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The international oil market has the tendency to affect any economy either developed or emerging. We examine the effect of structural oil shocks on the returns of developed and emerging stock markets. This effect is measured by employing the extreme dependence measure and connectedness approach across different quantile distributions. Our data ranges from January 1, 2006, to July 5, 2021. Oil shocks are extracted using the work of Ready (2018) as supply, demand and risk-related shocks. Our results highlight that demand shocks exhibit strong coherence as compared with supply and risk-driven shocks in the short-run. However, during extreme market conditions, we witness the pronounced effect of oil shocks on stock returns . Results of connectedness using Ando et al. (2022) show that France significantly transmits to the system across all quantile distributions. Among BRICs, Brazil appears as the most substantial net contributor. In terms of net receiver, Japanese equity market appears most sensitive to oil shocks followed by the Indian and Chinese stock markets . Our work carries important implications for investments and policymakers. Introduction Oil has been a significant energy commodity because of its overwhelming importance for the global economy (Oladosu et al., 2018; Mensi et al., 2023a, Mensi et al., 2023b). Movements on either side of oil prices can have profound effects on the dynamics of any industry, which are also reflected in a company's stock prices (Baffes, 2007). Political and geopolitical tensions have a substantial impact on oil prices and its supply and demand. Recent events, such as COVID-19 outbreak and the Russian invasion of Ukraine, disrupted oil production (Shah et al., 2022). As the international financial system evolves, these incidents gain more significance, leading to increased turbulence in the oil markets. However, in the last few decades, oil has spawned an enormous amount of research into how oil shocks affect the financial sector. Theoretically, oil prices can have several different effects on the stock market, depending on its consumption and production. For example, as oil prices rise, inflationary pressure can lead to lower discount rates, which can have a negative impact on the stock market (Huang et al., 1996). Also, oil price increase industry costs that lead to less corporate earnings and hence put a negative effect on share price (Awerbuch and Sauter, 2006). Demand and supply shocks explain wide contemporaneous swings in oil prices and therefore, a sizable portion of oil price changes can be attributed to precautionary demand shocks which may vary due to supply-related concerns and expectations of increased demand. Kilian (2009) uses crude oil production and shipping prices as proxies for the oil supply and demand, respectively to decompose shocks. Furthermore, Kilian and Park (2009) expanded on this approach to show the effect of oil price changes on US equity returns. Amid such developments, Ready (2018) offered an asset pricing index to disentangle oil price shocks into demand, supply and risk-related shocks. He contends that the oil-producing companies' stock indexes respond more to oil demand changes compared with the supply-related changes and that a positive demand shock will benefit their returns. Addressing oil as an asset class as well as a source of contagion, we attempt to contribute towards the existing strand of the literature by highlighting that the degree of dependence between oil shocks and international stock markets is distribution specific. For example, examining the tail distribution of returns can shed light on the spillover across extremely high (low) quantiles between oil shocks and stock returns. We examine whether oil shocks result in an asymmetric transmission toward the international equity market. A functional understanding of the time-varying impact on international equity markets driven by oil shocks necessitates an analysis measuring the behavior of these two asset classes across different returns distribution. Our study focuses on the distribution-specific dependence between oil shocks and international stock markets. Such an analysis based on different quantiles enables us to specifically examine asymmetric transmission and spillover effects. The application of the quantile coherency approach allows us to examine the presence of connectedness between oil shocks and stock markets across normal and extreme market conditions. This methodology appears more effective due to abrupt changes in the international oil market. For a similar purpose, instead of taking oil prices, we use preceding shocks that cause such price volatility. The overall approach that we used in this paper is a quantile-based analysis which further includes spillover connectedness on one-by-one as well as system-wide connectedness. The study is motivated by the impact of significant oil price shocks, i.e. supply, demand and risk shocks on stock returns during extreme events. This interest led us to examine the connectedness of developed (G7) and developing (BRICS) countries with oil shocks during bullish and bearish periods. Furthermore, we aim to contribute to the methodological front by measuring time-varying connectedness between disaggregated oil shocks and international equity markets. Theoretically, natural price hedging for oil-exporting countries is envisaged due to supply-side shocks, while the adverse effects of oil shocks are projected for oil importers (Das et al., 2018). To account for the fact that stock markets may react differently to oil shocks, we employ the recent decomposition structure of Ready (2018) to explore the asymmetric effect of oil demand and supply shocks on stock return. Our study contributes to the related literature in several ways. First, our work builds upon previous research by providing a more comprehensive analysis of the relationship between equity markets and the explicit structural shocks that characterize the endogenous nature of oil price changes affecting equity returns. While previous studies have focused primarily on either the extreme tail risk between oil and stock markets (Shahzad et al., 2018) or the time-varying characteristics and directional risk contagion (Kang et al., 2023; Mensi et al., 2023a, Mensi et al., 2023b), our study extends the literature by incorporating both of these perspectives by using disaggregated oil shocks (Ready, 2018) and applying quantile vector autoregression (QVAR) method proposed by Baruník and Kley (2019) and Ando et al. (2022). Our second contribution is to measure the presence of asymmetric dependence between oil shocks i.e., demand, supply and risk shocks and international equity markets. To model the asymmetric relationship between oil shocks and equity markets, we follow Ando et al. (2022) to examine the presence of quantile spillover between oil shocks and stock returns using quantile vector autoregression (QVAR). For the purpose of adding more robustness to our analysis, we also apply novel methods such as QVAR, Welch's different mean equality