Oil Price Volatility and MENA Stock Markets: A Comparative Analysis of Oil Exporters and Importers ^†

Abstract

This paper explores the transmission of volatility from Brent oil price evolution to the stock returns of 7 MENA countries, encompassing three importers and four exporters, after excluding four initial countries using the ARCH test. Employing the GARCH-BEKK estimation method, we detect this transmission from January 2008 to September 2022. The results reveal significant volatility persistence across six stock markets with three importer countries and three exporters. These findings align with Shiller’s theory, indicating high volatility in financial markets. Tunisia’s stock market shows sensitivity to oil market developments, while the Omani market demonstrates volatility transfer from Brent oil prices. However, Morocco’s market exhibits resilience, with no significant transmission from international oil prices. Exporting countries, except the UAE, display significant and positive coefficients, indicating volatility transmission. The study suggests further research into underlying mechanisms and recommends policymakers and investors implement strategies to mitigate volatility effects. Advanced modeling and behavioral insights can enhance risk management strategies.

1. Introduction

The global oil market is a crucial element of the global economy, impacting various sectors from the chemical industry to energy production. This strategic resource lies at the heart of global economic and geopolitical concerns. However, since the 1970s, this market has been subject to constant turbulence, often dictated by factors related to supply and demand, shaping economies on an international scale.

Middle Eastern countries, particularly those OPEC members, hold a central position in oil production and distribution, while major consumers are spread across developed and emerging countries, including China. The International Energy Agency forecasts a continuous increase in global oil demand, highlighting the enduring importance of this resource.

The evolution of oil prices since 2003, marked by notable fluctuations, profoundly impacts macroeconomic dynamics and arouses keen interest among economists. This inherent volatility also affects financial markets, prompting researchers to explore the transmission of this volatility.

Financial markets play a crucial role in financing the global economy. Fluctuations in stock prices raise questions about their origins, particularly concerning their relationship with the oil market. Studies have emphasized the influence of local, regional, and global factors, including the oil market, on stock markets.

The interplay between the oil market and the financial market has been a focal point of research, emphasizing how fluctuations in oil prices affect different stock indices. Notable contributions to this field include studies by [1,2,3,4].

These studies have also delved into the concept of “volatility transmission”, examining how oil price fluctuations impact stock returns. This perspective deepens our understanding of the interactions between the global oil market and stock markets.

In this context, our study aims to model the transmission of oil market volatility to the stock markets of MENA countries. We conduct a comparative analysis between oil-exporting and oil-importing nations, investigating the unique dynamics of these markets within a complex regional framework.

The critical importance of oil in MENA economies, where reliance on oil exports and imports is prevalent, motivates our investigation. We seek to answer the following questions: Are fluctuations in oil market prices and MENA stock market indices volatile? Is there a strong enough correlation between these two types of markets to lead to volatility transmission?

This article makes significant contributions to academic literature. Firstly, it focuses on a particularly crucial region, namely MENA countries, where oil is one of the major pillars of the economy. This study, therefore, takes a specific regional perspective, offering insights into the economic and financial dynamics of this region.

Secondly, our research delves deep into the complex phenomenon of oil market volatility transmission to the stock market of MENA countries. This analysis helps better understand the mechanisms of spreading oil price fluctuations to regional financial markets. To our knowledge, our study is the first systematic attempt to investigate this phenomenon in the specific context of MENA countries, adding an innovative dimension to existing research.

Finally, we provide a comprehensive comparative analysis between oil-exporting and oil-importing countries within the MENA region. This comparative approach highlights the differences and similarities in volatility transmission between these two groups of countries. By examining these nuances, our study offers valuable insights for policymakers, investors, and researchers interested in the interactions between the oil market and stock markets in MENA economies.

Our research makes an original contribution to understanding the complex links between the oil market, stock markets, and the economies of MENA countries, laying the groundwork for future studies in this constantly evolving field.

To ensure a coherent and organized presentation, this article is structured into several distinct sections.

In Section 2, we delve into a comprehensive review of both theoretical and empirical literature pertaining to the oil market and the stock market. This section serves as the cornerstone for our study, as it aims to establish a robust foundation by thoroughly exploring previous research works and identifying key theories and findings in this area of study.

Section 3 is dedicated to examining the transmission of volatility between oil markets and stock markets. Here, we further elaborate on the theoretical and empirical literature review, with a specific focus on elucidating the mechanisms and models that govern this volatility transmission. Our analysis encompasses a scrutiny of the various methodological approaches utilized in the literature to evaluate this transmission, thereby shedding light on current trends and identifying gaps in research.

Section 4, titled “Presentation of the Sample and Variables and Descriptive Statistics”, provides an overview of our research methodology. We detail our modeling approach, employing a specific ARCH modeling, notably GARCH BEKK, to quantify the volatility transmission from the oil market to the stock markets of MENA countries. This section offers an in-depth explanation of our analytical framework, variables of interest, and statistical methodology.

In Section 5, we present the results of the GARCH and GARCH-BEKK modeling estimations and discuss these findings for both oil-importing and oil-exporting countries. This section is the centerpiece of our analysis, where we interpret the implications of our results and evaluate their significance in the context of our research objectives.

