The impact of oil price fluctuations on stock market in Saudi Arabia (2000 – 2022)

 

Prepared by the researcher : Amany Mohamed Ezzat , Asmaa Mohamed Abdullah ,Ibrahim Hemaida Ibrahim , Sama Esmail Mohamed , Moatasm Osama Ahmed

Supervisor : Dr. Ali Mohamed Ali – Faculty of Politics and Economics  – Beni-Suef University

Democratic Arab Center

Abstract

over the previous centuries, oil prices evolved into a significant and necessary component of all global production and consumption processes. Oil prices has since evolved into a key driver of economic growth across world. In this study, we examine the relationship between oil prices fluctuations and stock market for monthly data over the period (2000:2022) for Saudi Arabia by using Autoregressive Distributed Lag (ARDL) and White method to correct standard error. Considering Saudi stock market as the dependent variable, and oil price fluctuations and exchange rate as explanatory variables on monthly base. Results declare that there is a positive relationship between oil price fluctuations and stock market in Saudi Arabia as an oil-exporting country.

1. Introduction

Permanent pricing fluctuations typically have an impact on the macro economy of either the exporting or importing nations. The increase in foreign exchange flows into the oil exporting nations is typically accompanied by a rise in oil prices, which increases their income and increases the value of their local currencies relative to other currencies. These outcomes are anticipated to have a beneficial impact on the stock markets of the exporting countries, along with an increase in inflation rates brought on by the expansion of the money supply in those nations When oil prices are low, the opposite occurs.

The stock market is a place where shares of publicly held companies can be bought and sold either over the counter (OTC) or through centralized exchanges. The stock market, as it was formerly known, has established itself as a free market economy insofar as it gives businesses access to cash in exchange for giving third parties a stake in the company’s ownership.

The exchange rate is the relative price of one currency compared to another. It is the number of units of a foreign currency that can be obtained for a unit of the national currency.

This study aims to test the impact of oil price fluctuations on stock prices in the Kingdom of Saudi Arabia in the period of (2000-2022).

In this study, section 1 talk about literature review related to Dutch disease and literature review related to Oil price fluctuations on stock market, Section 2 talk about the data, methodology and the theatrical framework, Section 3 talk about the discussion of the estimation and section 4 display conclusion and recommendations.

2. Research problems

Oil is the backbone of economic growth, and it is a great source of national income for countries, especially in the Arab Gulf countries. One of the most important of these oil countries that depend on oil exports and has large reserves of it is the Kingdom of Saudi Arabia. Oil price fluctuations have attracted great attention from many economists.  The world, and many theoretical and practical studies have emerged from it, especially about the subject of fluctuations in oil prices and their impact on the Saudi stock market.

Therefore, the main question on which the research depends is:

(To what extent do fluctuations in oil prices affect the price of the Saudi stock market?)

 From this question, we deduced the sub-questions: What are the factors affecting the Saudi stock market?

How does the oil price affect the Saudi stock market prices?

3. Research importance

Scientific importance, this study reinforces the scientific theory between fluctuation on oil price and stock market, as it works to estimate the extent of the impact of oil price fluctuation on stock market.

Practical importance, the practical importance of this study is that it is a tool that can be used by decision-makers in finding solutions to the question of oil price fluctuation and its impact on stock market. In the volume of in addition to that it covers a large part of this issue, which can be taken as a reference for researchers in the same field.

4. Research objectives

This study aims to:

• Explaining the volume of oil price in Saudi Arabia.
• Clarifying oil price fluctuation and the stock market
• Analyzing the relationship between oil price fluctuation and stock market in Saudi Arabia (2000: 2022)
• Estimate the effect of oil price fluctuation on stock market in Saudi Arabia.

5. research hypothesis

The study is testing the following hypotheses.

• The Saudi stock market is positively affected by fluctuations in oil prices.

6. Limitations

Through this study, we aim to measure the impact of oil price fluctuation on the stock market, it has two spatial and temporal frameworks, regarding the spatial framework, this study concerns Saudi Arabia limited to data availability. As for the time frame, it defined the Period (2000: 2022).

7. Literature Review
   • Literature review related to Dutch Disease:

Nancy C. Benjamin et al (1987) [1]

This study looks at the impact of an oil boom on a developing country. Recent work has confirmed the hypothesis that foreign revenues can be a mixed blessing. Despite their potential for raising incomes, these revenues can bring about structural changes in the economy that may be unwelcome. The almost inevitable appreciation of the real exchange rate leads to a contraction in the traditional export sectors and inflation in the non-traded sectors. The syndrome has been called the “Dutch disease”. The Australian model: Separate the effects of an oil boom into two effects: 1. The resource movement effect: movement of mobile factors into the oil sector, bidding up their wages. In the case of Cameroon, this effect will be small because the oil sector’s capital and labor are primarily foreign. 2. The spending effect: refers to the use of the increased revenues; is sufficient to produce Dutch disease-type effects. The increased wealth results in greater consumption of both traded and non- traded goods (unless one is inferior). The increased demand for non-traded goods puts upward pressure on their prices. Output expands by drawing resources out of the traded good sector. In contrast, the increased demand for traded goods is satisfied by imports at the (constant) world price, which are paid for with oil export revenues. Thus, the traded good sector must contract, and the price of traded relative to non-traded goods must fall, i.e., the real exchange rate appreciates. A CGE model of’ Cameroon: An eleven-sector version of the model.  Results: study simulates increased earnings from oil exports by injecting a specific amount of foreign earnings into the economy. This amount can be varied to reflect different levels of partied foreign earnings.in the first case oil revenues are channeled directly to the treasury, which is assumed to use them for investment. In the second case: study considers the case where oil revenues are distributed directly to consumers. Consumer demand for labor-intensive goods drives wages up and domestic prices increase. Investment grows by a small percentage as compared with in the earlier case. This latter specification is used in all the experiments presented below. It supplies a benchmark idea1 scenario where the first-round use of oil revenues is for improvement in the capita1 stock as opposed to expansion of consumption. The picture of the investment boom in the first experiment, with rising prices and wages, coincides with a general worsening of the trade position. However, evidence of the Dutch disease is best indicated in the sectoral breakdown of price, output, and trade changes. The effect of oil revenues on foreign trade is as expected. Imports rise in all traded sectors and exports decline, both because productive resources are drawn into non-traded sectors and because domestic traded goods prices, not perfectly tied to international prices, are allowed to rise. Turning to the impact of oil revenues on the labor market, the study finds labor in general leaving the traded for the non-traded sectors. Those sectors diverging from this expected pattern are the ones experiencing exceptional growth rates with the assumptions of a constant labor supply and flexible wages. While the wages for skilled urban workers rise at less than the average rate, it should be remembered that they are paid ten times as much as the other two labor groups at the outset. Also, the wage differential developing between urban and rural unskilled workers could have implications for the effect of oil revenues on rural-urban migration and productivity in agriculture. Any differences in rural and urban household consumption patterns will multiply the effects of this shifting income distribution. Results: The latter result is of particular importance to a developing country because it points to a potential asymmetry between importable and export- table sectors. In traditional trade theory, these are treated as symmetrical (indeed, they are often aggregated into one sector). However, our results show that the impact of an oil boom is very different in these two types of sectors. Being typically agricultural, the exportable sector suffers badly. It faces stiff competition from other primary exporters and hence the real exchange rate appreciation causes it to contract. By contrast, the importable sector is the industrial sector, which is somewhat insulated from foreign competition by its products are imperfect substitutes. Some form of subsidy to the harmed sector is warranted: First, a large share of the oil revenues was saved abroad, so that the phenomenon described in this study was, to a large extent, avoided.  Second, to the extent that oil revenues were spent domestically, the government used the increase in its revenues to raise the producer price of cash crops, lowering the effective export tax on these commodities, and counteracting the cost increase from the real exchange rate appreciation.

Graham A. Davis (1995)[2]

In Oil Windfalls: Blessing or Curse, the oil price rises of the 1970s may have made certain of the oil exporters worse off in the long run than they would have been with con­stant real oil prices. In Sustaining Development in Mineral Economies: The Resource Curse Thesis, finds that selected hard-rock mineral exporters did not respond well to terms of trade shocks induced by the oil price shocks of the 1970s. Auty has since advised that the resource curse the­sis is not an iron law, but a strong recurrent tendency. It is something that can be and has been avoided with careful mineral windfall manage­ment. Each country naturally wishes to exploit its resource endowments in the optimal way, even, if this means creating policy that reduces the effective mineral endowment by postponing or sti­fling mineral production until an adequate domestic environment can be ensured. That is, it is possible that a country that is for various reasons destined to become a victim of the resource curse thesis might wish to transform itself temporarily or permanently into one of those less well-endowed countries that should, according to the resource curse thesis, do bet­ter. If the resource curse thesis is substantiated on a wide basis, I see the roughly 25% of developing nations that can be termed minerals-based economies responding by downplaying or even ignoring their mineral endowments and proceeding to an apparently higher growth path via the production of other, non-mineral related goods. Minerals As Pariah in Development Economics: Primary resource industries, and the mining industry, have long been the pariah of development econ­omics. Mineral-rich countries are not said to have development advantages, but development problems industrialization was the key to economic growth, and that this would not be promoted by indefinite concen­tration on the expansion of primary resource exports in exchange for manufactured imports. Since miner­als were a predominant export of Latin America, min­ing was singled out as an undesirable economic activity. The dramatic swings in mineral prices created fluctuating export and fiscal revenues for mineral exporters. Making domestic demand unstable, these fluctuations were in turn expected to discourage investment and retard economic growth. Mining became disfavored for more emotive reasons. The “colonial” nature of mining and the dominance of the multinational firm in early interna­tional mining activities led certain developing-coun­try economists to view mining as detrimental to national development. mining was singled out as contributing to the “backwardness” of developing countries, both for alleged monopolistic purchasing of labor and its facilitation of discrimination of backward peoples The notion that poverty has external causes, and that multinational mining enterprises were one of those causes, was no doubt a reason for the sweeping nationalization of mines throughout the world in the postwar decades. There is also the overlooked matter of mining’s contributions to early economic development. In Chile, as in other mineral-based economies, the min­eral industry developed the first railways and modern­ized agriculture through direct investment linkages. Investments of the local mining elite encouraged urban development, banking, improved winemaking, and the production of guano and nitrate. In Australia, early gold mining encouraged the inflow of both skilled immigrants and overseas investment, resulting in the ultimate diversification of the economy. In other countries, min­ing supported rapidly growing populations and immi­gration. Zambia for example, although one of the world’s most backward countries in 1920, had by 1964 one of the highest per capita incomes in sub-Saharan Africa, due principally to its copper mining. If Zambian eco­nomic and development performance was neverthe­less below expected levels given its vast mineral endowment, study credits this not to the mineral industry, but to the low productivity in agri­culture and the sociopolitical policies of the European hegemony in Zambia. The negative impact of mining’s revenue instabil­ity has also been overstated. In Chile mining appears to have stabilized the economy during the debt crisis years following the 1982 crash. Moreover, any export instability caused by mining may have had, if anything, a positive effect on growth due to enhanced permanent income investment effects Unhealthy urban­ization, inflation, a lopsided distribution of oil receipts, out-of-control government spending, and weakened social and cultural values were of concern. Advised government tampering with trade flows widened, and there was pessimism about the ability to maintain living standards once the oil was gone. Oil was even seen as a “curse” upon the oil exporters, as it caused the populace to become depen­dent on the state.

