







































 
 

36 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

Asian Business Research Journal 
Vol. 10, No. 7, 36-49, 2025 
ISSN: 2576-6759 
DOI: 10.55220/25766759.494 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
The Oil Price-Stock Market Volatility Connection: Evidence from G20 Economies 

 
Ikram Ghamgui Frikha1

 
Mohamed ben Amar2 
 

 
 
 

1Economic Science at the university of Sfax, Tunisia. 
Email: frikhaikram@gmail.com    
2Faculty of Economics and Management of Sfax, Tunisia. 
Email: mohamed.benamar@fsegs.usf.tn  
( Corresponding Author) 
 
 

 
Abstract 

This research investigates how oil price fluctuations affect stock market performance across G20 
countries, utilizing the BEKK and DCC-GARCH models to model dynamic linkages and volatility 
cross-contagion between the two markets. An analysis is conducted from January 2004 to January 
2021, examining the impact of oil price volatility on stock market performance and also 
contemplating the reverse relationship. We perform a research study in the context of the 
COVID-19 pandemic. The BEKK model enables us to estimate fluctuating conditional 
correlations over time, whereas the DCC-GARCH model offers a deeper understanding of the 
progressing dynamics that govern the relationships among variables. Our data analysis shows 
sizable reciprocal causation; the impact of oil price fluctuations on stock market performance is 
noticeable across the G20 economies. Volatility in the stock market is shown to be influenced by 
shifts in oil prices, thereby presenting their complex connection. Grasping these dynamics is 
crucial for both investors and policymakers in an unstable market environment and times of 
worldwide uncertainty. The study sheds novel insights into how monetary shock transfers 
happen, providing significant contributions to understanding market interdependence and the 
influence of commodities on market conditions. 

 
Keywords: Bivariate GARCH, Dynamic relationship, G20 countries, Oil prices, Shocks transmission, Stock market returns. 

 
1. Introduction 

The link between oil prices and stock market returns is of great interest in both academic research and policy 
circles as well as among investors, especially on the G20 countries. Since these are all major players in the global 
economy, and they have large oil producers and consumers and also developed financial markets. However, oil 
prices have far-reaching consequences on global economic growth, inflation and corporate profitability that weigh 
heavily on stock market performance. For one, shocks are transmitted between international oil prices and stock 
market returns. Oil price volatility will have sector-specific effects, especially for industries that are high users of 
energy-inputs influencing valuations differently. Second, the same geopolitical events and global economic trends 
that to some extent define these relationships induce simultaneous movements in oil prices and stock markets, 
whilst enhancing concerns of causality as well as directionality. 

The hypothetical dependency can function as both a positive and contrasting force. Given the economies of 
shipping and receiving countries are enormously dependent on the price of oil, the price fluctuations of crude are 
responsible to influence the economies of these countries substantially as well as the changes in its price tend to 
have a great impact on them. The volatility in crude oil and alternative energy resources may immediately 
influence investment returns on the stock market. The interrelation between stock market values and oil prices has 
garnered substantial focus in fresh years. 

So, the IEA estimated that oil will account for 30% of universal energy supply by 2030. Investors especially 
portfolio managers experience disruption expected to the unpredictability of oil prices triggering risk and 
uncertainty in their investments. Research findings show oil prices straightforwardly affect stock markets by 
adapting forthcoming cash inflows, or influence stock markets obliquely via impacts on interest rates set assessing 
these cash inflows. Research has extensively studied how a high oil price causes stock market performance to 
decline by minimizing the possible growth of fiscal activities expected to loftier input costs, lessened business 
revenues, and increased comprehensive price inflation. The supplementary uncertainty, linked to loftier unrefined 
oil prices communicating high risk premiums, also lowers share prices. 

However, Changes to stock markets are transmitted through some diverse channels. Stock prices are 
influenced by oil prices both debiting to the cost of capital and upcoming cash flow expectations. Greater corporate 
cash flow is reduced debiting to ascending production costs caused by taller crude oil prices, which also inferior 
share prices. Dissecting the correlation between crude oil and time-honored stock markets offers significant 

mailto:frikhaikram@gmail.com
mailto:mohamed.benamar@fsegs.usf.tn
https://doi.org/10.55220/25766759.494


Asian Business Research Journal, 2025, 10(7): 36-49 

37 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

insights for investors. A precarious international crude oil market may result in delayed investment decisions, as 
uncertainty in the oil market can have a extensive impact on both the stock markets and the total economy. 

Uncertainty in the oil market's problems and risks are indeed transmitted to the authentic economy, with a 
ripple effect also hitting the capital market and affecting stock returns worldwide, including both established and 
developing countries. The G20's activities as a significant governing and fiscal grouping have a considerable effect 
on world energy markets and the universal economy in overall. The intense dependence of the G20 economies on 
energy exports and imports renders them vulnerable to oil prices and their volatility, with possible ramifications 
for the G20 region and its financial markets, particularly its stock market returns. The market fluctuations 
stemming from pronounced rises and falls in oil prices in current years underscore the value of examining the 
causal relationships between stock market performances and oil price volatility. 

Indeed, mainly extensive oil consumers aren't just limited to the US, china, japan, and india; rather, so are 
nations like canada, russia, and brazil that are also remarkable producers. World energy markets are chiefly 
dominated by them. Since the G20 countries are highly affected by events like global crises and the coronavirus, 
differentiating the effect of oil shocks on their stock market returns should be simpler. The global situation has 
substantially worsened, and the demand worldwide is now more perilous than before. The crisis has had a 
detrimental impact not exclusively on human health but also altered the way people live and produce. An economic 
blockade and a stock market crash have caused by all countries' measures taken to limit the spread of the epidemic 
resulting in a global economic downturn and a collapse of the energy market. Given the substantial fluctuation in 
oil prices in modern years, research should focus on the effects of these price variations on stock market 
performance. 

 Our research seeks to uncover any possible link between fluctuations in the oil and financial markets, precisely 
by assessing how disturbances and turbulence are passed from the oil market to the stock market. The study's 
findings will provide investors with precious insights into leading the complexities of global financial markets, 
permitting them to make knowledgeable decisions involving possible fluctuations in oil prices. Further research 
may provide greater efficacious and functional solutions for performing policies that help minimize the unfavorable 
effects of fickle oil prices on monetary outcomes. This research further contributes to succeeding scholarship on the 
interactions of commodity markets and investigates the separate characteristics of the G20 economies in relation to 
the global context. 

The objective of our study is to uncover correlations between volatility in oil prices and fluctuations in the 
financial sector, with a focus on comprehending how oil market shocks affect the general stock market 
performance. This research delves into the relationship between stock market performance and oil prices, with a 
focus on the manner in which fluctuations in both oil-exporting and oil-importing nations. Changes in oil price 
volatility are associated with variations in the level of stock market volatility which do fluctuate over time. Both 
definitely and adversely, the connection's influence can be observed at diverse instances, pivoting together or 
pivoting apart on average, and pulsating at divergent times. The price movement of oil differs in correlation with 
stock market fluctuations between oil-exporting nations and oil-importing nations in terms of extent. Oil prices on 
WTI and stock market returns data from 16 countries of G20.   

The results of this research will provide investors with substantial information to make judicious choices about 
market fluctuations engaging global financial investments in response to shifts in the price of oil. Future studies 
may contribute to the development of more effective and usable policy strategies to counter the negative effects of 
price fluctuations in oil on economic results. The research also contributes to current literature by exploring the 
dynamics between commodity markets and discovering individual traits of G20 economies within a broader global 
framework. 

 

2. Review of Literature 
Many studies have looked at how changes in oil prices affect stock markets. One study by Park and Ratti 

(2008) found that changes in oil prices caused changes in stock prices in 13 European countries. Another study by 
Kilian and Park (2009) found that the US stock market was affected by both changes in oil supply and demand, 
with demand changes having a bigger impact.  

