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Crude oil and stock market co-movement: Evidence from G7 and BRICS nations
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This paper examines the dynamic correlation between crude oil and stock market indices of major developed (G7) and emerging (BRICS) economies by analyzing weekly data from January 2000 to March 2017. Results suggest an existence of dynamic correlation between crude oil and stock markets throughout the study period. Results also indicate a considerable increase in dynamic correlation between crude oil and stock markets during the recent crisis. Interestingly , crude oil and indices of developed nations exhibit almost similar co-movements, but the correlation movements between crude oil and the stock indices of BRICS nations show weak homogenei-ty in the group. The impulse response function in variance (volatility) was employed to investigate the response of stock market indices of G7 and BRICS nations to the shock in the crude oil market and vice versa. Overall, these findings have significant implications for policy makers, investors and portfolio managers.
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... However, in some other studies, several countries are being studied. Of course, this type of studies have been done in two ways: Some researchers have studied the co-movement of financial markets using either global or some specific countries data (e.g., Nieh & Lee, 2001;Samanta & Zadeh, 2012;Ftiti et al. 2016;Arfaoui & Rejeb, 2017;Yarovaya & Lau, 2016;Mensi et al., 2018;Gourene & Mendy, 2018;Bhatiai & Mitra, 2018;El Abed & Zardoub, 2019;Pal & Mitra, 2019). Others have examined the co-movement of a financial variable (for example, the stock market) in several countries )e.g., Chen, 2018;Abounoori & Tour, 2019). ...
... The dominant econometric method used in these studies is the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) method (e.g., Akar et al., 2011;et al., 2014;Amiri & Fallahi, 2015;Yarovaya & Lau, 2016;El Abed & Zardoub, 2019;and Abounoori & Tour, 2019). Also, various other methods in econometrics have been used by researchers (e.g., Aggarwal, 1981;Nieh & Lee, 2001;Kim, 2003;Gilmore et al., 2009;Mirsha et al., 2010;Zhao, 2010;Hussin & Muhammad, 2012;Samanta & Zadeh, 2012;Biag et al., 2013;Arfaoui & Rejeb, 2016;Chen, 2018;Bhatiai & Mitra, 2018). However, several studies, particularly in recent years, have used WCA method for the co-movement analysis of financial markets (e.g., Ftiti et al., 2016;Nademi & Khochiany, 2017;Mensi et al., 2017;Huang et al., 2018;Gourene & Mendy, 2018;Khochiany, 2018;Pal & Mitra, 2019;Amalia & Purqon, 2019). ...
... On the other hand, some studies have examined the co-movement of a pair of stock-exchange rates (e.g., Aggarwal, 1981;Nieh & Lee, 2001;Kim, 2003;Zhao, 2010;Khochiany, 2018;El Abed & Zardoub, 2019), the co-movement of a pair of stock-gold price (e.g., Gilmore et al., 2009;Mirsha et al., 2010), the co-movement of a pair of stock-oil price (e.g., Ftiti et al., 2016;Huang et al., 2018;Gourene & Mendy, 2018;Bhatiai & Mitra, 2018;Pal & Mitra, 2019;Amalia & Purqon, 2019), the co-movement of stock, the exchange rate, and gold prices (e.g., Akar et al., 2011;Fallahi et al., 2018;Nademi & Khochiany, 2017), the co-movement of stock, oil, and gold prices (e.g., Biag et al., 2013;Mensi et al., 2017), the co-movement of stock, the exchange rate, and oil prices (e.g., Hussin & Muhammad, 2012), the co-movement of exchange rate, gold, and oil prices (e.g., Amiri & Fallahi, 2015), and the comovement of the exchange rate, stock, gold, and oil prices (e.g., Samanta & Zadeh, 2012;Arfaoui & Rejeb, 2016). ...