Balázs Égert and Carol S. Leonard (2007)[3]

study explore the evidence that would establish that Dutch disease is at work in, or poses a threat to, the Kazakh economy. Assessing the mechanism by which fluctuations in the price of oil can damage non-oil manufacturing and thus long-term growth prospects in an economy that relies heavily on oil production study find that non-oil manufacturing has so far been spared the perverse effects of oil price increases the real exchange rate in the open sector has appreciated over the last couple of years, largely due to the appreciation of the nominal exchange rate. study analyzes to what extent this appreciation is linked to movements in oil prices and oil revenues. Econometric evidence from the monetary model of the exchange rate.  The results makes us cautious about the use of aggregated data when studying Dutch Disease, because an apparent link between oil prices and the overall real exchange rate, regarding the nominal exchange rate, the monetary model indicates that the rise in the nominal price of oil and the rise in nominal oil revenue are possibly linked to an appreciation of the nominal exchange rate vis-à-vis the U.S. dollar but less so in effective terms.

Kareem Ismail (2010)[4]

The Dutch disease is the process by which a boom in a natural resource sector results in shrinking non-resource tradables. This study derives structural implications of the Dutch disease in oil-exporting countries due to permanent oil price shocks from a typical model. Study then tests these implications in manufacturing sector1977 to 2004. The results on oil-exporting countries are fourfold. First, study find that permanent increases in oil price negatively impact output in manufacturing as consistent with the Dutch disease. Second, evidence in the data shows that oil windfall shocks have a stronger impact on manufacturing sectors in countries with more open capital markets to foreign investment. Third, the study find that the relative factor price of labor to capital, and capital intensity in manufacturing sectors appreciate as windfall increases. Fourth, studies find that manufacturing sectors with higher capital intensity are less affected by windfall shocks than their peers, possibly due to a larger share of the effect being absorbed by more labor-intensive tradeable sectors. An implication of the fourth result is that having diverse manufacturing sectors in capital intensity helps cushion the volatility of oil shocks. This effectively appreciates the real exchange rate, which is the relative price. The appreciation in the real exchange rate moves factors of production from the non-resource tradables to non-tradables, which leads to an expansion in non-tradable services and shrinkage in tradable manufacturing and agriculture. There are fiscal and structural policies that may mitigate the Dutch disease. On the fiscal side, mitigating the Dutch disease effect comes down to decreasing the degree of spending out of windfall on non-tradable services. Thus, the two most direct fiscal policy measures to counter the Dutch disease is to decrease spending out of windfall income through investment in foreign assets or to direct that spending towards import-heavy expenditure. On the structural side, policies related to the openness of the factors market to inflows of labor and capital may help offset some of the impact of resource price shocks. Easier immigration policies can offset the pressures on the exchange rate by drawing labor from outside to supply the increased demand for non-tradables. Thus, a comparison across countries within sectors may be a more accurate method to measure the impact of real exchange rate appreciation from the Dutch disease on these sectors as it provides the closest proxy of the counterfactual sectoral growth in the absence of natural resources. Unlike in this paper, the bulk of the literature on the Dutch disease approached it on a country case level, and mostly centering on the effect of extraction or discovery as a onetime incident. The Dutch disease came first into the spotlight in the 1970s following the North Sea oil and gas discovery. It was coined as “Dutch” in reference to the shrinking of manufacturing and rising unemployment in the Netherlands at the time. While the fiscal expansion because of the extraction of natural gas is a factor in what occurred in this case, it is still unclear to what extent the shrinkage of the non-oil tradable sector can be attributed to resource extraction alone. The experience of the Netherlands, however, spread fears of similar incidence to other industrial countries with hydrocarbon resources including the United Kingdom, which saw a shrinkage in manufacturing in the late 70s. In cross-country studies, there has been limited evidence of Dutch disease due to oil discovery. Gelb (1988) provides an extensive empirical cross-country study of the Dutch disease, where the effect of windfall on oil exporters was examined for a group of oil exporting countries. Only a limited number of studies attempted to test for the Dutch disease directly through movements in oil price or revenue, with weak results. There are two caveats to this analysis: The first is that governments tend to adjust their fiscal policy slowly, especially following the negative experience with the oil price bust of the 1980s. This makes it difficult to assess how much of the fiscal spending was due to contemporary or past oil price shocks, or other factors. The second is that many countries buy most of their windfall revenues either through lump sum taxation of barrel exports, as is the case in Russia, or through royalties, as the case in Chad, which may cushion the windfall from short-term movements in oil price. Spadafore and Warner (1995) find a positive link between terms of trade shocks in oil-exporting countries and their real exchange rate as well as public spending. They find that the reaction of public spending to shocks was stronger than that of private spending. However, they could not find evidence of the Dutch disease. The results for oil-exporting countries are fourfold. First, I find that oil booms have resulted in reducing manufacturing output even after several robustness tests. Second, evidence in the data shows that windfall shocks have a stronger impact on manufacturing sectors in countries with more open capital markets to foreign investment. The model explains this result as due to an outflow of investment in manufacturing following a declining marginal return on capital, which is due to the expansion of labor-intensive non-tradables. Third, I find that the relative factor price of labor to capital, and capital intensity appreciate due to windfall increases. The second and third result are consistent with the model when non-tradables are labor-intensive. Fourth, I find that manufacturing sectors with higher capital intensity are less affected by windfall shocks, possibly due to a larger share of the effect being absorbed by the labor-intensive tradeable sectors. The conclusion of the fourth result is that a diverse manufacturing sector may be more cushioned from the effect of oil shocks. This has a direct policy implication for oil-exporters seeking to reduce the negative aspects of exposure of their tradable sector to oil volatility.

Sweder van Wijnbergen (2015)[5]

To refer to energy resources as the source of a ” disease”. It is becoming increasingly clear that high but temporary oil revenues may be somewhat of a mixed blessing, the quip of The Economist notwithstanding. Many third world oil producers are encountering serious problems in building up a diversified export base. while West European oil and gas producers (Netherlands, United Kingdom) are suffering a decline in their traded goods (manufacturing) sector induced by real wage pressures. The mechanism behind all this is clear enough: part of the oil revenues is spent on non-traded goods which leads to a sincere appreciation (i.e., a rise in the relative price of non-traded goods in terms of traded goods). This in turn draws resources out of the non-oil traded sector into the non-traded goods producing sector. the United Kingdom (Mexico, Egypt) simply should move into oil derived industries and non-traded goods and forget about their manufacturing sector until oil reserves are exhausted. technological progress: capital accumulation represents only a small part of economic growth. If most of economic growth is caused by Learning by Doing induced technological progress which moreover is largely confined to the traded goods sector, a temporary decline in that sector may permanently lower income per head compared with what could otherwise have been attained. This is the issue the study will address in this study. will present a simple two sector, two period model, where Learning by Doing (LD) is modelled via a positive link between second and first period Traded (T) goods output: study will first analyze the relation between the optimal subsidy to the T-sector and temporary oil revenues in the absence of foreign asset accumulation or foreign borrowing (an exogenous current account): expenditure is not smoothed across the drop in oil revenues between period 1 and 2. The results unambiguously confirm that high temporary oil revenues should lead to a higher subsidy to the T-sector during the period of high oil revenues. study introduce foreign capital markets and allow countries to smooth expenditure over time via foreign asset accumulation. As this leads to a shift of expenditure towards the (post-oil) future, and so to more demand for NT-goods in the post oil period, the case for an increase in subsidies to the T-sector is weakened. The increased subsidy will draw part of the resources diverted to the NT sector after the oil revenues increase back into the T-sector, thus leading to a further appreciation of the current real exchange rate Q. For conclusion: Oil producers across the development spectrum are or will be facing ‘Dutch Disease’ type crowding out of their non-oil traded goods sector. Those among them that are at an early stage of industrial development have voiced complaints that such a period of retrenchment of non-oil traded goods production will delay the learning by doing experience that would improve their comparative advantage (or lessen a comparative ‘disadvantage’) in the production of manufactured goods. Such industry-specific learning by doing effects will of course always present a case for production subsidies to the sector concerned. The question raised in this study is whether light but temporary oil revenues and the concomitant negative effects on the non-oil traded goods sector should lead to an increase in these subsidies during the period of higher oil revenues. The answer is unambiguously yes for those countries that have chosen not to use periods of high oil revenues to accumulate foreign assets but to use newly discovered (or revalued) oil wealth for consumption. If the current account is not used to smooth expenditure, subsidies to the non-oil traded goods sector should be increased if that sector shows the potential of significant learning by doing induced increases in productivity. In this sense the question asked in the title – is the Dutch Disease a disease after all? should be answered in the affirmative because more corrective medicine is needed. Dutch Disease phenomena will still be present, but it may not be necessary to switch back to non-oil traded goods production in the post-oil period as income from foreign assets accumulated when oil revenues were flowing in will allow continued concentration of resources in the non-traded goods sector. So, the case for subsidies to the traded goods sector during the period of oil revenues is weakened, and it is possible under this scenario that subsidies should be decreased (although they will always remain positive).

This study investigates the Dutch disease and its impact on economy. We discuss the studies which investigate Oil price fluctuations’ impact on stock market.

• Literature review related to Oil price fluctuations on stock market:

Roger D. Huang (1996):[6]

The study investigates the relation of oil futures returns to stock returns during 1980, the extent to which price changes or returns in one market lead returns in the others. If oil affects real GNP, it will affect earnings of companies for which oil is a direct or indirect cost of operation. Thus, an increase in oil prices will cause expected earnings to decline, and this will bring about an immediate decrease in stock prices if the stock market efficiently capitalizes the cash flow implications of the oil price increase. Study observed the period of the 1980s. They are looked at returns of heating oil or crude oil futures. For methodology: vector autoregressive (VAR) approach is used to examine the lead-lag relation between oil futures returns and stock returns while controlling for interest rate effects, seasonality’s, and other effects. The conclusions from the VAR approach are the same as from the simpler bivariate cross correlations estimated earlier in the study. Oil futures returns are not correlated with stock market returns, even contemporaneously, except in the case of oil company returns. Despite the frequently cited importance of oil for the economy, there is little evidence of such a link in the prices of stocks other than oil companies.

Mohamed El Hedi AROURI (2010):[7]

The aim of this study is to investigate the relationship between oil prices and stock markets in Gulf Corporation Council (GCC) countries. Using a weekly dataset covering the period from 7 June 2005 to 25 May 2010, Stock market price changes in the other GCC member countries do not Granger cause oil price changes, whereas oil price shocks Granger cause stock price changes. Therefore, investors in GCC stock markets should look at the changes in oil prices. For methodology: based on the following bivariate (OIL, STOCK) model. The results: show strong statistical evidence that the causal relationship is consistently bidirectional for Saudi Arabia. In the other GCC countries, stock market price changes do not Granger cause oil price changes, whereas oil price shocks Granger cause stock price changes.