Finally, Section 6 encapsulates the paper with a summary of key outcomes and offers recommendations for future research directions. This section serves as a conclusion, highlighting the main insights gleaned from our study and suggesting potential avenues for further exploration in this field.

2. Petroleum Market and Stock Market: Theoretical and Empirical Literature Review

The oil market and the stock market are two separate entities. The oil market focuses on commodities, involving the production, distribution, and sale of crude oil and petroleum products. In contrast, the stock market is a financial arena where investors buy and sell shares of publicly traded companies.

Numerous theoretical studies have investigated the relationship between crude oil and the stock market. Some have concentrated on the effects of crude oil price fluctuations on stock markets, while others have examined the linkage between oil prices and stock indices. Additionally, another group of researchers has explored the asymmetric causality between oil prices and stock returns. The following sections will summarize these different theoretical studies.

2.1. Crude Oil Price and Stock Markets

Researchers are encouraged to investigate the relationship between crude oil prices and the stock market, especially focusing on oil price volatility and its market impact, because theoretically, stock values represent the sum of expected future cash flows discounted to their present value. In practice, these cash flows are affected by macroeconomic variables, which are, in turn, influenced by fluctuations in oil prices.

Several articles have explored the relationship between oil prices and the stock market, including works by [3,5,6,7,8]. These studies aimed to determine whether there was a significant short-term relationship between oil prices and stock market performance. Ref. [9] investigated the long-term linear and nonlinear relationships between oil prices and stock prices across various sectors, focusing on the macroeconomic level rather than the overall market. They identified the effect of oil price fluctuations as a crucial channel through which oil price changes impact stock prices.

Other studies have shown that the relationship between oil prices and economic activity is nonlinear. Negative shocks to oil prices (price increases) generally have a greater impact on economic growth than positive shocks. This conclusion is supported by the research of [10,11,12,13,14,15].

2.2. Effects of Oil Price Fluctuations on Stock Markets in Gulf Countries

Several studies have examined the relationship between oil prices and economic activity, indicating that oil price shocks significantly impact macroeconomic variables in most developed and emerging countries [13,14,15,16,17,18,19]. While the relationship between oil prices and stock markets has garnered considerable interest, emerging market stock markets have often been overlooked. Additionally, research has specifically analyzed the short-term interaction between oil price shocks and stock returns.

The groundbreaking article by [3] investigates the short-term effects of crude oil price shocks on four developed stock markets: Japan, the United Kingdom, the United States, and Canada. Their findings reveal that fluctuations in crude oil prices affect financial asset returns in Canada and the United States. Conversely, ref. [20] analyzed how stock markets respond to crude oil price changes, differentiating between demand shocks and supply shocks in the oil market. Ref. [21] examined the long-term relationship between the stock markets of Gulf Cooperation Council (GCC) countries, considering the impacts of three global risk factors: US Treasury bond yields, crude oil prices, and the US stock market (S&P 500). Their results show that US Treasury bond yields have a direct effect on these markets, while the impact of crude oil prices and the S&P 500 is weaker and more indirect. Nevertheless, recent data indicate that, in the long term, crude oil price fluctuations significantly impact stock prices in GCC countries.

2.3. Interdependence between Oil Prices and Stock Indices

The relationship between crude oil and stock performance is significant, as oil is a major input that directly affects firms’ cost structures. This, in turn, impacts their profitability and stock prices. Specifically, cost increases resulting from higher oil prices lead to profit reductions, which decrease anticipated profits and ultimately result in a decline in stock prices [22,23,24]. Empirical evidence supporting these hypotheses has been found in studies conducted in the UK by [25] and in Greece by [26]. These studies demonstrate that oil shocks have a negative and moderate impact on stock returns in sectors unrelated to oil or gas, while companies in the oil and gas sector experience a positive effect on their stock returns due to increased crude oil prices.

The relationship between crude oil prices and stock market returns is a well-studied topic, with numerous studies examining the interdependence between the stock returns of companies heavily linked to the oil sector and crude oil prices. These studies include those by [6,24,25,26,27,28,29,30,31,32,33]. These studies have consistently shown that individual stock returns are influenced by fluctuations in crude oil prices, as indicated in the works of [3,5,8]. Additionally, researchers such as [26,33,34] have observed that movements in crude oil prices play a significant role in analyzing aggregate stock returns. Furthermore, examining the nonlinear or asymmetric relationship between crude oil prices and the economy, similar to stock markets, has revealed the existence of a nonlinear relationship between these two variables. For example, an increase in crude oil prices can have more damaging consequences on the US economy and financial markets than its decrease, as highlighted by [7,10,35,36]. Regarding the asymmetric impacts of crude oil price fluctuations on the stock returns of the Gulf Cooperation Council (GCC), they have been studied by [37]. It has also been established that crude oil price variations are influenced by exchange rate movements, as evidenced by the work of [12,38].

2.4. Asymmetric Causality between Oil Prices and Stock Returns

Several studies have delved into the issue of asymmetrical causality between oil prices and stock returns. For example, ref. [24] examined the presence of asymmetric effects of oil price fluctuations on stock market returns using asymmetric GARCH models. Their findings suggest that negative changes in oil prices exert a more pronounced influence on stock returns than positive changes, indicating differing reactions of stock markets to oil price increases versus decreases. Additionally, research by [2,20] has identified evidence of asymmetric impacts of oil price movements on stock returns across various countries and regions. These studies underscore the significance of incorporating asymmetrical effects into analyses of the oil price–stock return relationship.

