Volatility Spillovers between Oil Prices and Stock Markets in Oil-Importing Countries: Evidence from a DCC-GARCH Model

Volatility Spillovers between Oil Prices and Stock Markets in Oil-Importing Countries: Evidence from a DCC-GARCH Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Volatility Spillovers between Oil Prices and Stock Markets in Oil-Importing Countries: Evidence from a DCC-GARCH Model Haseen Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8450522/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the volatility spillover dynamics between international oil prices and stock markets in selected oil-importing countries using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework. The model captures time-varying correlations and conditional volatility transmission, allowing for a comprehensive assessment of both short-term and persistent spillover effects. The empirical findings reveal significant joint ARCH and GARCH effects for most oil-importing stock markets, indicating the presence of both immediate volatility spillovers from oil prices and persistent transmission of uncertainty over time. However, notable heterogeneity across countries is observed. In particular, the Indian stock market exhibits insignificant joint ARCH and GARCH effects, suggesting a relatively weaker volatility linkage with oil prices, while short-term volatility transmission is found to be insignificant for the Korean stock market. For the remaining countries, oil price shocks significantly influence stock market volatility and its persistence. These results highlight that oil-induced financial risk is time-varying and country-specific in oil-importing economies. The findings have important implications for portfolio diversification, dynamic hedging strategies, and macro-financial policy formulation aimed at mitigating the adverse effects of oil price uncertainty on equity markets. Finance Oil prices Stock markets Volatility spillovers DCC-GARCH Oil-importing countries Time-varying correlation Introduction Oil prices play a pivotal role in shaping macroeconomic conditions and financial market dynamics, particularly in oil-importing economies where fluctuations in energy costs directly influence production expenses, inflation, corporate profitability, and investor sentiment. As oil remains a critical input for industrial activity and transportation, volatility in crude oil prices can transmit rapidly to equity markets, generating return and volatility spillovers that pose challenges for policymakers, investors, and risk managers. Consequently, understanding the nature, magnitude, and time-varying characteristics of oil–stock market linkages has attracted sustained academic and policy interest. The theoretical and empirical literature highlights multiple transmission channels through which oil price movements affect stock markets, including cost-push effects, aggregate demand shocks, monetary policy responses, and exchange rate adjustments (Degiannakis, Filis, & Arora, 2018 ). For oil-importing countries, rising oil prices are generally associated with adverse stock market responses due to higher input costs and reduced disposable income, while oil price declines may support equity valuations (Wang, Wu, & Yang, 2013 ; Filis & Chatziantoniou, 2014 ). However, empirical findings reveal that these relationships are neither linear nor stable over time, exhibiting asymmetries, regime dependence, and heterogeneity across countries and market conditions (Hashmi, Chang, & Bhutto, 2021 ; de Jesus, Bezerra, & Besarria, 2020; Sadeghi & Roudari, 2022 ). A growing body of research emphasizes the importance of volatility transmission rather than mean spillovers, as uncertainty in oil markets often exerts a stronger influence on equity market behaviour than price changes alone (Bouri, 2015 ; Joo & Park, 2021 ). Studies document significant bidirectional return and volatility spillovers between oil prices and stock markets in both oil-exporting and oil-importing economies, with stronger effects observed during periods of financial stress and global economic uncertainty (Guesmi & Fattoum, 2014 ; Khalfaoui, Sarwar, & Tiwari, 2019 ). Moreover, the impact of oil price volatility on stock markets differs across quantiles, regimes, and economic structures, underscoring the need for flexible econometric frameworks capable of capturing dynamic dependence (Mokni, 2020 ; Chkir et al., 2020 ). In this context, multivariate GARCH models—particularly the Dynamic Conditional Correlation (DCC-GARCH) framework—have emerged as a powerful tool for analysing time-varying correlations and volatility spillovers between oil and equity markets. Unlike static correlation models, DCC-GARCH allows correlations to evolve over time, thereby capturing shifts in market integration, contagion, and hedging effectiveness (Filis, Degiannakis, & Floros, 2011 ; Boldanov, Degiannakis, & Filis, 2016 ). Empirical evidence using DCC-based approaches confirms that oil–stock market correlations are highly unstable and sensitive to global shocks, geopolitical tensions, and financial crises, especially in oil-importing economies (Aydoğan, Tunç, & Yelkenci, 2017 ; Bein & Mehmet, 2016 ; Silvapulle et al., 2017 ). Building on this rich literature, the present study employs the DCC-GARCH methodology to examine volatility spillovers between international oil prices and stock markets in selected oil-importing countries. By focusing on time-varying correlations and conditional volatility transmission, this study contributes to the existing literature by offering deeper insights into the dynamic risk interdependence between energy and equity markets. The findings have important implications for portfolio diversification, hedging strategies, and macro-financial policy formulation in oil-dependent economies, particularly in an era marked by heightened geopolitical risk, energy market uncertainty, and global financial integration (Basher, Haug, & Sadorsky, 2012 ; Ashfaq, Tang, & Maqbool, 2019 ; Ahmed et al., 2025 ). Literature Review The relationship between oil prices and stock markets has been extensively examined in the finance and energy economics literature due to the central role of oil as a production input and its macro-financial implications. Early studies primarily focused on return linkages and mean spillovers, while more recent contributions have emphasized volatility transmission, time-varying dependence, asymmetry, and nonlinearity, particularly distinguishing between oil-exporting and oil-importing economies. Theoretical explanations for the oil–stock market nexus highlight several transmission channels, including cost-push inflation, corporate earnings, aggregate demand effects, exchange rate movements, and monetary policy responses (Degiannakis, Filis, & Arora, 2018 ). For oil-importing countries, increases in oil prices typically exert negative pressure on stock markets by raising production costs and reducing household consumption, whereas oil price declines may support equity valuations (Wang, Wu, & Yang, 2013 ; Cunado & de Gracia, 2014 ). However, empirical evidence suggests that stock market responses to oil price movements are neither uniform nor stable across countries and periods, reflecting structural differences, policy frameworks, and global economic conditions (Filis & Chatziantoniou, 2014 ; Bouoiyour et al., 2017 ). Beyond return effects, volatility spillovers have gained prominence as oil price uncertainty often has a stronger and more persistent impact on financial markets than price changes alone. Guesmi and Fattoum ( 2014 ) document significant bidirectional return and volatility transmission between oil prices and stock markets for both oil-importing and oil-exporting countries. Similarly, Khalfaoui, Sarwar, and Tiwari ( 2019 ) show that volatility spillovers are economically meaningful and have important implications for portfolio diversification and risk management. Focusing on oil-importing economies, Bouri ( 2015 ) finds that oil volatility shocks significantly influence stock market volatility in MENA oil-importing countries, especially during periods of financial stress. Joo and Park ( 2021 ) further demonstrate that oil price volatility negatively affects stock market performance in oil-importing countries, with effects intensifying during high-uncertainty regimes. Ashfaq, Tang, and Maqbool ( 2019 ) provide evidence of volatility spillovers from global oil prices to Asian energy-importing stock markets, reinforcing the importance of oil-induced uncertainty in shaping equity market dynamics. A major advancement in the literature is the recognition that oil–stock market relationships evolve over time. Filis, Degiannakis, and Floros ( 2011 ) employ a DCC-GARCH framework to analyse dynamic correlations between oil prices and stock markets, revealing substantial time variation and clear differences between oil-importing and oil-exporting countries. Their findings indicate that correlations strengthen during periods of economic turmoil, reducing diversification benefits. Boldanov, Degiannakis, and Filis ( 2016 ) extend this line of research by focusing on volatility correlations and confirm that oil and stock market volatilities exhibit significant time-varying dependence across importing and exporting economies. Aydoğan, Tunç, and Yelkenci ( 2017 ) also report that oil price volatility has a stronger adverse impact on stock markets in oil-importing countries, particularly during global uncertainty episodes. Evidence from European markets further supports the presence of dynamic oil–stock linkages (Bein & Mehmet, 2016 ; Silvapulle et al., 2017 ). Recent studies emphasize that the oil–stock market relationship is asymmetric and nonlinear. Hashmi, Chang, and Bhutto ( 2021 ) show that stock markets respond differently to oil price increases and decreases, with stronger adverse effects observed in oil-importing countries. Nonlinear dynamics are further documented by de Jesus et al. (2020) and Chkir et al. ( 2020 ), who highlight the role of exchange rates and structural breaks in shaping oil–stock interactions. Mokni ( 2020 ) employs a dynamic quantile regression framework and demonstrates that oil price effects vary across stock return distributions, indicating heightened vulnerability during extreme market conditions. Sadeghi and Roudari ( 2022 ) reinforce these findings by showing heterogeneous effects of oil shocks across regimes and economic structures. He et al. ( 2022 ) further reveal that oil price uncertainty significantly alters the risk–return relationship in stock markets, with distinct patterns across oil-importing and exporting economies. Studies focusing on emerging and developing economies highlight stronger and more unstable oil–stock market linkages due to higher energy dependence and macroeconomic vulnerability (Basher, Haug, & Sadorsky, 2012 ; Am & Shanmugasundaram, 2017 ). Arouri and Rault ( 2012 ) document long-run relationships between oil prices and stock markets in GCC countries, while Basher, Haug, and Sadorsky ( 2018 ) emphasize the role of oil-market shocks in driving stock returns. Recent contributions extend the analysis to broader macro-financial connectedness frameworks. Ahmed, Siddiqui, and Naushad ( 2025 ) examine oil prices, exchange rates, and stock markets in BRICS economies, highlighting dynamic interdependence during global turmoil. Ahmed and Kaur ( 2025 ) provide evidence of evolving connectedness among oil prices, inflation, and exchange rates in India, while Ahmed ( 2025 a, 2025 b, 2025 c) explores the role of green finance and advanced modelling techniques in understanding market dynamics. Despite extensive literature on oil–stock market interactions, several gaps remain. First, empirical evidence on time-varying volatility spillovers in oil-importing countries remains fragmented, particularly in the context of heightened global uncertainty. Second, many studies focus on returns or static correlations, potentially overlooking dynamic volatility transmission. Third, comparative insights across oil-importing economies using a unified