Leila Dagher, Sadika El Hariri (2013)[8]

Study aimed to investigate the dynamic linkage between oil prices and stock market and examine the dynamic interactions between daily Brent spot prices and Lebanese stock prices. Its method is investigating the order of integration of the variables used in the empirical study, the ADF (Augmented Dickey Fuller) test will be used, complemented with the PP (Phillips Perron) test. And as an alternative to simultaneous equation models, the study used the vector autoregression (VAR) model. To test for causality, the study used Granger causality testing. And to determine the direction of causality, a simple Wald test in an unrestricted VAR setting is applied to a group of coefficients to test whether they are jointly significant or not. According to variables, a study used daily closing prices for the period 10/16/2006 to 7/10/2012 were obtained from the BSE website for the stock prices and Energy Information Administration for the oil prices. As a proxy for the world price of crude oil (OIL), the study used the Brent spot price (Measured in US dollars per barrel), which is the most used benchmark for pricing in the crude oil market. As a proxy for BSI, in addition to the BLOM BSI (Beirut Stock Index), the stocks that fall under the development and reconstruction sector, SOLA (Solider A) and SOLB (Solider B). Causality test: Model 1: BSI is the dependent variable and Oil is the explanatory variable. Model 2: Oil is the dependent variable and BSI is the explanatory variable. As a result, studies find evidence of oil prices Grange causing stock prices, but no evidence of the opposite relationship. The estimated level of the impact of an oil price shock on the Lebanese market is positive but marginal.

Samih Antoine Azar (2013) [9]

Oil Prices and the Kuwaiti and the Saudi Stock Markets: The observed period is from 2008 to 2014, study looked at two variables, Stock market returns and Crude oil shocks. Four models will be estimated to explain each of the Kuwaiti stock market log returns and the Saudi stock market log returns. Log returns are calculated by taking the first difference of the natural logs. Since non-linearity is pervasive in the literature, three types of nonlinearities are considered. All models include one kind of non-linear relation which is a GARCH (1,1) process for the conditional variance. In this case the non-linear relation is the temporal dependency of the squared residuals. For result, the two equity markets of Kuwait and Saudi Arabia have different dynamics and react differently to oil price shocks. oil price shocks do not have any effect on Kuwaiti stock markets, neither linearly nor non-linearly. Oil prices have non-linear effects on Saudi log returns. This result supports the main finding in this study, that there is a contrast between the response of the Kuwaiti and Saudi stock markets to oil price shocks.

David C. Broadstock, George Filis (2014).[10]

The objective of This study examines the time-varying correlations between oil prices shocks of different types (supply-side, aggregate demand and oil-market specific demand). The study applies to both aggregate stock market and industrial sectors returns oil-market specific demand). They are looked at variables: world oil production (OILPROD), oil Prices (OILP) and global economic activity (GEA) Index, which are used for the estimation of the oil price shocks, study use monthly data starting from January 1995 through to July 2013 on Aggregate US and Chinese stock market indices. Methodology: 1-Structural VAR model and historical price decomposition. 2- study apply BEKK. For results: study find that the stock market response to oil price shocks is different over Time. the evidence suggest That the US stock market is more responsive to oil price shocks compared to the Chinese stock Market, as it shows a higher level of correlation with oil price shocks throughout Studied period. In addition, the US market is Always positively related with the aggregate demand shocks, whereas this does not hold true for China.

Abdulrahman Adnan Alqattan (2016).[11]

The aim of this study that find whether a relationship exists between oil prices and the GCC stock markets price in both the short run as well as long run. the observed data from November 2006 to February 2015. For methodology: This study applied Autoregressive Distributed Lag (ARDL) approach by developing a version of the autoregressive distributed lag (ARDL) model as an alternative cointegration procedure known as the error correction version. This result suggests that in the long run stock market price is not sensitive to oil price fluctuations in GCC countries while it is sensitive in Oman. In the short run model, it is found that there is a relationship between oil prices fluctuation and stock market prices in all GCC countries. Oil price has a positive and significant impact on stock market, but the degree of impact varies from one country to another.

Suliman Zakaria Suliman Abdalla (2013)[12]

Study aimed to modeling the Impact of Oil Price Fluctuations on the Stock Returns in an Emerging Market: The Case of Saudi Arabia, the observed period is from 2007 to 2011, study looked at two variables, Stock market returns and Crude oil shocks. For a wide range of financial data series. To investigate the impact of crude oil price fluctuations on stock market returns in Saudi Arabia. This study looked at the impact of crude oil price fluctuations on stock market returns. Based on daily observations of Tadawul all share index (TASI) and crude oil prices over the period 1st January 2007 to 31st December 2011, the results of the study indicate that an increase in oil prices leads to increase stock market returns volatility. In addition, there was evidence to suggest that past volatility of oil market is transmitted to stock market in the Kingdom of Saudi Arabia during the period of the studs.

Olamide T. Ojikutu et al (2017).[13]

The aim of this study sets out to fill the gaps of already existing literature by establishing the linkages between oil price, EXR and stock Market performance in Nigeria. covering the period from (1985-2014). study looked at three variables: are all share index (ASI) which serves as a proxy for market performance, COP, and EXR. This study applied: 1- Unit Root tests are usually performed on variables to find if they are stationary (i.e., zero mean and constant variance) or otherwise, to determine their order of integration. 2- Cointegration is the idea that the linear combinations of non-stationary series can be stationary, implying a long- run relationship, thus they can be models. 3- ECM This is the final specification that includes a short run dynamic process, consistent with data and converging to the long run equilibrium. 4- The ordinary least square estimation technique was used in the single equation models. For results: 1- there exists a long-run relationship amongst ASI, COPs and EXR at 5% level of significance. 2- A unit root test was conducted using the ADF and P-P tests the P-P test result shows that all the variables are homogenous of order one. 3- the negative sign of the ECM that about 20.6% of the means errors were corrected yearly and were found to be significant.

Hussein Alzyoud et al (2018).[14]

The aim of this study is to analyses the impact of crude oil prices (COP) on exchange rate and stock market returns in Canada for the period Of 1986–2015. And to analyze the long run relationship between the variables. In addition to examine the long run elasticity. the observed period from 1986 to 2015. Study looked at three variables: Canadian Stock Market, the CAD/USD dollar exchange rate and the COP. This study applied unit root, study further the time series properties of the variables where study found that the variables displayed a unit root and stationery at first difference. This means that the variables had a similar ordered (1) at first difference. study proceeded to measure the long run elasticity of the variables with the help of DOLS. The regression coefficients were highly significant. the study used the VECM to analyses the short run and long run error Correction mechanism of the Canadian Stock Market return. The model suggested that both COP and exchange rates have significant impact on stock market which means that the stock market is influenced by these two variables.

Nouf Bin Ayyaf AlMogren (2020)[15]

Study explained the impact of oil price fluctuations on Saudi Arabia stock market: A Vector Error Correction Model Analysis, the observed period is from 2000 to 2019, used the variable of interest represented by the Saudi Stock Index called Tadawul All Share Index (TASI) and New York Stock Exchange (NYSE), which is included as a proxy for US market risk and oil price. The methodological framework used in this study is based on a p–th order VAR. these results suggest that: 1- WTI holds a greater magnitude of long– term effect over TASI, and that an increase in both WTI and NYSE will be associated with a positive increase in TASI in the long run. 2- The results revealed in this study give us reason to reject our original null hypothesis that oil prices are statistically significant predictors of the Saudi stock market movement s. 3- The New York stock exchange (NYSE) is noticed to be a better predictor of TASI, and therefore additional study must be conducted to closely examine this relationship.

Noureddine Benlagha (2020).[16]

This study aimed to show the stock market dependence between the Qatari stock market and other country stock markets, focusing on the period of the pre- and post-oil price shock and the pre- and post-2017 GCC political crisis period. Covering the period from 1998 to 2018, study is looked at two variables: dependence structure between the Qatar stock market and the other stock markets and oil price. For method, the study uses a flexible copula approach that accommodates the presence of tail dependence and the time variation due to various economic and political events. In the first stage, study estimated a battery of static copula models and their corresponding time varying constructions (Gaussian, Student’s, and SJC). Then selected the Suitable copula by using the log likelihood and AIC information criteria. The results show Regarding the dependence between Qatari and each of the developed stock markets studied, the results indicate that the dependence between Qatar and US daily stock returns is the highest among the studied developed markets followed by the UK. The lowest level of dependence is between Qatar and Germany. the dependence between the Qatar market and the developed financial markets is significantly weak. This implies that the Qatari financial market is isolated from most developed financial markets. the results show that the dependence between the Qatar stock market and the other international financial markets is time varying and change significantly after political, economic, and financial crises.

Rıza Demirer et al (2020).[17]

This study aimed to investigate the relationship between oil price Shocks and financial markets by examining the effect of oil shocks on the sovereign bond markets of many advanced and Emerging Economies and exploring the Impact of oil shocks on the degree of connectedness among international financial markets. study observed the period of (1990:2007), study is looked at variables: Supply, demand and risk shocks, sovereign bond market returns, connectedness among stock and sovereign Bond markets, Oil price changes. For methodology: 1- study examined their effect on stock and sovereign bond market returns via a multifactor linear model. 2- Using the generalized VAR framework, the Haste-ahead forecast error variance decomposition of the I-variable due to shocks to the variable. The results suggest that Global stock and sovereign bond Markets react differently to oil price shocks depending on the underlying cause of the Structural shock. While oil demand shocks are found to have a robust positive effect on stock market returns regardless of the status of the country as an importer/exporter or advanced/emerging Economy, study observe that the effect of supply Related shocks is more heterogeneous Across markets, with generally an adverse impact on stock market returns.

Bashayr Albulayhi and Dr. M. Junaid Khawaja (2021)[18]

Study aimed to explain the impact of shocks oil prices on the stock market Saudi Arabia, the observed period is from 1995 to 2018, study looked at two variables, stock market returns and crude oil shocks. This study objective is to analyze the response of the Saudi Arabia stock market to the shocks of crude oil prices. To determine the asymmetric stock market response from the positive and negative changes in crude oil prices, the nonlinear ARDL model is implemented that is basically the Shin et al, the study utilizes crude oil weekly price (the OPEC crude Basket price) obtained from OPEC and Saudi stock index TASI that obtained from the World Bank expressed in the US dollar, over the period weekly from January 1995 until December 2018. The results of this study show that the shocks of crude oil price affect the Saudi Arabia stock market (index TASI) in the long-term in an asymmetric fashion. The traditional cointegration tests show that there is no correlation linear in the long or short run between stock markets and shocks of crude oil price in the crude oil dependent Saudi economy, The Nonlinear ARDL model illustrates a positive association among the Saudi Arabia stock market and positive and negative crude oil price shocks in a long run.

Dinmukhamed Kelesbayev et al (2021).[19]

The aim of study is to investigate the effect of oil prices on the Kazakhstan stock exchange (KASE) and real exchange rate. Covering the period from 2016 to 2021, the study looked at three variables: real exchange rate, KASE closing prices and crude oil prices. The study applies VAR model, using the monthly data on variables such as Brent crude oil prices, real exchange rate, and KASE closing price for the period 2016-2021. First, the series is evaluated for stationary and the ADF and Zivot-Andrews unit root tests showed that the series is stationary at the first difference. Results show that KASE closing prices are affected negatively by the real exchange rate and positively by oil price shocks. The findings also show that the effect of the real exchange rate is statistically significant.

