A wavelet analysis of mean and volatility spillovers between oil and BRICS stock markets

This paper investigates the spillover effects of volatility and shocks between oil prices and the BRICS stock markets using multivariate approach and wavelet analysis at different time horizons. Hence, we combine a multivariate ARMA-GARCH model and wavelet multiresolution analysis to study this phenomenon. A bivariate ARMA(1,1)-GARCH(1,1)-cDCC-Student-t model was joined with MODWT filter to capture a broad range of possible spillover effects in mean and variances of level prices at various time horizons. Generally, empirical results provide strong evidence of time-varying volatility in all markets under study. However, our proposed approach shows that oil price and stock market prices are directly affected by their own news and volatilities and indirectly affected by the volatilities of other prices and wavelet scale. The results show also, that mean and volatility spillover effects was decomposed into many sub-spillovers on different time scales according to heterogeneous investors and market participants. The practical implications of this study are critical, innovative and useful for the local and international investors and also for the portfolio managers. They can utilize this study to formulate the optimal oil-BRICKS stock portfolios as well as lead to more accurate predictions of volatility spillovers patterns also in developing their hedging strategies.     

Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States

The relationship between oil and stock markets is a hot topic, but little research has focused on the time-varying asymmetric volatility spillover in a quantitative manner. In this study, we use a new spillover directional measure and asymmetric spillover measures to investigate the dynamic asymmetric volatility spillover between oil and stock markets during the period of 2007 to 2016. Using the intra-day data of WTI future prices, the S&P 500 index, and the Shanghai stock market composite index, we find that there exists an asymmetric spillover effect between the oil market and stock markets and that bad volatility spillovers dominate good volatility spillovers for most of the sampling period. In addition, participants are more pessimistic about the oil market than they are about the stock market. We further investigate the presence of asymmetric response to volatility shocks using the asymmetric generalized dynamic conditional correlation (AG-DCC) model; the results also show strong evidence of asymmetries in volatility shocks between the oil and stock markets due to bad volatility.     

Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective

We examine the frequency dynamics of volatility spillovers between crude oil and China's stock markets in a spectral representation framework of generalized forecast error variance decomposition using sectoral stock indices data. We find evidence of total volatility spillover driven mainly by short-term spillovers. The net spillovers of the oil market are almost all positive and dominated by short-ter.m components, although the spillover during China's 2015 financial crisis is negative and attributable to long-term components. In addition, there exists heterogeneity in net pairwise (frequency) spillovers between the oil and sectoral stock markets. Moreover, structural breaks in volatilities appear to be a significant feature of volatility spillovers. Finally, frequency spillovers in our system can predict future stock market volatility. These results have economic implications for investors and policymakers.     

Extreme risk spillovers between crude oil and stock markets

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.     

Analyzing time-varying volatility spillovers between the crude oil markets using a new method

The spillover effect is an important factor affecting the volatility of crude oil price. Basing on the study of Diebold and Yilmaz (2009, 2012, 2014), we propose a new method that calculates the time-varying volatility spillover indexes by the generalized forecast error variance decomposition of TVP-VAR-SV model. Then, using the new method, we study the time-varying volatility spillovers between four major crude oil markets (WTI, Brent, Oman, Tapis) from November 29, 2002 to July 13, 2018. By comparing the results of our new method and traditional rolling window method, we verify the superiority of our new method. The results show that the volatility spillovers calculated by the new method are clearer, more stable and not outlier sensitive. From the estimated results of time-varying volatility spillovers, we find that the volatility spillover between crude oil markets is slowly increasing, but there are obvious cyclical changes. And from the correlation analysis and the Granger causality test, we find that the volatility and volatility spillovers are positively correlated and are two-way Granger causality, which supported for the market infection hypothesis of King and Wadhwani (1990).     

Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19

We test for the existence of volatility spillovers and co-movements among energy-focused corporations during the outbreak of the COVID-19 pandemic, inclusive of the April 2020 events where West Texas Intermediate (WTI) oil future prices became negative. Employing the spillover index approach of Diebold and Yilmaz (2012); as well as developing a DCC-FIGARCH conditional correlation framework and using estimated spillover indices built on a generalised vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we examine the sectoral transmission mechanisms of volatility shocks and contagion throughout the energy sector. Among several results, we find positive and economically meaningful spillovers from falling oil prices to both renewable energy and coal markets. However, this result is only found for the narrow portion of our sample surrounding the negative WTI event. We interpret our results being directly attributed to a sharp drop in global oil, gas and coal demand, rather than because of a sudden increase in oil supply. While investors observed the US fracking industry losing market share to coal, they also viewed renewables as more reliable mechanism to generate long-term, stable and low-cost supply.     