Satterthwaite (1946) and ANOVA (Welch, 1951), variance equality tests of Levene (1960) and Brown and Forsythe (1974) to analyze the asymmetric effects across various quantiles. We also use the connectedness framework (Diebold and Yılmaz, 2014) approach to examine the dynamic directional spillover risk between different oil price changes and market conditions. The application of QVAR enables more flexible copulas and generates a richer asymmetric dependence structure. It allows multiple autoregressive structure at different quantile distributions. The quantile coherency method estimates the dynamic dependence structure between oil shocks and equity returns with cross-spectral densities across frequencies and quantiles. This quantile-based technique explores tail-risk analysis under different market conditions as the dependence pattern during extreme positive (bullish) quantile distribution may vary from the infrequent periods of extreme negative (bearish) and normal market conditions. This technique is also useful for highlighting the potential for short-, medium-, and long-term investment horizons, by identifying accurate correlation coefficients across different time scales. Our last contribution is analyzing the presence of spillover between these oil shocks and international stock markets. For this purpose, the spillover methodology proposed by Diebold and Yılmaz (2014, 2016) is extended to develop the net pairwise directional networks between oil shocks and stock markets. In addition to emphasizing dependence among extreme tail returns across the frequency domain, this approach also entails the benefit of separately analyzing positive and negative spillover. The results of our work highlight that G7 and BRIC economies highlight strong dependence on oil supply, demand, and risk shocks under bearish and bullish conditions in the long-run. Japan, India, and China are net receivers from the overall system (comprising of oil shocks, G7 and BRIC stock markets) across all quantiles. Risk shocks remain the strongest transmitter of spillover towards the US across all quantiles. Japan appears to be the second most vulnerable market from risk shocks across all quantile distributions, while Indian and Chinese equity markets are affected by risk and demand shocks across extreme positive returns distribution. We conclude that oil price shocks are an integral part of the financial system which is significantly affected by structural oil shocks. The co-movements between oil shocks and stock returns for G7 and BRIC countries highlight substantial differences and asymmetries across investment time frames. We suggest that portfolios trading with multiple rebalancing horizons may yield optimal returns as the responses of stock markets to oil shocks differ across stock markets and their return distribution which carries important implications for investors and policymakers. We organize our work in the following manner. Section 2 reviews studies related to our topic. Section 3 provides details about the data and the applied methodology. Section 4 presents a detailed analysis of the results followed by Section 5 in which we present implications based on our results. Section 6 provides concluding remarks. Section snippets Literature review The literature on the effect of oil shocks on emerging stock markets is comparatively limited however, few studies examine the oil shocks-stock nexus for Chinese stock markets (Smyth and Narayan, 2018). Limited studies can be traced for India (Ghosh and Kanjilal, 2016; Fang and You, 2014), ASEAN stock markets (Mensi et al., 2022), Central and Eastern Europe (Asteriou and Bashmakova, 2013), BRICS (Reboredo and Ugolini, 2015; Naeem et al., 2022) along with few multi-country studies (Basher et Methodology Following Ready (2018), we first employ the recent decomposition technique to explore oil demand and supply shocks from oil price changes to estimate their effect on stock return. Second, we use the quantile coherency econometric model and quantile VAR approach given by Baruník and Kley (2019) and Ando et al. (2022) to detect the dependence structure between equity returns and oil shocks. Data and preliminary analysis We focus on the daily dataset of returns from the G7 and the BRIC stock markets spanning from January 1, 2006, to July 5, 2021. These stock markets include FTSE MIB (Italy), S&P500 composite (US), CAC 40 (France), Nikkei 225 (Japan), S&P/TSX composite Canada), DAX 30 (Germany), and FTSE100 (UK) from the G7 region whereas Shanghai SE (China), S&P BSE 30 (India), RTS (Russia) and BOVESPA (Brazil) from the BRIC region. Global daily oil shocks data is calculated by decomposing oil price changes Quantile coherency analysis We apply the quantile coherency method of Baruník and Kley (2019) to present results for general dependence structures of G7 and BRIC stock markets with oil related shocks i.e., demand, supply and risk shocks. It visualizes the estimation of three forms of quantile coherency matrices under different quantile distributions, i.e., the 5th, 50th, and 95th. The results for quantile coherence across different investment windows are presented in Fig. 4, Fig. 5, Fig. 6. The quantile coherency matrices Conclusion This study examines the presence of coherence between structural oil shocks and the equity markets of G7 and BRIC economies following Ready (2018). For this purpose, we adopt a connectedness measure proposed by Baruník and Kley (2019) to estimate coherence across quantiles of joint distributions in frequency. Our results highlight that the negative coherence of oil demand shocks is stronger than risk and supply-driven shocks for G7 and BRIC stock market returns in the short run. During extreme CRediT authorship contribution statement Mobeen Ur Rehman: Conceptualization, Writing – original draft. Neeraj Nautiyal: Data curation, Formal analysis, Writing – review & editing. Xuan Vinh Vo: Investigation, Funding acquisition. Wafa Ghardallou: Methodology, Software, Formal analysis. Sang Hoon Kang: Supervision, Writing – review & editing, Funding acquisition. Declaration of competing interest There are no conflicts of interest to declare. Acknowledgment Princess Nourah bint Abdulrahman University Researchers Supporting Project number ( PNURSP 2023R261 ), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia . This research is partly funded by the University of Economics Ho Chi Minh City, Vietnam . This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea ( NRF-2022S1A5A2A01038422 ). References (84) M. Ahmadi et al. Global oil market and the US stock returns Energy (2016) E. Al-hajj et al. Oil price shocks and stock returns nexus for Malaysia: Fresh evidence from nonlinear ARDL test Energy Rep. (2018) D. Asteriou et al. Assessing the impact of oil returns on emerging stock markets: a panel data approach for ten Central and Eastern European Countries Energy Econ. (2013) S. Awerbuch et al. Exploiting the oil–GDP effect to support renewables deployment Energy Pol. (2006) J. Baffes Oil spills on other commodities Resour. Pol. (2007) J. 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