Many studies have looked at how changes in the price of oil affect stock markets around the world. Wen et al. 
(2012) found that during the 2008 financial crisis, big swings in the price of oil affected both the US and Chinese 
stock markets. Ghorbel and Boujelbene (2013) showed that oil price swings also affected stock markets in many 

countries, including those in the Middle East, Brazil, Russia, India, and China. Also, Büyükşahin and Robe (2014) 
suggested that future studies should consider how economic crises affect the relationship between oil prices and 
stock prices. 

Guesmi and Fattoum (2014) found that big changes in the global economy affected the relationship between oil 
prices and stock prices in both countries that import and export oil. This relationship was stronger during the 
financial crisis  

The MENA countries studied by Bouri (2015) included Lebanon, Jordan, Tunisia, and Morocco from 2003 to 
2013. Prior to the financial crisis, data indicates that there is restricted interdependence in the transfer of volatility 
between the oil and stock markets in Middle Eastern and Northern Afro countries. During the post-monetary 
crisis period, links to monetary growth can be seen in sure countries.  

Du and He (2015) investigate the risk cross effects between oil and stock markets running data from September 
2004 through September 2012 regularly. Research indicates that before the financial crisis, the stock market had a 
positive effect on the oil market, while the oil market had a negative influence on the stock market. Across the post-
monetary crisis timeframe, instances of mutual risk transmission have been observed.  

A number of researchers, embracing Khalfaoui (2015), collaborated on a study. A restricted number of studies 
have precisely analyzed the g7 nations. Researchers utilize a multivariate GARCH approach in combination with 
wavelet analysis to examine the correlation between West Texas Intermediate (WTI) oil prices and substantial 
stock markets of the Group of Seven (G7) economies. The research reveals a significant transfer of risk between the 



Asian Business Research Journal, 2025, 10(7): 36-49 

38 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

oil market and the stock market, where heightened oil market fluctuations mainly cause heightened stock market 
uncertainty. 

The study reveals that diverse chronological correlations relating to oil trade do not vary between countries 
that import and countries that export oil. Maghyereh  and his team. Oil-exporting countries used during the period 
of 2008-2015 are Algeria, Iraq, and Libya.Research findings indicate that fluctuations in oil prices serve as the main 
channel by which volatility affects stock market fluctuations, and the data fails to distinguish between oleaginous-
importing and oleaginous-exporting countries. Possessing comprehensive knowledge of conventional stock 
markets can be key in helping investors make knowledgeable decisions across distinct scenarios. Research after the 
commodities liberalization has indicated a direct correlation between unprocessed oil markets and diverse 
worldwide equity markets. The justification for placing the DCC-GARCH model to the relationship between oil 
prices and equities is not completely warranted, as it adopts a multivariate approach that implies mutually 
beneficial effects on volatility between the oil market and the stock market. When bringing into account worldwide 
patterns, generalizations are regularly essential to make out the difference between countries reliant on oil exports 
versus oil imports in terms of the relationship between oil prices and equities markets 

Several studies have examined the relationship between oil prices and stock markets in different regions. 
Roberto and his colleagues (2017) looked at six Latin American countries (Argentina, Brazil, Chile, Colombia, 
Mexico, and Peru) from 2000 to 2015. They found that higher oil prices generally led to higher stock returns, 
regardless of whether the country was a major oil exporter or importer. 

Horobet and his team (2019) studied the connection between the European Union's financial sector and the oil 
market from 2010 to 2018. Their research showed that stocks in the financial sector are affected by oil price 
changes over long periods.The Middle East is a significant oil-producing region. Studies have explored the link 
between oil and stock markets in this area, particularly in the Gulf Cooperation Council (GCC) countries. Ammar 
and Mahmoud (2020) analyzed the Dubai market from 2010 to 2018 and found that oil market volatility influences 
the volatility of energy stocks.  

Lin et al. (2019) showed that changes in the price of oil directly affected the Chinese and European stock 
markets during times when the markets were acting unusually. These studies all show that big changes in the price 
of oil can have a big impact on stock markets, especially during times of economic trouble. 

Abdulrahaman (2020) investigated the long-term relationship between oil and stock markets in Saudi Arabia, a 
major oil exporter, using data from 2000 to 2017. Their research confirmed a strong connection between the two 
markets. 

 

3. Methodology  
The process of volatility can't be straightforwardly remarked or measured. Market anxiety usually revolves 

around one crucial factor. The tool is also used to assess the shock transmission between diverse markets. The 
frequency of shocks and volatility between oil markets and stock markets in selected G20 countries such as Japan, 
Mexico, Russia, and so on, and a few alternative G20 countries, were evaluated through two GARCH family 
models. This choice aims at acquiring accurate and relevant outcomes which have been steadily supported by  
sundry anteceding investigations. 

The BEKK-GARCH model's reputation is one of an elaborate model that is appropriate for the study of two-
way effects. The DCC-GARCH model has earned a reputation for designing better outcomes. Different 
investigations in new times have employed it, thereby authenticating its uniqueness (Tsuji, 2018; Fills et al, 2011). 
Based on specifications of dependent volatility, several single-variable models basic the DCC model can incorporate 
the Glosten - Jagannathan - Runkle (GJR) unbalanced model as well as the exponential GARCH (EGARCH) model 
with the unbalanced leverage effect of Nelson (1991). It is also potential to modify the BEKK and DCC models in 
order to incorporate considerations of asymmetry, leverage effects and alternative ordinarily observed variance and 
correlation features of monetary returns into the structures. 

The BEKK model 
The multivariate GARCH models, known as the BEKK class, were introduced by Engle and Kroner (1995). 

Bauwens et al (2006) suggest a general formulation that accounts for some factor structures (see particularly, e.g. 
The year they published their work). We consider within this paper the easiest BEKK formulation with all model 
orders set to: 

Σt=CCj+Aεt−1 εtj−1Aj+BΣt−1Bj 
 
Where A and B are two (N*N) matrices of constant parameters and C’ is a (N*N) matrix of symmetric 

parameter. The fully parameterized model comprises 2.5 𝑁2 + 0.5 N parameters. 
The DCC model  
Engle (2002) introduced the DCC model as a broader adaptation of Bollerslev's (1990) consistent conditional 

correlation (CCC) model. The intention here is to model the conditional variances and the conditional correlations 
individually. The covariance matrix is broken down consequently to the subsequent formula. 

 ∑ 𝑡 =  𝐷𝑡  𝑅𝑡  𝐷𝑡  
𝐷𝑡  = 𝑑𝑖𝑎𝑔 ( 𝜎1 , 𝑡, 𝜎2 , 𝑡, … … . 𝜎𝑘 , 𝑡)  

              𝑅𝑡 =  𝑄𝑡
1/2

 𝑄𝑡  𝑄𝑡
1/2

   ;   𝑄𝑡  =   𝑑𝑔( 𝑄𝑡   ) 

Where 𝑄𝑡 includes the conditional variances characterized by a series of univariate GARCH equations (see 

Baba and al. (1990); Engle (2002)). The dynamic correlation matrix, 𝑅𝑡, does not come directly from a dynamic 

equation, but is derived by normalizing a different matrix, 𝑄𝑡, which has a dynamic structure. The configuration of 

𝑄𝑡  defines the complexity and feasibility of the model in large cross-sectional dimensions. 

Proposals for 𝑄𝑡  specifications have been put forth. The subsequent analysis focuses solely on the most 
uncomplicated model and applies merely to the BEKK specifications of equations (1) through (4). Hadamard DCC, 
which is also referred to as the DCC model, was first introduced by Engle in 2002 

                          𝑄𝑡  = 𝑆 + 𝐴 ∗  𝐷𝑡−1 𝜀𝑡−1 𝜀𝑡−𝑗   𝐷𝑡−1 - ∑ 𝑆 + 𝐵 ∗ (𝑄𝑡−1 − 𝑆) 



Asian Business Research Journal, 2025, 10(7): 36-49 

39 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

With A and B as symmetrical parameter matrices and S as the long-term covariance matrix. 
 