Ismail O. Fasanya et al (2021).[20]

Investigate the impact of Oil price fluctuations on stock market in GCC countries. Study employ both the Symmetric ARDL by Pesaran et al. (2001) and Nonlinear ARDL by Shin et al. (2014) and account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models. Study employs and modifies the Shin et al. to account for structural breaks in the model as there might have been some significant shifts in the series. Not paying proper attention to these breaks when they exist may bias regression. The NARDL model is an asymmetric extension of the linear ARDL model of Pesaran et al. For variables, Dependent Variable: Stock Returns, Independent Variable: Oil Price. study obtained weekly data for the six GCC countries for four (4) variables, namely the Brent oil price, stock market indices for individual countries, geopolitical risk index, and global economic activity. The sample period differs for each country in the GCC region based on the availability of data; Kuwait (26/11/1995–11/4/2016), Qatar (10/8/ 1998–11/4/2016), UAE (30/9/2001–11/4/2016), Kingdom of Saudi Arabia (26/1/1994–11/4/2016), Oman (1/1/1992–11/4/2016), and Kingdom of Bahrain (05/07/2004–11/4/2016). Study finds that oil price is a significant driver of stock returns in all countries, as its coefficients are largely significant and positive. Differentially, the long-run relationship is not found to exist for the symmetric models when no breaks are put into consideration. Still, for the asymmetric models, both short- and long-run relationships exist. For models with breaks, evidence of significant impact is observed both in the short and long run. Most of the break dates are also observed to be significant. All these signify the critical roles played by asymmetries and structural breaks. Meanwhile, the large positive relationship is not unexpected.

Inwook Hwang, Jaebeom Kim (2021).[21]

This study aimed to determine the extent to which the role of supply and demand shocks in the global crude oil market can be estimated and tested in terms of the degree of asymmetry of their effects over the business cycle. Covering the period from January 1973 to December 2018. Study looked at four variables: The aggregate US real stock returns, the percentage change in global crude oil production, a measure of global real economic activity, the real price of crude oil imported by the United State. The study applied the smooth transition VAR (STVAR) model by Granger and Teräsvirta (1993) to analyze the possible nonlinear effect of the state- dependent dynamics of structural shocks on US stock market returns. The results show that the response of US stock returns to disaggregate d shocks is asymmetric over the business cycle and the impact of demand- driven shocks on US stock returns is stronger and more persistent especially when economic activity is depressed. In contrast to previous oil price-related studies, this study suggests that the uncertainty effect, among many others, seems to play an important role in understanding the state- dependent stock market reaction to oil price shocks during recessions.

Abdorasoul Sadeghi_ Soheil Rounder (2022).[22].

This study aimed to examine whether the oil structure of economies, the Source of oil Shocks, and Regime changes Affect stock price Responses. After Dividing the entire Period into two Regimes (bull and Bear market) and Considering three Different oil Shocks: global oil Price, global oil Demand, and oil supply, study attempted to select countries that are considered developed economies while demonstrating differences and similarities in their oil structures. They are looked at using monthly Data from 1990 to 2020, study use three types of oil shocks: an indicator of global real activity as the oil demand shock (Demands), the Percentage Change in Global crude oil Production as The Oil supply shock (Supply), The results indicate a strong Link between the Stock market, the source and type of oil shocks (negative or positive), the oil structure of economies, and regime changes. China and Japan’s stock markets respond similarly to oil shocks, and Canada and Norway, as two oil-Exporting Economies, face Similar conditions. The Responses of the two Oil-importing economies are strikingly dissimilar to those of the two oil-exporting economies.

Aktham Maghyereh, Hussein Abdoh (2022).[23]

The object of the study is to investigate how extreme oil price shocks affect the stock market returns of major oil-exporter countries (Gulf Cooperation Council [GCC] countries) at different time horizons. For method, study consists of a two-step approach. First, decomposing oil price shocks into supply shock, demand shock, and risks shock using a vector autoregression (VAR). In the second step, the study examines the extreme co-movements (tail dependence) between each of the three oil-market shocks and stock market returns, using a novel quantile cross-spectral dependence approach (QS). The study also applies the wavelet coherence analysis of Whitcher and Craigmile (2004). Study uses daily time series that covers the period from June 1, 2006, to February 28, 2020, with 3845 daily observations. Working in study builds firstly on a VAR model for the purpose of decomposing oil prices into risk, supply, and demand shocks. Following, study used the World Integrated Oil and Gas Producer Index as a proxy for the global stock price index of oil-producing firms. The WTI futures price is used as a proxy for global crude oil price. The VIX index is used to estimate risk changes. For results, the overall dependence (coherence) between oil shocks (demand and supply) and stock market returns is stronger than between risk shocks and the stock market returns. This finding stems from the evidence that the response of the stock market to oil price shocks is more important for oil-exporting countries. The coherence between high quantiles of demand and supply shocks and that of stock market returns decreased significantly from year to the monthly frequency in Bahrain, Kuwait, Qatar, and UAE. The coherence between risk shocks and stock market returns in all GCC countries increases when time-frequency rises from yearly to monthly. At low time frequency (e.g., yearly), the coherence between high quantile of demand or supply shocks and GCC stock market returns is higher than the coherence of low quantiles with that of stock returns.

Fenghua Wen et al (2022).[24]

This study aimed investigate the impact of the supply, demand, and risk shocks of oil prices on the Chinese stock risk-return relation by using newly developed decomposition method. study looked at variables: (1)- The world Integrated oil and gas Producer indexes global stock Price index of oil-producing firms. (2)- The nearest maturity crude oil (light sweet) future contract traded in NYMEX, which reflects the oil prices. and (3)- the implied volatility index of the US stock market (VIX). For the methodology: This model is specifically introduced 2- This study uses a bivariate VAR to Perform the Granger-causality analysis. The results: study find that The Granger causality from OS to γSHCI is not significant, while the Granger causality from OD and OR to γSHCI is significant. However, studies cannot find γSHCI significantly Grange causes any oil shock. study observe that in the pre-financialization period, both Granger causality from oil prices shocks to γSHCI and from γSHCI to oil price shocks are insignificant.

Sajjadur Rahman (2022).[25]

Study investigates the asymmetric relation between the price of crude oil and U.S. aggregate real stock returns. For methodology, Study estimate a nonlinear bivariate structural vector autoregression that includes the effects of oil price volatility on stock returns. study use the estimates of nonlinear model to calculate the responses of stock returns to unexpected increases and decreases in prices of crude oil and conduct a test of symmetry on these responses. Study also use the methodologies described in Koop et al. (1996), Grier et al. (2004) and Kilian and Vigfusson (2011) to calculate Nonlinear Impulse Response Functions (NIRF) to investigate the dynamic effects of real oil price shocks on U.S. real stock returns. According to variables, Dependent Variable: Stock Returns, Independent Variable: Oil Price. Study use monthly data for the United States over the period from 1973:01 to 2015:08 on two variables: changes in the real price of oil and aggregate stock returns. Study’s nonlinear impulse response analysis suggests that aggregate stock returns decrease in response to a positive oil price shock and increase due to a negative oil price shock. However, the effects on returns of a positive oil price shock are relatively higher in magnitude than those of a negative oil price shock, showing an asymmetric relation between the price of crude oil and aggregate stock returns. Study also finds that increased volatility about the change in the real price of oil has a negative effect on stock returns, and oil price volatility plays a major role in asymmetries in the transmission of oil price shocks to aggregate stock returns.

Sh Zeinedini et al (2022).[26]

This study aimed to investigate the influence of global oil and gold prices on the Iran stock Market during the Covid-19 pandemic. The observed period from (February 20, 2020, until January 30, 2021). They are looked at variables: 1-The total stock price Index is used as a (dependent Variable). 2- Oil Price – Gold Price Used a (independent variable). For Methodology: Using the quantile regression method, it is possible to cover the full range of conditional quantile Functions. This method assumes That the conditional distribution is homogeneous. On the other hand, quantile regression allows different estimations to be calculated at different points in the conditional distribution. By estimating quantile processes, some potential nonlinear relationships can be obtained between the dependent and explanatory variables. 2- The augmented Dickey and Fuller test was used to achieve the stationary or no stationary time Series variables applied in the model. To this end, the augment Dickey Fuller (ADF) unit root test was employed to the data and the results have been presented in the section of evaluation of Variables stationery. Results: the study indicates a negative relationship between oil price and stock market returns. The results show That generally there is an insignificant relationship between the global gold price and the stock index of Iran stock exchange during the Covid-19 pandemic. In all deciles, there is a negative and significant relationship between OPEC oil price and the Iran stock exchange Index during the Covid-19 pandemic.

Suganya Balakumar (2022).[27]

Study investigates the different structural oil shocks—oil supply global aggregate demand shock, speculative shock, and other oil demand shocks. The study is looked at: the vector of variables such as global crude oil production changes, global real economic activity, the change in above-ground crude oil inventory, real oil price changes, and momentum returns or payoffs of each economy is the vector of variables such as global crude oil production changes, global real economic activity. For methodology: study elaborates on the approach for the structural vector autoregressive model to find the time varying responses on momentum payoffs. For results: find that an aggregate demand shock followed by a speculative demand shock has significant implications for momentum profits. The supply shock and other oil specific demand shocks have a marginal impact on momentum profits. The conditional asset pricing model tests indicate that structural oil shocks, especially oil demand and speculative shocks, have important implications as conditioning information in predicting stock return momentum behavior. sensitive to oil price shocks or have a higher chance of momentum profit.

Thai Hong Le, Anh Tram Luong (2022).[28]

Study aimed to examine the dynamic spillovers between oil price shocks, stock market returns and investor sentiment in the US and Vietnam during the period 2010–2020. Its methodology is to explore the dynamic connectedness in a time-varying manner, study employ the TVP-VAR approach introduced by Anton kakis and Gabauer (2017). The TVP-VAR methodology combines the connectedness approach of Diebold and Yilmaz (2009, 2012, 2014) and Koop and Korobilis (2014). According to variables, Model 1: Dependent variable: U.S stock market returns. Explanatory variables: U.S oil Price shocks and U.S investor sentiment. Model 2: Dependent variable: VN stock market return explanatory variables: VN Oil Price shocks and VN investor sentiment. study employ monthly data of stock market indices for the US (S&P500) and Vietnam (VN30) from the FiinPro platform, which is a financial database in Vietnam. The stock market indices are then converted into stock market returns by taking the first difference of the natural logarithms. To proxy oil price shocks, study collect monthly data of Brent crude oil price from the Energy Information Administration (EIA). The study computes an investor sentiment index following the method of Baker and Wurgler (2007). Chen et al. (2021) The investor sentiment series for Vietnam is constructed from five proxies: market turnover, number of IPOs, average first-day return on IPOs, equity share of new issuances, and the log difference in book-to-market ratios between dividend payers and dividend non-payers. Results show a moderate interdependence among the variables (oil price, stock market, and investor sentiment). In emerging markets, investor sentiment tends to be the most critical factor. The relationship between oil price, stock market returns, and investor sentiment are time-varying and entirely driven by time-specific developments and events. Results suggest that a change in one of the three variables above likely impacts the others, thus posing spillover risks to the financial system.