DCC-GARCH framework are still limited. Addressing these gaps, the present study employs the DCC-GARCH methodology to provide robust evidence on volatility spillovers and dynamic correlations between oil prices and stock markets in oil-importing countries, contributing to both academic discourse and practical risk management strategies. Methodology and Data DCC-GARCH (Dynamic Conditional Correlation GARCH) Developed by Robert Engle in 2002, DCC-GARCH is the extended form of the GARCH model that models the correlation between the time-series data over the period. The parameters of the DCC-GARCH, model the volatility, and the linkages with the volatilities in the long and short-run. It is crucial tool to measure the diversification potential among the financial variables. The variance and mean equations of the DCC GARCH are the following. The α and β , parameters measure the ARCH and GARCH effects respectively. $$\:{\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:r}_{t}={u}_{t}+{e}_{t}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ \(\:{r}_{t}\) is the return of the series, \(\:{u}_{t}\) and \(\:{e}_{t}\) are constant and vector of residual respectively. $$\:{h}_{t}=c+\alpha\:{e}_{t-1}^{2}+\beta\:{h}_{t-1}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ 2 \(\:{h}_{t}\) here stands for the conditional volatility, whereas \(\:c\) is the constant. \(\:\alpha\:\) is the ARCH effect, whereas \(\:\beta\:\:\) measure the GARCH effect. H t = D t R t D t (3) matrix of conditional co-variance matrix is represented by the H t , whereas D t represents the k x k diagonal matrix with the time-varying standard deviations on the diagonal. R t is the time-varying correlation matrix. R t further can be defined as - R t = Q t *−1 Q t Q t *−1 (4) Q t = (1- \(\:\left(1-{\theta\:}_{1}-{\theta\:}_{2}\right){Q}^{*}+{\theta\:}_{1}{\phi\:}_{t-1}+{\theta\:}_{2}{\phi\:}_{t-1\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:}\) (5) Q t is the conditional variance that follows a GARCH process. Q t * is the unconditional co-variance estimated in the steps above. \(\:{\theta\:}_{1}\) and \(\:{\theta\:}_{2}\) are the scalar parameter measuring the long, and short-term effect on the conditional correlation. The conditional correlation estimator can be written as – $$\:{\rho\:}_{ijt}=\:\frac{{q}_{ijt}}{\sqrt{{q}_{ii\:}{q}_{jj}}}$$ 6 This study uses data stock markets of major oil importing countries given in Table 1 . The data ranges from 2013 to 2023. Table 1 Stock Markets in Oil Importing Countries Country Stock Market China Shanghai Composite India NSE USA Dow Jones Japan Nikkie225 Britain FTSE100 Netherlands AEX NETHERLANDS Germany DAX Spain IBEX35 Italy FTSE ITALIA South Korea KOSPIKO Results and Findings Volatility Spillover among Oil Prices and Stock Markets This section shows the result of the volatility spillover among oil prices and stock markets of oil-importing countries. The volatility transmission results are obtained by using the DCC GARCH introduced by Engle (2009). The parameters, mu, and omega show the estimated mean and constant term in the volatility equation. The alpha and beta parameters represent the ARCH and GARCH effect, whereas the dcca1 and dccb1 show the joint ARCH and GARCH between the two variables. Oil Prices and Shanghai Composite In the Chinese stock market, the Shanghai Composite, there is no significant volatility clustering in the series. However, there is a high and significant GARCH effect indicating the persistence in the volatility. The parameter beta1 is high and significant, indicating the spillover of volatility in the long run. The volatility transmission among the prices of oil and equity market in the short term is low as joint ARCH effect α (dcca1) is 0.02, whereas the long-term spillover, denoted by the β (dccb1) from oil prices to the stock market is high and significant. The model fits well, as the total α + β |t|) ROIL. Mu 0.000116 0.000340 0.34099 0.733108 ROIL.omega 0.000006 0.000004 1.37749 0.168361 ROIL.alpha1 0.122578 0.016371 7.48062 0.00000 ROIL.beta1 0.876422 0.013338 65.70812 0.00000 RSHANGHAICOMP. Mu 0.000221 0.000199 1.11205 0.266116 RSHANGHAICOMP.omega 0.000001 0.000003 0.37417 0.708280 RSHANGHAICOMP.alpha1 0.069225 0.045662 1.51602 0.129514 RSHANGHAICOMP.beta1 0.925512 0.042610 21.72059 0.000000 Dcca1 0.023457 0.012390 1.89318 0.058333 Dccb1 0.926205 0.045310 20.44163 0.000000 Oil Prices and NSE The stock market of India, the National Stock Exchange of India shows the significant ARCH and GARCH effects. The ARCH term is 0.08, indicating the presence of volatility clustering. Whereas the GARCH term is 0.89 indicating the strong persistence of volatility in the Indian stock market. Further, there is the transmission of volatility between oil and the stock market. The parameters α (dcca1), and β (dccb1) indicating spillover of volatility clustering and persistence of volatility are not significant. The oil prices do not cause volatility either in the short-term or long-term in the Indian stock market. Table 3 Oil Prices and NSE Estimate Std. Error t-value Pr (>|t|) ROIL. Mu 0.000116 0.000340 0.34054 0.733447 ROIL. omega 0.000006 0.000004 1.37991 0.167614 ROIL.alpha1 0.122578 0.016336 7.50359 0.000000 ROIL.beta1 0.876422 0.013372 65.54195 0.000000 RNSE.mu 0.000697 0.000175 3.98350 0.000068 RNSE. omega 0.000002 0.000001 2.02081 0.043300 RNSE.alpha1 0.083059 0.018420 4.50917 0.000007 RNSE.beta1 0.897417 0.017825 50.34613 0.000000 Dcca1 0.058878 0.045067 1.30644 0.191403 Dccb1 0.609562 0.426350 1.42972 0.152797 Oil Prices and Dow Jones There is significant and strong and significant volatility clustering and the persistence of volatility in the US stock market. The ARCH term and GARCH term are 0.20 and 0.75 respectively. There is a presence of spillover among the oil prices and the US stock market in the short-run is significant. The value of dcca1 is 0.04. Further, the spillover of persistence of volatility is high and significant. The value of dccb1, the value of the joint GARCH parameter is 0.88. Table 4 Oil Prices and Dow Jones Estimate Std.Error t-value Pr(>|t|) ROIL. Mu 0.000116 0.000340 0.34082 0.733236 ROIL.omega 0.000006 0.000004 1.37726 0.168431 ROIL.alpha1 0.122578 0.016326 7.50801 0.00000 ROIL.beta1 0.876422 0.013334 65.82688 0.00000 USDJ. Mu 0.000728 0.000168 4.32613 0.000015 USDJ.omega 0.000004 0.000006 0.67815 0.497673 USDJ.alpha1 0.208520 0.022731 9.17327 0.000000 USDJ.beta1 0.758959 0.081256 9.34037 0.000000 Dcca1 0.045859 0.013540 3.38683 0.000707 Dccb1 0.880438 0.041611 21.15868 0.000000 Oil Prices and NIKKIE225 The volatility clustering in the stock market of Japan is low as the ARCH term is 0.09, but significant. The persistence of volatility in Nikkie225, represented by the GARCH term is high at a value of 0.87 and is significant. The spillover of volatility clustering from oil prices to the Nikkie225 is low at a value of 0.03 but is significant, as shown by the parameter dcca1. The parameter dccb1 indicates that the spillover of persistence of volatility is high at 0.77. The spillover in the long run is high and significant, as compared to the spillover in the short run. Table 5 Oil Prices and NIKKIE225 Estimate Std. Error t-value Pr (>|t|) ROIL. Mu 0.000116 0.000339 0.34131 0.732870 ROIL.omega 0.000006 0.000004 1.37852 0.168043 ROIL.alpha1 0.122578 0.016304 7.51808 0.000000 ROIL.beta1 0.876422 0.013314 65.82472 0.000000 RNIKKIE225JP.mu 0.000569 0.000214 2.65814 0.007857 RNIKKIE225JP.omega 0.000005 0.000002 2.03959 0.41391 RNIKKIE225JP.alpha1 0.093133 0.011964 8.11896 0.000000 RNIKKIE225JP.beta1 0.873468 0.020186 43.27052 0.000000 Dcca1 0.032792 0.016641 1.97050 0.048781 Dccb1 0.770167 0.145049 5.30969 0.000000 Oil Prices and FTSE100 The British stock market, FTSE100 shows significant ARCH and GARCH effects. The ARCH and GARCH terms are 0.15 and 0.79 respectively, indicating the volatility clustering and persistence of volatility in the series. Further, the spillover of volatility clustering and the persistence of volatility are significant. The parameters dcca1 and dccb1, parameters of spillover of volatility clustering and, spillover of persistence of volatility are 0.05 and 0.79 respectively. Table 6 Oil Prices and FTSE100 Estimate Std.Error t-value Pr(>|t|) ROIL. Mu 0.000116 0.000339 0.34109 0.733037 ROIL.omega 0.000006 0.000004 1.37900 0.167896 ROIL.alpha1 0.122578 0.016340 7.50165 0.000000 ROIL.beta1 0.876422 0.013315 65.82399 0.000000 RFTSE100.mu 0.000291 0.000152 1.91415 0.055600 RFTSE100.omega 0.000005 0.000001 3.83200 0.000127 RFTSE100.alpha1 0.150354 0.014410 10.43372 0.000000 RFTSE100.beta1 0.795148 0.21768 36.52818 0.000000 Dcca1 0.052744 0.013130 4.01712 0.000059 Dccb1 0.790173 0.058525 13.50155 0.000000 Oil Prices and AEX Netherlands The table below indicates that the parameters mu and omega, representing the estimated mean and constant term in the volatility equation, are approximate—zero and not significant. The ARCH and GARCH terms are 0.15 and 0.81 respectively. The volatility clustering in the stock market of Amsterdam is higher than the volatility clustering in the oil prices. Further, the persistence in the volatility is higher than the volatility clustering. The volatility clustering transmission among oil and AEX is 0.05, whereas the spillover of persistence of volatility is high at a value of 0.80. There is a clear indication that the spillover is high in the long run. Table 7 Oil Prices and AEX Netherlands Estimate Std.Error t-value Pr(>|t|) ROIL. Mu 0.000116 0.000340 0.34085 0.733215 ROIL.omega 0.000006 0.000004 1.37848 0.168054 ROIL.alpha1 0.122578 0.016397 7.47559 0.000000 ROIL.beta1 0.876422 0.013328 65.75631 0.000000 RAEXNETH.mu 0.000644 0.000224 2.87256 0.004072 RAEXNETH.omega 0.000004 0.000006 0.70659 0.479822 RAEXNETH.alpha1 0.152681 0.018405 8.29566 0.000000 RAEXNETH.beta1 0.814253 0.057337 14.20121 0.000000 Dcca1 0.051327 0.017451 2.94116 0.003270 Dccb1 0.806225 0.069573 11.58824 0.000000 Oil Prices and German DAX Similar to the volatility results of the Netherlands stock market, the German stock market also follows a similar pattern. There is a presence of volatility clustering, as the ARCH effect is 0.10, whereas the persistence of volatility is high, as indicated by the high value of the GARCH effect, 0.85. Further, the spillover of volatility clustering from the oil prices to the DAX is low at 0.04, whereas it is 0.86 in case of spillover of persistence of volatility, as indicated by the dcca1 and dccb1 respectively. Overall, the long-term spillover between the volatility of oil prices and the German share market is present. Table 8 Oil Prices and DAX Estimate Std.Error t-value Pr(>|t|) ROIL. Mu 0.000116 0.000339 0.34154 0.732695 ROIL.omega 0.000006 0.000004 1.37864 0.168007 ROIL.alpha1 0.122578 0.016342 7.50069 0.000000 ROIL.beta1 0.876422 0.013310 65.84524 0.000000 RDAX.mu 0.000602 0.000196 3.07040 0.002138 RDAX.omega 0.000005 0.000004 1.16693 0.243239 RDAX.alpha1 0.109750 0.015744 6.97095 0.000000 RDAX.beta1 0.858325 0.028888 29.71230 0.000000 Dcca1 0.042159 0.014278 2.95278 0.003149 Dccb1 0.869890 0.047338 18.37599 0.000000 Oil Prices and Korean Stock Exchange KOSPI, the stock market of South Korea shows an ARCH effect of 0.08 and a high GARCH effect of 0.87, indicating the presence of volatility clustering and persistence of volatility respectively. The spillover of volatility clustering is not significant, however, there is a high and significant spillover of persistence of volatility. The parameter of persistence of spillover dccb1, β is 0.78. Table 9 Oil Prices and Korean Stock Exchange Estimate Std.Error t-value Pr(>|t|) ROIL. Mu 0.000116 0.000339 0.34109 0.73304 ROIL.omega 0.000006 0.000004 1.37873 0.16798 ROIL.alpha1 0.122578 0.016360 7.49241 0.000000 ROIL.beta1 0.876422 0.013353 65.63464 0.000000 RKOSPIKO.mu 0.000185 0.000163 1.13058 0.25823 RKOSPIKO.omega 0.000003 0.000004 0.68774 0.49162 RKOSPIKO.alpha1 0.089322 0.012437 7.18170 0.000000 RKOSPIKO.beta1 0.878663 0.029517 29.76821 0.000000 Dcca1 0.018308 0.012249 1.49469 0.13500 Dccb1 0.782184 0.116808 6.69630 0.000000 Oil Prices and FTSE ITALIA The results