4. Data and Descriptive Statistics 
4.1. Data Availability Statement  

We analyzed data from the former month for the two series in question: the oil prices and the stock market 
returns of G20, comprised of 16 countries such as Australia, Brazil, Canada, China, France, Germany, India, Italy, 
Japan, Mexico, Russia, South Africa, South Korea, the United Kingdom, Turkey, and the United States. The years 
from 2004 to 2020 have been classified into five distinct intervals. From 2004 to 2007, a period of stability preceded 
the Subprime crisis. The Subprime crisis happened between 2008 and 2009. Between 2010 and 2014, the transition 
from the Subprime crisis to the debt crisis, which culminated in the 2014 Oil crisis, took place. The years 2015 to 
2019 were marked by comprehensive universal financial stability. The COVID health crisis defined the period from 
2021. 

These data were collected from the data stream data base (a global platform of Financial and macroeconomic 
Data) and the international database The Global economy. 

 

4.2. Descriptive Statistics 
 

Table 1. Descriptive statistics. 

 WTI SIAUS SIBR SICA SICH SIFR SIGER SIIND 

 Mean  0.006813  0.003753  0.009067  0.003964  0.005761  0.003105  0.004792  0.011041 

 Median  0.014827  0.008181  0.011651  0.010836  0.000921  0.009164  0.013215  0.018394 

 Maximum  0.728814  0.103200  0.200413  0.109348  0.213908  0.106783  0.139292  0.220859 

 Minimum -0.447122 -0.222921 -0.280195 -0.221203 -0.195488 -0.245601 -0.245390 -0.240469 

 Std. Dev.  0.110172  0.036991  0.060911  0.036843  0.066295  0.043721  0.047034  0.054451 

 Skewness  0.681695 -1.609310 -0.806945 -2.240944  0.401592 -1.563778 -1.502868 -0.629307 

 Kurtosis  13.53196  10.36139  6.290208  14.85418  4.268968  9.276804  8.854649  7.130637 

 Jarque-Bera  944.5410  540.6032  112.4771  1345.099  18.88885  411.8815  362.7326  156.1625 

 Probability  0.000000  0.000000  0.000000  0.000000  0.000079  0.000000  0.000000  0.000000 

 Sum  1.369365  0.754279  1.822367  0.796774  1.157987  0.624119  0.963130  2.219212 
 Sum Sq. Dev.  2.427590  0.273667  0.742023  0.271480  0.879015  0.382308  0.442434  0.592981 

 Observations  201  201  201  201  201  201  201  201 

 SIITA SIJAP SIMEX SIRUS SISAF SISKOR SITUR SIUKING SIUSA 
 Mean  0.00076  0.00331  0.00801  0.01048  0.00885  0.00609  0.01117  0.002044  0.004216 
 Median  0.00584  0.00640  0.01104  0.01576  0.01500  0.00973  0.01511  0.005745  0.010500 
 Maximum  0.18303  0.10371  0.13378  0.18220  0.07437  0.15923  0.18698  0.088798  0.126605 
 Minimum -0.26430 -0.21957 -0.19152 -0.38059 -0.19895 -0.17549 -0.22643 -0.214878 -0.224787 
 Std. Dev.  0.05173  0.04781  0.04408  0.06527  0.03866  0.04406  0.06350  0.036727  0.039460 
 Skewness -0.98230 -0.80481 -0.72613 -1.32672 -1.56614 -0.78610 -0.31197 -1.777610 -2.044221 

 Kurtosis  7.52766  5.14891  5.21863  9.32853  8.69577  5.72779  4.06934  11.16674  13.05484 
 Jarque-Bera  204.010  60.3731  58.8885  394.387  353.869  83.0188  12.8372  664.4323  986.7019 
 Probability  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000  0.00163  0.000000  0.000000 
 Sum  0.15346  0.66525  1.61052  2.10651  1.77998  1.22549  2.24573  0.410784  0.847391 
 Sum Sq. Dev.  0.53529  0.45731  0.38862  0.85205  0.29898  0.38841  0.80657  0.269774  0.311418 
 Observations  201  201  201  201  201  201  201  201  201 

 
Descriptive statistics are presented for daily returns based on oil indices and stock indices on the table. The 

pre-pandemic and pandemic era is divided into the pre-recession period, the crisis period, the post-recession period, 
the crisis period and the pre-recession period. Data on level, risk, standard deviation, evolution over time, and the 
lowest and peak statistics in the field of descriptive statistics provide an idea. Subsequent consecutive crises that 
have impacted the oil and stock markets, a majority of indices are displaying unfavorable lowest values. Series 
studied permit normality to be tested by the "Skewness" and "Kurtosis" coefficients as well as the Jarque-Bera test 
statistic. The degree of distribution's flatness is measured by the "Kurtosis" coefficient. The normal distribution 
follows as distribution when it equals three. A value of coefficient fewer than 3 for kurtosis indicates a distribution 
is more flattering than a normal distribution, while a value more significant than 3 suggests a leptokurtic 
distribution. 

The Skewness coefficient measures the degree of distribution asymmetry. The distribution skews to the left 
when this coefficient is adverse, and it skews to the right when it is affirmative. At zero, the distribution being 
balanced suggests it follows a normal distribution. The null hypothesis of the Jarque-Bera test for the normality of 
the distribution is the normality itself of data. An estimated value of the k-squared statistic that is larger than the 
listed value of the test statistic leads to the rejection of this hypothesis. 

 

5. Empirical Findings  
5.1. Stationarity Test: Augmented Dickey-Fuller (ADF) 

To understand how data changes over time, we first need to make sure it's behaving in a predictable way. This 
is called checking for "stationarity."  We use a special test called The ADF test which helps us figure out if our data 
is stable or not, even if it looks like it's changing a lot. This test helps us get a clearer picture of whether our data is 
reliable for studying changes. 
Over time. 
 
 

 



Asian Business Research Journal, 2025, 10(7): 36-49 

40 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

Table 2. Stationarity test : Augmented Dickey-Fuller (ADF). 

 SIAUS SIBR SICA SICH SIFR SIGER SIIND SIITA 

ADF test 
in level 

-11.31345 

0.0000*** 
 -10.05088 
0.0000*** 

 -11.21867 

0.0000*** 
 -9.221951 
0.0000*** 

-11.46416 

0.0000*** 
 -11.54625 
0.0000*** 

-10.23061 

0.0000*** 
-11.81599 

0.0000*** 

ADF test 
first 
difference 

-11.83443-
0.0000*** 

-11.73217 
0.0000*** 

-12.57950-
0.0000*** 

-15.51194 
0.0000*** 

-9.692905 

0.0000*** 
-9.796026 

0.0000*** 
-12.88532 

0.0000*** 

-12.51546 

0.0000*** 

 SIJAP  SIMEX SIRUS  SISAF SISKOR   SITUR SIUKING   SIUSA 
ADF test 
in level 

 -11.14152 
 0.0000*** 

 -11.47700 
  0.0000*** 

-9.894833 

0.0000*** 
 -11.99281 

 0.0000*** 
 -10.92912 
0.0000*** 

 -10.89029 
0.0000*** 

 -12.49528 
 0.0000*** 

 -11.14580 
 

0.0000*** 
ADF test 
first 
difference 

-13.55871 

0.0000*** 
-14.42846 

0.0000*** 
-15.15411 

0.0000*** 
-9.895130 

0.0000*** 
-9.688681 

0.0000*** 
-13.05923 

0.0000*** 
-13.03901 

0.0000*** 
-14.29963 

0.0000*** 

 

5.2. Vector Auto Regression (Var) Test 
Vector Autoregression (VAR) is a powerful tool for understanding how different economic factors, like 

inflation, unemployment, and interest rates, affect each other over time. It's like a system of equations that shows 
how these factors are connected. For example, if inflation goes up, VAR can help us see how that might affect 
unemployment and interest rates. It doesn't assume one factor causes another, but instead looks at how they all 
influence each other. This makes VAR a flexible tool for understanding the complex relationships in the economy. 
 