Modeling oil price–US stock nexus: A VARMA–BEKK–AGARCH approach

This study adds to the existing literature on oil price–US stock nexus in three ways. First, it employs the VARMA–AGARCH model developed by McAleer et al. (2009) within the context of BEKK framework using West Texas Intermediate (WTI) and Brent as proxies for oil market and S&P stocks as a proxy for US stock market. Secondly, it modifies the model to include endogenously determined structural break using the general structure for analyzing breaks with unit roots in Perron (2006). Third, it uses the adopted model to compute optimal portfolio weight and hedge ratios between oil price and US stocks using different sample data based on the break date. On average, our empirical evidence suggests a significant positive return spillover from US stock market to oil market and bi-directional shock spillovers between the two markets. In addition, there is significant own asymmetric shock effect in both markets while volatility spillover from oil market to stock market became pronounced after the break which coincides with the period of global economic slowdown. Similarly, the results of portfolio management differ across the sample data. More importantly, we find that ignoring structural break when it exists may exaggerate hedging effectiveness.     

Time-varying co-movements between energy market and global financial markets: Implication for portfolio diversification and hedging strategies

This study explores the time patterns of volatility spillovers between energy market and stock prices of seven major global financial markets including clean energy, energy, information technology corporations, equity markets and United States economic policy index over the period vary from December 28, 2000 to December 31, 2018. We employ a time domain connectedness measures of Diebold and Yilmaz (DY, 2009, 2012 and 2014) to examine spillover mechanism of volatility shocks across future markets. Optimal weights and hedge ratios are calculated for portfolio diversification and risk management. The main findings of the study conclude that oil shocks are exogenous and contribution of oil market volatility to global financial markets is insignificant. The returns of World Stock Index and World Energy Index are major transmitters of volatility to clean energy market. Moreover, the impact of energy market become strong in global financial market when data is divided into pre, during and post financial crisis periods. Finally, the hedge ratios are volatile over time and their maximum value is observed during the financial crisis period of 2008–09. The optimal portfolio between energy and stock prices are heavily weighted to the stock markets.     

Oil volatility shocks and the stock markets of oil-importing MENA economies: A tale from the financial crisis

The role of oil price volatility in predicting the stock-market volatility of small oil-importing countries that have a substantial number of investors from neighboring oil-exporting countries remains unexplored. To refine our basic understanding of this role, this paper proposes a methodological extension of the recently developed causality-in-variance procedure and considers the case of Lebanon and Jordan. These two heavy importers of oil are interesting in the sense that they are located in a region with a large number of rich oil-exporting countries, so their stock markets are tied to oil-exporters by way of foreign investors. The conditional mean and variance of returns are modeled within an ARMAX–GARCH framework that accommodates three salient features of the data, namely: autocorrelation, day-of-the-week effects, and movements in international markets. For comparison purposes, the stock markets of Morocco and Tunisia are also included in the study. Empirical analyses highlight the dynamic effects of the global financial crisis on the volatility spillovers between oil and the stock markets of oil-importing countries and provide more insights into the seemingly contradictory effects of being oil-importers while having investors from oil-exporting countries. The main results indicate that the volatility spillover is much more apparent from the world oil market to the stock market of Jordan than the other way around, whereas oil volatility is not a good predictor of Lebanese stock market volatility. Finally, policy/practical implications and conclusions for future research are drawn.     

How to effectively estimate the time-varying risk spillover between crude oil and stock markets? Evidence from the expectile perspective

With the integration and financialization of world economy, massive hot money has frequently flowed between crude oil and stock markets, and has brought significant extreme risks and their spillover. For this reason, this paper develops the ARCH-Expectile model with embedded Conditional AutoRegressive structure (namely CAR-ARCHE model) and expectile-based VaR (EVaR) approach, and investigates the time-varying risk spillover between WTI futures market and US, UK, Japanese and global stock markets, respectively. The results indicate that, for one thing, the EVaR approach based on CAR-ARCHE model is more adequate than the conventional quantile-based VaR (QVaR) approach based on GED-GARCH for WTI and stock markets, which is due to the evident advantages of expectile compared to quantile. For another, the unidirectional downside risk spillover effects from WTI to the four stock markets and vice-versa are only remarkable during major events and present variations with jumps, but the bidirectional downside risk spillover effects between them are significant for each time point during the in-sample period, which indicate that the simultaneous risk spillover between WTI and stock markets are fairly pronounced.     

Volatility spillovers for energy prices: A diagonal BEKK approach

We examine the relationship between return and volatility as well as the covolatility spillover for energy, foreign currency, and stock markets using the diagonal BEKK model. Using daily crude oil, natural gas, and the coal prices as proxies for energy prices, the S&P500 index as a proxy for the U.S. stock market, and the EUR/USD exchange rate as a proxy for the exchange rate, we find robust evidence for the volatility spillover effects among the three markets. Also, in the 16 out of 20 pairwise relationships in the five markets, there are significant negative covolatility spillover effects. In the four pairs involving coal, there are positive and significant covolatility spillover effects. We conclude that the energy markets and the stock market have stronger covolatility spillovers than others.     