The literature, both theoretical and empirical, on the correlation between crude oil prices and stock markets is extensive and diverse. While some studies have identified significant connections between the two variables over both short and long terms, others have underscored the presence of asymmetries and nonlinearities in their interactions. Grasping these dynamics is crucial for investors, policymakers, and researchers aiming to navigate the intricate terrain of financial markets influenced by energy price fluctuations.

3. Volatility Transmission between Oil and Stock Markets: Theoretical and Empirical Literature Review

The concept of volatility transmission between oil markets and stock markets refers to how fluctuations in oil prices can impact price fluctuations in financial markets, particularly the stock market. This interconnection can be complex and is influenced by various factors such as underlying conditions in the oil market, the global economic context, geopolitical considerations, and investor attitudes.

Oil price fluctuations have garnered global interest in recent years due to marked changes in commodity prices. Between 2008 and 2009, significant volatility in oil prices raised many questions regarding the determinants of oil prices and the complexity of relationships between physical and financial markets. Several fundamental economic factors explain this volatility, including increasing demand from emerging countries, the limited production capacity of producing countries to increase production, and political tensions in certain key regions. Additionally, financial factors also contribute to volatility, such as exchange rates, interest rates, the rise of oil-related financial products, the entry of new players into markets, investor herd behavior, arbitrage operations between spot and futures markets, market participant forecasts, and speculative funds.

The relationship between oil prices and financial markets has attracted the interest of numerous researchers, resulting in abundant literature on the subject. The pioneering work of [3] sought to answer whether financial market reactions to oil shocks were primarily due to the anticipation of reduced cash flows or revenues. Their results suggested that the effects of oil shocks on financial markets in the United States and Canada after World War II were primarily related to anticipated reductions in cash flows. Other researchers, such as [1], have shown that rising oil prices have a depressive effect on stock markets. They also highlighted the risk of speculative bubbles related to high oil prices and their impact on market instability. Finally, ref. [39] linked the last recession of the US economy in 2007–2008 to the rise in oil prices.

The literature extensively investigates the interconnectedness between oil and stock markets, emphasizing the transmission of volatility and shocks between these two key financial domains. Ref. [40] scrutinized the transmission of volatility and shocks between oil prices and stock returns across five major market sectors. Their findings unveiled the presence of shock and volatility transmission between oil prices and specific market sectors. Similarly, ref. [21] analyzed data spanning from 1990 to 2006 to explore the volatility of oil and industrial commodity markets in relation to the stock market. Their examination revealed both high and low volatility regimes among the prices of five commodities and the S&P 500 index. Employing a CCC-GARCH model, they illustrated that correlations among commodities surged post the Iraq war, while correlations with the US S&P 500 stock index decreased. Furthermore, ref. [2] delved into the transmission of returns and volatility between global oil prices and stock markets in Gulf Cooperation Council (GCC) countries from 2005 to 2010. Their research showcased significant volatility effects between oil prices and stock markets in certain GCC countries, with positive impacts observed on the stock markets of Qatar and Oman, but negative impacts noted on the stock market of Bahrain. Volatility transmission was more prominently observed from oil markets to stock markets.

Furthermore, several studies have delved into the complexity of relationships between stock markets and commodity markets, including oil. Ref. [41] used a dynamic conditional correlations (DCC-GARCH) model to analyze the volatility of prices of 25 commodities from different sectors over a period from 3 January 2001 to 28 November 2011. They found that correlations between stock markets and commodity markets are highly volatile. Ref. [42] examined commodity markets and stock markets using a VAR-GARCH model applied to daily data from 3 January 2000 to 31 December 2011. Their results highlighted significant correlations and volatility transmission between commodity markets and stock markets. Ref. [43] employed multivariate GARCH models (BEKK and DCC) to analyze monthly data on crude oil prices and the stock indices of the United States, GCC member countries, as well as Brazil, Russia, India, and China (BRIC) from May 2005 to December 2011. Their results showed a permanent level of common volatility between crude oil markets and the mentioned stock markets. Ref. [44] shed light on the influence of the US financial market on the oil market and other financial markets of oil-producing countries such as Russia, Kuwait, Indonesia, and Venezuela. This close relationship between physical markets and financial markets was studied using a trivariate GARCH (BEKK) model, which is of great importance for the development of asset valuation models in these countries as well as for forecasting oil prices and volatility. Ref. [45] addressed the issue of the financialization of commodities, prompted by the abundance of investment capital in commodity future markets over the past decade. They questioned how financial investors influence information search in commodity markets and risk allocation, concluding that this financialization has significantly altered commodity markets. Furthermore, ref. [46], based on BEKK and CCC models applied to daily data from 2009 to 2014, found that the volatility of crude oil future contracts impacts the volatility of carbon emission future contracts.