Table 3. Vector Auto Regression (VAR) Test. 

 SIAUS SIBR SICA SICH SIFR SIGER SIIND SIITA 

Lag (1) 
 (0.682820) 
2.98678*** 

 (0.494898) 
3.51950*** 

 (1.194674) 
5.21337*** 

 (0.183159) 
1.44766 

 (0.635556) 
3.46356*** 

 (0.618769) 
3.67097*** 

 (0.441845) 
2.92883*** 

 (0.478478) 
3.09398*** 

Lag (2) 
(-0.519184) 
-2.24309** 

(-0.206186) 
-1.44985 

(-0.388098) 
-1.59487 

(-0.09351) 
-0.74253 

(-0.485421) 
-

2.60233*** 
(-0.3781) 

-2.19122** 
(-0.371497) 
-2.4711*** 

(-0.348812) 
-2.22377** 

 
SIJAP  SIMEX SIRUS  SISAF SISKOR   SITUR SIUKING   SIUSA 

Lag (1) 
 (0.386582) 
 2.28087** 

 (0.47173) 
 2.5147*** 

 (0.441596) 
3.38716*** 

 (1.003438) 
 

4.91113*** 
 (0.823615) 
4.53435*** 

 (0.328258) 
2.59288*** 

 (0.805366) 
 3.7126*** 

 (0.904141) 
 

4.34468*** 
Lag (2) 

(-0.217494) 
-1.2714 

(-0.364704) 
-1.92433* 

(-0.096434) 
-0.71955 

(-0.190675) 
-0.87921 

(-0.292946) 
-1.55113 

(-0.189414) 
-1.47995 

(-0.434526) 
-1.93969* 

(-0.472278) 
-2.21189** 

Note(s): ***, **, * statistical significance at 1%, 5 and 10% levels, respectively 

 
The analysis of the VAR model shows that a one-period delay in oil prices has a positive and significant impact 

on stock market returns for most countries, including Australia, Brazil, Canada, France, Germany, India, Italy, 
Japan, Mexico, Russia, South Africa, South Korea, Turkey, the United Kingdom, and the United States, except for 
China. This finding is consistent with previous research by Roberto et al. (2017). 

 
However, when the oil price is delayed by two periods, the impact on stock market returns becomes negative 

and significant for a smaller group of countries, including Australia, Germany, India, Italy, and the United States. 
For the other countries, the impact is negative but not statistically significant. This finding aligns with previous 
studies by Filis et al. (2011) and Khan et al. (2019). It's important to note that the results for the first lag (one-
period delay) are generally more relevant than those for the second lag (two-period delay). This is because the 
immediate consequences of oil price shocks are fully reflected in the first lag, while these effects are diminished in 
the second lag. 

 

5.3. Analysis of the Correlation Between Crude Price and G20 Stock Market Indexes 
The BEKK model, proposed by Baba, Engle, Kraft, and Kroner (1995), is known as the highest exhaustive and 

highest convoluted model of the models considered for this study in terms of computation. Results in Chart 8 
illustrate the effects of incorporating oil price shocks on the performance of the various stock indices in our selected 
dual-variable BEKK-GARCH model. The period has been categorized into five unique sub-periods. The first 
interval spans from January 1, 2004, to June 30, 2007, while the following interval spans from July 1, 2007, to 
December 31, 2009, followed by another interval encompassing from January 1, 2010, to December 31, 2014, then 
another interval from January 1, 2015, to December 31, 2019, and the ultimate interval occurs from January , 2020, 
to January, 2021. This paper examined the volatility transmission between the oil and stock markets of 16 G20 
countries divided into oil exporting and comprising nations over five unique sub-periods. 

The transmission is quantified in two phases through 𝛼2,1and variance is represented by𝛽2,1. Three diverse 
significance levels are studied: one percent, five percent, and several percent. ARCH coefficients measure the 
postponed shocks impact while GARCH explains how volatility affects the equation. The assessed results of 
BEKK-GARCH analysis show that both ARCH and GARCH effects are substantial in the oil and stock markets. 
 
 
 



Asian Business Research Journal, 2025, 10(7): 36-49 

41 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

5.3.1. Analysis of Results for Importing Countries 
 
 

Table 4. Analysis of results for importing countries. 

 
 
 
 
 
 
 
 

Period 1 : 2004-2007 before the subprime crises 

Countries Australia Brasil Canada China France Germany India Italy 

𝛼1,2 (0.169597216) 
0.01518284** 

(0.05877782) 
0.61825417 

(0.116275985) 
0.17805003 

(-0.06208051) 
0.49479682 

(0.03767483) 
0.65624020 

(-0.09130473) 
0.41291163 

(0.141097745) 
0.21186811 

(0.114155928) 
0.22540396 

𝛼2,1 (1.654782729) 
0.00968508*** 

(-0.36805293) 
0.06716381* 

(0.390316497) 
0.62253815 

(-0.97635438) 
0.0006742*** 

(-0.2300705) 
0.74310132 

(-0.2394867) 
0.64728079 

(-0.282727397) 
0.32347168 

(-0.514984745) 
0.38707364 

𝛽1,2 (0.202658597) 
0.00841906*** 

(-0.1804604) 
0.0000031*** 

(0.054327556) 
0.43950938 

(-0.59129083) 
0.0000439*** 

(-0.00103684) 
0.98838929 

(-0.04101038) 
0.41504148 

(-0.091468981) 
0.45663033 

(-0.164737136) 
0.14092334 

𝛽2,1 (2.463787408) 
0.00081607*** 

(0.26959268) 
0.0014616*** 

(-1.076989534) 
0.1912885 

(-0.58214004) 
0.0094340*** 

(-1.64296696) 
0.0072258*** 

(-1.03020196) 
0.04714491** 

(0.219497394) 
0.39915079 

(0.154874933) 
0.91892794 

Period 2 : 2008-2009 the subprime crises 
Countries Australia Brasil Canada China France Germany India Italy 

𝛼1,2 (-0.088484494) 
0.23127608 

(-0.04852523) 
0.56726119 

(0.03981804) 
0.55905529 

(0.16391328) 
0.0095533*** 

(-0.21192893) 
0.04594817** 

(-2.47572676) 
0.0000000*** 

(-0.9028) 
0.00000000*** 

(-8.2139e-03)   
0.00000001*** 

𝛼2,1 (-1.019532424) 
0.00008945*** 

(-1.07727442) 
0.0001345*** 

(1.678801628) 
0.00118638*** 

(-0.47705689) 
0.30095772 

(0.43774818) 
0.04168180** 

(0.92545761) 
0.0000000*** 

(0.1327) 
0.00000000*** 

(0.2469) 
0.00000000*** 

𝛽1,2 (-0.16821894) 
0.19645864 

(-0.11805167) 
0.45999088 

(-0.206672244) 
0.00072169*** 

(0.201490849) 
0.21488241 

(-0.38726908) 
0.0000000*** 

(-0.01461998) 
0.06138959* 

(0.4002) 
0.00000000*** 

(0.2402) 
0.00000000*** 

𝛽2,1 (-0.646021254) 
0.02412485** 

(-0.44903077) 
0.08148306* 

(1.087060641) 
0.04658286** 

(1.01273258) 
0.07657895* 

(0.6721346) 
0.0000000*** 

(0.00513251) 
0.1270042 

(0.1955) 
0.00000000*** 

(0.2035) 
0.00000309*** 

Period 3 : 2010 -2014 after the subprime crises and on the  Sovereign debt crisis 
Countries Australia Brasil Canada China France Germany India Italy 