The main financial markets in the Iranian Economy include the stock exchange, foreign exchange, oil, and gold markets. The sharp fluctuations in these markets, especially those caused by the severe sanctions imposed on Iran in May 2018, and the pandemic outbreak of Covid-19 have led to more confusion and uncertainty among investors. One of the effective approaches to examine such unstable conditions is to study the co-movement(s) between markets to identify the leading variable(s). Thus, in the present study, Wavelet Coherence Analysis was applied to examine the co-movements between markets in a time period from September 2014 to June 2020, as an intense period of uncertainty in Iran. In other words, in this study, the markets were investigated in different sub-periods. Also, the Segmented Regression was performed to estimate the impact of sanctions and the Covid-19 pandemic on the co-movements of financial markets in Iran. The results showed that the oil price had a low co-movement with the other three markets, i.e. stock exchange, exchange rate, and gold markets. Thus, the oil market can be a suitable alternative for risk aversion investors. Meanwhile, the oil market could also act as a source of finance for the government during the sanctions period. That possibly explains the recent decision by the Iranian government to use the oil market to finance its budget deficit. Between the exchange rate and gold price, the gold price was identified as the leading variable. While the exchange rate and gold price did not show a significant co-movement in stable conditions, they did show a significant co-movement in unstable conditions, as in times of sanctions or during a global pandemic and thus influenced the investors’ portfolio risk. This result is important from a policy-making perspective. Based on this result, the policymakers can, especially during crises and unstable conditions, control the gold market and make it more stable by managing the foreign exchange market.
... Other studies focus on different regions of the world, such as developed versus developing countries. For instance, Bhatia andMitra (2018), Yang et al (2018), Bhuyan et al (2016), Hammoudeh et al (2016, Mensi et al (2016a,b), Yarovay andLau (2016) andZhang et al (2013) studied Brazil, Russia, India, China and South Africa (BRICS) and developed stock markets. Bhar and Nikolova (2009) examined the integration and dynamic relationship between BRICS countries and the rest of the world and found that India shows the highest regional and global integration, followed by Brazil and Russia and lastly by China. ...
This paper examines the dynamics and spillover behavior between time-varying optimal weights and hedge ratios in order to analyze optimal volatility allocation spillover and characteristic structure.We estimate the vector autoregression moving average-Baba–Engle–Kraft–Kroner-generalized autoregressive conditional heteroscedasticity model of Ling and McAleer based on weights and hedge ratios following the 2017 work of Majdoub and Ben Sassi. Optimal dynamic portfolio compositions are obtained from Brent oil and Gulf region stock markets estimations based on Kroner and Ng’s approach from 1998. The results reveal that our proposed time-varying weights and hedge ratios are dynamic, show active time-varying behavior and have a different performance than the mean value used in all previous studies in order to hedge different portfolios. Our findings are interesting for local and international
portfolio holders wishing to more precisely forecast hedged portfolio composition and structure within the framework of international portfolio diversification.
... Consequently, the relationship is captured by linear correlation. For example, Bhatiai and Mitra [13] employed GO-GARCH to investigate the dynamic co-movement between crude oil and stock market by G7 and BRICS (Brazil, Russia, India, China and South Africa) based on linear and no-tail dependence assumption. Many researchers have found that the stock price is asymmetric [14] [15] and the relationship between financial variables have a non-linear relationship [16]. ...
This paper aims to investigate the impact of oil price on the stock price of developed countries (represented by the Group of seven) and the stock price of developing countries (represented by BRICS) by using C-vine copula approach. A C-vine based GJR-GARCH is used to measure the conditional dependence between stock price and oil price and investigate the difference between developed countries and developing countries. The empirical evidence shows that G7 and BRICS countries have a positive and significant dependence between oil returns, and there is an even stronger dependence between oil returns for the G7 countries. Some countries in the developed and the developing countries have the tail relationship in the low market or in bear market, further, both groups do not have a relationship with oil return when the stock is in a boom market.
This study investigates the asymmetric effect of crude oil shocks on emerging sectoral stock indices using a non-parametric causality-in-quantiles approach. This study considers the origin of the oil shock i.e. demand shock or supply shock to investigate its impact on various sectors in mean and volatility. We find that the impact of crude oil is heterogeneous across shocks (demand or supply), market states (bullish, bearish and normal) and to a limited extent across sectors. Observing a similar pattern of crude oil's impact on emerging sectors, we argue that the influence of crude oil shocks extends beyond energy-intensive sectors.
