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THE INTER-RELATIONSHIP BETWEEN COMMODITY ENERGY PRICES AND STOCK MARKET VOLATILITY IN SAUDI-ARABIA

Background and Purpose: The present study examines the inter-relationship that exists between commodity energy price as well as stock market volatility in Saudi-Arabia. The focus of the study is to test if changes in commodities energy prices (oil related) cause significant changes in the stock market volatility of Saudi Arabia. Methodology: This study made use of a generalized autoregressive conditional heteroscedasticity model which has exogenous variables (GARCH-X), thus able to employ the commodity energy price inform of an exogenous so as to test the conditional variance of the Saudi-Arabia stock market return. Findings: The findings from the estimated model provide evidence that only the ARCH and GARCH parameters are significant while the exogenous variables are insignificant. It is concluded that other factors affect the volatility of the Saudi-Arabia stock market, but not the commodity energy price. Contributions: This study recommends that, policy makers, investors, and regulators should give emphasis on macro-economic variables and volatility interdependence with other correlated markets, especially during energy price shock that affected the volatility of Saudi-Arabia stock market.

tied up in the energy industry and are determined by commodity prices, in which volatility affects the entire stock market. It is also imperative to mention that stock market volatility may depend on the importance of some macroeconomics situations, information and price of some important commodities of a country.
Figures below are graphs of selected volatility variables of this study. From the figures, it can be seen that the behaviors of the selected variables are different. The plots of Tadawul All Share Index (TASI) and Saudi Petrochemical Price (SPP) show that the data revolve around the mean, and the preliminary data show some sign of volatility cluster. The price is almost similar to that of TASI and SPP while that of SEP shows upward trending. The plots of all the data set exhibit features of economic and financial variables.      Available literature shows that the rise or fall in the oil and gas price will no doubt affects the stocks of the companies related to the oil and gas business market volatility. Awerbuch and Berger (2003) argued that beginning from 1980s. the oil price volatility despite energy options, is more detrimental to the stock market as well as the economy at large.
Therefore, this paper examines if changes in commodities energy prices (oil related) cause significant changes in the stock market volatility of Saudi Arabia. As such, through the empirical approach employed in the present study, the issue of the relationship that exists between commodity energy prices as well as stock market volatility in Saudi-Arabia are addressed. The paper employs a generalized autoregressive conditional heteroskedasticity model with exogenous variables (GARCH-X). This implies that the Saudi-Arabia market is run by more of external factors than internal factors. This represents that the Saudi market is more of export to foreign countries than importation.
Previous studies that were carried out on the relationship between oil price in relation to stock market return by several scholars are Sadorsky (2001), Abhyankar, Xu, and Wang (2013), and many others. Their studies discovered that oil price shock caused by supply possess a higher negative impact on the stock market than the ones which are caused by the demand of oil price shock (Cunado & Gracia, 2014). Most studies carried out on the impact of oil price in the developed countries stock markets, which in most cases oil importing nations have discovered that a negative relationship exists between oil price as well as stock market (Miller & Rati, 2009;Driesprong, Jacobsen, & Benjiman, 2008;Basher, Haug, & Sadorsky, 2012).
According to some other researches, there exists a positive relationship between oil price in relation to stock market among the oil-exporting nations (Lescaroux & Mignon, 2008;Mohanty, Nandha, Turkistani, & Alaitani, 2011;Hammoudeh & Choi, 2006;Fayyad & Daly, 2011). According to Samih and Loucine (2013), oil price impact on stock log returns has been statistically significant for both nations (Saudi Arabia and Kuwait). However, there has been double impact coefficient of oil price for the Saudi market, which is put at 0.1779 and seen to be a second contrast. Whereas the third contrast is found in the estimates of the model equation has explicitly explained that both expected return and undesired volatility matters in portfolio construction. Sadorsky (2003) argued that negative macroeconomic affects resource price volatility that even non-energy stocks seem to be driven by oil price volatility. This leaves a huge gap that the energy resource price and stock market volatility relationship has been ignored. These dynamics have not been observed together with exogenous energy price movements and oil price volatilities. Therefore, the objective of this paper is to determine if commodity energy price contributes to the stock market volatility of Saudi-Arabia. This study employs a new set of variables in the GARCH-X model, which has not been used in other studies.