𝛼1,2 (0.132698966) 
0.07887568* 

(-0.48163288) 
0.0002231*** 

(0.023698546) 
0.72654426 

(-0.10363660) 
0.33341714 

(0.23725172) 
0.03931942** 

(0.34502674) 
0.0021888*** 

(-0.252033511) 
0.00406265*** 

(0.417888805) 
0.00023534*** 

𝛼2,1 (0.109813916) 
0.76098620 

(0.58939818) 
0.0059907*** 

(1.177612153) 
0.00032657*** 

(0.77049669) 
0.01041155** 

(1.87535075) 
0.0071684*** 

(2.31959778) 
0.0000000*** 

(0.732344154) 
0.00091239*** 

(0.082522089) 
0.77651123 

𝛽1,2 (0.246884312) 
0.00094687*** 

(0.28028483) 
0.17080017 

(0.276047027) 
0.00066263*** 

(-0.14903503) 
0.52190468 

(0.03564804) 
0.77842593 

(-0.00986098) 
0.91148162 

(-0.148293055) 
0.03431159** 

(0.345025627) 
0.00426592*** 

𝛽2,1 (-1.317857651) 
0.00000000*** 

(-0.90497577) 
0.0019739*** 

(-0.513797813) 
0.07081295* 

(-0.22580676) 
0.65567468 

(0.67706337) 
0.0000343*** 

(0.57412898) 
0.0000004*** 

(0.331816235) 
0.03092979** 

(-0.814393585) 
0.00010788*** 



Asian Business Research Journal, 2025, 10(7): 36-49 

42 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

 
 
Period 4 : 2015-2019 befor COVID-19 
Countries Australia Brasil Canada China France Germany India Italy 

𝛼1,2 (-0.208841593) 
0.00000006*** 

(-0.00994103) 
0.91445574 

(-0.157164953) 
0.01063880** 

(-0.1761913) 
0.04758599** 

(-0.17555448) 
0.0005954*** 

(-0.2085324) 
0.0004671*** 

(0.012373638)  
 0.79718606 

(-0.286751766) 
0.04930797** 

𝛼2,1 (2.119996952) 
 0.00000139*** 

(0.69774481)   
0.0015485*** 

(1.771425028)   
0.00010945*** 

(-0.66997731) 
0.01138941** 

(1.74552908)   
0.0000027*** 

(1.45438818)   
0.0000213*** 

(-0.101279972) 
0.82270552 

(1.300753303) 
0.00000324*** 

𝛽1,2 (-0.006792305) 
0.80154871 

(-0.35277126) 
0.0000001*** 

(0.188201227)   
0.00007816*** 

(-0.19042913) 
0.04964350** 

(0.3553212)   
0.0000069*** 

(-0.22824673) 
0.0021967*** 

(-0.138685438) 
0.02300313** 

(0.028248952)   
0.85308336 

𝛽2,1 (0.175208863)  
 0.66110155 

(0.64265948)   
0.0000206*** 

(-0.515641162) 
0.62058348 

(0.57775992)   
0.0005418*** 

(-1.83542528)       
0.0000001*** 

(1.23724630)   
0.07175053* 

(2.465201662)   
0.00000000*** 

(-0.757196580) 
0.48959280 

Period 5 : 2020 -2021 the COVID-19 

countries Australia Brasil Canada China France Germany India Italy 

𝛼1,2 (-0.477091562) 
0.00000000*** 

(0.049127390)  
 0.12419103 

(-0.462383259) 
0.00000000*** 

(-0.03684975) 
0.29608677 

(-0.38278084) 
0.0000000*** 

(0.02626215)   
0.0000000*** 

(-0.085750679)   
0.00000000*** 

(-0.255296741) 
0.00000000*** 

𝛼2,1 (4.332756787)   
0.00000000*** 

(0.62702503)  
 0.0005431*** 

(6.990584107)  
 0.00000000*** 

(3.56421437)  
 0.0000000*** 

(5.80258697)  
 0.0000000*** 

(3.0944353)   
0.0000000*** 

(3.848406714)   
0.00000000*** 

(6.736233798)   
0.00000000*** 

𝛽1,2 (-0.012216566) 
0.30846432 

(0.01063818)  
 0.56761257 

(-0.009020603) 
0.00000000*** 

(-0.00046059) 
0.96708326 

(-0.01053482) 
0.0000000*** 

(0.11371150)   
0.0000000*** 

(0.047138925)   
0.00000000*** 

(-0.023758603) 
0.00000000*** 

𝛽2,1 (0.053369942)   
0.00015037*** 

(0.06946938)   
0.32793668 

(0.077423311)  
 0.00000000*** 

(1.32254806)  
 0.0000000*** 

(0.0472128)  
 0.0000000*** 

(0.5113333)   
0.0000000*** 

(0.559194167)  
0.00000000*** 

(0.626585326) 
0.00000000*** 

Note(s): ***, **, * statistical significance at 1%, 5 and 10% levels, respectively.



Asian Business Research Journal, 2025, 10(7): 36-49 

43 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

This study examined how changes in oil prices affected stock market returns in various oil-importing 
countries. During a period of rising oil prices, the study found that oil prices had a significant impact on stock 
market performance.  

The analysis, using a statistical model called BEKK-GARCH, showed that before the 2008 financial crisis, oil 
price changes influenced both the average return and the volatility of stock markets in Australia, Brazil, China, and 
Italy. This means that oil price fluctuations affected both the overall direction and the riskiness of stock markets in 
these countries.  

However, in France and Germany, oil price changes only affected the volatility of the stock market, not the 
average return. This suggests that while oil price fluctuations increased risk in these countries, they didn't 
necessarily lead to higher or lower overall stock market returns.  

Overall, the study found that the impact of oil price changes on stock markets varied across different oil-
importing countries, with some experiencing both positive and negative effects. Crude oil is a very important 
commodity that has a big impact on the economy. When oil prices go up, it costs more to produce goods and 
services, as well as to transport things and heat homes. This can lead to higher prices for consumers, which can 
make them buy less. When people buy less, it can hurt businesses, make people less confident about the economy, 
and have a negative impact on the overall economy. 

There are a few reasons why oil prices can affect the stock market. One reason is that the value of a company's 
stock is based on how much money it is expected to make in the future. If oil prices go up, it can cost companies 
more to operate, which could reduce their profits. This could lead to lower stock prices. However, higher oil prices 
could also mean that companies that produce oil will make more money, which could lead to higher stock prices. 
Studies have shown that there is a connection between oil prices and stock market prices. This means that changes 
in oil prices can affect the stock market. This is similar to what researchers Malik and Ewing (2009) and Arouri 
and Nguyen (2010) found in their studies. 

Our study found no evidence of transmission from oil markets to stock markets in most of the countries we 
examined. This aligns with previous research by Cong et al. (2008) and Jammazi and Alouli (2010). However, 
during the second period of our study, which coincided with the global financial crisis, we observed a significant 
impact on oil markets. The price of crude oil surged from $96 in January 2008 to $144 in July, likely due to the 
subprime crisis and its effect on oil supply. This sharp increase affected industries heavily reliant on fuel.The 
combination of the global economic crisis and efforts by major oil-consuming countries to reduce their dependence 
on oil led to a dramatic drop in oil prices, reaching as low as $32 per barrel. Our analysis revealed that this period 
saw a transmission of effects from oil markets to stock markets in all G20 oil-importing countries, both in terms of 
average price and volatility. Interestingly, the transmission was negative for Australia, Brazil, and China, while it 
was positive for the other countries. 