Moreover, a series of studies have explored financialization as a factor influencing prices, volatility, and the correlation of commodity futures contracts. Ref. [47] showed that financial markets transfer shocks not only to futureprices but also to the spot prices of commodities and stocks. Ref. [48] used a VAR model to examine the impact of fund managers, index investors, and macroeconomic variables on the prices of coffee, cotton, wheat, and oil. They found that, unlike index investors, net long positions of fund managers have a significant impact on commodity prices, which may hinder the fundamental role of commodity derivative markets. Conversely, ref. [49] looked at the volatility between oil future contracts and oil company stocks in oil-producing countries. Using the DCC-MEGARCH model applied to the daily data of oil and gas stock indices from the United States, Russia, Australia, and Canada from January 2000 to 15 August 2017, they observed a unidirectional transmission of the volatility of crude oil future returns to oil company stocks. Mean while, ref. [50] analyzed directional impacts and the temporal relationship between oil markets, gold, and stock markets before and during the COVID-19 pandemic period. They found that before the pandemic, the S&P 500 series and crude oil were net risk receivers, although gold triggered the shock. Conversely, during the pandemic, crude oil and S&P 500 markets were the transmitters.

3.1. Empirical Examination of Transmission Direction

In this section, we outline empirical investigations focusing on the transmission of volatility between the two variables under scrutiny. Below, we provide a synopsis of seven studies that explore volatility transmission between oil prices and stock indices, elucidating the direction of this transmission.

3.1.1. Unidirectional Transmission

Among the four studies identifying unidirectional volatility transmission, ref. [51] investigated oil prices and six major global financial markets (France, USA, Germany, Japan, UK, and Canada). Their analysis involved two indicators for oil prices (West Texas Intermediate and Brent). Results indicated a positive transmission of volatility from the stock index to the global oil price (WTI), except for the USA, where the transmission direction was reversed. Ref. [40] also explored volatility transmission between oil prices and stock indices, focusing on five different sectoral indices in the USA: finance, industry, consumption, health, and technology. Utilizing weekly data from 1992 to 2008, they observed volatility transmission from the financial, industrial, and consumption sectors to oil prices. However, the transmission direction was reversed for health and technology sector volatilities. Ref. [52] centered their analysis on oil prices and the stock prices of oil and gas companies in the USA, India, and the UK. Employing daily data from 2003 to 2008 and a GJR-GARCH model, they uncovered volatility transmission in each of these countries, from oil prices to the stock prices of these companies.

3.1.2. Unidirectional and Bidirectional Transmission

Among the empirical investigations yielding results indicating both unidirectional and bidirectional transmissions, refs [2,53] emerge as notable studies. Ref. [2] conducted an analysis encompassing oil prices and European and American sectoral stock indices, spanning automotive, financial, industrial, technology, basic materials, telecommunications, and utilities sectors. Their study utilized weekly data from 1998 to 2009, employing a VAR-GARCH model. Findings revealed unidirectional volatility transmission from oil prices to industrial sector indices in Europe, while in the USA, the transmission was bidirectional. However, the intensity of this transmission varied across sectors, attributed to sector-specific factors such as oil dependence, consumption, competition, and concentration.

Ref. [53] directed their research towards emerging and developed financial markets, employing a broader sample than previous studies. They considered the national indices of each country (21 countries) for emerging markets, along with the “MSCI frontier markets” index designed for measuring the performance of emerging stock markets. For developed markets, the “MSCI world” index was utilized. Results unveiled significant volatility transmission between oil prices and stock markets, predominantly from oil prices to financial markets. In certain emerging market economies like Jordan, Oman, Kazakhstan, Kuwait, and the United Arab Emirates, bidirectional transmission was observed. Notably, this study covered the period from 2008 to 2013, characterized by financial market turmoil following the 2008 financial crisis. The authors suggested that cyclical factors associated with this tumultuous period may have influenced volatility transmission outcomes, as contagion or transmission effects tend to be amplified during crises.

3.1.3. Bidirectional Transmission

Ref. [54] investigated the dynamic effects of returns and volatility between oil and stocks in Gulf Cooperation Council (GCC) countries over the period 2004–2012. Their findings unveiled the presence of bidirectional and asymmetric transmissions between these two markets. The authors attributed this asymmetry to the fact that an upsurge in oil prices tends to result in heightened income and wealth in oil-exporting nations, thereby stimulating economic activity and financial markets. Consequently, when examining these variables from an income perspective, the causality is observed from oil prices to financial markets. This elucidates why the transmission is more pronounced from oil prices to the stock market compared to the reverse direction.

3.1.4. No Transmission

Ref. [55] conducted a study focusing on Iran and found no evidence of volatility propagation between oil prices and the Iranian stock market. The author suggested that this lack of volatility transmission indicates the Iranian stock market’s sustainable long-term performance and relative immunity to external shocks, particularly oil-related shocks. This observation could be interpreted as a positive indicator for foreign investors and international portfolio managers.

It is crucial to acknowledge that research findings on volatility transmission between oil prices and financial markets exhibit significant variability. There is not a consistent direction systematically associated with the characteristics of a country or a specific category of countries. For instance, studies by [51,53,55], demonstrate different transmission directions, even among oil-exporting nations like Canada, Iran, and Kuwait. Thus, it is evident that results differ among countries sharing the common trait of oil exports. However, it is noteworthy that when transmission is unidirectional, it tends to be from oil prices to the stock market index.