While much research uses multivariate GARCH to model volatility dynamics and risk measures, one particular type of multivariate GARCH model, GO-GARCH, has been underutilized. This paper uses DCC, ADCC and GO-GARCH to model volatilities and conditional correlations between emerging market stock prices, oil prices, VIX, gold prices and bond prices. A rolling window analysis is used to construct out-of-sample one-step-ahead forecasts of dynamic conditional correlations and optimal hedge ratios. In most of the situations we study, oil is the best asset to hedge emerging market stock prices. Hedge ratios from the ADCC model are preferred (most effective) for hedging emerging market stock prices with oil, VIX, or bonds. Hedge ratios estimated from the GO-GARCH are most effective for hedging emerging market stock prices with gold in some instances. These results are reasonably robust to choice of model refits, forecast length and distributional assumptions.
In this article, we study whether the behaviour of oil prices can be used as a reliable predictor for the disaggregated industry-level stock market indices. We find strong evidence for the relevance of changes in oil price as a predictor for the returns of UK industry portfolios, while this relevance is heterogeneous across industries. In an out-of-sample framework, we find that both the contemporaneous and lagged oil price changes do predict UK industry stock market returns. The predictive power is more transient for the latter case, and mostly appearing after allowing for time variation in the relative performance. In addition, we find some evidence of asymmetry in the oil–stock price relationships.
This paper investigates the presence of time-varying causal linkages in mean and variance between oil price changes and stock returns for six major oil-importing countries (France, Germany, Italy, Spain, the UK and the US) in a multiscale framework that combines wavelet analysis and a modified version of the dynamic causality test of Lu et al. (2014). The results show significant bidirectional causal relations between oil and stock markets at the different time horizons for all countries. The causal links tend to be stronger at coarser scales and in periods of financial turmoil, mainly during the recent global financial and European sovereign debt crises. This evidence provides useful insights to participants in oil and stock markets and to policymakers.
This paper considers the causal relationships between WTI and Dubai crude oil returns and five stock index returns (S&P 500, Nikkei, Hang Seng, Shanghai, and KOSPI) within the quantile causality framework by using daily data for a period from January 1, 1996, to October 12, 2012. The quantile causality test is useful for a comprehensive understanding of the causal relationship between two returns. The test reveals several noteworthy results. First, although WTI returns are not closely related to Asian countries, some financial markets such as Nikkei and Hang Seng Granger-cause WTI returns. Second, the significance of causality from one market to another derives only from lower and upper levels of quantiles except for the case of causality from Nikkei to WTI returns. Third, all stock index returns Granger-cause Dubai crude oil returns over almost all quantile levels except for Shanghai returns. Fourth, Dubai crude oil returns Granger-cause all Asian stock index returns except for S&P 500 returns. Finally, the results indicate asymmetric causality from Dubai crude oil returns to Shanghai returns and KOSPI returns to Dubai crude oil returns.
This paper investigates the spillovers of extreme risks between crude oil and stock markets using daily data of the S&P 500 stock index and West Texas Intermediate (WTI) crude oil futures returns. Based on the method of Granger causality in risk, Value at Risk (VaR) is employed to measure market risk, and a class of kernel-based tests is used to detect negative and positive risk spillover effects. Empirical results reveal that there are significant risk spillovers between the two markets. Extreme movements, past or current, in one market may have a significant predictive power for those in the other market. Prior to the recent financial crisis, there are positive risk spillovers from stock market to crude oil market, and negative spillovers from crude oil market to stock market. After the financial crisis, bidirectional positive risk spillovers are strengthened markedly. The risk spillovers may occur instantaneously, and/or with a (long) time delay. Both positive and negative risk spillover effects exhibit asymmetric correlations.
This paper examines the relationship between US crude oil and stock market prices, using a Markov-Switching vector error-correction model and a monthly data set from 1859 to 2013. The sample covers the entire modern era of the petroleum industry, which typically begins with the first drilled oil well in Titusville, Pennsylvania in 1858. We estimate a two-regime model that divides the sample into high- and low-volatility regimes based on the variance-covariance matrix of the oil and stock prices. We find that the high-volatility regime more frequently exists prior to the Great Depression and after the 1973 oil price shock caused by the Organization of Petroleum Exporting Countries. The low-volatility regime occurs more frequently when the oil markets fell largely under the control of the major international oil companies from the end of the Great Depression to the first oil price shock in 1973. Using the National Bureau of Economic research business cycle dates, we also find that the high-volatility regime more likely occurs when the economy experiences a recession.