During a period when oil prices rose to their highest point in July 2008, the impact on stock markets was 
expected to be positive. This was because the price increase was driven by strong global demand for oil.  However, 
things changed after mid-2008 when the global financial crisis hit.  The crisis made financial markets around the 
world more connected, and the relationship between oil prices and stock markets in oil-importing countries became 
stronger.  As the crisis deepened, both the stock market and the oil market experienced a downturn, leading to a 
negative impact on stock markets. 

The price of oil rose to $80 per barrel in the early 2000s. This was partly due to oil-producing countries 
cutting back on production to deal with economic problems. The global economy improved in 2010, which also 
helped push oil prices higher.  

However, things changed after mid-2008. The financial crisis that year made the world's financial markets 
more connected. This led to a stronger relationship between oil prices and stock market prices. The crisis caused 
stock markets to decline and also led to a sharp drop in oil prices.  

Research shows that changes in oil prices can affect stock markets, especially in countries that import a lot of 
oil. This is similar to a study by Nazlioglu and al.  (2015). They found that oil price changes affected financial 
markets before the 2008 crisis. After the crisis, they found that financial market problems could also affect oil 
prices. In 2015, the price of oil plummeted to $50 per barrel due to a surplus of oil, mainly from increased 
production in the United States. Even though OPEC countries kept their production levels the same, the price fell 
even further, reaching below $30 per barrel. However, a few months later, the price started to rise slightly after 
some oil-producing countries decided to cut back on production. This period saw a significant impact on both the 
oil and stock markets. The volatility in the oil market directly affected the stock markets of many oil-importing 
countries.The global oil price experienced a dramatic decline in mid-2014. The price of Brent crude oil dropped 
from $114 per barrel in June 2014 to $28 per barrel in February 2016, a decrease of over 70%. This sharp drop was 
caused by a combination of factors: so, the rapid growth of North American shale oil production, fueled by 
technological advancements, led to a surplus of oil in the market and the Slow economic growth in many countries 
resulted in a decrease in the demand for crude oil. 

In essence, the combination of too much oil and not enough demand drove down the global oil price.The year 
2020 saw a major global crisis with the emergence of the COVID-19 virus. This pandemic caused a worldwide 
slowdown, with economies shrinking rapidly. The price of oil plummeted to a very low level, falling below $20 per 
barrel. This was particularly concerning for countries that rely heavily on oil revenue. Studies have shown a strong 
connection between oil prices and stock market performance, especially for countries that import oil, like those in 
the G20. 

 
 



Asian Business Research Journal, 2025, 10(7): 36-49 

44 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

5.3.2. Results Analysis for Exporting Countries 
 

Table 5. Analysis of results for exporting countries. 

   

 
 
 
 
 
 
 
 
 
 

Period 1: 2004-2007 before the subprime crises 

Countries Japan Mexico Russia South Africa South korea Turkey United Kingdom United States 

𝛼1,2 (-0.078614113) 
0.04683363** 

(0.118139173) 
0.11838453 

(0.090898471) 
0.60764701 

(0.024565310) 
0.62296385 

(-0.015471428) 
0.86654871 

(0.298644868) 
0.04347037** 

(0.093390965) 
0.28751143 

(0.068722648) 
0.16479182 

𝛼2,1 (0.629441175) 
0.04228121** 

(-2.046654616) 
0.00000000*** 

(-0.570704965) 
0.05499495* 

(-0.299380340) 
0.52687744 

(0.989177744) 
0.00065098*** 

(0.081494083) 
0.75291683 

(0.482239577) 
0.44731582 

(1.631732194) 
0.00498912*** 

𝛽1,2 (0.050338795) 
0.00675580*** 

(0.012776838) 
0.85571849 

(0.560832485) 
0.12375177 

(0.296423264) 
0.00000000*** 

(0.095708620) 
0.23417511 

(-0.198037412) 
0.04473297** 

(-0.002182587) 
0.98441788 

(0.043389611) 
0.02811906** 

𝛽2,1 (0.045329318) 
0.81567868 

(0.000022833) 
0.66588500 

(-0.793421135) 
0.00011313*** 

(-1.524642491) 
0.00000215*** 

(-0.083462599) 
0.68882748 

(0.360575869) 
0.05075932* 

(1.421263015) 
0.10750330 

(-0.243810237) 
0.34272385 

Period 2 : 2008-2009 the subprime crises 
Countries Japan Mexico Russia South Africa South korea Turkey United Kingdom United States 

𝛼1,2 (0.736870)   
0.00000000*** 

(1.905891351) 
0.00000000*** 

(-0.4828) 
0.02175166** 

(-0.255682150) 
0.44298475 

(2.872868155) 
0.00000000*** 

(1.704645662) 
0.00000000*** 

(0.733566370) 
0.00956585*** 

(0.990075141) 
0.00000000*** 

𝛼2,1 (-1.178209)   
0.00000000*** 

(-3.870827938) 
0.00000000*** 

(0.7983) 
0.00000000*** 

(0.226935724) 
0.39406317 

(-2.185993924) 
0.00000000*** 

(-1.249361306) 
0.00000000*** 

(0.000427886) 
0.88273101 

(-0.685673638) 
0.00000000*** 

𝛽1,2 (-0.275630)    
0.03295570** 

(0.071544855) 
0.00000000*** 

0.4162 
0.00000000*** 

(0.687851839) 
0.00000000*** 

(0.184002179) 
0.00000000*** 

(-0.036585403) 
0.00000000*** 

(0.000238667) 
0.99863318 

(-0.059910751) 
0.26308020 

𝛽2,1 (-0.000030)    
0.00000000*** 

(0.051779347) 
0.00000000*** 

(0.4036) 
0.00000000*** 

(0.047515482) 
0.00000000*** 

(-0.051367532) 
0.00000000*** 

(-0.111789730) 
0.00000000*** 

(-0.000152489) 
0.87345512 

(0.002644439) 
0.05328588/ 

Period 3 : 2010 -2014 after the subprime crises and on the  Sovereign debt crisis 
Countries Japan Mexico Russisa South Africa South korea Turkey United Kingdom United States 

𝛼1,2 (0.235484924) 
0.00415880** 

(0.129175514) 
0.31332353 

(0.085619930) 
0.53628264 

(0.269305965) 
0.00000008*** 

(0.274816236) 
0.00304131*** 

(-0.305297434) 
0.01145705** 

(-0.024634476) 
0.57389825 

(0.010687475) 
0.88090202 

𝛼2,1 (0.741362220) 
0.00259140*** 

(0.946708199) 
0.00082963*** 

(-1.056231161) 
0.00086158*** 

(2.148355505) 
0.00001024*** 

(1.523403063) 
0.00000002*** 

(0.778437494) 
0.00002074*** 

(0.897276963) 
0.00015000*** 

(1.397380759) 
0.00000475*** 

𝛽1,2 (-0.037826041) 
0.67119522 

(-0.335788406) 
0.00067670*** 

(-0.624162719) 
0.00473837*** 

(-0.101695146) 
0.08499822* 

(0.018345071) 
0.88795741 

(-0.138625772) 
0.47739579 

(0.411457872) 
0.00000000*** 

(0.115832802) 
0.15014561 

𝛽2,1 (-0.317796756) 
0.14723725 

(0.780934117) 
0.00551131*** 

(0.350511635) 
0.38615008 

(-0.670527977) 
0.01879517** 

(-0.655047913) 
0.08504442* 

(-0.373070609) 
0.34907580 

(-1.920249931) 
0.00000000*** 

(0.443486676) 
0.00673259*** 



Asian Business Research Journal, 2025, 10(7): 36-49 

45 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

 
 
Note(s): ***, **, * statistical significance at 1%, 5 and 10% levels, respectively. 

