A reassessment of oil market volatility and stock market volatility: Evidence from selected SAARC countries

Aziz, Tariq Article A reassessment of oil market volatility and stock market volatility: Evidence from selected SAARC countries Comparative Economic Research. Central and Eastern Europe Provided in Cooperation with: Institute of Economics, University of Łódź Suggested Citation: Aziz, Tariq (2023) : A reassessment of oil market volatility and stock market volatility: Evidence from selected SAARC countries, Comparative Economic Research. Central and Eastern Europe, ISSN 2082-6737, Lodz University Press, Lodz, Vol. 26, Iss. 3, pp. 179-196, https://doi.org/10.18778/1508-2008.26.27 This Version is available at: https://hdl.handle.net/10419/289750 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ 179 AReassessmentofOilMarketVolatility andStockMarketVolatility:Evidencefrom SelectedSAARCCountries Tariq Aziz https://orcid.org/0000‑0001‑6073‑0628 Ph.D., Assistant Professor, Prince Muhammad Bin Fahd University, Finance and Accounting, Khobar, Dhahran Saudi Arabia, e‑mail: [email protected] Abstract Volatility spillover informs whether the information in one market impacts the information in an‑ other. This paper examines whether oil market volatility spills over to the equity markets of se‑ lected SAARC countries. The study uses data from February 2013 to September 2019 to ob‑ tain updated evidence about the transmission of global oil price volatility to the equity markets of the SAARC member countries. The bivariate EGARCH model is used to test for volatility trans‑ mission from the oil market to the stock market. It is found that oil price shocks do not signifi‑ cantly impact equity market volatility, except in Bangladesh. Policymakers can use these findings when making policy decisions. Keywords: stock market, oil market, volatility spillovers, information transmission, EGARCH JEL: C32, G15 Comparative Economic Research. Central and Eastern Europe Volume 26, Number 3, 2023 https://doi.org/10.18778/1508‑2008.26.27 © by the author, licensee University of Lodz – Lodz University Press, Poland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license CC‑BY‑NC‑ND 4.0 (https://creativecommons.org/licenses/by‑nc‑nd/4.0/) Received: 17.01.2023. Verified: 13.02.2023. Accepted: 10.06.2023 180 Tariq Aziz Introduction Emerging countries’ stock markets have grown rapidly over thelast few decades. It has created many investment opportunities inthose markets, increasing capital in‑ flows from developed markets toemerging markets (Beckmann, Berger, andCzudaj 2015). Despite this, global news andevents also affect emerging markets’ stock re‑ turns, making them more volatile andcreating anuncertain environment. Oil is one oftheworld’s most widely used andtraded products, andit remains theback‑ bone ofany economy. Because ofrecent major oil price fluctuations, research onoil markets received aboost as it has awide impact onthebusiness cycle ofany econ‑ omy. Studies have demonstrated that therelationship between stock returns andoil prices is still inconclusive, even though much research has been carried out onthis topic. Thestock market represents theeconomic condition ofany country (Ham‑ ilton andHerrera 2004; Kilian 2008; Korhonen andLedyaeva 2010). Therefore, oil market andstock market volatility spillover is crucial fordecision‑makers, such as energy policymakers andothers who mitigate therisk generated through these fluc‑ tuations inoil price changes. This is one ofthemost debatable topics, as evidenced by increased research inthis area (Filis, Degiannakis, andFloros 2011; Awartani andMaghyereh 2013; Ewing andMalik 2016; Kang, McIver, andYoon 2017). Intherecent past, theanalysis ofstock market returns has revealed ahigh level ofvolatility. Different countries have different macroeconomic conditions anddifferent types ofinvestors with different perspectives onthestock market, which causes this high volatility. Because oftheuncertainty inthestock market, risk‑averse investors are always reluctant toinvest inthestock market (Jones andKaul 1996). TheFinancial Crisis of2008 andtheEuropean sovereign debt crisis of2011 adversely affected investor