8. Theoretical framework, Data, and Methodology.
   • Theoretical framework

Our theoretical frame followed by Degiannakis (2018)[29] which we relied on its assumptions to develop the empirical analysis.

Over the past ten years, there have been notable changes in the price of oil. After a twenty-year era of relative peace, from 1986 to 2006, Brent crude oil prices increased from $60 to $145 between 2007 and 2009, before dropping precipitously to $30. Several years later, in 2014 and 2015, oil prices dropped by roughly 75% in just a few short months. According to these fluctuations, we are simply the relationship between oil prices and stock market depending on Degiannakis (2018).

• Theoretical transmission mechanisms between oil and stock market returns:

Stock valuation channel: The stock valuation channel is the direct channel by which oil prices influence stock markets. stock returns are impacted by factors that can alter the expected cash flows and/or the discount rate, including oil prices. Oil price changes can alter a firm’s future cash flows either positively or negatively, depending on whether the firm is an oil-user (oil-consumer) or oil-producer. For an oil-consuming firm, oil is one of the major production factors and consequently an increase in oil prices will result in an increase of production costs (assuming that there are no perfect substitution effects between production factors, which, in turn, will reduce profit levels and thus future cash flows. On the other hand, for an oil producer the oil price increase will result in increased profit margins and thus increased expected cash flows.

Monetary channel: The discount rate is at least partially composed of expected inflation and expected real interest rates. Thus, the second transmission mechanism by which oil price changes impact stock returns is through inflation and interest rates.  These costs will be transferred to consumers, leading to higher retail prices and thus higher expected inflation. If a central bank follows some type of rules, we expect monetary policy makers to increase short-term interest rates in response to higher inflationary pressures. There are two main effects of the increased short-term interest rates on stock markets. First, increases in short-term interest rates lead to an increase in commercial borrowing rates (i.e., discount rates) for any future firm investments, raising the borrowing costs of firms. Furthermore, the increased borrowing costs lead to fewer positive NPV projects (lower cash flows). Thus, either due to increased discount rates and/or lower cash flows, stock prices decrease in value.

Output channel: According to this channel, positive oil price changes are expected to have both an income and a production cost effect, which will lead to changes in aggregate output, so we will concentrate on the income effect. Specifically, increased oil prices tend to lead to lowering the discretionary income of households, due to the changes in retail prices (because of increased production costs), but also due to the increased prices of gasoline and heating oil. Lower income leads to lower consumption and thus aggregate output, which further leads to lower labor demand. Stock markets tend to respond negatively to such developments. We maintain that this will be the response of the stock market, lower aggregate demand leads to lower expected cash flows for firms, which further leads to lower stock prices. On the other hand, even though an oil-exporting economy will also experience negative production cost effects, it will benefit from a positive income effect, due to increased oil revenues (the value of export demand for oil rises), leading to higher aggregate demand and thus higher output.

Fiscal channel: The fiscal channel is primarily concerned with oil-exporting economies, which are financing physical and social infrastructure using their oil revenues. Increased oil prices tend to lead to a transfer of wealth from oil-importing economies to oil-exporting ones, which allow for increased government purchases. Assuming that consumption and government purchases are considered complements, then the latter will lead to higher household consumption. In such a case, private firms are expected to increase their cash flows and thus their profitability. Such developments will push stock prices to higher levels and the stock market will exhibit a bullish period.

Uncertainty channel: The final transmission channel is the uncertainty channel. Rising oil prices cause higher uncertainty in the real economy, because of the former on inflation, output, consumption, etc. Thus, increased oil prices will reduce firms’ demand for irreversible investments, which in turn, reduce expected cash flows. Furthermore, uncertainty is also propagated to households which reduce their consumption of durable goods. Rising uncertainty about future oil costs increases the incentives of households to save rather than consume. It is worth noting here that as uncertainty rises due to increased oil prices, the value of postponing both investment and consumption decisions increases and thus, a decrease in the incentive to invest or consume is observed, which thereby dampens economic growth prospects and thus stock market returns.

• Relationship between oil price and stock market returns

Empirical evidence: Hamilton (1983) was among the first to document that oil price changes regularly exercise a significant impact on economic activity in the US. Hamilton (1983) went as far as to suggest that most US recessions from the end of WWII up until 1983 were the result of energy price surges. Hamilton (1983) was among the first to document that oil price changes regularly exercise a significant impact on economic activity in the US. Hamilton (1983) went as far as to suggest that most US recessions from the end of WWII up until 1983 were the result of energy price surges. Jones and Kaul (1996) report that oil exerts a significantly negative impact on aggregate stock market returns, whereas Huang et al. (1996) do not offer support to these findings, claiming that the effects of oil on stock markets are non-existent. The picture painted from the studies suggests that positive oil price changes lead to negative stock market returns. For instance, Sadorsky (1999) focuses on the US market and reports that positive changes in the price of oil are associated with decreased stock market returns, whereas the reverse does not hold. Even more, his findings provide evidence that the effects of oil on stock markets became more important between 1986 and 1996—a period that saw significant oil price declines.

Symmetric and asymmetric effects: The financial literature also tries to identify whether oil prices exercise asymmetric effects on stock market returns. It is worth noting that these studies focus on either aggregate or sectoral stock market returns. There are three types of asymmetric specifications that these studies are exploring, namely positive and negative oil price returns, scaled oil price increases and decreases (SOPI and SOPD) and net oil price increases (NOPI). They maintain that different specifications for capturing the asymmetric effects of oil prices could yield different results and, thus, authors should be very careful when choosing the asymmetric specification.

Oil-importing countries and oil-exporting countries: The effects of oil price changes on stock markets returns do not necessarily hold for all countries. Rather, Mohanty et al. (2011) maintains that oil price effects are different in countries that are oil-exporters, compared to these that are oil-importers. Hence, the negative relationship that was established in the previous sections does not necessarily hold for stock markets operating in oil-exporting countries.

Time-varying relationship: The fact that the relationship between oil and stock markets may not be stable over time. On the contrary, a time-varying relationship may prevail. examine the relationship between oil price movements and stock market performance for the period from 1971 to 2008. More specifically, the authors claim that a negative relationship was held during the 70s and the 90s. By contrast, in the 80s the authors cannot report any significant effects of oil prices on stock returns. Finally, they find evidence that the negative effects of oil prices on stock markets are reversed into positive effects after 1999. studies corroborate that the relationship between oil prices and stock market is time-varying and mainly driven by economic or geopolitical developments. Thus, there are periods when the two markets exhibit a positive relationship, whereas in other periods a negative relationship prevails.

• Relationship between oil price shocks and stock market returns

Defining oil price shocks: Oil price shocks are identified based on the sources that cause oil prices to change. The emergence of these shocks is important in understanding better the relationship between oil and stock market performance. In short, an oil price shock reflects a change in the price of oil due to an unanticipated change in oil market fundamentals (i.e., global supply or demand of oil). Kilian (2008b, 2009) maintains that there are three types of oil price shocks, namely, the supply-side, aggregate demand and precautionary demand shocks. supply-side shocks are related to restrictions in oil supply by OPEC, via cartel behavior, as a strategy to inflate oil prices. On the other hand, geopolitical unrest, primarily observed in the Middle East region, does not lead to supply-side oil price shocks. On the contrary, they posit that these events trigger precautionary demand shocks, which result due to the uncertainty that the geopolitical turbulence imposes on economic agents about the future availability of oil.  The aggregate demand shocks, according to Kilian’s studies, are related to oil price changes which are influenced by movements in the global business cycle. For instance, the remarkable growth of the Chinese and other emerging economies from 2004 to 2007 significantly increased oil demand from these countries, while oil supply did not follow suit, driving oil prices to unprecedented levels.

Empirical evidence:

Aggregate, sectoral, and firm level analysis: study finds that stock market returns do not really respond to supply-side shocks, whereas positive (negative) responses are observed during aggregate demand (precautionary demand) shocks. stock markets do not seem to react to OPEC decisions to restrict oil supply to generate increases in the price of oil. Such findings might be justified by the fact that OPEC decisions are somewhat anticipated and, thus, they are discounted by market participants. positive aggregate demand shocks seem to be regarded as positive news for stock markets (hence the positive response), even though they create an upward movement in oil prices. This is expected, as positive aggregate demand shocks reflect periods of economic growth, which are positive news for financial markets. Finally, the negative responses of the stock markets to positive precautionary demand shocks suggest uncertainty in the oil market. Finally, there are studies that investigate the effects of the three oil price shocks on stock market volatility. Degiannakis et al. (2014), who focus on the European stock market, show evidence that stock market volatility responds negatively (i.e., reduces) to positive aggregate demand shocks, whereas no significant response is evident to supply-side and precautionary demand shocks. Their findings hold true for aggregate stock market indices, as well as for ten industrial sectors.

Oil-importing countries and oil-exporting countries: They report heterogeneous responses to the two demand side shocks (i.e., aggregate demand and precautionary demand shocks), which stem from the fact that Norway is an oil-exporter, whereas Korea is an oil-importing country. Even though they find that the aggregate demand shocks exercise a positive effect on both the Norwegian and Korean stock markets, the effects are more prevalent for the Norwegian stock market, given the oil-importing character of the country. A clear difference in findings exists for the effects of the precautionary demand shocks. Interestingly, the latter shocks exercise a positive effect on Norwegian stock markets (although only in the short run), whereas the opposite effect holds true for the Korean market.

Time-varying relationship: There is a recent strand of literature which suggests that the results may be time-varying. One of the early findings in this line of research is by Filis et al. (2011), who show that the correlation between oil and stock markets is time-varying and responds to the various oil price shocks. They show that precautionary demand (aggregate demand) shocks lead to lower (higher) correlations between oil and stock market returns and though the magnitude of these correlations is not always the same, suggesting that there is an element of event-specific effects. Supply-side events do not seem to trigger changes in the correlation. The results remain qualitatively similar for both oil-importing and oil-exporting economies.

Conclusions: The main conclusions that can be drawn are as follows. There are various channels that impact firm cash flow and/or their discount rates. These transmission channels suggest that higher oil prices lead to lower stock market returns—for stock markets operating in oil-importing economies. The reverse applies for oil-exporting countries. Evidence mainly supports the theoretical premise that higher oil prices lead to lower stock market returns, yet only for the oil-importing countries, as the reverse holds true for oil-exporting countries. At a more detailed level, though, higher oil prices due to supply-side or precautionary demand shocks trigger negative responses from stock markets, whereas higher oil prices resulting from a boost in the global economy (aggregate demand shocks) are received as positive news by stock markets. More recent evidence shows that the relationship between the two markets is time- varying. Oil price volatility exercises a significant effect on stock market volatility, whereas the reverse holds true only in the case of the US market. Furthermore, additional evidence suggests that the volatility relationship is time-varying, which tends to intensify during the global financial crisis period. Interestingly, there are no studies that focus on firm-level data when considering volatility interconnectedness between the two markets, making this an interesting avenue for further research. Finally, given that the oil market has become more financialized in recent years due to the increased participation of hedge funds studies should investigate further the role of the speculative activity in the oil market and how this financialization has altered its nature.

