricate relationship between 

economic factors and stock market returns in Nigeria. These implications hold significance for investors, 

policymakers, and market participants in shaping investment decisions and economic policies. The strong 

positive impact of the lagged All-Share Index (ASI) on the current ASI indicates significant autocorrelation in 

stock market returns. Past performance holds a substantial influence over present stock market returns. 

Investors and policymakers must consider historical market trends to make well-informed investment choices 

and devise effective economic strategies. The substantial and significant effect of oil prices on the All-Share 

Indices reveals the Nigerian stock market's sensitivity to oil price fluctuations. Fluctuations in exchange rates 

can affect various sectors, particularly import-dependent companies listed on the stock exchange and should be 

carefully managed by policymakers. The positive and significant effect of the inflation rate on stock market 

returns indicates that inflation plays a pivotal role in shaping investor behavior and expectations. As inflation 

erodes purchasing power, investors may seek refuge in the stock market to counter the impact of rising prices. 

Policymakers should adopt measures to control inflation, as it can influence stock market dynamics. The 

positive and significant impact of the GDP growth rate on stock market returns underscores the positive 

correlation between economic growth and stock market performance. A thriving economy fosters a conducive 

environment for businesses, leading to heightened investor confidence and increased stock market returns. 

Investors and policymakers should consider these insights to make informed decisions that bolster stock market 

performance and foster economic growth. Additionally, the presence of time-varying volatility necessitates 

proactive risk management strategies to effectively navigate market uncertainties. By incorporating these 

advanced economic implications, Nigeria can work towards a more robust and sustainable stock market that 

contributes to overall economic prosperity. 

5.3 Recommendations 

The study recommends the following based on the findings. 

i Given the significant positive impact of lagged All-Share Indices (ASI) on current ASI, investors and 

policymakers should place emphasis on monitoring market sentiment and historical performance trends. A 

comprehensive analysis of market sentiment can help in making informed investment decisions and 

developing effective economic policies to bolster stock market returns. 

ii Considering the positive and significant effect of oil prices on All-Share Indices, it is crucial for investors 

to closely monitor oil price fluctuations. Policymakers should also keep a close eye on oil market dynamics, 

as Nigeria's status as a major oil producer can make the stock market susceptible to oil price changes. 

Implementing measures to mitigate the impact of oil price volatility on the stock market is essential for 

sustainable economic growth. 



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iii Government should institute mechanisms like benchmarking, through policy instruments that flags 

sensitivity of internal and eternal risk elements that interferes against natural flow of the domestic economy. 

This will guide for prompt mitigation measures. 

iv To enhance decision-making processes, continuous data analysis and research should be conducted to better 

comprehend the causal relationships between economic variables and stock market returns. Data-driven 

insights will assist in formulating effective economic policies and investment strategies that align with 

market dynamics. 

v The presence of time-varying conditional volatility, as evidenced by ARCH and GARCH parameters, 

necessitates the adoption of robust risk management strategies by investors. Diversification, hedging, and 

other risk mitigation techniques should be employed to navigate the dynamic nature of the Nigerian stock 

market. 

vi The positive and significant impact of the GDP growth rate on stock market returns indicates the importance 

of fostering economic growth. Policymakers should prioritize economic policies that stimulate growth and 

create a conducive environment for businesses to thrive, ultimately boosting investor confidence and stock 

market performance. 

Suggestions for Future Research:  

1) Future studies could broaden their scope by examining the influence of oil price shocks on stock market 

returns in various regions, including West Africa, Sub-Saharan African countries, or the entire African 

continent who are members of OPEC.  

2) Researchers may explore alternative estimation techniques, such as Dynamic OLS or Generalized Methods 

of Moment (GMM), to complement the current ARDL model and strengthen the findings. 

3) Delving deeper into the complex dynamics between oil price volatility and stock market returns, future 

research could incorporate additional factors like education, technological advancements, and government 

policies. This would offer a more comprehensive analysis of the subject matter. 

REFERENCES 

Agren, M. (2006). Does oil price uncertainty transmit to stock markets? (No. 2006: 23). Working Paper.  the 

Gulf Corporation Council countries? 

Akigbo, S. (2014). Macroeconomic uncertainty and conditional stock-price volatility in frontier African 

markets: Evidence from Ghana. The Journal of Risk Finance, 10(4), 333- 349. 

Alamgir, F., & Amin, S. B. (2021). The nexus between oil price and stock market: Evidence from South 

Asia. Energy Reports, 7, 693-703. 

Alzyoud, H., Wang, E. Z., & Basso, M. G. (2018). Dynamics of Canadian oil price and its impact on exchange 

rate and stock market. International journal of energy economics and policy, 8(3), 107-114. 