LITERATURE REVIEW
The study by Hamilton (1983) is among the pioneer and forefront studies on the relationship of oil price changes on the economy. Subsequent studies such as Hamao (1988), Ferson and Harvey (1995), Kaneko and Lee (1995), Basher and Sadorsky (2006) However, just like the stock market volatility, it is difficult to predict the reactions of stocks to oil price (Ross, 2017). Studies conducted by Bashir and Sadorsky (2006), Bjornland ISSN 0127-9386 (Online) http://dx.doi.org/10.24200/jonus.vol5iss1pp270-293 Hooker (1996), Hamilton (2005), and Chinn, LeBlanc, and Coibion (2005) suggested that opposite result is found in the increase of the oil price because a hike in oil price will lead to major decrease in the stock market returns. In fact, several researchers like Kilian (2009), Balaz andLondarev (2006), Cunado and Gracia (2014), as well as Cologni and Manera (2008), have all revealed in their study that in some selected emerging and developed countries that import oil, there exists a negative relationship between economic activities in regard to oil prices. More recently, Kang, Ratti, and Vespignani (2017) analysed how oil price shock affects the oil and gas return in selected companies in the United States (US). The study selected Exxon mobile, BP, Royal Dutch Shell, and Chevron, and found that negative energy supply shock triggers negative returns in the oil and gas sector returns. Moreover, the study found that at in individual company level, the result is different that both positive and negative energy supply shock trigger positive responses to firm-level stock returns. This indicates that the relationship is not company-specific but sector or industry-specific. Furthermore, that study analysed the effect of commodity energy price on upstream, down-stream and middle stream companies (Valero Energy Corporation, Conoco Philips and Trans-Canada Corporation) and found that the responses of the companies are similar to that of the major oil companies that are previously explained.
Available studies carried out by Apergis and Miller (2009), Kilian (2009), and Jung and Park (2011) decomposed their study based on net oil producers as well as consumers' nations respectively. The study by Jung and Park (2011) specifically carried out analysis on the commodity energy price in regard to stock market in the country of Norway as well as Korea, where the study was able to prove that there is a more prevalent impact on the shock of the oil importing nations. Similarly, Wang, Wu, and Yang (2013) investigated 16 stock markets of nine oil-importing and seven oil-exporting countries and found that none of the stock markets responded to supply-side oil price shock. However, the study did not fond evidence of positive oil response from oil importing stock markets to positive aggregate demand shocks.
Another plausible explanation in the literature is how changes in commodity energy price affect stock market volatility. Ross (1989) opined that the volatility of the stock market could come from the volatility of some assets or important variables in an economy. Huang, Masulis, and Stoll (1996) justified that oil price movement and stock market volatility could be realized through their volatilities. Further studies in this regard are that of Malik and Ewing (2009), Vo (2011), and Arouri, Jouini, and Nguyen (2011. However, Boldanov, Degiannakis, and Filis (2016) as well as Maghyereh, Awartani, and Bouri (2016) recently found evidence that the volatility of the oil price is the major factor transmitting volatility to stock markets. 277 This supports the findings of Du and He (2015) that significant risk spillovers between oil and stock markets and negative spillovers flowed from oil volatility to stock market volatility. Also, of recent, Shaeri and Katircioğlu (2018) employed Carrion-i-Silvestre, Kim, and Perron unit root tests and Maki cointegration tests, allowing for multiple breaks. The study provided empirical evidence of long-run equilibrium relationships between these stock indices, crude oil prices, short-term interest rates, and the S&P 500 in the US. That study results portrayed that stock prices of oil companies are positively affected by crude oil prices to a greater degree than that of technology and transportation stocks.