• Data

To investigate the impact of oil price fluctuations on the stock market, we collected monthly data about three variables which are oil price as a dependent variable, stock market index and exchange rate as an explanatory variable. We used Cushing, OK WTI Spot Price FOB (Dollars per Barrel) as a proxy for crude oil price and its abridgement is OIL_PRICE_WTI, we collect the data of this variable from Energy Information Administration from the period January 2000 to December 2022 in a monthly way. According to explanatory variables, we used TASI stock market index, The shares of all companies listed on the Saudi stock market are represented by the TASI index, which in turn gives an idea of the performance of the market and gives an idea of the extent of progress or decline in the Saudi economy. Until this time, the number of companies included in the TASI index has reached nearly 200 companies, and among the most famous shares traded within this index are Aramco shares, SABIC shares, Al-Rajhi, Al-Inma, Riyad Bank shares, Almarai, Jarir and Communications, TASI is an acronym for: Tadawul All Share Index. We used TASI stock market as a proxy for stock market and its abridgement is SM_CHANGE. In addition to the exchange rate variable, we used USD/SAR exchange rate as a proxy for exchange rate and its abridgement is EXCHANGERATE_CHANGE. Both explanatory variables, SM_CHANGE and EXCHANGERATE_CHANGE, are also monthly data from the period January 2000 to December 2022 and are collected from investing.com website. The data consists of 828 observations for all variables and 276 observations for each variable.

Figure 1 illustrates the explanation of data graph. Figure 1.a, by observing the chronological development of the oil price throughout the study period, we find that the oil price began in the year 2000 to reach $27.65 per barrel until the barrel price reached about $76.52 per barrel in 2022. During this period, fluctuations occurred in oil prices for various reasons. Including:  The significant rise in oil prices in 2008, with a barrel recording about $140, due to In the wake of the bursting of the real estate bubble in the United States and the resulting severe financial crisis, The decline in the price of oil in the period 2014-2016: due to oversupply , The decline in the oil price for the period 2019-2020: During the year 2020, global oil markets witnessed a significant decline in crude oil prices as a result of the outbreak of the Corona virus.

Figure 1.b, In the same period, we notice the change in the Saudi stock price, which is characterized by stability, and we find that in the year 2000, the change in the stock price was about -2.68%, In 2022, it reached -0.07%. The change in the stock market price faced many fluctuations due to several reasons for the rise in stock prices in 2006, and that is because the market witnessed the entry of new investors into the Saudi market, and they began looking for good sectors with. The stock market price changed in 2008 due to the market decline after the fall in oil prices and global stock markets at the weekend, following the failure of OPEC and Russia to reach a new agreement to reduce production after the expansion of the Corona virus, so that OPEC decided to cancel all restrictions on its production starting from April, and to end an agreement that lasted about  3 years between producers in OPEC and non-OPEC producers. The stock market price changed in 2014 due to the significant market rise in the third quarter following the Cabinet’s approval to open the stock market to qualified foreign financial institutions. After this decision, the market witnessed an increase for several sessions, achieving gains of 14%, equivalent to about 1342 points.

Figure 1.c, on the other hand, we find the change that occurred in the exchange rate in the same period, as in the year 2000 the change in the exchange rate was equivalent to 0.00% and reached in 2022 about -0.05%. So, we find that there is stability in the exchange rate in this period and there has been a rise in the change in the exchange rate in the period 2007 and 2008, due to the financial crisis.

Figure 1. a

Figure 1.b

Figure 1.c

Figure 1: Time trend of oil price, Saudi stock market and exchange rate.

Data source: constructed by authors using data from Energy Information Administration and investing.com website.

Figure 1 illustrates the explanation of data graph. Figure 1.a describe the chronological development of the oil price throughout the study period, figure 1.b illustrates the change in the Saudi stock price and figure 1.c show the change that occurred in the exchange rate in the same period.

According to the collected data, figure 2 illustrates the relationship between independent variable and explanatory variables.

Figure 2.a indicates a positive but weak correlation between oil prices and stock market change over the 2000-2022 period in Saudi Arabia. During the period (2000-2022) oil price rose steadily from 33.16$ in Jan 2004 to100.7$ in Sep 2008 because of many financial crises and declined to 65.94$ in 2014 because of Boom in US oil production, receding geopolitical concerns, and changing OPEC policies. and stock market change rose in 2004 by 0.54% to 6.65% in 2008 because of the increase of oil price and decline in 2009 by4.76%.

Figure 2.b indicates a positive but weak correlation between exchange rate and stock market change over the 2000-2022 period In Saudi Arabia, during the period (2000-2022) exchange rate and stock market rose steadily in 2007 by 1.09% because of decreasing the imports and declined in 2020 by –0.20%.

Figure 2.a

Figure 2.b

Figure 2: Scatter diagram of Saudi stock market with oil price and Saudi stock with exchange rate.

Data source: constructed by authors using data from Energy Information Administration and investing.com website.

Table 1 exhibits the descriptive statistics for the three variables.

Table1. Descriptive statistics: crude oil price, stock market change and exchange rate:

OIL_PRICE_WTI		SM_CHANGE	EXCHANGERATE_CHANGE
Mean	62.75	0.001	5.43
Median	60.35	0.001	0
Standard Deviation	26.13	0.01	0.0009
Minimum	19.23	-0.04	-0.008
Maximum	139.96	0.06	0.01
Count	276	276	276

• Methodology

According to economic theory, changes in one economic variable over time may have an impact on other economic variables as well. These changes do not occur instantly but rather over time. A single equation time series has been used for decades to investigate the relationship between variables using the autoregressive distributed lag (ARDL) model. One of the most comprehensive dynamic unrestricted models in the literature on econometrics is the ARDL model. The dependent variable in this model is represented by the lag, current and own lag values of the independent variables.

There are various ways to express this relation:

we have y as a dependent variable, and it is a function of its lag as in lagged dependent variable model beside the current value of x (independent variable) as following:

 (1)

 the current value of dependent variable

 the current value of independent variable

 the value of previous one period of y

 if we have the dependent variable y as a function of current and past values of an independent variable x then it’s called distributed lag model which is a dynamic model while we can say that the impact of an explanatory (x) on dependent (y) happens over time not only in the same level time.

The simplest case of DL model is when we have one explanatory variable, the model as described as follow:

(2)

Combining the two prior techniques’ equations (1) and (2) will result in a dynamic model with lagged dependent and explanatory variables, as shown in the following:

The distributed lag (DL) component represents the lag effect of x’s, while the autoregressive (AR) component expresses a regression of  on its lags. The ARDL (p, q) model is expressed in the following way:

When there is a serial correlation issue, the ARDL model is frequently used to produce a transformed model with uncorrelated errors.

The advantages of using ARDL:

1- The crucial feature is that it is applicable when the variables are integrated in various orders, which is consistent with the claim that using the ARDL technique prevents the issue of non-stationary time series data.

2- By including a suitable number of lags of y and x that can handle serial correlation problem, it reflects a dynamic influence of lagged x’s and lagged y’s.

And we used white method to correct standard error to get robust standard error as Jarque Bera test suggest that the residuals are not normally distributed.

Bound test the null hypotheses is formed to test βi since.

 H0: β1 = β2 = … = βk+1 = 0

H1: at least one parameter not equal to zero

F-statistics is calculated to compare with critical values. We cannot rule out the null hypothesis that there is no association between the times series if the estimated F-statistics are found to be less than the lower critical values previously specified. It is violated to make a particular choice and is referred to other cointegration tests if estimated F-statistics fall between the lower and upper bounds of critical values. We can infer that there is a relationship between time series if estimated F-statistics is higher than the critical value’s upper bound. That is to say, the null hypothesis is not acceptable.

a dynamic error correction model (ECM) integrates the short-run dynamics with the long-run equilibrium without losing long-run information can be derived from ARDL by using a simple linear transformation:

∆ =  +  ×∆+ ×∆ ×∆

The Akaike information criteria (AIC), created by Akaike (1973), which studied the technique to make a balance between the two instances under fitting and over fitting, is typically used to calculate the number of lags.

The AIC is different from a conventional hypothesis test because it uses a scoring system rather than accepting or rejecting the null hypothesis to identify the “best” model.

                                                AIC = -2 log (L) + 2m

m is denoted as the number of parameters in the model (degrees of freedom) and the value of the log of the likelihood function of the estimated model.

The Jarque-Bera (JB) test is used for checking normality of the residuals, the null hypothesis of JB test is the residuals are normally distributed, the probability (p-value) highly recommends the normality of residuals as we can’t reject the null hypothesis event at the very high level of significance. And as our data isn’t normally distributed, we used the white method to correct standard error.

9. Discussion
   • Stationarity

Engle and Granger (1987) demonstrated that cointegration analysis is not applicable when variables are integrated in different orders (i.e., some series are I(1) and others are I(0)), but Johansen and Juselius (1990) demonstrated that ARDL cointegration is applicable in these situations. Although ARDL cointegration technique does not necessitate pre-testing for unit roots, stationary condition must be checked for all series as a first step of model estimation to prevent ARDL model crash in the presence of integrated stochastic trend of I (2), If a series’ mean, variance, and structural characteristics remain constant across time, it is stationary. A non-stationary time series is a stochastic process with unit roots or structural breaks according to the unit root notion. Unit roots, however, are important non-stationary sources. When a time series is non-stationary, it is implied by the presence of a unit root, but when it is stationary, it is implied by the absence of one. Dickey and Fuller (Dickey and Fuller 1979, Fuller 1976) invented the unit root method for testing stationarity. The unit root test is based on the idea that if a non-stationary series (X) needs to be differentiated d times to become stationary, then this series must have d unit roots at its level and be integrated of order d, it can be written as (X) ~ I (d). The Dickey-Fuller (DF) test contains as its null hypothesis (H0) “series has a unit root” and as its alternative hypothesis (H1) “the series is stationary”. The DF test assumes that the disturbance term is subject to white noise, therefore if the dependent variable exhibits autocorrelation, the error term will also exhibit autocorrelation, invalidating the DF test. Dickey and Fuller created the DF test in 1981 to enhance the Dickey-Fuller test (ADF) by accounting for p lag values. As with the DF test, the same critical values table and null hypothesis are utilized. Table 2 show Augmented Dickey–Fuller Test Results:

Table 2: Augmented Dickey–Fuller Test Results

Series	Integrated order
SM_CHANGE	I (0)
OIL_PRICE_WTI	I (0)
EXCHANGERATE_CHANGE	I (0)

Table 2 indicates that the ADF test confirmed that the included variables are stationary at I (0) (stationary at their level).

• Empirical Result

The estimating ARDL model with automatic lag selection using R and R studio is ARDL (1,0,1) model, it was selected depending on the least AIC as shown in table 3 and table 4.