4. Presentation of the Sample and Variables

The datasets utilized in this study comprise the monthly evolution of global Brent oil prices and the stock index returns of seven net oil-importing countries and four net oil-exporting countries in the MENA region. The study period spans from January 2008 to September 2022, encompassing countries such as Tunisia, Morocco, Lebanon, Jordan, Egypt, Bahrain, and Oman as net oil importers, and Kuwait, Saudi Arabia, the UAE, and Qatar as net oil exporters. The data source was collected from the website investing.com.

The estimation of this relationship is based on two variables: the changes in returns of the stock market indices (Re) and the evolution of Brent oil prices (OPE).

$$OPE={\displaystyle \frac{P_t-P_{t-1}}{p_{t-1}}}$$

(1)

With $P$: Brent oil prices

Descriptive Statistics

The descriptive statistics of the two variables used, namely the stock market index returns and the evolution of Brent oil prices, for both net oil-importing and net oil-exporting countries in the MENA region, are presented in

Table 1. Descriptive statistics.

	Mean	Max	Min	Std.Dev	Skewness	Kurtosis	Jarque_Bera
MENA countries importing oil	−0.0001	0.146	−0.199	0.028
Tunisia: Tunindex	0.003	0.039	−0.062	0.015	−0.6	4.986	39.631 ***
Morocco: Masi	0.003	0.094	−0.145	0.040	−0.490	3.944	13.607 ***
Lebanon: BLSI	−0.001	0.146	−0.084	0.024	1.4	13.822	921.711 ***
Jordan: MSCI	−0.004	0.102	−0.199	0.027	−2.176	19.991	2256.6 ***
Egypt: EGX30	−0.0001	0.135	−0.175	0.039	−0.478	5.951	70.583 ***
Bahrain: BAX	−0.0009	0.035	−0.090	0.015	−1.529	9.615	391.784 ***
Oman: MSCI	−0.0014	0.055	−0.154	0.025	−2.043	12.638	771.914 ***
MENA countries exporting oil	−0.0001	0.176	−0.142	0.030
Saudi Arabia: TASI	0.00008	0.078	−0.129	0.028	−0.914	5.641	76.103 ***
Kuwait: BKP	−0.001	0.075	−0.142	0.025	−5.862	54.797	14,522 ***
Qatar MSCI	0.0002	0.176	−0.134	0.033	−1.743	7.697	206.534 ***
UAE: MSCI	0.0004	0.176	−0.094	0.038	1.254	7.348	177.505 ***
Evolution of Brent oil prices	0.007	0.598	0.112	0.112	0.102	9.038	273.229 ***

Notes: *** Statistical significance at 1%.

.

This preliminary table offers a comprehensive descriptive analysis of the main variables under investigation. It furnishes valuable insights into various statistical measures such as the mean, maximum, minimum, standard deviation, Skewness, Kurtosis, and Jarque-Bera statistics for each variable across individual countries as well as for different country groups. By presenting these statistics, the table aids in gaining a deeper understanding of the sample’s characteristics and distributions. This information is instrumental in discerning the key features and trends within the observed dataset, providing a foundational understanding for further analysis and interpretation.

The average statistic shows that the Moroccan stock index yield is relatively higher compared to a lower value in Jordan, which is one of the net oil importers in the sub-sample. Moreover, the stock index yields in the net oil-importing region seem less volatile, although in Morocco, the standard deviation value is higher than that of Jordan and even other countries, indicating that the dispersion around the mean is more pronounced in this country. Furthermore, the summary statistics of the monthly global oil price evolution in terms of the average is 0.007 for Brent.

The detected non-normality is crucial for GARCH modeling, suggesting that approaches suitable for asymmetric distributions and heavy tails are necessary for a more accurate analysis of yields and price fluctuations. These results underline the complexity of the dynamics underlying yields and encourage a more sophisticated methodological approach to capture the nuances of this financial data.

To visually inspect any possible co-movement between oil price evolution and stock index yields, however, we plot the oil price evolution against each of the stock index yields (see Figure 1 and Figure 2). The interaction between stock index yield and oil price evolution seems evident at the level of these countries and proves that the stock markets of importing countries as well as exporting countries are affected by crises:the financial crisis of 2008, COVID-19 in 2020, and the energy crisis of 2022. Furthermore, remarkable volatility in the oil market occurred throughout the study period.