Period 4 : 2015-2019 befor COVID-19 

Countries Japan Mexico Russia South Africa South korea Turkey United Kingdom United States 

𝛼1,2 (-0.333458450) 
0.00000002*** 

(0.091482568) 
0.03534234** 

(-0.130495291) 
0.01709291** 

(0.052813212) 
0.54296427 

(-0.144886642) 
0.01331436** 

(0.204168958) 
0.01163456** 

(-0.090327266) 
0.01633754** 

(-0.184766909) 
0.00029484*** 

𝛼2,1 (1.260740101) 0.00010189*** (-1.067431292) 
0.01639655** 

(0.191924464) 
0.61075365 

(-1.839373478) 
0.00010838*** 

(-1.252690998) 
0.00082615*** 

(-0.585282064) 
0.04533999** 

(-0.122378159) 
0.87175971 

(2.162645372) 
0.00054887*** 

𝛽1,2 (-0.119562008) 0.10412902 (0.009211532) 
0.61907059 

(-0.032591921) 
0.81322184 

(0.150708089) 
0.06148021* 

(0.111312674) 
0.28305473 

(0.494753180) 
0.00000000*** 

(-0.182813531) 
0.00015196*** 

(-0.056742643) 
0.28453314 

𝛽2,1 (0.645365505) 
0.12287292 

(-0.395127970) 
0.02962722** 

(1.533809029) 
0.06161001* 

(-1.131276466) 
0.04189184** 

(1.491161958) 
0.00848143*** 

(1.287563812) 
0.00249113*** 

(2.550829986) 
0.00000140*** 

(2.585720915) 
0.00000100*** 

Period 5 : 2020 -2021  the COVID-19 

Countries Japan Mexico Russia South Africa South korea Turkey United Kingdom United States 

𝛼1,2 (-0.223829)   0.00000000*** (0.045375397) 
0.14050291 

(-0.452885299) 
0.00000000*** 

(-0.428430313) 
0.00000000*** 

(-0.144246)    
0.00000000*** 

(-0.323017340) 
0.00000000*** 

(-0.264343875) 
0.00000000*** 

(-0.022242536) 
0.00000000*** 

𝛼2,1 (2.400173) 
0.00000000*** 

(0.838961721) 
0.00056712*** 

(2.550117657) 
0.00000000*** 

(5.114567261) 
0.00000000*** 

(3.894208)      
0.00000000*** 

(3.780508237) 
0.00000000*** 

6.522721173) 
0.00000000*** 

(4.657193488) 
0.00000000*** 

𝛽1,2 (-0.006642)     0.00000000*** (-0.187096122) 
0.00022621*** 

(-0.075075329) 
0.00000000*** 

(-0.055198048) 
0.00000000*** 

(-0.003750)     
0.00000000*** 

(0.022203886) 
0.00024299*** 

(0.012380458) 
0.25204329 

(0.041809063) 
0.00000019*** 

𝛽2,1 (0.529698)     0.00000000*** (0.344380352) 
0.36964152 

(0.423503370) 
0.00000000*** 

(0.626387100) 
0.00000000*** 

(0.894262)   
0.00000000*** 

(-0.000035945) 
0.98698921 

(1.397264296) 
0.00001365*** 

(0.866032057) 
0.00000000*** 



Asian Business Research Journal, 2025, 10(7): 36-49 

46 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

During the subprime mortgage crisis, oil prices and stock markets in oil-exporting countries like Japan, 
Mexico, Russia, South Korea, the United States, Turkey, and South Africa were closely linked. This means that 
changes in one market often caused changes in the other. The strength of the country's economy influenced how 
this connection worked. Sometimes, a rise in oil prices led to a drop in stock prices, and vice versa.  However, the 
overall impact was similar across these countries during this period. 

Several factors contributed to this close relationship. The housing boom in the early 2000s created a positive 
atmosphere for global markets, including both oil and stocks.  This led to higher prices in both areas.  Additionally, 
events like the 9/11 attacks and the Iraq War caused uncertainty in all economies, leading to similar movements in 
stock markets and a stronger connection to oil prices. Finally, China's rapid economic growth and its impact on 
global trade created a sense of optimism in stock markets worldwide, regardless of the country's origin. During the 
subprime mortgage crisis, oil prices and stock markets generally moved in opposite directions for most oil-
exporting countries. The only exception was the United Kingdom.  

The global financial crisis of 2008-2009 had a similar impact on all stock markets, causing them to move 
together.  During this period, oil prices and stock markets generally moved in opposite directions, with both 
average prices and price fluctuations being negatively affected. The crisis was triggered by the widespread issuance 
of risky US mortgage loans, which led to a global financial shock. This shock can be seen as an oil price shock, as it 
reduced global demand for oil.  

After the subprime crisis, the sovereign debt crisis in Europe further impacted oil and stock markets. This 
crisis affected many European countries and led to a significant connection between oil prices and stock markets for 
most countries. 

This study looked at how oil price changes affect stock market volatility, both before and during the COVID-
19 pandemic. The results show that oil price volatility and stock market volatility are strongly connected, and this 
connection is even stronger during the pandemic. This means that changes in oil prices have a bigger impact on 
stock markets during the pandemic.  

The study found that the relationship between oil price volatility and stock market volatility is stronger during 
the pandemic than before. This suggests that the COVID-19 outbreak has made the global financial markets more 
interconnected and vulnerable to shocks.  Other studies have also found that the pandemic has increased the risk of 
financial contagion, meaning that problems in one market can quickly spread to others.This research aligns with 
previous studies that found a connection between oil market changes and emerging stock markets.  

Overall, our findings show that oil price volatility directly impacts stock market returns in many countries.  
The influence usually flows from oil to stocks, not the other way around. However, there are differences between 
countries, likely due to the varying economic situations of emerging markets. It's important to remember that this 
research was conducted during a period of significant financial instability. This means that the impact of oil on 
stock markets might have been stronger than usual due to the general uncertainty and volatility in the global 
economy. 
 

 

 
 



Asian Business Research Journal, 2025, 10(7): 36-49 

47 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

 

 
Figure 1. Dynamic Conditional Correlation between Oil Price and Stock Returns of Importing Countries. 

 

 

 

 



Asian Business Research Journal, 2025, 10(7): 36-49 

48 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

 
Figure 2. Dynamic Conditional Correlation between oil price and stock market returns for oil exporting countries. 

 
We studied how the oil price (WIT oil index) and stock markets in G20 countries moved together between 

2004 and 2021. This period included several major crises, like the 2008 financial crisis, the European debt crisis, 
and the COVID-19 pandemic. We looked at 16 G20 countries with available data, focusing on 8 oil-exporting and 8 
oil-importing countries. We used a statistical model called DCC-GARCH (1,1) to understand how the relationship 
between oil prices and stock markets changed over time. This model is helpful because it allows the volatility (how 
much prices change) and the correlation (how much they move together) to vary over time.  

Our results clearly show the impact of the major crises on both oil-exporting and oil-importing countries. We 
can see how these events affected the relationship between oil prices and stock markets. The 2008-2009 financial 
crisis was a major event that shook the world. It started with problems in the housing market in 2006, where many 
people couldn't pay their mortgages. This spread throughout the financial system, causing a global crisis.  One big 
effect was a drop in the prices of oil and natural gas. Oil went from $133.88 a barrel to $39.09, and natural gas went 
from $12.69 to $4.52. Looking at the period when the housing crisis was at its worst 2007, we see some interesting 
things. For countries that import oil, lower oil prices were good news. This is because they could buy oil cheaper, 
which helped their businesses and stock markets.  

On the other hand, countries that export oil were hurt by the lower prices. They made less money from selling 
oil, which negatively affected their stock markets. Market movements are interconnected, and their relationships 
change over time. During crises, like the 2010 European sovereign debt crisis, markets tend to move more closely 
together. This was also seen in the 1980s Latin American debt crisis, which had a lasting negative impact on the 
region. The current situation in Europe is concerning because it shares similarities with past crises.  One major 
worry is that countries that rely heavily on exports could face a high risk of default if oil prices fall. This is because 
lower oil prices often lead to higher interest rates, which can make it harder for these countries to manage their 
finances. 