sentiment (Malmendier andNagel 2011; Hoffmann, Post, andPennings 2013). Many papers have investigated thecorrelation be‑ tween stock andcommodity prices because oftheir diversification advantage intheeconomy. Tiwari andSahadudheen (2015) studied oil andgold as themost highly liquid commodities andfound movement instock market returns when oil andgold are included intheportfolio. That is why investors are motivated toadd commodities (gold andoil) totheir portfolios.Dur‑ ing economic downturns, which negatively impact investors’ returns, e.g., theeconomic crisis of1970, theRussian crisis in1997, theAsian financial crisis in1998, andtheglobal financial crisis of2007–2009, investors are encouraged tolook foralternative investments todiversify their assets andavoid return losses. Tohedge therisk, aportfolio construction strategy re‑ mains thefocus forall individual investors suggested by researchers andinvestment manag‑ ers. It will bring back investors’ confidence inthefinancial markets andhelp them prevent sudden losses because ofmarket turmoil. Much research has been conducted onoil as anasset inportfolio optimization inconnec‑ tion with stock returns. Researchers have found asignificant association ofthis asset class 181 A Reassessment of Oil Market Volatility and Stock Market Volatility… inportfolio optimization (Park andRatti 2008; Degiannakis, Filis, andKizys 2014; Lin andAppiah 2014; Khalfaoui, Boutahar, andBoubaker 2015; Wang andLiu 2016). According toa2015 report by Jadwa Investment, investor sentiment is affected by oil price fluctuations intheinternational market because 90%oftrading volumes are generated by individual investors, andthese sentiments drive investors totake invest‑ ment decisions. InSaudi Arabia (amajor oil exporting country), 86%ofgovernment revenue comes from oil andoil‑related products. Whenever theprice ofoil fluctu‑ ates intheinternational market, Saudi Arabia’s government expenditures andstock market returns are affected. Inaddition, according toRaza etal. (2016), all emerg‑ ing markets are less impacted by oil price changes. Thus, emerging markets have negative relationship with oil price. Global oil prices showed arapid decline from January 2013 tomid‑2015, andthen they increased steadily. Noor andDutta (2017) noted that volatility spillover exists between theoil market andthestock market forselected countries (i.e., Sri Lanka, India, andPakistan). Fowowe (2017) investigat‑ ed Nigeria andSouth Africa forvolatility spillover between oil prices andthestock market. They note that inNigeria, volatility spillover is not significant. Themixed results could be attributed totheuse ofdifferent data frequencies, different econo‑ metric models, or different variable measurements. Regarding themixed results andchanging patterns ofglobal oil prices, this study aims toprovide updated evi‑ dence ofvolatility spillover from global oil prices tothestock markets oftheSouth Asian Association forRegional Cooperation(SAARC) member countries. Thestudy contributes totheliterature inmultiple ways. First, it examines thevolatili‑ ty spillover between theoil market andthestock market forselected SAARC member countries. Second, thestudy uses theprices oftwo different oil standards, including Brent oil andWTI, which ensures consistent andreliable results. Third, thestudy em‑ ploys theEGARCH model, which allows fortheleverage effect andis more efficient than thesimple GARCH model andother linear regression models. Because therise andfall inoil prices donot cause uniform shocks tostock returns; thus, that behav‑ ior can be captured efficiently using theEGARCH model. Fourth, oil prices andstock prices are time‑varying variables; hence continuous screening andinvestigation are needed. Therefore, thecurrent study provides up‑to‑date evidence about thelink be‑ tween oil prices andstock returns. Therest ofthepaper is organized as follows. Thesecond part provides aliterature re‑ view, while thethird part provides theresearch methodology. Thefourth part contains theresults anddiscussion, while thefifth part provides conclusions. 