Table 3: Akaike Information Criteria (top 20 models)

MODEL	SM_CHANGE	OIL_PRICE_WTI	EXCGANGERATE GHANGE	AIC
1	1	0	1	-1683.59
2	1	0	0	-1682.07
3	1	1	1	-1681.59
4	2	2	1	-1675.08
5	2	2	0	-1673.65
6	2	2	2	-1673.09
7	2	1	1	-1672.78
8	2	1	2	-1671.10
9	3	2	1	-1667.22
10	3	2	0	-1665.96
11	3	3	1	-1665.56
12	3	2	2	-1665.22
13	3	3	2	-1663.57
14	3	2	3	-1663.54
15	4	2	1	-1662.93
16	3	3	3	-1661.76
17	4	2	0	-1661.60
18	4	3	1	-1661.34
19	4	2	2	-1660.96
20	4	2	3	-1659.55

Table 4: Akaike Information Criteria (best order)

SM_CHANGE	OIL_PRICE_WTI	EXCGANGERATE_GHANGE
1	0	1

Table 5 shows that there are no significant effects of the lags of the variables on SM_CHANGE. And there is no lag of OIL_PRICE_WTI is chosen for describe on SM_CHANGE, and there is no effect of the first lag of EXCHANGERATE_CHANGE on SM_CHANGE. This result by using the white method as residuals were not normally distributed.

Table 5: Robust standard error ARDL Results

Variables	Coefficient	Standard error	T-test	Pr(>|t|)
L (SM_CHANGE, 1)	5.549	8.799	0.6305	0.52889
OIL_PRICE_WTI	2.440	2.069	1.1791	0.23940
EXCHANGERATE_CHANGE	1.220	1.346	0.9061	0.36569
L (EXCHANGERATE_CHANGE, 1)	1.492	8.501	1.7554	0.08033
C	3.365	1.426	0.2358	0.81373
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

• Bound test

the bound test is the test to determine if there is a long-run relationship as a null hypothesis says that; there is no long-run relationship, according to the value of F-statistics, first case; if this value lower than I(0) we don’t reject the null hypothesis and there is no long rum relationship, second one; if this value greater than I(1) we reject the null hypothesis and we can indicate that there is long relationship, the last case; if this value lies between two bounds we cannot judge. Here, we are in the second case as the value of F-statistic greater than upper bound which includes that there is a long-run relationship at all levels of significance 10% as shown in table 6.

Table 6: f- Bound Test:

I (1)	I (0)	Significant level	value	Test Statistic
-4.097	-3.424	10%	64.191	F-statistic
			2	K
			-15.7136	t- statistic

• Error correction model

The ECT shows how much of the disequilibrium is being corrected, that is, the extent to which any disequilibrium in the previous period is being adjusted in current point. A positive coefficient indicates a divergence, while a negative coefficient indicates convergence. If the estimate of ECT = 1, then 100% of the adjustment takes place within the period, or the adjustment is instantaneous and full, if the estimate of ECT = 0.5, then 50% of the adjustment takes place each period/year. ECT = 0, shows that there is no adjustment, and to claim that there is a long-run relationship does not make sense any.  In our case the ECT is a negative sign and highly significant which indicate convergence and we can conclude that 94% of adjustment from short run to long-run is take place each month, i.e., adjustment is taken place after 1 year (four quarters).as shown in table 7 and table 8.

Table 7: unrestricted Error correction model estimation:

Variables	Coefficient	Std. Error	t-statistic	Pr (> [t])
intercept	3.365	1.765	0.191	0.8489
L(SM_CHANGE,1)	-9.445	6.011	-15.714	<2e-16***
OIL_PRICE_WTI	2.440	2.604	0.973	0.3496
L(EXCHANGERATE_CHANGE,1)	2.713	1.379	1.967	0.0502
D(EXCHANGERATE_CHANGE)	1.220	8.053	1.516	0.1308

Table 8: Restricted error correction model

Variables	Coefficient	Std. Error	t-statistic	Pr (> [t])
D(EXCHANGERATE_CHANGE)	1.22038	0.41002	2.976	0.00318**
ECT	-0.94451	0.05862	-16.113	<2e-16***

• Diagnostics tests

• Checking stability:

A further step of estimating model is checking this model adequacy before making a forecast, these checking steps divided to checking model stability and diagnostic of residuals performance. For checking the stability and the accuracy of the estimated model CUSUM is used.  Figure 3 confirms that the estimated model satisfies the stability condition as there is no root lying outside the significance level.

Figure 3:

• Checking serial correlation and Heteroscedasticity

As widely used for checking serial correlation of the residuals, the LM test is used, and it is confirmed that there is no longer serial correlation between residuals. As shown in Table 9; the null hypothesis that there is no serial correlation is not rejected at level 0.05 which means that there is no evidence for serial correlation in the residuals term of the estimated model. Also, Table 9 shows that there is no heteroscedasticity (or the variance is constant) in the residuals since we don’t reject the null hypothesis of no heteroscedasticity at level 0.05.

Table 9: serial correlation and Heteroscedasticity results

Test	Chi-squared value	P-value
Lagrange Multiplier (LM) for Serial Correlation	0.85569	0.8361
Breusch-Pagan-Godfrey for Heteroskedasticity	4.4232	0.3518

• Checking Normality

The Jarque-Bera test is a goodness-of-fit test that determines whether sample data have skewness and kurtosis that matches a normal distribution. The test statistic of the Jarque-Bera test is always a positive number and if it’s far from zero, it indicates that the sample data do not have a normal distribution. Table 10 tells us that the test statistic is 371.22 and the p-value of the test is 2.2e-16 as shown in table 10. In this case, we would reject the null hypothesis that the data is normally distributed. We have sufficient evidence to say that the data is not normally distributed as shown in figure 4.

Table 10: Normality Test

Jarque Bera Test

data:  bestARDL$residuals X-squared = 371.22, df = 2, p-value < 2.2e-16

Figure 4:

10. Conclusion and recommendations

In our study, we attempted to investigate the relationship between oil price fluctuations and stock market in Saudi Arabia during the period (2000: 2022). The analysis was based on time Autoregressive Distributed Lag model (ARDL) and White method. The estimation of Stock market equation indicated that Oil price fluctuations are positive related to stock market.

The analysis lets us state that there is significant positive impact for oil price fluctuations on stock market, so we find a long run relationship between two variables.

Our findings are consistent with the major study related oil price fluctuations on stock market Mohamed El Hedi AROURI (2010), Leila Dagher, Sadika El Hariri (2013) and Ismail O. Fasanya et al (2021).

To sum up, the empirical result of the study shows that Saudi Exchange respond to the fluctuations in oil prices as an oil-exporting country. The empirical results of this study provide policy makers a better understand of oil price fluctuations_stock market nexus to formula financial and productive policies in Saudi Arabia or any oil-exporting county for sustainable and long-term economic development.

In this research, we investigate new case study during recent period to coping with updates all over the world. Also, we applied autoregressive distributed lag and white method (new method) which is not commonly used, and this was our contribution.

In our study, we analyzed only one oil-exporting country and econometrics model included two explanatory variables.

For policy makers, we find that the relationship between oil price changes and the Saudi stock market is a direct relationship, meaning that any change in the price of oil will lead to a change in the price of the Saudi stock market, so decision-makers and policies must consider that point and not rely on oil only as a source of income and economic growth. And we must diversify the sources and consider that any changes that may occur in the use of oil, such as the lack of dependence on it as a basic source in energy or in many industries, will lead to a decrease in the value of exports of oil, and thus this negatively affects the Saudi stock market. In addition to reducing dependence on imports of goods by setting up factories and trying to produce these goods locally to reduce their cost and reducing dependence on goods imports to reduce spending from hard currency. Exploiting oil revenues to use these returns in investment, establishing projects, developing economic sectors, establishing modern infrastructure, attracting foreign investments, and this will provide an appropriate environment for foreign investors and the diversity of income sources for the state. Expanding the petrochemical industries by establishing partnerships with investors and foreign partnerships, exploiting oil derivatives in the petrochemical industry, and expanding the establishment of factories for refining crude oil and its derivatives to obtain more revenue than exporting it as crude oil.

Country leaders should pay attention to the concept of sustainable development by make attention to the tourism sector as one of the most important economic sectors and important sources of income for the state and the exploitation of sea and water areas, especially that Saudi Arabia overlooks more than one water source and several islands that can be used in the development of tourism as well as religious tourist attractions and the exploitation of revenues from them and increase national income.

For future researchers, they can focus their analysis on exporting and importing oil countries and add additional explanatory variables. Also investigate the daily impact of oil price fluctuations on the stock market.