indicate that there is a presence of volatility clustering and persistence of volatility in the series. The ARCH term alpha1 is 0.12, and the GARCH term beta is 0.83. The volatility spillover between the oil prices and the stock market of Italy is low but significant. The value of the ARCH term is 0.03, whereas the long-term spillover of the persistence of the volatility is high as well as significant. The joint GARCH parameter, dccb1 is 0.87. Table 10 Prices and FTSE ITALIA Estimate Std.Error t-value Pr(>|t|) ROIL. Mu 0.000116 0.000339 0.34121 0.732948 ROIL.omega 0.000006 0.000004 1.37780 0.168267 ROIL.alpha1 0.122578 0.016340 7.50168 0.00000 ROIL.beta1 0.876422 0.013351 65.8693 0.00000 RFTSEITALIA.mu 0.000622 0.000200 1.42271 0.154821 RFTSEITALIA.omega 0.000008 0.000002 4.97644 0.000001 RFTSEITALIA.alpha1 0.127574 0.011538 11.05637 0.000000 RFTSEITALIA.beta1 0.831644 0.016878 49.27332 0.000000 Dcca1 0.035912 0.012398 2.89667 0.003772 Dccb1 0.877517 0.041084 21.35898 0.000000 Oil Prices and IBEX35 Spain There is a significant presence of volatility clustering and persistence of volatility in the stock market of Spain. The values of parameters alpha1 and beta are 0.13 and 0.82 respectively. Further, the spillover of volatility clustering is low. The value of the joint ARCH parameter, dcca1 is 0.04, whereas, the spillover of persistence is high and significant. The value of joint GARCH parameter dccb1 is 0.86. Table 11 Oil Prices and IBEX35 Spain Estimate Std.Error t-value Pr(>|t|) ROIL. Mu 0.000116 0.000339 0.34108 0.733043 ROIL.omega 0.000006 0.000004 1.37454 0.169275 ROIL.alpha1 0.122578 0.016315 7.51337 0.00000 ROIL.beta1 0.876422 0.013336 65.71815 0.00000 IBEX35.SPAIN.mu 0.000285 0.000200 1.42271 0.154821 IBEX35.SPAIN.omega 0.000007 0.000002 3.09826 0.001947 IBEX35.SPAIN.alpha1 0.135740 0.011901 11.40559 0.000000 IBEX35.SPAIN.beta1 0.824641 0.018081 45.60705 0.000000 dcca1 0.043875 0.015490 2.83243 0.004620 dccb1 0.866509 0.056951 15.21499 0.000000 The DCC GARCH results indicate the significant joint ARCH and GARCH effect, except the relationship with the stock market of India, which shows the insignificant joint ARCH and GARCH effects. Further, the short-term transmission is not significant between the oil and the Korean Stock Exchange. Among the other countries, there is significant ARCH, GARCH effect, the spillover of volatility, and the spillover of persistence of volatility. Discussion The findings from the DCC-GARCH analysis provide important insights into the dynamic volatility spillovers between international oil prices and stock markets of oil-importing countries. The presence of significant joint ARCH and GARCH effects for most markets indicates that oil price shocks and their associated volatility are transmitted to equity markets not only contemporaneously but also persist over time. This suggests that oil-related uncertainty plays a crucial role in shaping stock market risk dynamics in oil-importing economies, consistent with the volatility transmission hypothesis documented in earlier studies (Filis, Degiannakis, & Floros, 2011 ; Guesmi & Fattoum, 2014 ; Khalfaoui, Sarwar, & Tiwari, 2019 ). The insignificance of the joint ARCH and GARCH effects in the case of India suggests a relatively weaker volatility linkage between oil prices and the Indian stock market. This finding may reflect India’s diversified economic structure, policy interventions such as fuel price regulation and strategic reserves, and the growing role of non-energy sectors in equity market valuation. Similar evidence of heterogeneous oil–stock market dependence across countries has been reported in the literature, highlighting the importance of country-specific macroeconomic conditions and institutional frameworks (Wang, Wu, & Yang, 2013 ; Hashmi, Chang, & Bhutto, 2021 ). Moreover, recent evidence points to the increasing role of alternative financial channels and policy buffers in mitigating oil-induced financial volatility in emerging markets (Ahmed & Kaur, 2025 ). The absence of significant short-term volatility transmission between oil prices and the Korean Stock Exchange suggests that oil price shocks do not immediately translate into equity market volatility in Korea. This may be attributed to Korea’s strong export-oriented industrial base, effective hedging practices, and the ability of firms to absorb energy cost fluctuations in the short run. Previous studies have similarly observed delayed or weak short-term oil–stock linkages in economies with advanced financial markets and strong institutional resilience (Boldanov, Degiannakis, & Filis, 2016 ; Bein & Mehmet, 2016 ). However, the lack of short-run spillovers does not necessarily imply long-run independence, as oil price volatility may still affect equity markets through indirect macroeconomic channels over time. For the remaining oil-importing countries in the sample, the significant ARCH effects indicate immediate volatility spillovers from oil prices to stock markets, while the significant GARCH effects reflect persistence in volatility transmission. This implies that oil price shocks not only generate immediate uncertainty in equity markets but also lead to prolonged periods of elevated volatility. Such persistence is particularly relevant during periods of global economic stress, when oil market uncertainty intensifies and amplifies financial market instability (Bouri, 2015 ; Joo & Park, 2021 ). These findings align with the view that oil price volatility acts as a systemic risk factor for stock markets in oil-dependent economies (Degiannakis, Filis, & Arora, 2018 ). From a portfolio management perspective, the observed volatility spillovers and persistence reduce the effectiveness of diversification between oil and equity assets during turbulent periods. The time-varying nature of volatility transmission underscores the importance of dynamic hedging strategies rather than static portfolio allocations, particularly for investors exposed to oil-importing markets (Khalfaoui et al., 2019 ; Ashfaq, Tang, & Maqbool, 2019 ). Policymakers should also note that sustained oil price volatility can have destabilizing effects on financial markets, warranting proactive energy and macro-financial policies aimed at reducing exposure to external energy shocks. Overall, the results reinforce the notion that oil–stock market volatility linkages are heterogeneous across oil-importing countries and evolve over time. While some markets exhibit strong and persistent spillovers, others display relative insulation in the short run, reflecting differences in economic structure, policy frameworks, and financial market maturity. By employing the DCC-GARCH framework, this study contributes to the literature by capturing these nuanced dynamics and providing robust evidence on the transmission of oil price volatility to equity markets in oil-importing economies. Conclusion This study examines the volatility spillover dynamics between international oil prices and stock markets of selected oil-importing countries using the Dynamic Conditional Correlation GARCH (DCC-GARCH) framework. The empirical results reveal strong evidence of time-varying volatility transmission from oil prices to equity markets in most of the sampled countries. The presence of significant joint ARCH and GARCH effects confirms that oil price shocks not only generate immediate volatility in stock markets but also lead to persistent uncertainty over time. These findings underscore the importance of oil price volatility as a key source of systemic risk for oil-dependent economies. However, the results also highlight notable cross-country heterogeneity. The Indian stock market exhibits insignificant joint ARCH and GARCH effects, suggesting a relatively weaker volatility linkage with oil prices. This indicates a degree of insulation from oil-induced financial volatility, potentially reflecting economic diversification, policy interventions, and evolving market structures. Similarly, the absence of significant short-term volatility transmission between oil prices and the Korean stock market points to delayed or indirect adjustment mechanisms, possibly driven by institutional strength, effective risk management practices, and industrial resilience. Overall, the findings confirm that oil–stock market relationships in oil-importing countries are neither uniform nor static. Instead, they are shaped by country-specific economic structures, financial market maturity, and policy frameworks. By capturing these dynamics through a DCC-GARCH approach, the study contributes to the literature by providing robust evidence on the evolving nature of volatility spillovers between energy and equity markets. The documented volatility spillovers and persistence effects have important implications for portfolio diversification and risk management. The presence of time-varying correlations implies that diversification benefits between oil and equity assets diminish during periods of heightened oil market uncertainty. Investors operating in oil-importing markets should therefore adopt dynamic hedging strategies that account for changing volatility conditions rather than relying on static asset allocations. In markets exhibiting strong and persistent spillovers, oil price movements should be closely monitored as a leading indicator of equity market risk. For policymakers in oil-importing economies, the findings highlight the importance of reducing vulnerability to external energy price shocks. Persistent oil-induced volatility in stock markets can undermine financial stability, discourage investment, and amplify macroeconomic uncertainty. Policies aimed at energy diversification, strategic petroleum reserves, and effective fuel pricing mechanisms can help mitigate the transmission of oil price volatility to financial markets. Strengthening financial market depth and promoting risk-hedging instruments may further enhance resilience against external shocks. The heterogeneous spillover patterns observed across countries suggest several avenues for future research. Subsequent studies may incorporate regime-switching or nonlinear frameworks to capture structural breaks and crisis-specific dynamics. Expanding the analysis to include exchange rates, inflation, or climate-related financial instruments could provide a more comprehensive understanding of macro-financial connectedness. Additionally, comparative analyses between oil-importing and oil-exporting countries using high-frequency data may yield further insights into the evolving role of oil in global financial markets. Declarations The author declares that this manuscript is an original work and has not been published previously, nor is it under consideration for publication elsewhere. The research presented in this paper forms a part of the author’s doctoral thesis submitted to Jamia Millia Islamia, New Delhi. All sources of data and references have been appropriately acknowledged, and the manuscript complies with ethical standards of academic research. The author declares no conflict of interest related to this study. References Ahmed H (2025) From Integration to Strategy. Deep Learning Insights into Turkey’s Global Financial Linkages Ahmed H (2025) Is It Getting Green? Insights into Green Bond Influence on Stock Markets of Major Oil-Exporting Countries. Insights into Green Bond Influence on Stock Markets of Major Oil-Exporting Countries (September 01, 2025) Ahmed H (2025) Green Finance and Market Dynamics: Insights from Deep Learning based LSTM-Copula and MS-VAR Model. Available at SSRN 5641457 Ahmed H, Kaur R (2025) Dynamic Connectedness Among Oil Prices, Exchange Rate and Consumer Inflation: New Evidence from India. IIM Kozhikode Soc Manage Rev, 22779752251346247 Ahmed H, Siddiqui TA, Naushad M (2025) Navigating global economic turmoil: The dynamics of oil prices, exchange rates, and stock markets in BRICS. Invest Manage Financial Innovations 22(1):94 Am MA, Shanmugasundaram G (2017) Nexus between crude oil price, exchange rate and stock market: Evidence from oil exporting and importing economies. Int J Humanit Manage