182 Tariq Aziz Literature review Numerous researchers have examined global economies andfound that fluctuations inoil prices have significant andinsignificant impacts. Whenever there is anew oil price shock intheinternational market, themovement ofoil prices gains significance intheliterature, emphasizing anew research horizon. Earlier research (Hamilton 1983; Mork 1989; Kilian 2009) found significant evidence that oil prices negatively correlate with GDP, which means that oil price shocks will not be affected much andeconom‑ ic recessions are under control. Infour developed markets, including theUSA, Japan, Canada andtheUK, Jones andKaul (1996) found that oil prices pose asignificant risk factor forthestock market because they negatively impacted stock market returns. Incontrast, Sadorsky (2001) found that oil prices andstock market returns are positive‑ ly correlated. He found that key factors that affect thestock market are interest rates, risk premiums, andexchange rates. It may also be possible that both equity returns andoil prices have no relationship. Inthis regard, Wei (2003) found no relationship between stock market returns andoil prices because their correlation was insignifi‑ cant, which shows that oil prices cannot determine movements instock prices. Fayyad andDaly (2011) used (VAR) andused data from seven counties (theUAE, theUK, theUSA, Oman, Kuwait, Bahrain, andQatar) toinvestigate therelationship between stock market returns andoil prices. Empirical findings suggested that there is asig‑ nificant relationship between fluctuations inoil prices andGulf Coordination council (GCC) countries’ stock market returns. They also found that developed countries’ (UK andUSA) equity market returns are also affected by oil prices. Further, Arouri andNguyen (2010) showed that macroeconomic variables are af‑ fected by oil price fluctuations, including inflation, income level, interest rate, in‑ vestor confidence, andproduction costs. Thestudy conducted by Arouri, Lahiani, andNguyen (2011) investigated thespillover effects between oil andstock market re‑ turns by utilizing data from theUS andEuropean stock markets. Toexamine therole ofoil as anasset inhedging portfolio risk, they employed theVAR‑GARCH mod‑ el. Their analysis revealed that oil plays avital andnoteworthy role inboth hedg‑ ing portfolio risk andoptimizing portfolios.Further, Arouri, Jouini, andNguyen (2012) studied stock index returns andoil prices inEuropean countries interms ofvolatility spillovers. They found that price fluctuations ofoil andequity market returns have significant volatility transmission. This means that wemust under‑ stand this correlation first tomake anoptimal portfolio, which should include oil prices. Raza etal. (2016) examined developed countries andexamined therelation‑ ship by using asymmetric effects between oil andstock market returns. They found asignificant negative relationship between them. 183 A Reassessment of Oil Market Volatility and Stock Market Volatility… Emerging economies are negatively influenced by oil price volatilities. Lin andAppi‑ ah (2014) studied oil prices andstock market returns inGhana andNigeria interms ofvolatility spillover. They found when hedging stock market risk, oil is thebetter al‑ ternative asset forinvestment. They also found that theNigerian stock market was af‑ fected by oil price fluctuations; it means volatility spillover was higher inthat market. Meanwhile, theGhanaian stock market was more affected when hedging theportfo‑ lio risk. Singhal andGhosh (2016) studied thelink between theoil market andthestock mar‑ ket inIndia. They utilized theVAR_DCC_GARCH model tostudy thelink between two variables from January 1, 2006, toFebruary 28, 2015. They did not find significant volatility spillover between theoil market andthestock market. Dedi andYavas (2016) examined five countries forvolatility spillover between oil prices andthestock market. They employed various GARCH techniques tostudy this link forGermany, Turkey, Rus‑ sia, theUnited Kingdom, andChina. They noted that volatility spillover existed between all thecountries between March 31, 2011, andMarch 11, 2016. Many researchers have also found that news regarding oil price fluctuations specifical‑ ly affects emerging economies andcreates amore