11. References
12. Abdorasoul Sadeghi _ Soheil Rounder. 2022. “Heterogeneous effects of oil structure and oil shocks on stock prices in different regimes: Evidence from oil-exporting and oil-importing countries” Resources policy 0301-4207. DOI: 101016/j.resourpol.2022.102596.
13. Abdlrahman Adnan Alqattan. 2016. “Impact of Oil Prices on Stock Markets: Evidence from Gulf Cooperation Council GCC Financial Markets” Amity Journal of Finance: 1(1), (1-8).
14. Aktham Maghyereh and Hussein Abdoh. 2022. “Extreme dependence between structural oil shocks and stock markets in GCC countries” Resources Policy 76: 0301-4207. DOI: 1016/j.resourpol.2022.102626.
15. Balázs Égert and Carol S. Leonard. 2007. “Dutch Disease Scare in Kazakhstan: Is It Real?” William Davidson Institute Working Paper No 866.
16. Bashayr Albulayhi and Dr. M. Junaid Khawaja. 2021. “Impact of shocks oil prices on the stock market Saudi Arabia” International Journal of Psychosocial Rehabilitation: 1475-7192. DOI: 10.13140/RG.2.2.13633.28000.
17. David C. Broadstock, George Filis. 2014. “Oil price shocks and stock market returns: new evidence from the United States and China. Journal of international financial markets& money 1042-4431. DOI: 1016/j.intfin.2014.09.007.
18. Dinmukhamed Kelesbayev, Kundyz Myrzabekkyzy, Artur Bolganbayev, and Sabit Baimaganbetov. 2021. “The Impact of Oil Prices on the Stock Market and Real Exchange Rate: The Case of Kazakhstan”. International Journal of Energy Economics and Policy 12(1): 163-168. DOI: 10.32479/ijeep.11880.
19. Fenghua Wen, Minzhi Zhang, Jihong Xiao, Wei Yue. 2022. “The impact of oil price shocks on the risk-return relation in the Chinese stock market” Finance research letters 1544-6123. DOI: 10.1016/j.frl.2022.102788.
20. Graham A. Davis. 1995. “Learning to Love the Dutch Disease: Evidence from the Mineral Economies.” World Development, Vol. 23, No. 10, pp. 1765-1779, 1995. 0305-750x.
21. Hussein Alzyoud, Eric Zengxiang Wang, and Michael Glenn Basso. “Dynamics of Canadian Oil Price and its Impact on Exchange Rate and Stock Market”. International Journal of Energy Economics and Policy 8(3): 107-114.
22. Inwook Hwang, Jaebeom Kim. 2021. “Oil price shocks and the US stock market: A nonlinear approach” journal of empirical finance 0927-5398. DOI: 10.1016/j.jempfin.2021.08.004.
23. Ismail O. Fasanya, Oluwatomisin J. Oyewole, Oluwasegun B. Adekoya and Fopesaye O. Badaru, 2021. “Oil price and stock market behavior in GCC countries: Do asymmetries and structural breaks matter?” Energy Strategy Reviews 36: 2211-467X. DOI: 1016/j.esr.2021.100682.
24. Kareem Ismail. 2010. “The Structural Manifestation of the `Dutch Disease’: The Case of Oil Exporting Countries” IMF Working Paper, Strategy, Policy, and Review Department.
25. Leila Dagher and Sadika El Hariri. “Impact of global oil price shocks on the Lebanese stock market” Energy 63: 366-374. DOI: 10.1016/j.energy.2013.10.012.
26. Mohamed El Hedi Arouri. 2010. “Causal relationships between oil and stock prices: some new evidence from gulf oil-exporting countries” International Economics: 1240-8093. DOI: 10.3917/ecoi.122.0041
27. Nncy C. Benjamin, Shantayanan Devarajan and Robert J. Weiner. 1987. “The ‘Dutch’ Disease in A Developing Country Oil Reserves in Cameroon” Journal of Development Economics 30. 0304-3878.
28. Nouf Bin Ayyaf AlMogren. 2020. “The Impact of Oil Price Fluctuation on Saudi Arabia Stock Market: A Vector Error Correction Model Analysis” International Journal of Energy Economics and Policy: 310-317. DOI: 10.32479/ijeep.10525.
29. Noureddine Benlagha. 2020. “Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade” International Business and Finance 0275-5319. DOI: 10.1016/ j. ribaf.2020.101285.
30. Olamide T. Ojikutu, Rita U. Onolemhemhen, Sunday O. Isehunwa. 2017. “Crude Oil Price Volatility and its Impact on Nigerian Stock Market Performance 1985-2014” International Journal of Energy Economics and Policy 302-311.
31. Rıza Demirer, Román Ferrer, Syed Jawad Hussain Shahzad. 2020. “Oil price shocks, global financial markets, and their connectedness” Energy economics: 0140-9883. DOI: 10.1016/j.eneco.2020.104771.
32. Roger D. Huang. 1996. “Energy Shocks and Financial Markets” Journal of Futures Markets 16:
33. Sajjadur Rahman. 2022. “The asymmetric effects of oil price shocks on the U.S.” Energy Economics 105: 0140-9883. DOI: 10.1016/j.resourpol.2022.102626.
34. Samih Antoine Azar. 2013. “Oil Prices and the Kuwaiti and the Saudi Stock Markets” International Journal of Economics and Financial Issues: 2146-4138.
35. Sh Zeinedini, M. Sh Karimi, A. Khanzadi. “Impact of global oil and gold prices on the Iran stock market returns during The Covid-19 pandemic using the quantile regression approach” Resources policy 0301-4207. DOI: 10.1016/j.resoupol.2022.102602.
36. Stavros Degiannakis, George Filis, Vipin Arora. 2018. “oil prices and stock markets: a review of the theory and empirical evidence” The energy Journal, Vol.39, No.5 (September 2018), pp. 85-130.
37. Suganya Balakumar. 2022. “Do oil price shocks have any implications for stock return momentum?” Economic Analysis and Policy: 0313-5926. DOI: 10.1016/j.eap.2022.6.016.
38. Suliman Zakaria Suliman Abdalla. 2013. “Modelling the Impact of Oil Price Fluctuation on the Stock Returns in an Emerging Market: The Case of Saudi Arabia” Interdisciplinary Journal of Research in Business: 2046-7141.
39. Sweder van Wijnbergen. 2015. “The `Dutch Disease’: A Disease After All?” The Economic Journal, Vol. 94, No. 373 (Mar. 1984), pp. 41-55.
40. Thai Hong Le and Anh Tram Luong. 2022. “Dynamic spillovers between oil price, stock market, and investor sentiment: Evidence from the United States and Vietnam” Resources Policy 78: 0301-4207. DOI: 10.1016/j.resourpol.2022.102931.

12. Appendix
    • Augmented Dicky Fuller test
    • ARDL Model
    • White method
    • Serial correlation test
    • Error Correction Model
    • Bound test and Multiplier test
    • Stability and Normality test

[1] Nancy C. Benjamin, Shantayanan Devarajan and Robert J. Weiner. 1987. “The ‘Dutch’ Disease in A Developing Country Oil Reserves in Cameroon” Journal of Development Economics 30. 0304-3878.

[2] Graham A. Davis. 1995. “Learning to Love the Dutch Disease: Evidence from the Mineral Economies.” World Development, Vol. 23, No. 10, pp. 1765-1779, 1995. 0305-750x.

[3] Balázs Égert and Carol S. Leonard. 2007. “Dutch Disease Scare in Kazakhstan: Is It Real?” William Davidson Institute Working Paper No 866.

[4] Kareem Ismail. 2010. “The Structural Manifestation of the `Dutch Disease’: The Case of Oil Exporting Countries” IMF Working Paper, Strategy, Policy, and Review Department.

[5] Sweder van Wijnbergen. 2015. “The `Dutch Disease’: A Disease After All?” The Economic Journal, Vol. 94, No. 373 (Mar. 1984), pp. 41-55.

[6] Roger D. Huang. 1996. “Energy Shocks and Financial Markets” Journal of Futures Markets 16:

[7] Mohamed El Hedi Arouri. 2010. “Causal relationships between oil and stock prices: some new evidence from gulf oil-exporting countries” International Economics: 1240-8093. DOI: 10.3917/ecoi.122.0041

[8] Leila Dagher and Sadika El Hariri. 2013. “Impact of global oil price shocks on the Lebanese stock market” Energy 63: 366-374. DOI:  10.1016/j.energy.2013.10.012.

[9] Samih Antoine Azar. 2013. “Oil Prices and the Kuwaiti and the Saudi Stock Markets” International Journal of Economics and Financial Issues: 2146-4138.

[10]  David C. Broadstock, George Filis. 2014. “Oil price shocks and stock market returns: new evidence from the United States and China. Journal of international financial markets& money 1042-4431. DOI:  10.1016/j.intfin.2014.09.007.

[11] Abdulrahman Adnan Alqattan. 2016. “Impact of Oil Prices on Stock Markets: Evidence from Gulf Cooperation Council GCC Financial Markets” Amity Journal of Finance: 1(1), (1-8).

[12] Suliman Zakaria Suliman Abdalla. 2013. “Modelling the Impact of Oil Price Fluctuation on the Stock Returns in an Emerging Market: The Case of Saudi Arabia” Interdisciplinary Journal of Research in Business: 2046-7141.

[13] Olamide T. Ojikutu, Rita U. Onolemhemhen, Sunday O. Isehunwa. 2017. “Crude Oil Price Volatility and its Impact on Nigerian Stock Market Performance 1985-2014” International Journal of Energy Economics and Policy 302-311.

[14] Hussein Alzyoud, Eric Zengxiang Wang, and Michael Glenn Basso. 2018. “Dynamics of Canadian Oil Price and its Impact on Exchange Rate and Stock Market”. International Journal of Energy Economics and Policy 8(3): 107-114.

[15] Nouf Bin Ayyaf AlMogren. 2020. “The Impact of Oil Price Fluctuation on Saudi Arabia Stock Market: A Vector Error Correction Model Analysis” International Journal of Energy Economics and Policy: 310-317. DOI: 10.32479/ijeep.10525.

[16] Noureddine Benlagha. 2020. “Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade” International Business and Finance 0275-5319. DOI: 10.1016/ j. ribaf.2020.101285.

[17] Rıza Demirer, Román Ferrer, Syed Jawad Hussain Shahzad. 2020. “Oil price shocks, global financial markets, and their connectedness” Energy economics: 0140-9883. DOI: 10.1016/j.eneco.2020.104771.

[18] Bashayr Albulayhi and Dr. M. Junaid Khawaja. 2021. “Impact of shocks oil prices on the stock market Saudi Arabia” International Journal of Psychosocial Rehabilitation: 1475-7192. DOI: 10.13140/RG.2.2.13633.28000.

[19] Dinmukhamed Kelesbayev, Kundyz Myrzabekkyzy, Artur Bolganbayev, and Sabit Baimaganbetov. 2021. “The Impact of Oil Prices on the Stock Market and Real Exchange Rate: The Case of Kazakhstan”. International Journal of Energy Economics and Policy 12(1): 163-168. DOI: 10.32479/ijeep.11880.

[20] Ismail O. Fasanya, Oluwatomisin J. Oyewole, Oluwasegun B. Adekoya and Fopesaye O. Badaru, 2021. “Oil price and stock market behavior in GCC countries: Do asymmetries and structural breaks matter?” Energy Strategy Reviews 36: 2211-467X. DOI:  10.1016/j.esr.2021.100682.

[21] Inwook Hwang, Jaebeom Kim. 2021. “Oil price shocks and the US stock market: A nonlinear approach” journal of empirical finance 0927-5398. DOI: 10.1016/j.jempfin.2021.08.004.

[22] Abdorasoul Sadeghi _ Soheil Rounder. 2022. “Heterogeneous effects of oil structure and oil shocks on stock prices in different regimes: Evidence from oil-exporting and oil-importing countries” Resources policy 0301-4207. DOI:  101016/j.resourpol.2022.102596.

[23] Aktham Maghyereh and Hussein Abdoh. 2022. “Extreme dependence between structural oil shocks and stock markets in GCC countries” Resources Policy 76: 0301-4207. DOI:  10.1016/j.resourpol.2022.102626.

[24] Fenghua Wen, Minzhi Zhang, Jihong Xiao, Wei Yue. 2022. “The impact of oil price shocks on the risk-return relation in the Chinese stock market” Finance research letters 1544-6123. DOI: 10.1016/j.frl.2022.102788.

[25] Sajjadur Rahman. 2022. “The asymmetric effects of oil price shocks on the U.S.” Energy Economics 105: 0140-9883. DOI: 10.1016/j.resourpol.2022.102626.

[26] Sh Zeinedini, M. Sh Karimi, A. Khanzadi. 2022. “Impact of global oil and gold prices on the Iran stock market returns during The Covid-19 pandemic using the quantile regression approach” Resources policy 0301-4207. DOI: 10.1016/j.resoupol.2022.102602.

[27] Suganya Balakumar. 2022. “Do oil price shocks have any implications for stock return momentum?” Economic Analysis and Policy: 0313-5926. DOI: 10.1016/j.eap.2022.6.016.

[28] Thai Hong Le and Anh Tram Luong. 2022. “Dynamic spillovers between oil price, stock market, and investor sentiment: Evidence from the United States and Vietnam” Resources Policy 78: 0301-4207. DOI: 10.1016/j.resourpol.2022.102931.

[29]  Stavros Degiannakis, George Filis, Vipin Arora. 2018. “OIL PRICES AND STOCK MARKETS: A REVIEW OF THE THEORY AND EMPIRICAL EVIDENCE” The energy Journal, Vol.39, No.5 (September 2018), pp. 85-130.