Sci, 5 (1) Arouri MEH, Rault C (2012) Oil prices and stock markets in GCC countries: empirical evidence from panel analysis. Int J Finance Econ 17(3):242–253 Ashfaq S, Tang Y, Maqbool R (2019) Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies’ stock returns. Energy 188:116002 Aydoğan B, Tunç G, Yelkenci T (2017) The impact of oil price volatility on net-oil exporter and importer countries’ stock markets. Eurasian Economic Rev 7(2):231–253 Basher SA, Haug AA, Sadorsky P (2012) Oil prices, exchange rates and emerging stock markets. Energy Econ 34(1):227–240 Basher SA, Haug AA, Sadorsky P (2018) The impact of oil-market shocks on stock returns in major oil-exporting countries. J Int Money Finance 86:264–280 Bein MA, Mehmet AGA (2016) International crude oil price and stock markets: Evidence from the Nordic and other European oil importing and oil exporting countries. Romanian J Economic Forecast 19(4):115 Boldanov R, Degiannakis S, Filis G (2016) Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries. Int Rev Financial Anal 48:209–220 Bouoiyour J, Selmi R, Shahzad SJH, Shahbaz M (2017) Response of stock returns to oil price shocks: Evidence from oil importing and exporting countries. J Econ Integr, 913–936 Bouri E (2015) Oil volatility shocks and the stock markets of oil-importing MENA economies: A tale from the financial crisis. Energy Econ 51:590–598 Chkir I, Guesmi K, Brayek AB, Naoui K (2020) Modelling the nonlinear relationship between oil prices, stock markets, and exchange rates in oil-exporting and oil-importing countries. Res Int Bus Finance 54:101274 Cunado J, de Gracia FP (2014) Oil price shocks and stock market returns: Evidence for some European countries. Energy Econ 42:365–377 de Jesus DP, Bezerra BFL (2020) S., & da Nóbrega Besarria, C. The non-linear relationship between oil prices and stock prices: Evidence from oil-importing and oil-exporting countries. Research in International Business and Finance , 54 , 101229 Degiannakis S, Filis G, Arora V (2018) Oil prices and stock markets: A review of the theory and empirical evidence. Energy J 39(5):85–130 Filis G, Chatziantoniou I (2014) Financial and monetary policy responses to oil price shocks: evidence from oil-importing and oil-exporting countries. Rev Quant Financ Acc 42(4):709–729 Filis G, Degiannakis S, Floros C (2011) Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. Int Rev Financial Anal 20(3):152–164 Guesmi K, Fattoum S (2014) Return and volatility transmission between oil prices and oil-exporting and oil-importing countries. Econ Model 38:305–310 Hashmi SM, Chang BH, Bhutto NA (2021) Asymmetric effect of oil prices on stock market prices: New evidence from oil-exporting and oil-importing countries. Resour Policy 70:101946 He Z, Chen J, Zhou F, Zhang G, Wen F (2022) Oil price uncertainty and the risk-return relation in stock markets: Evidence from oil‐importing and oil‐exporting countries. Int J Finance Econ 27(1):1154–1172 Joo YC, Park SY (2021) The impact of oil price volatility on stock markets: Evidences from oil-importing countries. Energy Econ 101:105413 Khalfaoui R, Sarwar S, Tiwari AK (2019) Analysing volatility spillover between the oil market and the stock market in oil-importing and oil-exporting countries: Implications on portfolio management. Resour Policy 62:22–32 Mokni K (2020) A dynamic quantile regression model for the relationship between oil price and stock markets in oil-importing and oil-exporting countries. Energy 213:118639 Sadeghi A, Roudari S (2022) Heterogeneous effects of oil structure and oil shocks on stock prices in different regimes: Evidence from oil-exporting and oil-importing countries. Resour Policy 76:102596 Silvapulle P, Smyth R, Zhang X, Fenech JP (2017) Nonparametric panel data model for crude oil and stock market prices in net oil importing countries. Energy Econ 67:255–267 Wang Y, Wu C, Yang L (2013) Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries. J Comp Econ 41(4):1220–1239 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8450522","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565937052,"identity":"21982833-833e-4d70-b19a-ca7ae406cd26","order_by":0,"name":"Haseen Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACCSDmbQAzGR8kGNiA6MYDxGphNnhQkQbS0kC0FjbJB2cOg1l4tfDP7k588HaHnT0/e+8BicS283Zr2w8DbamxicZpyZ2zmw3nnklOnNlzLsEgse128rYziUAtx9JyG3DpuZG7TZq3jTnB4EaOQQJIi9kBoBbGhsM4tcjfyN3+m7et3t7+/huDA4lt55LNzj/Er8UAaAszb9thxg0SPIYNCWcO2JndIGCL4Y3czZJz244nzjiTY8yQUJGcYHYDaEsCHr/I3cjd+OFtW7U9f/sZ858/DOzszc6nP3zwocYGt/fRQSJYZQKxykHAnhTFo2AUjIJRMDIAAKuea8RUYZtmAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-1323-5162","institution":"New Delhi Institute of Management","correspondingAuthor":true,"prefix":"","firstName":"Haseen","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2025-12-25 18:13:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8450522/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8450522/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99679865,"identity":"22f12ca2-f449-40e3-b999-20437c4ae89f","added_by":"auto","created_at":"2026-01-07 08:48:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60768,"visible":true,"origin":"","legend":"","description":"","filename":"22023.docx","url":"https://assets-eu.researchsquare.com/files/rs-8450522/v1/996683635bd526d3b2533492.docx"},{"id":99795330,"identity":"5830c347-c925-4386-b2c3-670e63838ecc","added_by":"auto","created_at":"2026-01-08 13:37:47","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8450522.json","url":"https://assets-eu.researchsquare.com/files/rs-8450522/v1/59a7503ac68289bb3b708af0.json"},{"id":99797016,"identity":"7d436045-01c2-40a9-b055-a524ffddb082","added_by":"auto","created_at":"2026-01-08 13:44:23","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130507,"visible":true,"origin":"","legend":"","description":"","filename":"rs84505220enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8450522/v1/324aeee8d6c481d8a6733ff2.xml"},{"id":99679867,"identity":"6b41bb59-3093-41f2-9a2e-b36248ed70a0","added_by":"auto","created_at":"2026-01-07 08:48:25","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128213,"visible":true,"origin":"","legend":"","description":"","filename":"rs84505220structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8450522/v1/acd3194b24b02b40ada80d3c.xml"},{"id":99679869,"identity":"2faba1cb-db76-4397-9eea-dbbde7f689f2","added_by":"auto","created_at":"2026-01-07 08:48:25","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137054,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8450522/v1/3719b239a18675489cc64c72.html"},{"id":100356752,"identity":"fbf3ba2b-9a46-4627-bde9-613c44ab72f4","added_by":"auto","created_at":"2026-01-16 07:17:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999363,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8450522/v1/89053aad-07b8-4fb5-aeb1-8118151988dc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eVolatility Spillovers between Oil Prices and Stock Markets in Oil-Importing Countries: Evidence from a DCC-GARCH Model\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOil prices play a pivotal role in shaping macroeconomic conditions and financial market dynamics, particularly in oil-importing economies where fluctuations in energy costs directly influence production expenses, inflation, corporate profitability, and investor sentiment. As oil remains a critical input for industrial activity and transportation, volatility in crude oil prices can transmit rapidly to equity markets, generating return and volatility spillovers that pose challenges for policymakers, investors, and risk managers. Consequently, understanding the nature, magnitude, and time-varying characteristics of oil–stock market linkages has attracted sustained academic and policy interest.\u003c/p\u003e \u003cp\u003eThe theoretical and empirical literature highlights multiple transmission channels through which oil price movements affect stock markets, including cost-push effects, aggregate demand shocks, monetary policy responses, and exchange rate adjustments (Degiannakis, Filis, \u0026amp; Arora, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For oil-importing countries, rising oil prices are generally associated with adverse stock market responses due to higher input costs and reduced disposable income, while oil price declines may support equity valuations (Wang, Wu, \u0026amp; Yang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Filis \u0026amp; Chatziantoniou, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, empirical findings reveal that these relationships are neither linear nor stable over time, exhibiting asymmetries, regime dependence, and heterogeneity across countries and market conditions (Hashmi, Chang, \u0026amp; Bhutto, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; de Jesus, Bezerra, \u0026amp; Besarria, 2020; Sadeghi \u0026amp; Roudari, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA growing body of research emphasizes the importance of volatility transmission rather than mean spillovers, as uncertainty in oil markets often exerts a stronger influence on equity market behaviour than price changes alone (Bouri, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Joo \u0026amp; Park, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies document significant bidirectional return and volatility spillovers between oil prices and stock markets in both oil-exporting and oil-importing economies, with stronger effects observed during periods of financial stress and global economic uncertainty (Guesmi \u0026amp; Fattoum, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Khalfaoui, Sarwar, \u0026amp; Tiwari, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, the impact of oil price volatility on stock markets differs across quantiles, regimes, and economic structures, underscoring the need for flexible econometric frameworks capable of capturing dynamic dependence (Mokni, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chkir et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, multivariate GARCH models—particularly the Dynamic Conditional Correlation (DCC-GARCH) framework—have emerged as a powerful tool for analysing time-varying correlations and volatility spillovers between oil and equity markets. Unlike static correlation models, DCC-GARCH allows correlations to evolve over time, thereby capturing shifts in market integration, contagion, and hedging effectiveness (Filis, Degiannakis, \u0026amp; Floros, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Boldanov, Degiannakis, \u0026amp; Filis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Empirical evidence using DCC-based approaches confirms that oil–stock market correlations are highly unstable and sensitive to global shocks, geopolitical tensions, and financial crises, especially in oil-importing economies (Aydoğan, Tunç, \u0026amp; Yelkenci, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bein \u0026amp; Mehmet, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silvapulle et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on this rich literature, the present study employs the DCC-GARCH methodology to examine volatility spillovers between international oil prices and stock markets in selected oil-importing countries. By focusing on time-varying correlations and conditional volatility transmission, this study contributes to the existing literature by offering deeper insights into the dynamic risk interdependence between energy and equity markets. The findings have important implications for portfolio diversification, hedging strategies, and macro-financial policy formulation in oil-dependent economies, particularly in an era marked by heightened geopolitical risk, energy market uncertainty, and global financial integration (Basher, Haug, \u0026amp; Sadorsky, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ashfaq, Tang, \u0026amp; Maqbool, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ahmed et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLiterature Review\u003c/h3\u003e\n\u003cp\u003eThe relationship between oil prices and stock markets has been extensively examined in the finance and energy economics literature due to the central role of oil as a production input and its macro-financial implications. Early studies primarily focused on return linkages and mean spillovers, while more recent contributions have emphasized volatility transmission, time-varying dependence, asymmetry, and nonlinearity, particularly distinguishing between oil-exporting and oil-importing economies.