5. Methodology

The econometric methodology to be adopted comprises three phases. To analyze the correlation between the yield series and co-movements, the first step is to study the correlation using the ordinary parametric Pearson test and the non-parametric Spearman test. Through correlation analysis, we can measure and compare the movements of the yield series as well as the ranking in relation to their relationship. Subsequently, we are called to use the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to measure the magnitude of the volatility of the selected series’ yields. However, the GARCH(1,1) model is the most relevant design among the models belonging to the volatility family [56]. The latter was applied by [57] to estimate the volatility returns of our sample, which includethe stock market yields of 11 countries, including 7 oil importers (Tunisia, Morocco, Lebanon, Jordan, Egypt, Bahrain, Oman) and 4 exporters (Saudi Arabia, Kuwait, Emirates, and Qatar), as well as the evolution of the global Brent oil price. However, the GARCH(1,1) model indicates the conditional variance, which is considered as a linear function of its own lags. Furthermore, the conditional variance of all variables is required to depend on different lags. Moreover, the first lag of squared residuals will generate a mean equation and provide information about the volatility of the previous period. In practical terms, it is the mean equation and the variance equation that characterize the GARCH(1,1) model, which, respectively, take the following forms:

$$r_t=\mathit{\mu }+{\epsilon }_t$$

(2)

$${\sigma }_t^2=\omega +\alpha {\epsilon }_{1_{t-1}}^2+\beta {\sigma }_{1_{t-1}}^2$$

(3)

Furthermore, the mean equation indicates that the series yields over time are defined by the sum of the average yields marked by $\mu$. Meanwhile, the residual yields have been marked by ${\epsilon }_t$. Moreover, the assumptions of the variance equation prove that the value of the constant is $\omega$ greater than zero, accompanied by values $\alpha$ and $\beta$. The third phase involves estimating the GARCH model using the Baba-Engle-Kraft-Kroner (BEKK) parameterization determined by [58], which is presented as follows:

$$H_t=C^{\prime }C+{\displaystyle \sum _{k=1}^K{\displaystyle \sum _{i=1}^q{A}_{ik}^{\prime }{\epsilon }_{t-i}{\epsilon }_{t-i}^{\prime }{A}_{ik}+{\displaystyle \sum _{k=1}^K{\displaystyle \sum _{j=1}^P{B}^{\prime }{H}_{t-j}{B}_{jk}}}}}$$

(4)

During the estimation of the GARCH(1,1) BEKK model, the model transforms as follows:

$$H_t=C^{\prime }C+A_{ik}^{\prime }{\epsilon }_{t-i}{\epsilon }_{t-i}^{\prime }{A}_{ik}+B^{\prime }{H}_{t-j}{B}_{jk}$$

(5)

It is worth noting that in the BEKK model, the matrix of conditional covariances is defined as positive by construction. It helps indicate how shocks are transmitted among various markets over time. This type of model is preferred for its robustness.

To study the transmission of volatilities from oil markets to stock markets for MENA countries, the selected systems are presented as follows. The choice of endogenous and exogenous returns for each equation is explained by the abundance of oil price movements in these countries on the one hand and the specificity of the relationships between these stock indices on the other hand.

$${\mathrm{Re}}_i={\alpha }_0+{\alpha }_{1}OPE+{\epsilon }_i$$

(6)

6. Results and Discussion

In the presentation of our findings, we adhere to a structured approach. Initially, our focus is on assessing the relationship between stock index returns and the fluctuation in Brent oil prices. To achieve this, we employ both parametric (Pearson) and non-parametric (Spearman) correlation tests. These tests serve to provide complementary perspectives on the associations between the variables under scrutiny.

Table 2. Results of the correlation test between oil price evolution and stock index returns.

Country	Index	Pearson	Spearman
MENA countries importing oil
Tunisia	TUNINDEX	0.165 **	0.079
Morocco	MASI	0.022	0.051
Lebanon	BLSI	0.205 ***	0.190 **
Jordan	MSCI	0.153 **	0.139 *
Egypt	EGX30	0.172 **	0.072
Bahrain	BAX	0.308 ***	0.124 *
Oman	MSCI	0.384 ***	0.271 ***
MENA countries exporting oil
Saudi Arabia	TASI	0.300 ***	0.247 ***
Kuwait	BKP	0.041	0.180 **
Qatar	MSCI	0.255 ***	0.241 ***
UAE	MSCI	0.017	0.011

Notes: *** Statistical significance at 1%, ** Statistical significance at 5%, * Statistical significance at 10%.

summarizes the outcomes of these correlation tests across the countries included in our analysis. The results reveal a consistent pattern of positive correlations between stock index returns and changes in Brent oil prices across the examined countries. This indicates that, in general, as the prices of Brent oil fluctuate, so too do the returns on the respective stock indices.

Importantly, the significance of these correlations varies across the countries and the tests conducted. However, it is noteworthy that in at least one of the tests, the correlations emerge as statistically significant. This implies that the relationship between stock index returns and Brent oil prices is not merely coincidental but rather demonstrates a meaningful statistical association.

Overall, these findings underscore the interdependence between oil markets and stock markets within the MENA region. Understanding and analyzing these correlations can provide valuable insights for investors, policymakers, and researchers seeking to comprehend the dynamics of financial markets and their responses to changes in oil prices.

Additionally, Figure 1 and Figure 2 illustrate clustering volatility, wherein periods of substantial changes are followed by other significant changes, and periods of minor changes are succeeded by further minor changes in both stock market returns and oil price evolutions. To assess the normality of the data, we conducted a Jarque-Bera normality test. Results presented in Table 1 indicate that none of the series of stock market returns and oil price evolutions follow a normal distribution, leading to the rejection of the null hypothesis of normality at the 1% significance level. Moreover, skewness values suggest that virtually all series exhibit symmetry. The kurtosis values, exceeding 3, confirm that the distributions are leptokurtic, characterized by a wider or flatter shape with more significant tails, thus increasing the likelihood of extremely positive or negative events.