During the European sovereign debt crisis (2010-2016), the gap between interest rates on government bonds 
in different European countries widened significantly. This happened at the same time as major events in the 
Middle East and a sharp drop in oil prices (almost 75%) between 2014 and 2015. After accounting for economic 
factors, our research shows that the widening of these interest rate gaps was strongly linked to increased demand 
for safe assets due to the instability in the Middle East and North Africa (MENA) region.   

The oil price crash also caused a decline in global demand, which had a negative impact on interest rate gaps, 
especially in countries on the edge of the Eurozone (EMU periphery). This is likely because these countries are 
more sensitive to disruptions in the oil market.  

Finally, our findings suggest that changes in the supply of goods and services had little impact on interest rate 
gaps during this period, except for some positive correlations in Belgium and France. The Arab Spring had a 
significant impact on oil prices. It caused people to buy more oil than usual, which is called a "precautionary 
demand shock," because they were worried about future supply disruptions. At the same time, actual problems with 
oil production in the region also led to supply shocks. Interestingly, only Belgium and France saw their bond prices 
change in response to these supply shocks. This is likely because they have strong trade relationships with oil-
producing countries in the Arab world. For example, France imported a huge amount of oil from North Africa and 
the Middle East in 2015.When oil prices dropped between 2014 and 2015, it was mainly due to a combination of 
factors: people buying less oil (aggregate demand shock) and problems with oil production (supply shock). During 
this time, bond prices didn't change much in response to the precautionary demand shock. However, they did move 
in the way we expected when oil prices fell due to lower demand.  The fact that bond prices didn't react much to 
supply shocks during this period suggests that these shocks weren't very important for financial markets. 

This study examined the relationship between crude oil prices and stock market prices before and during the 
COVID-19 pandemic. Using a technique called cross wavelet transform, the researchers found that oil prices and 
stock prices move together, especially in the short-term (high-frequency). This means that when oil prices go up, 
stock prices tend to go up as well, and vice versa. However, the study also found that this relationship was weaker 
in the long-term (low-frequency) during the pandemic. This suggests that the short-term connection between oil 
and stock markets became more important during the crisis.  

Another study by Salisu et al. (2020) found that oil prices influenced stock prices before the pandemic, but after 
the pandemic, the relationship became two-way. This means that both oil and stock prices affected each other. The 
study also noted that oil prices were more volatile than stock prices both before and during the pandemic. 
However, all stock markets showed positive returns, even during the crisis, and these returns were actually higher 
during the pandemic. 

  

6. Conclusion  
Oil prices have been very unpredictable lately, going up and down quickly. This is more extreme than we've 

seen since the oil crisis of 1979. These big changes in oil prices have a big impact on the economies and financial 
markets of countries that buy and sell oil. 



Asian Business Research Journal, 2025, 10(7): 36-49 

49 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

The oil industry is very important and affects many markets, especially the financial market.  The financial 
market also affects the oil market, so they are connected. This chapter looks at how the financial markets of 
countries that export and import oil, as well as the financial markets of the G20 countries (which include the 
biggest oil producers and consumers), are all connected. The goal is to understand how much the price of oil can 
make stock markets in some G20 countries more likely to have problems. 

 the study found that both oil and stock prices had unusual patterns, with more extreme values than expected 
(asymmetry and leptokurtosis). This research investigated how changes in oil prices affect stock markets in 16 G20 
countries. We used two different models, BEKK-GARCH and DCC-GARCH, to analyze this relationship. To get a 
clearer picture, we divided the study period into five smaller periods and separated the countries into oil exporters 
and importers. This helped us understand how oil price volatility impacts the economies of major oil producers and 
consumers differently. 

Our analysis revealed that the relationship between oil prices and stock returns is dynamic and changes over 
time. We observed that the correlation between stock returns tends to increase during periods of crisis. 

Furthermore, we found strong evidence of a direct transmission of volatility between oil and stock markets in 
many of the countries studied.  Generally, shocks and volatility tend to flow from oil markets to stock markets 
more often than the other way around. However, there are differences between countries, which is expected given 
the diverse nature of their economies. 

 

References   
Abdulrahaman. (2020). Long run association of stock prices and crude oil prices: Evidence from Saudi Arabia. International Journal of Energy 

Economics and Policy. 
Arouri, M. E. H., & Nguyen, D. K. (2010). Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over 

the last decade. Energy Policy, 38(8), 4528–4539. 
Baba, Y., Engle, R. F., Kraft, D., & Kroner, K. (1990). Multivariate simultaneous generalized ARCH. [Working paper or journal name 

needed if available]. 
Bouri, E. (2015). Oil volatility shocks and the stock markets of oil-importing MENA economies: A tale from the financial crisis. Energy 

Economics, 51, 590–598. 

Büyükşahin, B., & Robe, M. A. (2014). Speculators, commodities and cross-market linkages. Journal of International Money and Finance, 42, 
38–70. 

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. 

Du, L., & He, Y. (2015). Extreme risk spillovers between crude oil and stock markets. Energy Economics, 51, 455–465. 
Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122–150. 
Filis, G., Degiannakis, S., & 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(3), 152–164. 
Ghorbel, A., & Boujelbene, Y. (2013). Contagion effect of the oil shock and US financial crisis on the GCC and BRIC countries. International 

Journal of Energy Sector Management, 7(4), 430–447. 
Guesmi, K., & Fattoum, S. (2014). Return and volatility transmission between oil prices and oil-exporting and oil-importing countries. 

Economic Modelling, 38, 305–310. 
Horobet, A., Dumitrescu, D. G., Belascu, L., & Popescu, D. M. (2019). Oil price and stock prices of EU financial companies: Evidence from 

panel data modeling. Energies, 12(6), 1112. 
Khan, K., Ahmed, M., & Umar, M. (2020). Do crude oil price bubbles occur? Resources Policy, 65, 101553. 
Kilian, L., & Park, C. (2009). The impact of oil price shocks on the US stock market. International Economic Review, 50(4), 1267–1287. 

Lin, B., & Zhu, J. (2019). Determinants of renewable energy technological innovation in China under CO₂ emissions constraint. Journal of 
Environmental Management, 247, 662–671. 

Malik, F., & Ewing, B. T. (2009). Volatility transmission between oil prices and equity sector returns. International Review of Financial 
Analysis, 18(3), 95–100. 

Nazlioglu, S., Soytas, U., & Gupta, R. (2015). Oil prices and financial stress: A volatility spillover analysis. Energy Policy, 82, 278–288. 
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. 
Park, J., & Ratti, R. A. (2008). Oil price shocks and stock markets in the US and 13 European countries. Energy Economics, 30(5), 2587–2608. 
Roberto, C. C., da Silva, F. J., & Garcia, M. T. M. (2017). Impact of oil prices on stock markets in major Latin American countries (2000–

2015). International Journal of Energy Economics and Policy, 7(5), 152–160. 
Salisu, A. A., Akanni, L., & Raheem, I. D. (2020). The COVID-19 global fear index and the predictability of commodity price returns. Journal 

of Behavioral and Experimental Finance, 27, 100371. 
Tsuji, C. (2018). Return transmission and asymmetric volatility spillovers between oil futures and oil equities: New DCC-MEGARCH 

analyses. Economic Modelling, 74, 167–185. 
Un, K., & Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business & 

Economic Statistics, 20(3), 339–350. 
Wen, X., Wei, Y., & Huang, D. (2012). Measuring contagion between energy market and stock market during financial crisis: A copula 

approach. Energy Economics, 34(5), 1435–1446. 

 