\u003c/p\u003e \u003cp\u003eTheoretical explanations for the oil–stock market nexus highlight several transmission channels, including cost-push inflation, corporate earnings, aggregate demand effects, exchange rate movements, and monetary policy responses (Degiannakis, Filis, \u0026amp; Arora, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For oil-importing countries, increases in oil prices typically exert negative pressure on stock markets by raising production costs and reducing household consumption, whereas oil price declines may support equity valuations (Wang, Wu, \u0026amp; Yang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cunado \u0026amp; de Gracia, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, empirical evidence suggests that stock market responses to oil price movements are neither uniform nor stable across countries and periods, reflecting structural differences, policy frameworks, and global economic conditions (Filis \u0026amp; Chatziantoniou, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bouoiyour et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond return effects, volatility spillovers have gained prominence as oil price uncertainty often has a stronger and more persistent impact on financial markets than price changes alone. Guesmi and Fattoum (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) document significant bidirectional return and volatility transmission between oil prices and stock markets for both oil-importing and oil-exporting countries. Similarly, Khalfaoui, Sarwar, and Tiwari (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) show that volatility spillovers are economically meaningful and have important implications for portfolio diversification and risk management.\u003c/p\u003e \u003cp\u003eFocusing on oil-importing economies, Bouri (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) finds that oil volatility shocks significantly influence stock market volatility in MENA oil-importing countries, especially during periods of financial stress. Joo and Park (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further demonstrate that oil price volatility negatively affects stock market performance in oil-importing countries, with effects intensifying during high-uncertainty regimes. Ashfaq, Tang, and Maqbool (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) provide evidence of volatility spillovers from global oil prices to Asian energy-importing stock markets, reinforcing the importance of oil-induced uncertainty in shaping equity market dynamics.\u003c/p\u003e \u003cp\u003eA major advancement in the literature is the recognition that oil–stock market relationships evolve over time. Filis, Degiannakis, and Floros (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) employ a DCC-GARCH framework to analyse dynamic correlations between oil prices and stock markets, revealing substantial time variation and clear differences between oil-importing and oil-exporting countries. Their findings indicate that correlations strengthen during periods of economic turmoil, reducing diversification benefits. Boldanov, Degiannakis, and Filis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) extend this line of research by focusing on volatility correlations and confirm that oil and stock market volatilities exhibit significant time-varying dependence across importing and exporting economies. Aydoğan, Tunç, and Yelkenci (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also report that oil price volatility has a stronger adverse impact on stock markets in oil-importing countries, particularly during global uncertainty episodes. Evidence from European markets further supports the presence of dynamic oil–stock linkages (Bein \u0026amp; Mehmet, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silvapulle et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies emphasize that the oil–stock market relationship is asymmetric and nonlinear. Hashmi, Chang, and Bhutto (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that stock markets respond differently to oil price increases and decreases, with stronger adverse effects observed in oil-importing countries. Nonlinear dynamics are further documented by de Jesus et al. (2020) and Chkir et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who highlight the role of exchange rates and structural breaks in shaping oil–stock interactions. Mokni (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employs a dynamic quantile regression framework and demonstrates that oil price effects vary across stock return distributions, indicating heightened vulnerability during extreme market conditions. Sadeghi and Roudari (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reinforce these findings by showing heterogeneous effects of oil shocks across regimes and economic structures. He et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) further reveal that oil price uncertainty significantly alters the risk–return relationship in stock markets, with distinct patterns across oil-importing and exporting economies.\u003c/p\u003e \u003cp\u003eStudies focusing on emerging and developing economies highlight stronger and more unstable oil–stock market linkages due to higher energy dependence and macroeconomic vulnerability (Basher, Haug, \u0026amp; Sadorsky, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Am \u0026amp; Shanmugasundaram, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Arouri and Rault (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) document long-run relationships between oil prices and stock markets in GCC countries, while Basher, Haug, and Sadorsky (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasize the role of oil-market shocks in driving stock returns. Recent contributions extend the analysis to broader macro-financial connectedness frameworks. Ahmed, Siddiqui, and Naushad (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examine oil prices, exchange rates, and stock markets in BRICS economies, highlighting dynamic interdependence during global turmoil. Ahmed and Kaur (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provide evidence of evolving connectedness among oil prices, inflation, and exchange rates in India, while Ahmed (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003ec) explores the role of green finance and advanced modelling techniques in understanding market dynamics.\u003c/p\u003e \u003cp\u003eDespite extensive literature on oil–stock market interactions, several gaps remain. First, empirical evidence on time-varying volatility spillovers in oil-importing countries remains fragmented, particularly in the context of heightened global uncertainty. Second, many studies focus on returns or static correlations, potentially overlooking dynamic volatility transmission. Third, comparative insights across oil-importing economies using a unified DCC-GARCH framework are still limited. Addressing these gaps, the present study employs the DCC-GARCH methodology to provide robust evidence on volatility spillovers and dynamic correlations between oil prices and stock markets in oil-importing countries, contributing to both academic discourse and practical risk management strategies.\u003c/p\u003e "},{"header":"Methodology and Data","content":"\u003ch2\u003eDCC-GARCH (Dynamic Conditional Correlation GARCH)\u003c/h2\u003e\u003cp\u003eDeveloped by Robert Engle in 2002, DCC-GARCH is the extended form of the GARCH model that models the correlation between the time-series data over the period. The parameters of the DCC-GARCH, model the volatility, and the linkages with the volatilities in the long and short-run. It is crucial tool to measure the diversification potential among the financial variables. The variance and mean equations of the DCC GARCH are the following. The \u003cem\u003eα\u003c/em\u003e and \u003cem\u003eβ\u003c/em\u003e, parameters measure the ARCH and GARCH effects respectively.\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:r}_{t}={u}_{t}+{e}_{t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{t}\\)\u003c/span\u003e \u003c/span\u003e is the return of the series, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{t}\\)\u003c/span\u003e\u003c/span\u003eare constant and vector of residual respectively.\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{h}_{t}=c+\\alpha\\:{e}_{t-1}^{2}+\\beta\\:{h}_{t-1}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{t}\\)\u003c/span\u003e \u003c/span\u003ehere stands for the conditional volatility, whereas \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003eis the constant. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003eis the ARCH effect, whereas \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003emeasure the GARCH effect.\u003c/p\u003e\u003cp\u003e \u003cem\u003eH\u003c/em\u003e \u003csub\u003e \u003cem\u003et =\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e (3)\u003c/p\u003e\u003cp\u003ematrix of conditional co-variance matrix is represented by the \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e, whereas \u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e represents the k x k diagonal matrix with the time-varying standard deviations on the diagonal. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e is the time-varying correlation matrix. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e further can be defined as -\u003c/p\u003e\u003cp\u003e \u003cem\u003eR\u003c/em\u003e \u003csub\u003e \u003cem\u003et\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e= Q\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e*−1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e*−1\u003c/em\u003e\u003c/sup\u003e (4)\u003c/p\u003e\u003cp\u003e \u003cem\u003eQ\u003c/em\u003e \u003csub\u003e \u003cem\u003et =\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(1-\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(1-{\\theta\\:}_{1}-{\\theta\\:}_{2}\\right){Q}^{*}+{\\theta\\:}_{1}{\\phi\\:}_{t-1}+{\\theta\\:}_{2}{\\phi\\:}_{t-1\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:}\\)\u003c/span\u003e\u003c/span\u003e(5)\u003c/p\u003e\u003cp\u003e \u003cem\u003eQ\u003c/em\u003e \u003csub\u003e \u003cem\u003et\u003c/em\u003e \u003c/sub\u003e is the conditional variance that follows a GARCH process. \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e is the unconditional co-variance estimated in the steps above. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e are the scalar parameter measuring the long, and short-term effect on the conditional correlation. The conditional correlation estimator can be written as –\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\rho\\:}_{ijt}=\\:\\frac{{q}_{ijt}}{\\sqrt{{q}_{ii\\:}{q}_{jj}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cp\u003eThis study uses data stock markets of major oil importing countries given in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The data ranges from 2013 to 2023.