Subsequent to this analysis, we observed clustering characteristics within our volatility series. The next step involved testing for the ARCH effect using the ARCH heteroskedasticity test to determine the suitability of GARCH models. Results in

Table 3. ARCH test results.

Country	Index	F-Statistic	ARCH Effect
MENA countries importing oil
Tunisia	TUNINDEX	6.532 **	ARCH
Morocco	MASI	16.356 ***	ARCH
Lebanon	BLSI	0.147	NO ARCH
Jordan	MSCI	0.011	NO ARCH
Egypt	EGX30	1.864	NO ARCH
Bahrain	BAX	0.471	NO ARCH
Oman	MSCI	3.929 **	ARCH
MENA countries exporting oil
Saudi Arabia	TASI	6.031 **	ARCH
Kuwait	BKP	37.839 ***	ARCH
Qatar	MSCI	48.535 ***	ARCH
UAE	MSCI	37.419 ***	ARCH
Brent	OPE	141.749 ***	ARCH

Notes: *** Statistical significance at 1%, ** Statistical significance at 5%.

indicate that evidence of an ARCH effect is present only for seven stock market returns, specifically for importing countries like Tunisia, Morocco, and Oman, and for exporting countries such as Saudi Arabia, Kuwait, Qatar, and the Emirates, along with the evolution of oil prices. This implies the acceptance of the alternative hypothesis of heteroskedasticity.

Next, we proceed to estimate ARCH and GARCH models for our eight series. Based on the results presented in

Table 4. Results of GARCH (1,1) model estimation.

Country	Index	$\widehat{\mathit{\mu }}$	$\widehat{\mathit{\omega }}$	$\widehat{\mathit{\alpha }}$	$\widehat{\mathit{\beta }}$
		MENA countries importing oil
Tunisia	TUNINDEX	0.003 **	0.00005	0.143	0.645 ***
Maroc	MASI	0.005 *	0.0006	0.125 **	0.448
Oman	MSCI	−6.38 × 10−5	0.0003 ***	0.246 ***	0.151 *
		MENA countries exporting oil
Saudi Arabia	TASI	0.003	0.00004 *	0.140 **	0.797 ***
Kuwait	BKP	0.007 ***	0.0002 ***	5.417 ***	−0.011 *
Qatar	MSCI	0.001	0.0002 ***	0.517 ***	0.219
UAE	MSCI	−0.0009	0.00005	0.328 ***	0.702 ***
Brent	OPE	0.007	0.002 **	0.740 ***	0.196

Notes: *** Statistical significance at 1%, ** Statistical significance at 5%, * Statistical significance at 10%.

, the ARCH coefficients demonstrate significant levels at 1% for the stock returns of Oman, Kuwait, Qatar, and the Emirates. However, the ARCH coefficient for Saudi Arabia’s stock returns, standing at 0.14 and significant at the 5% threshold, contrasts with the non-significance of the ARCH coefficient for Tunisian stock returns.

Conversely, the GARCH coefficients exhibit high significance levels with null probabilities for the stock returns of Tunisia, Saudi Arabia, and the Emirates. This is in contrast to the non-significance of the GARCH parameters for the stock returns of Morocco and Qatar.

It is important to note that the significance of the GARCH coefficient indicates that a substantial excess return value (whether positive or negative) suggests an increase in future variance forecasts over an extended period. Consequently, during periods of heightened volatility, the GARCH model is deemed more efficient for forecasting compared to the ARCH model.

Finally, emphasis will be placed on estimating the BEKK system for both importers and exporters. Both systems are represented as follows:

MENA countries importing oil

$$\left\{\begin{array}{c}{\mathrm{Re}}_{Tun}={\alpha }_0+{\alpha }_{1}OPE\\ {\mathrm{Re}}_{Maro}={\beta }_0+{\beta }_{1}OPE\\ {\mathrm{Re}}_{Oma}={\delta }_0+{\delta }_{1}OPE\end{array}\right.$$

(7)

MENA countries exporting oil

$$\left\{\begin{array}{c}{\mathrm{Re}}_{Saudi}={\gamma }_0+{\gamma }_{1}OPE\\ {\mathrm{Re}}_{Kuwait}={\lambda }_0+{\lambda }_{1}OPE\\ {\mathrm{Re}}_{Qatar}={\theta }_0+{\theta }_{1}OPE\\ {\mathrm{Re}}_{UAE}={\phi }_0+{\phi }_{1}OPE\end{array}\right.$$

(8)

The parameter estimation results of the two equation systems above from the GARCH BEKK modeling are summarized in

Table 5. Estimation Results of the GARCH BEKK Model.

	Importing Oil			Exporting Oil
	Tunindex	MASI	MSCI	TASI	BKP	MSCI	MSCI
	Tunisia	Maroc	Oman	Saudi Arabia	Kuwait	Qatar	UAE
aii	0.282 ***	0.228 ***	0.345 ***	0.290 ***	1.834 ***	0.773 ***	0.228 ***
bjj	0.874 ***	0.830 ***	0.838 ***	0.908 ***	0.004	0.775 ***	0.948 ***
OPE	0.026 ***	0.007	0.078 ***	0.078 ***	0.054 ***	0.071 ***	0.014

Note: *** Statistical significance at 1%.