Arouri, M., & Rault, C. (2009). On the influence of oil prices on stock markets: Evidence from panel analysis 

in GCC countries. 



Ikemenogo, Eze Solomon (2024)  

  

American Interdisciplinary Journal of Business and Economics |  

60   https://sadijournals.org/index.php/AIJBE 

  

Arouri, M., & Rault, C. (2010). Oil prices and stock markets: What drives what in the Gulf. 

Arouri, M. E. H. (2011). Does crude oil move stock markets in Europe? A sector investigation. Economic 

Modelling, 28(4), 1716-1725. 

Ashamu, S. O., Adeniyi, O., & Kumeka, T. (2017). The effects of oil price volatility on selected banking stock 

prices in Nigeria. NDIC Quarterly, 32(34), 35-53. 

Atoi, N. V. (2014). Testing volatility in Nigeria stock market using GARCH models. CBN Journal of Applied 

Statistics (JAS), 5(2), 4. 

Babatunde, O. A. (2013). Stock market volatility and economic growth in Nigeria (1980-2010). International 

review of management and business research, 2(1), 201-209. 

Basher S. A., Haug, A. A., & Sadorsky, P. (2012). Oil prices, exchange rates and emerging stock markets.  

Briggs, A. P. (2015). Stock Market and Economic Growth of Nigeria. Research Journal of Finance and 

Accounting; Vol.6, No.9, 2015. 

Basher, S. A., & Sadorsky, P. (2006). Oil price risk and emerging stock markets. Global finance journal, 17(2), 

224-251. 

Boyer, M. M., & Filion, D. (2007). Common and fundamental factors in stock returns of Canadian oil and gas 

companies. Energy economics, 29(3), 428-453. 

Brown, S. P., & Yucel, M. K. (2002). Energy prices and aggregate economic activity: An interpretative survey. 

The Quarterly Review of Economics and Finance, 42(2), 193-208. 

Cong, R. G., Wei, Y. M., Jiao, J. L., & Fan, Y. (2008). Relationships between oil price shocks and stock market: 

An empirical analysis from China. Energy Policy, 36(9), 3544-3553. 

Degiannakis, S., Filis, G., & Arora, V. (2018). Oil prices and stock markets: A review of the theory and 

empirical evidence. Energy Journal, 39(5).  

Erdem, E., Gozbasi, O., Ilgun, M. F., & Nazlioglu, S. (2010). Stock market and economic growth nexus in 

emerging markets: cointegration and causality analysis. International Journal of Business Forecasting 

and Marketing Intelligence, 1(3-4), 262-274. 

Fowowe, B. (2013). Jump dynamics in the relationship between oil prices and the stock market: Evidence from 

Nigeria. Energy, 56, 31 - 38. 

Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 

111-120. 



Ikemenogo, Eze Solomon (2024)  

  

American Interdisciplinary Journal of Business and Economics |  

61   https://sadijournals.org/index.php/AIJBE 

  

Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. Journal of political economy, 91(2), 

228-248. 

Hamilton, J. D. (2009). Causes and Consequences of the Oil Shock of 2007-08 (No. w15002). National Bureau 

of Economic Research. 

Iyoha, O. M. (2017). Oil price volatility, exchange rate movements and stock market reaction: The Nigerian 

experience (1985-2017). American Finance & Banking Review, 3(1), 12-25 

Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil 

market. American economic review, 99(3), 1053-1069. 

Kumar, B. R. (2014). Study on dynamic relationship among gold price, oil price, exchange rate and stock market 

returns. International Journal of Applied Business and Economic Research, 9(2), 145-165. 

Ndlovu, I. (2019). Commodity price volatility, stock market performance and economic growth: evidence from 

BRICS countries (Doctoral dissertation). 

Olayungbo, D. O., & Ojeyinka, T. A. (2022). Crude oil prices pass-through to retail petroleum product prices 

in Nigeria: evidence from hidden cointegration approach. Economic Change and Restructuring, 55(2), 

951-972. 

Salisu, A. A., & Oloko, T. F. (2015). Modeling oil price–US stock nexus: A VARMA–BEKK–AGARCH 

approach. Energy Economics, 50, 1-12. 

Stock Market Index in China and India, International Journal of Economics and Finance, Vol. 3, No. 6, pages 

233-243. 

Su, C. W., Khan, K., Tao, R., & Umar, M. (2020). A review of resource curse burden on inflation in 

Venezuela. Energy, 204, 117925. 

 Tobin, and Shiller. ISBN 9781137292216.  

Wang, G., Sharma, P., Jain, V., Shukla, A., Shabbir, M. S., Tabash, M. I., & Chawla, C. (2022). The relationship 

among oil prices volatility, inflation rate, and sustainable economic growth: Evidence from top oil 

importer and exporter countries. Resources Policy, 77, 102674. 

 