In terms of the methodology, previous studies employed either Time-Varying Parameter VAR model of Primiceri (2005), multivariate GARCH models (such as the Dynamic Conditional Correlation) of Engle (2002) or Baba-Engle-Kraft and Kroner (BEKK) model by Baba, Engle, Kraft, and Kroner (1991) and Engle and Kroner (1995). In this regard, Kilian Compared with other models, it is envisaged in the present study that the GARCH-X process substantially exhibits different characteristics, in particular during the time the covariate is persistent in memory. The covariate is allowed to be stationary of long as well as short memory, and integrated or nonstationary of long memory. As such, here the study models the covariate as a fractionally integrated process that has a wide range of order of integration for it to be able to represent different kind of time series of the covariates employed in the model of the GARCH-X. This no doubt seems desirable, based on the fact that each of the employed covariate in the GARCH-X models presents various kind of degree of persistence.
On the other hand, there exist nonstationary of some of the time series covariates, which can therefore be modeled as unit root processes, while there is a clear rejection of the time series of other covariates towards the unit root hypothesis. More so, even if there is a rejection of the unit root hypothesis for these variables, yet the degree of persistence is mostly high in the economic variables that are employed in the GARCH-X models. Moreover, it is well-known that the time series of realized measures are also persistent. Taking as an instance, Andersen, Bollerslev, Diebold, and Labys (2003) Conversely, various empirical studies might have benefited from the use of correlation models, paving more rooms for the influence of exogenous variables. Taking as an instance, an ongoing debate has been going on with regards to the existence of contagion among stock market returns in relation to its potential triggers. As defined in many studies, contagion is a change in conditional correlations (Forbes & Rigobon, 2002;King & Wadhwani, 1990;Journal of Nusantara Studies 2020, Vol 5(1)  could be used to determine contagion as well as its causes. Another area of study has been channeled towards the benefits of international diversification (Longin & Solnik, 1995, 2001Solnik, Boucrelle, & Fur, 1996;Goetzmann, Li, & Rouwenhorst, 2008) and asset-allocation (Guidolin & Timmermann, 2008;d'Addona & Kind, 2006;Ang & Bekaert, 2002). Based on this, it is interesting to see why the existing correlations between markets change. In the same vein, there are fewer researches which have focused on the convergence in the Eurozone (Berben & Jansen, 2005) and searched for drivers that describe the increasing correlations among Eurozone markets.

METHODOLOGY
The objective of this paper is to determine whether commodity energy prices have impact on the volatility of Saudi-Arabia stock market. Therefore, this study tests if changes in commodities energy prices (oil related) cause significant changes in the Saudi stock market volatility. Since the development of the Autoregressive Conditional Heteroskedasticity (ARCH) model by Engle (1982) and the extension to generalized ARCH (GARCH) model by Bollerslev (1986), the ARCH family model has been widely used in determining volatility. The model witnesses various developments over the period, such as Exponential GARCH (EGARCH) of Nelson (1991), the GJR-GARCH of Glosten, Jagannathan, and Runkle (1993), the family GARCH (FGARCH) models of Hentschel (1995), Baillie, Bollerslev, and Mikkelsen (1996), fractional integrated GARCH (FIEGARCH) the threshold GARCH of Zako ïan (1992), and the multivariate GARCH by Engle and Kroner (1995), amongst others.
The present study employs a variant of the GARCH model which accommodates exogenous variables (GARCH-X) to meet the objective of the study. The development of the model started from the work of Sharma, Mougoue, and Kamath (1996) Starting with GARCH (p,q) which is defined as Equation (1) = ~ IID (0,1) (1) Here, is the innovation term and the conditional variance (volatility of the Saudi stock market) is given in Equation (2) Where, 2 is a non-negative and weak stationary, thus making the required conditions to be; Following the above GARCH model, the GARCH-X model is expressed in Equation (4) Where: σt 2 is its variance conditional on the information available at time t-1, ɛ 2 t-1 is the covariate, X is the exogenous variables in which the conditional variance 2 is determined by ARCH ( ),

ANALYSIS AND DISCUSSION
To meet the objective of the study, the monthly time series data of five variables, which are Saudi stock market return (TASI), Saudi petroleum price (SPP), global oil price (GOP), Saudi gas price (SGP), and Saudi interest rate (SIR) for the period of April 2007 to December 2017 are employed. Scholes and Williams (1977) argued that monthly data has some advantages over daily or weekly data because low trading data may induce errors in variables problems.