.

Table 5 corresponds to the systems of Equations (7) and (8) addressing the transmission of volatility from the evolution of crude oil prices to the stock index returns of seven markets, including three importers (Tunisia, Morocco, and Oman) and fourexporters (the Saudi Arabia, Kuwait, Qatar and UAE).

The results displayed in this table indicate that the robustness coefficients, a_ii and b_jj, are significant at the 1% level throughout the examination period from January 1 2008 to 31 September 2022, except for b_jj for the Kuwaiti stock return. This proves the existence of a volatility persistence phenomenon for six stock markets, with three belonging to the importer countries (Tunisia, Morocco, and Oman) and threeto the exporter subset (the Saudi Arabia, Kuwait, Qatar and UAE), except for the Kuwaiti stock market. These results are consistent with Shiller’s theory (1981), suggesting that high volatility is a characteristic of financial markets.

For the Tunisian stock market, the table indicates that it is influenced by news from the oil market throughout the examination period. Moreover, the presence of a significant value of 0.026 at the 1% threshold demonstrates that the evolution of crude oil impacts this market positively and significantly. Similarly, for the Omani stock market, the presence of a significant and positive coefficient indicates that the stock return of this market is affected by the volatility of Brent oil price, signifying volatility transfer.

Regarding Morocco, the result in the table reflects the insignificance of the coefficient of the evolution of crude oil prices, indicating the non-transmission of volatility from the international Brent crude oil price to the Moroccan stock return, the evidence of this market’s stagnation against disruptions that could affect the oil market in general and mark volatility in Brent evolution in particular.

For the exporting countries, reaction coefficients related to Brent price changes are all significant and positive at the 1% threshold for the three respective countries, namely Saudi Arabia, Kuwait, and Qatar, except for the UAE, where a non-significant reaction coefficient value suggests the non-transmission of volatility from Brent evolution to the stock market return of this country, demonstrating the robustness of this market capable of withstanding disturbances transferred by other markets.

In this context, several researchers have attempted to identify the source of increased volatility affecting stock markets. The famous concept of “mimicry” is one of the cognitive biases that contradicts the rationality spirit of contemporary financial theory and is considered a primary source of volatility in stock markets. Thus, investigators have sought to confront the reality of markets with the theory of mimicry, focusing on behavior models, especially mimetic rationality [59]. Following an examination of the behaviors of Japanese institutional investors before and after the market downturn in the United States, they demonstrated that the downturn in the US market was the focus of Japanese investors, who ignored local information, focusing on external information (i.e., from the US market).

This shows that the volatility of the variable OPE significantly influenced the behavior of investors present in the various stock exchanges of affected countries and consequently affected stock returns.

7. Conclusions and Recommendations

This paper has delved into the analysis of the volatility transmission phenomenon from the evolution of Brent oil prices to the stock returns of seven MENA countries, including three importers and four exporters, after eliminating four from the initial sample which are Lebanon, Jordan, Egypt, and Bahrain using the ARCH test. The appropriate estimation method, GARCH-BEKK, has been employed to detect this transmission.

The results indicate a significant volatility persistence phenomenon across six stock markets, with three belonging to importer countries (Tunisia, Morocco, and Oman) and three to the exporter subset (Saudi Arabia, Kuwait, Qatar, and the UAE), except for the Kuwaiti stock market. These findings align with Shiller’s theory (1981), suggesting that high volatility is inherent in financial markets.

Throughout the examination period, the Tunisian stock market appears to be significantly influenced by developments in the oil market. The notable coefficient value of 0.026 at the 1% threshold demonstrates the positive and substantial impact of crude oil evolution on this market. Similarly, the Omani stock market exhibits a significant and positive coefficient, indicating volatility transfer from Brent oil prices to its stock returns.

In contrast, Morocco’s stock market displays insignificance in the coefficient of crude oil price evolution, suggesting a lack of volatility transmission from international Brent crude oil prices to Moroccan stock returns. This underscores the market’s resilience against disruptions that may affect the oil market, particularly in Brent evolution.

For the exporting countries, reaction coefficients related to Brent price changes are all significant and positive, except for the UAE, where non-significant coefficients suggest a lack of volatility transmission from Brent evolution to its stock market returns. This highlights the robustness of the UAE market in withstanding disturbances from other markets.

In light of these findings, several researchers have attempted to identify the source of the increased volatility affecting stock markets. The concept of “mimicry” has emerged as a primary cognitive bias contradicting the rationality spirit of contemporary financial theory and contributing to stock market volatility. Investigating behavior models, particularly mimetic rationality, has shed light on investor behaviors in various stock exchanges of affected countries, illustrating the significant influence of volatility on stock returns.

Moving forward, it is recommended to conduct further research into the underlying mechanisms driving volatility transmission, exploring additional factors beyond crude oil prices. Moreover, policymakers and investors should remain vigilant and implement strategies to mitigate the adverse effects of volatility on stock markets, fostering stability and resilience in the face of external shocks and uncertainties. Additionally, incorporating advanced modeling techniques and behavioral insights can enhance our understanding of market dynamics and inform more effective risk management strategies in the future.