sensitive, ambiguous economic envi‑ ronment. Using data from eleven countries from 2008to2015, Maghyereh, Awartani, andBouri (2016) found that both oil andstock market returns are affected by news spill‑ overs. Guesmi (2014) used amultivariate GJRDCC‑GARCH model incountries that export oil (i.e., Venezuela, theKingdom ofSaudi Arabia (KSA), Kuwait, andtheUAE) andthose countries that import oil (theUSA, France, Italy, Germany, andtheNether‑ lands) toanalyze theimpact ofoil prices fluctuations onstock markets interms ofvola‑ tility spillover. They found that inperiods ofglobal turmoil, oil prices significantly im‑ pacted thestock market returns ofthese exports andimport‑oriented countries. Bouri (2015) studied theshock effects ofthe2008 global financial crisis onJordan andLeb‑ anon, which are small oil importers, by focusing onvolatility spillover tosee whether thefinancial crisis shocks had any impact ontheir price ofoil andstock market returns. He found that price ofoil andreturns ofstock market both were significantly affected inJordan but not inLebanon. Gbatu etal. (2017) applied theADL bounds test approach ofPesaran, Shin, andSmith (2001) toinvestigate theimpact ofoil price fluctuations ontheLiberian economy. They showed that oil prices have asignificant impact intheshort run, andReal GDP has anexus with these oil price fluctuations. Using data from February 2007 toJuly 2016, Trabelsi (2017) examined three major oil‑exporting economies (theUAE, Saudi Ara‑ bia, andRussia). He used thestock market indices ofthese countries tosee theimpact ofoil price spillovers. He adopted theDCC‑GARCH andCo Var measure. ForSaudi Arabia, he used theSaudi Arabia (TASI) Index, fortheUAE, he used theDFM index, 184 Tariq Aziz andforRussia, he used theRSI index. He noted asignificant negative relationship be‑ tween stock market indices andoil prices. Bouri etal. (2018) used quantile response methods andamultivariate regression quantile technique tosee therelationship between countries that export oil (i.e., Bra‑ zil andRussia) andthose that import oil (i.e., India andChina) by applying ashock transmission mechanism. He also used oil prices toexamine thedependence oftheoil shocks andBRICS sovereign risk onstock volatility. Theempirical results showed that there is anasymmetric effect. Inaddition, anegative shock inoil volatility impacts oil‑importing economies more, whereas apositive shock inoil volatility impacts oil‑ex‑ porting economies more. Ping etal. (2018) examined theenergy stock market incon‑ nection with fuel oil spots andfuel oil futures by focusing onvolatility spillover using DCC‑GARCH andVAR‑BEKK‑GARCH frameworks.They found these markets have bidirectional effects inconnection with volatility spillovers. Methodology Data and variables Thepaper uses monthly time series data from February 2013 toSeptember 2019 tostudy thevolatility spillover between stock market returns andoil prices. Forafair exam‑ ination, thestudy used thetwo most traded andpopular benchmark oil commodi‑ ties, i.e., West Texas Intermediate (WTI) andBrent North Sea Crude (Brent). Thedata ofboth benchmarks were obtained from Investing.com. Thetarget sample ofthestudy is SAARC member countries, although another four countries are included inthesam‑ ple, i.e., Pakistan, Sri Lanka, India, andBangladesh. These countries account formore than 90% market capitalization oftheSAARC region. Themarket data ofthecountries are taken from theglobal market data forum, Investing.com. Theprices are used atthepercentage difference level calculated by using thefollowing equations: ( ) 1 ,, 1 100, tt equity WTI Brent t price price Ret price - - éù - êú =× êú ëû (1) where 𝑅 (𝑒𝑞𝑢𝑖𝑡𝑦, 𝑊𝑇𝐼, 𝐵𝑟𝑒𝑛𝑡) shows thereturns/percentage change inoil prices andequity prices. 𝑃𝑟𝑖𝑐𝑒 t shows theprices ofthecurrent month, while 𝑃𝑟𝑖𝑐𝑒 t−1 represents theprices oftheprevious month. 