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStock Markets in Oil Importing Countries\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStock Market\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShanghai Composite\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDow Jones\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNikkie225\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBritain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFTSE100\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAEX NETHERLANDS\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAX\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBEX35\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFTSE ITALIA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKOSPIKO\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Results and Findings","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVolatility Spillover among Oil Prices and Stock Markets\u003c/h2\u003e \u003cp\u003eThis section shows the result of the volatility spillover among oil prices and stock markets of oil-importing countries. The volatility transmission results are obtained by using the DCC GARCH introduced by Engle (2009). The parameters, mu, and omega show the estimated mean and constant term in the volatility equation. The alpha and beta parameters represent the ARCH and GARCH effect, whereas the dcca1 and dccb1 show the joint ARCH and GARCH between the two variables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOil Prices and Shanghai Composite\u003c/h3\u003e\n\u003cp\u003eIn the Chinese stock market, the Shanghai Composite, there is no significant volatility clustering in the series. However, there is a high and significant GARCH effect indicating the persistence in the volatility. The parameter beta1 is high and significant, indicating the spillover of volatility in the long run. The volatility transmission among the prices of oil and equity market in the short term is low as joint ARCH effect \u003cem\u003eα\u003c/em\u003e (dcca1) is 0.02, whereas the long-term spillover, denoted by the \u003cem\u003eβ\u003c/em\u003e (dccb1) from oil prices to the stock market is high and significant. The model fits well, as the total \u003cem\u003eα\u0026thinsp;+\u0026thinsp;β\u0026thinsp;\u0026lt;\u0026thinsp;1.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil prices and Shanghai Composite\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.48062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.70812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSHANGHAICOMP. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.266116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSHANGHAICOMP.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.708280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSHANGHAICOMP.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.069225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.51602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSHANGHAICOMP.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.925512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.72059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.89318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.926205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.44163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eOil Prices and NSE\u003c/h3\u003e\n\u003cp\u003eThe stock market of India, the National Stock Exchange of India shows the significant ARCH and GARCH effects. The ARCH term is 0.08, indicating the presence of volatility clustering. Whereas the GARCH term is 0.89 indicating the strong persistence of volatility in the Indian stock market. Further, there is the transmission of volatility between oil and the stock market. The parameters \u003cem\u003eα\u003c/em\u003e (dcca1), and \u003cem\u003eβ\u003c/em\u003e (dccb1) indicating spillover of volatility clustering and persistence of volatility are not significant. The oil prices do not cause volatility either in the short-term or long-term in the Indian stock market.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and NSE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr (\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.54195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNSE.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.98350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNSE. omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNSE.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.083059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.50917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNSE.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.897417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.34613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.058878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.191403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.609562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.426350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and Dow Jones\u003c/h2\u003e \u003cp\u003eThere is significant and strong and significant volatility clustering and the persistence of volatility in the US stock market. The ARCH term and GARCH term are 0.20 and 0.75 respectively. There is a presence of spillover among the oil prices and the US stock market in the short-run is significant. The value of dcca1 is 0.04. Further, the spillover of persistence of volatility is high and significant. The value of dccb1, the value of the joint GARCH parameter is 0.88.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and Dow Jones\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.82688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSDJ. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.32613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSDJ.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.497673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSDJ.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.208520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.17327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSDJ.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.758959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.34037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.045859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.38683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.880438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.15868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and NIKKIE225\u003c/h2\u003e \u003cp\u003eThe volatility clustering in the stock market of Japan is low as the ARCH term is 0.09, but significant. The persistence of volatility in Nikkie225, represented by the GARCH term is high at a value of 0.87 and is significant. The spillover of volatility clustering from oil prices to the Nikkie225 is low at a value of 0.03 but is significant, as shown by the parameter dcca1. The parameter dccb1 indicates that the spillover of persistence of volatility is high at 0.77. The spillover in the long run is high and significant, as compared to the spillover in the short run.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and NIKKIE225\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr (\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.51808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.82472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNIKKIE225JP.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.65814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNIKKIE225JP.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.03959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNIKKIE225JP.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.093133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.11896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNIKKIE225JP.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.873468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.27052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.032792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.97050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.770167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.30969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and FTSE100\u003c/h2\u003e \u003cp\u003eThe British stock market, FTSE100 shows significant ARCH and GARCH effects. The ARCH and GARCH terms are 0.15 and 0.79 respectively, indicating the volatility clustering and persistence of volatility in the series. Further, the spillover of volatility clustering and the persistence of volatility are significant. The parameters dcca1 and dccb1, parameters of spillover of volatility clustering and, spillover of persistence of volatility are 0.05 and 0.79 respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and FTSE100\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.82399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSE100.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.91415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSE100.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.83200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSE100.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.150354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.43372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSE100.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.795148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.52818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.052744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.01712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.50155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and AEX Netherlands\u003c/h2\u003e \u003cp\u003eThe table below indicates that the parameters mu and omega, representing the estimated mean and constant term in the volatility equation, are approximate\u0026mdash;zero and not significant. The ARCH and GARCH terms are 0.15 and 0.81 respectively. The volatility clustering in the stock market of Amsterdam is higher than the volatility clustering in the oil prices. Further, the persistence in the volatility is higher than the volatility clustering. The volatility clustering transmission among oil and AEX is 0.05, whereas the spillover of persistence of volatility is high at a value of 0.80. There is a clear indication that the spillover is high in the long run.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and AEX Netherlands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.47559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.75631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAEXNETH.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.87256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAEXNETH.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAEXNETH.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.152681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.29566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAEXNETH.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.814253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.20121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.051327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.806225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.58824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and German DAX\u003c/h2\u003e \u003cp\u003eSimilar to the volatility results of the Netherlands stock market, the German stock market also follows a similar pattern. There is a presence of volatility clustering, as the ARCH effect is 0.10, whereas the persistence of volatility is high, as indicated by the high value of the GARCH effect, 0.85. Further, the spillover of volatility clustering from the oil prices to the DAX is low at 0.04, whereas it is 0.86 in case of spillover of persistence of volatility, as indicated by the dcca1 and dccb1 respectively. Overall, the long-term spillover between the volatility of oil prices and the German share market is present.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and DAX\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.84524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDAX.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.07040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDAX.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.243239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDAX.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.109750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.97095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDAX.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.858325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.71230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.042159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.869890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.37599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and Korean Stock Exchange\u003c/h2\u003e \u003cp\u003eKOSPI, the stock market of South Korea shows an ARCH effect of 0.08 and a high GARCH effect of 0.87, indicating the presence of volatility clustering and persistence of volatility respectively. The spillover of volatility clustering is not significant, however, there is a high and significant spillover of persistence of volatility. The parameter of persistence of spillover dccb1, \u003cem\u003eβ\u003c/em\u003e is 0.78.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and Korean Stock Exchange\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.49241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.63464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRKOSPIKO.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRKOSPIKO.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRKOSPIKO.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.089322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.18170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRKOSPIKO.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.878663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.76821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.782184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.69630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and FTSE ITALIA\u003c/h2\u003e \u003cp\u003eThe results indicate that there is a presence of volatility clustering and persistence of volatility in the series. The ARCH term alpha1 is 0.12, and the GARCH term beta is 0.83. The volatility spillover between the oil prices and the stock market of Italy is low but significant. The value of the ARCH term is 0.03, whereas the long-term spillover of the persistence of the volatility is high as well as significant. The joint GARCH parameter, dccb1 is 0.87.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrices and FTSE ITALIA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.8693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSEITALIA.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSEITALIA.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.97644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSEITALIA.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.127574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.05637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFTSEITALIA.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.831644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.27332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.89667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.877517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.35898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOil Prices and IBEX35 Spain\u003c/h2\u003e \u003cp\u003eThere is a significant presence of volatility clustering and persistence of volatility in the stock market of Spain. The values of parameters alpha1 and beta are 0.13 and 0.82 respectively. Further, the spillover of volatility clustering is low. The value of the joint ARCH parameter, dcca1 is 0.04, whereas, the spillover of persistence is high and significant. The value of joint GARCH parameter dccb1 is 0.86.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOil Prices and IBEX35 Spain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL. Mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.122578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.51337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROIL.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.71815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBEX35.SPAIN.mu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBEX35.SPAIN.omega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.09826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBEX35.SPAIN.alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.135740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.40559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBEX35.SPAIN.beta1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.824641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.60705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edcca1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.83243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edccb1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.866509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.21499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe DCC GARCH results indicate the significant joint ARCH and GARCH effect, except the relationship with the stock market of India, which shows the insignificant joint ARCH and GARCH effects. Further, the short-term transmission is not significant between the oil and the Korean Stock Exchange. Among the other countries, there is significant ARCH, GARCH effect, the spillover of volatility, and the spillover of persistence of volatility.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from the DCC-GARCH analysis provide important insights into the dynamic volatility spillovers between international oil prices and stock markets of oil-importing countries. The presence of significant joint ARCH and GARCH effects for most markets indicates that oil price shocks and their associated volatility are transmitted to equity markets not only contemporaneously but also persist over time. This suggests that oil-related uncertainty plays a crucial role in shaping stock market risk dynamics in oil-importing economies, consistent with the volatility transmission hypothesis documented in earlier studies (Filis, Degiannakis, \u0026amp; Floros, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Guesmi \u0026amp; Fattoum, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Khalfaoui, Sarwar, \u0026amp; Tiwari, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe insignificance of the joint ARCH and GARCH effects in the case of India suggests a relatively weaker volatility linkage between oil prices and the Indian stock market. This finding may reflect India\u0026rsquo;s diversified economic structure, policy interventions such as fuel price regulation and strategic reserves, and the growing role of non-energy sectors in equity market valuation. Similar evidence of heterogeneous oil\u0026ndash;stock market dependence across countries has been reported in the literature, highlighting the importance of country-specific macroeconomic conditions and institutional frameworks (Wang, Wu, \u0026amp; Yang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hashmi, Chang, \u0026amp; Bhutto, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, recent evidence points to the increasing role of alternative financial channels and policy buffers in mitigating oil-induced financial volatility in emerging markets (Ahmed \u0026amp; Kaur, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe absence of significant short-term volatility transmission between oil prices and the Korean Stock Exchange suggests that oil price shocks do not immediately translate into equity market volatility in Korea. This may be attributed to Korea\u0026rsquo;s strong export-oriented industrial base, effective hedging practices, and the ability of firms to absorb energy cost fluctuations in the short run. Previous studies have similarly observed delayed or weak short-term oil\u0026ndash;stock linkages in economies with advanced financial markets and strong institutional resilience (Boldanov, Degiannakis, \u0026amp; Filis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bein \u0026amp; Mehmet, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the lack of short-run spillovers does not necessarily imply long-run independence, as oil price volatility may still affect equity markets through indirect macroeconomic channels over time.\u003c/p\u003e \u003cp\u003eFor the remaining oil-importing countries in the sample, the significant ARCH effects indicate immediate volatility spillovers from oil prices to stock markets, while the significant GARCH effects reflect persistence in volatility transmission. This implies that oil price shocks not only generate immediate uncertainty in equity markets but also lead to prolonged periods of elevated volatility. Such persistence is particularly relevant during periods of global economic stress, when oil market uncertainty intensifies and amplifies financial market instability (Bouri, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Joo \u0026amp; Park, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings align with the view that oil price volatility acts as a systemic risk factor for stock markets in oil-dependent economies (Degiannakis, Filis, \u0026amp; Arora, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a portfolio management perspective, the observed volatility spillovers and persistence reduce the effectiveness of diversification between oil and equity assets during turbulent periods. The time-varying nature of volatility transmission underscores the importance of dynamic hedging strategies rather than static portfolio allocations, particularly for investors exposed to oil-importing markets (Khalfaoui et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ashfaq, Tang, \u0026amp; Maqbool, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Policymakers should also note that sustained oil price volatility can have destabilizing effects on financial markets, warranting proactive energy and macro-financial policies aimed at reducing exposure to external energy shocks.\u003c/p\u003e \u003cp\u003eOverall, the results reinforce the notion that oil\u0026ndash;stock market volatility linkages are heterogeneous across oil-importing countries and evolve over time. While some markets exhibit strong and persistent spillovers, others display relative insulation in the short run, reflecting differences in economic structure, policy frameworks, and financial market maturity. By employing the DCC-GARCH framework, this study contributes to the literature by capturing these nuanced dynamics and providing robust evidence on the transmission of oil price volatility to equity markets in oil-importing economies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examines the volatility spillover dynamics between international oil prices and stock markets of selected oil-importing countries using the Dynamic Conditional Correlation GARCH (DCC-GARCH) framework. The empirical results reveal strong evidence of time-varying volatility transmission from oil prices to equity markets in most of the sampled countries. The presence of significant joint ARCH and GARCH effects confirms that oil price shocks not only generate immediate volatility in stock markets but also lead to persistent uncertainty over time. These findings underscore the importance of oil price volatility as a key source of systemic risk for oil-dependent economies.\u003c/p\u003e \u003cp\u003eHowever, the results also highlight notable cross-country heterogeneity. The Indian stock market exhibits insignificant joint ARCH and GARCH effects, suggesting a relatively weaker volatility linkage with oil prices. This indicates a degree of insulation from oil-induced financial volatility, potentially reflecting economic diversification, policy interventions, and evolving market structures. Similarly, the absence of significant short-term volatility transmission between oil prices and the Korean stock market points to delayed or indirect adjustment mechanisms, possibly driven by institutional strength, effective risk management practices, and industrial resilience.