All the data were retrieved from data stream international, which were log-transformed except for TASI, which was converted from the market index.
The study examines whether commodity energy price volatility, Saudi petrochemicals price index SPP, Global oil price GOP, Saudi gas price SGP, and short-term interest rate SIR affect the conditional variance of the Saudi-Arabia stock market return (TASI). Table 1 reports the result of the descriptive statistics of the variables in this study. It is evidenced from the result that TASI has a positive mean of 0.000, which corresponds with a standard deviation of 0.068 that measures the risk of investing in the market. To measure the normality of data, this study employs skewness, kurtosis and Jarque-Bera statistics. The result shows that TASI variable is negatively skewed with a value of −0.561 and indicates that the skewness is non zero; hence, the variable is asymmetric in nature. The kurtosis of TASI is 4.521 which indicates the distribution of the series to be leptokurtic since it is greater than 3.
The Jarque-Bera statistics do the overall normality analysis; the result for the dependent variables indicates that the distribution is not normal. However, it is widely agreed that financial variables such as stock return are not normal. The result of the exogenous variables is almost similar to that of the dependent variable. It can be observed that all the exogenous variables have positive mean, and SPP is one of the less volatile of the variables with a standard deviation of 0.092. Furthermore, the Jarque-Bera test indicates that the distributions of one of the exogenous variables (GOP) are normally distributed. TASI, SPP, SGP and SIR were significant at 1% level of probability.     285 however is found in contrast with studies such as Ljungqvist and Palmqvist (2014), and Maghyereh et al. (2016).
The reliability of the inferences made from the estimated GARCH-X model depends upon the model passing the diagnostic checks. In the univariate GARCH models, the most widely and accepted diagnostic checks are autocorrelation, heteroscedasticity, and normality test. This study employs the Breusch-Godfrey test for autocorrelation and heteroscedasticity, which fails to reject the null hypothesis of no autocorrelation from lag 1-36. Similarly, there is no evidence of heteroscedasticity which concludes that the model is homoscedastic. Finally, the Jarque-Bera statistics indicate that the residuals of the model are normally distributed. The model has passed all the diagnostic checks whereby inference from the estimated model are statistically reliable.

CONCLUSION
This paper examined the relationship between commodity energy price and stock market volatility in Saudi-Arabia. Besides TASI, the study focused on three-commodity energy price: SPP, SGP and GOP and also included SIR in the estimations and analysis. The findings from the estimated GARCH-X model provided evidence of significant ARCH and GARCH parameter. Furthermore, the result did not provide empirical evidence that the exogenous variables have impact on the conditional variance (volatility) of the Saudi-Arabia stock exchange market. This indicates that the volatility of oil related energy price has no dynamic relationship in the Saudi stock market volatility. Hence, changes in commodities energy prices (oil related) do not cause significant changes in the Saudi stock market volatility. To conclude, it is important to stress that the analysis above has shown that the ARCH and GARCH parameter are the main factors that affect stock market volatility in Saudi-Arabia, not the commodity energy price. These findings indicate that the insignificance of the explanatory variables is because they are long-run determinants of the stock market volatility and not shortrun. As such, it cannot be properly captured by the method employed. It is recommended for further study to employ other methods that could be used to determine the significance of the exogenous variables. Therefore, the policy implication and recommendations from the analysis above are that policy makers, investors and regulators should give emphasis on how other macroeconomic variables and volatility interdependence with other correlated markets especially during energy price shock that affected the volatility of Saudi-Arabia stock market.