185 A Reassessment of Oil Market Volatility and Stock Market Volatility… Unit root test As stationarity influences thebehavior ofvariables, thestationary andnon‑stationary variables are treated differently (Brooks 2019). Therefore, before selecting theeconomet‑ ric model, thelevel ofstationarity foreach variable is important. Thestudy uses theAug‑ mented Dickey‑Fuller test (ADF) andthePhillip‑Perron unit root test (PP) totest thesta‑ tionarity ofthevariables. Econometric model Toexamine thevolatility spillover effect between stock market returns andoil pric‑ es, Generalized Autoregressive Conditional Heteroscedastic (GARCH) andExponen‑ tial Generalized Autoregressive Conditional Heteroscedastic (EGARCH) models are employed. TheEGARCH model is anextended version oftheGARCH model toin‑ vestigate theleverage effect. TheGARCH model is awidely used model tostudy volatility that makes it possible topredict theconditional variance onits own lag terms (Brooks 2019). Thegeneral equations oftheGARCH model are given below: ( ) 2 1 µ , ~ 0, , t t tt t y yu Nad - =+ + ò(2) 2 22 0 11 1 . t tt ud b b gd -- =+ + (3) This is aunivariate standard GARCH (1,1) model, where equation (2) shows thecondi‑ tional mean andequation (3) represent theconditional variance. Themodel can convert toorder (p, q) by extending previous lags of𝑢𝑡 totheqth order, andthelag terms of 2 t δ tothepth order, as given below: 2 22 0 11 11 , qp t t jt j ij ud b b gd -- == =+ + åå (4) where equation (4) shows theconditional variance; 𝛽 0 , 𝛽 𝑖 , and𝛾 𝑗 are thecoefficient ofthemodel; 2 1t u - is theprevious lags oftheerror term, and 2 1t δ − is thelag terms ofthevar‑ iance. Thestudy employed abivariate GARCH (1,1) model tocheck thevolatility persis‑ tence. Thespecification ofthemodels is shown below: ( ) ( ) 2 22 1111 1 1 1 , tt t Ret t Ret u WTId a b gd w -- - =+ + + (5) 186 Tariq Aziz ( ) ( ) 2 22 2 21 2 2 1 1 , tt t Ret t Ret u Brentd a b gd w -- - =+ + + (6) where ( ) 2 t Ret d , ( ) 2 t WTI d and ( ) 2 t Brent d are theconditional variance ofstock market returns, WTI, andBrent, respectively. 𝛼1–𝛼4 are intercepts ofthemodels. 𝛽1–𝛽4 are thecoefficients ofmoving average terms (MA), which show theimpact oftheerror term onthecondi‑ tional variance. Similarly, 𝛾 1 𝛾 4 measures theimpact ofown lags ofthevariance onthevar‑ iance ofthecurrent month. 𝜔1−𝜔4 shows theimpact ofthelag term oftheindependent variable inthemodel ontheconditional variance ofthedependent variable. Toinvestigate theleverage effect, theEGARCH model is abetter tool that also does not necessitate thecondition ofpositive variances (Brooks 2019). Theconditional variance oftheEGARCH model is given below: ( ) 22 1 1 01 22 11 2 ln( ) ln . t t tt tt u u d abd g j d dp - - - -- éù êú =+ + + - êú ÖÖ ëû (7) Intheequation, ln(𝛿𝑡 2 ) shows thenatural log ofconditional variance; b measures thepersistence ofvolatility; g gives theexistence ofanasymmetric or leverage effect inthemodel; j provides thesymmetric effect. Thespecific equations fortheconditional mean andtheconditional variance ofthebi‑ variate EGARCH model are developed, following Kanas (2000) andJebran andIqbal (2016). Volatility transmission from WTI oil prices to stock market Toexamine thevolatility spillover or volatility transmission from WTI crude oil mar‑ ket returns tostock market returns, thealgebraic equations ofthebivariate EGARCH model are given below: 01 12 1 , t t tt Ret Ret WTI uaa a -- =+ + + (8) ( ) ( ) ( ) ( ) ( ) 22 1 1 01 2 12 21 1 2 ln ln , t t t Ret t Ret WTI t t u uVd bbd b j r dp d - - - - - éù êú =+ + + - + êú Ö ëû (9) where Ret shows thestock market returns, andV represents thevolatility ofWTI andρ provides thespillover effects. 193 A Reassessment of Oil Market Volatility and Stock Market Volatility… References Arouri, M.E., Nguyen, D.K. (2010), Oil prices, stock markets andportfolio investment: Evidence from sector analysis inEurope over thelast decade, “Energy Policy”, 38, pp.4528–5439, https://doi.org/10.1016/j.enpol.2010.04.007 Arouri, M.E.H., Jouini, J., Nguyen, D.K. 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Wykorzystano dwuwymiarowy model EGARCH do testowania przenoszenia zmienności z rynku ropy naftowej na rynek akcji. Należy zauważyć, że szoki cenowe na rynku ropy naftowej nie mają znaczącego wpływu na zmienność na rynkach akcji, z wyjątkiem rynku akcji w Bangladeszu. De‑ cydenci mogą wykorzystać te ustalenia przy podejmowaniu decyzji w obszarze polityki. Słowa kluczowe: giełda, rynek ropy naftowej, przenoszenie zmienności, transmisja informacji, EGARCH