\u003c/p\u003e \u003cp\u003eOverall, the findings confirm that oil\u0026ndash;stock market relationships in oil-importing countries are neither uniform nor static. Instead, they are shaped by country-specific economic structures, financial market maturity, and policy frameworks. By capturing these dynamics through a DCC-GARCH approach, the study contributes to the literature by providing robust evidence on the evolving nature of volatility spillovers between energy and equity markets.\u003c/p\u003e \u003cp\u003eThe documented volatility spillovers and persistence effects have important implications for portfolio diversification and risk management. The presence of time-varying correlations implies that diversification benefits between oil and equity assets diminish during periods of heightened oil market uncertainty. Investors operating in oil-importing markets should therefore adopt dynamic hedging strategies that account for changing volatility conditions rather than relying on static asset allocations. In markets exhibiting strong and persistent spillovers, oil price movements should be closely monitored as a leading indicator of equity market risk.\u003c/p\u003e \u003cp\u003eFor policymakers in oil-importing economies, the findings highlight the importance of reducing vulnerability to external energy price shocks. Persistent oil-induced volatility in stock markets can undermine financial stability, discourage investment, and amplify macroeconomic uncertainty. Policies aimed at energy diversification, strategic petroleum reserves, and effective fuel pricing mechanisms can help mitigate the transmission of oil price volatility to financial markets. Strengthening financial market depth and promoting risk-hedging instruments may further enhance resilience against external shocks.\u003c/p\u003e \u003cp\u003eThe heterogeneous spillover patterns observed across countries suggest several avenues for future research. Subsequent studies may incorporate regime-switching or nonlinear frameworks to capture structural breaks and crisis-specific dynamics. Expanding the analysis to include exchange rates, inflation, or climate-related financial instruments could provide a more comprehensive understanding of macro-financial connectedness. Additionally, comparative analyses between oil-importing and oil-exporting countries using high-frequency data may yield further insights into the evolving role of oil in global financial markets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe author declares that this manuscript is an original work and has not been published previously, nor is it under consideration for publication elsewhere. The research presented in this paper forms a part of the author\u0026rsquo;s doctoral thesis submitted to Jamia Millia Islamia, New Delhi. All sources of data and references have been appropriately acknowledged, and the manuscript complies with ethical standards of academic research. The author declares no conflict of interest related to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed H (2025) From Integration to Strategy. Deep Learning Insights into Turkey\u0026rsquo;s Global Financial Linkages\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed H (2025) Is It Getting Green? Insights into Green Bond Influence on Stock Markets of Major Oil-Exporting Countries. \u003cem\u003eInsights into Green Bond Influence on Stock Markets of Major Oil-Exporting Countries (September 01, 2025)\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed H (2025) Green Finance and Market Dynamics: Insights from Deep Learning based LSTM-Copula and MS-VAR Model. \u003cem\u003eAvailable at SSRN 5641457\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed H, Kaur R (2025) Dynamic Connectedness Among Oil Prices, Exchange Rate and Consumer Inflation: New Evidence from India. IIM Kozhikode Soc Manage Rev, 22779752251346247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed H, Siddiqui TA, Naushad M (2025) Navigating global economic turmoil: The dynamics of oil prices, exchange rates, and stock markets in BRICS. Invest Manage Financial Innovations 22(1):94\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAm MA, Shanmugasundaram G (2017) Nexus between crude oil price, exchange rate and stock market: Evidence from oil exporting and importing economies. Int J Humanit Manage Sci, \u003cem\u003e5\u003c/em\u003e(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArouri MEH, Rault C (2012) Oil prices and stock markets in GCC countries: empirical evidence from panel analysis. Int J Finance Econ 17(3):242\u0026ndash;253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshfaq S, Tang Y, Maqbool R (2019) Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies\u0026rsquo; stock returns. Energy 188:116002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAydoğan B, Tun\u0026ccedil; G, Yelkenci T (2017) The impact of oil price volatility on net-oil exporter and importer countries\u0026rsquo; stock markets. Eurasian Economic Rev 7(2):231\u0026ndash;253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasher SA, Haug AA, Sadorsky P (2012) Oil prices, exchange rates and emerging stock markets. Energy Econ 34(1):227\u0026ndash;240\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasher SA, Haug AA, Sadorsky P (2018) The impact of oil-market shocks on stock returns in major oil-exporting countries. J Int Money Finance 86:264\u0026ndash;280\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBein MA, Mehmet AGA (2016) International crude oil price and stock markets: Evidence from the Nordic and other European oil importing and oil exporting countries. Romanian J Economic Forecast 19(4):115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoldanov R, Degiannakis S, Filis G (2016) Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries. Int Rev Financial Anal 48:209\u0026ndash;220\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouoiyour J, Selmi R, Shahzad SJH, Shahbaz M (2017) Response of stock returns to oil price shocks: Evidence from oil importing and exporting countries. J Econ Integr, 913\u0026ndash;936\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouri E (2015) Oil volatility shocks and the stock markets of oil-importing MENA economies: A tale from the financial crisis. Energy Econ 51:590\u0026ndash;598\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChkir I, Guesmi K, Brayek AB, Naoui K (2020) Modelling the nonlinear relationship between oil prices, stock markets, and exchange rates in oil-exporting and oil-importing countries. Res Int Bus Finance 54:101274\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunado J, de Gracia FP (2014) Oil price shocks and stock market returns: Evidence for some European countries. Energy Econ 42:365\u0026ndash;377\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jesus DP, Bezerra BFL (2020) S., \u0026amp; da N\u0026oacute;brega Besarria, C. The non-linear relationship between oil prices and stock prices: Evidence from oil-importing and oil-exporting countries. \u003cem\u003eResearch in International Business and Finance\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 101229\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDegiannakis S, Filis G, Arora V (2018) Oil prices and stock markets: A review of the theory and empirical evidence. Energy J 39(5):85\u0026ndash;130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilis G, Chatziantoniou I (2014) Financial and monetary policy responses to oil price shocks: evidence from oil-importing and oil-exporting countries. Rev Quant Financ Acc 42(4):709\u0026ndash;729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilis G, Degiannakis S, Floros C (2011) Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. Int Rev Financial Anal 20(3):152\u0026ndash;164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuesmi K, Fattoum S (2014) Return and volatility transmission between oil prices and oil-exporting and oil-importing countries. Econ Model 38:305\u0026ndash;310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashmi SM, Chang BH, Bhutto NA (2021) Asymmetric effect of oil prices on stock market prices: New evidence from oil-exporting and oil-importing countries. Resour Policy 70:101946\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Z, Chen J, Zhou F, Zhang G, Wen F (2022) Oil price uncertainty and the risk-return relation in stock markets: Evidence from oil‐importing and oil‐exporting countries. Int J Finance Econ 27(1):1154\u0026ndash;1172\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoo YC, Park SY (2021) The impact of oil price volatility on stock markets: Evidences from oil-importing countries. Energy Econ 101:105413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalfaoui R, Sarwar S, Tiwari AK (2019) Analysing volatility spillover between the oil market and the stock market in oil-importing and oil-exporting countries: Implications on portfolio management. Resour Policy 62:22\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMokni K (2020) A dynamic quantile regression model for the relationship between oil price and stock markets in oil-importing and oil-exporting countries. Energy 213:118639\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadeghi A, Roudari S (2022) Heterogeneous effects of oil structure and oil shocks on stock prices in different regimes: Evidence from oil-exporting and oil-importing countries. Resour Policy 76:102596\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilvapulle P, Smyth R, Zhang X, Fenech JP (2017) Nonparametric panel data model for crude oil and stock market prices in net oil importing countries. Energy Econ 67:255\u0026ndash;267\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Wu C, Yang L (2013) Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries. J Comp Econ 41(4):1220\u0026ndash;1239\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Jamia Millia Islamia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Oil prices, Stock markets, Volatility spillovers, DCC-GARCH, Oil-importing countries, Time-varying correlation","lastPublishedDoi":"10.21203/rs.3.rs-8450522/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8450522/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the volatility spillover dynamics between international oil prices and stock markets in selected oil-importing countries using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework. The model captures time-varying correlations and conditional volatility transmission, allowing for a comprehensive assessment of both short-term and persistent spillover effects. The empirical findings reveal significant joint ARCH and GARCH effects for most oil-importing stock markets, indicating the presence of both immediate volatility spillovers from oil prices and persistent transmission of uncertainty over time. However, notable heterogeneity across countries is observed. In particular, the Indian stock market exhibits insignificant joint ARCH and GARCH effects, suggesting a relatively weaker volatility linkage with oil prices, while short-term volatility transmission is found to be insignificant for the Korean stock market. For the remaining countries, oil price shocks significantly influence stock market volatility and its persistence. These results highlight that oil-induced financial risk is time-varying and country-specific in oil-importing economies. The findings have important implications for portfolio diversification, dynamic hedging strategies, and macro-financial policy formulation aimed at mitigating the adverse effects of oil price uncertainty on equity markets.\u003c/p\u003e","manuscriptTitle":"Volatility Spillovers between Oil Prices and Stock Markets in Oil-Importing Countries: Evidence from a DCC-GARCH Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 08:48:16","doi":"10.21203/rs.3.rs-8450522/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7eb6b22f-9ad5-4e41-8471-8d5ea800b395","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60250421,"name":"Finance"}],"tags":[],"updatedAt":"2026-01-07T08:48:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-07 08:48:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8450522","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8450522","identity":"rs-8450522","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}