Do energy prices stimulate food price volatility? Examining volatility transmission between US oil,ethanol and corn markets

This paper examines volatility transmission in oil, ethanol and corn prices in the United States between 1997 and 2011. We follow a multivariate GARCH approach to evaluate the level of interdependence and the dynamics of volatility across these markets. The estimation results indicate a higher interaction between ethanol and corn markets in recent years, particularly after 2006 when ethanol became the sole alternative oxygenate for gasoline. We only observe, however, significant volatility spillovers from corn to ethanol prices but not the converse. We also do not find major cross-volatility effects from oil to corn markets. The results do not provide evidence of volatility in energy markets stimulating price volatility in the US corn market.     

Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach

This paper reveals some new evidence on the volatility spillover between fuel oil and stock index futures markets in China by considering its time-variant feature. Time-varying effect is specified by the Legendre polynomials in a DCC GARCH model. Moreover, the semi-nonparametric (SNP) approach accounting for marginal excess kurtosis and asymmetry is applied. We find different time-evolving patterns of volatility spillover across multiple periods due to the structural breaks. The direction of mean volatility spillover is found to be bilateral. Moreover, the strength of mean volatility spillover shows that the effect from stock index to fuel oil futures is much stronger than the other way around. This implies that the stock index futures market plays a leading role in information processing. These findings have important implications for the fuel oil stakeholders as well as risk management.     

Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test

This paper utilizes the newly developed method of a generalized spectral test to examine the weak-form efficiency of the main worldwide crude oil markets. The generalized spectral test, unlike other methods, can detect both linear and nonlinear serial dependence in the conditional mean and allows for different forms of unknown conditional heteroscedasticity. By using a “rolling sample” approach instead of an analysis of different time periods, we find that the efficiency of oil markets may depend on time periods. The main global crude oil markets reach weak-form efficiency in the long-term and the degree of efficiency of global oil markets changes over time. Among the oil markets examined in this study, the Brent and the WTI oil markets possess the highest efficiency levels, whereas the Daqing oil market has the lowest efficiency level. Apparent anti-synchronization is detected between the efficiency of Brent and WTI markets in recent years, whereas synchronization is found between the efficiency of Daqing and Dubai oil markets during the last decade.     

Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach

Since oil prices are typically governed by nonlinear and chaotic behavior, it’s become rather difficult to capture the dominant properties of their fluctuations. In recent years, unprecedented interest emerged on the decomposition methods in order to capture drifts or spikes relatively to this data. Together, our understanding of the nature of crude oil price shocks and their effects on the stock market returns has evolved noticeably. We accommodate these findings to investigate two issues that have been at the center of recent debates on the effect of crude oil shocks on the stock market returns of five developed countries (USA, UK, Japan, Germany and Canada). First, we analyze whether shocks and or volatility emanating from two major crude oil markets are transmitted to the equity markets. We do this by applying, the Haar A Trous Wavelet decomposition to monthly real crude oil series in a first step, and the trivariate BEKK Markov Switching GARCH model to analyze the effect of the smooth part on the degree of the stock market instability in a second step. The motivation behind the use of the former method is that noises and erratic behavior often appeared at the edge of the signal, can affect the quality of the shock and thus increase erroneous results of the shock transmission to the stock market. The proposed model is able to circumvent the path dependency problem that can influence the prediction’s robustness and can provide useful information for investors and government agencies that have largely based their views on the notion that crude oil markets affect negatively stock market returns. Second, under the hypothesis of common increased volatility, we investigate whether these states happen around the identified international crises. Indeed, the results show that the A Haar Trous Wavelet decomposition method appears to be an important step toward improving accuracy of the smooth signal in detecting key real crude oil volatility features. Additionally, apart from UK and Japanese cases, the responses of the stock market to an oil shock depend on the geographic area for the main source of supply whether from the North Sea or from the North America (as we take two oil benchmarks WTI and Brent respectively).     

Does speculation in the oil market drive investor herding in emerging stock markets?

This paper examines whether the time variation in the level of investor herding in the stock markets of major oil exporting nations relates to speculation and volatility in the global oil market. We find that speculative activities in the oil market, rather than oil price movements, are positively correlated with anti-herding in the stock markets of major exporters. We argue that traders take the speculative signals from the oil market as a sign of positive expectations and try to generate superior profits by going against the crowd in their local market. While this pattern largely holds during calm (low volatility) market periods, we also find that significant herd behavior takes place during high volatility (or crisis) periods. The findings suggest that policy makers who are concerned about stability in their stock markets should monitor measures of speculative activities in the energy market in order to model and monitor volatility and/or risk transmissions into their markets.     

Food–energy nexus in Europe: Price volatility approach

The literature on food–biofuel price volatility spillovers is growing. Published articles so far have widely ignored nonlinearities and the influence of exogenous variables on volatility patterns. This article allows for these issues when characterizing EU biodiesel industry price dynamics. While Brazilian and US ethanol markets have been thoroughly investigated, less attention has been paid to EU biodiesel markets. Pure EU biodiesel and rapeseed oil prices are the object of our research. Two different methods are applied to model these data: a parametric approach and Long et al.'s (2011) semiparametric approach. Empirical results suggest significant asymmetries in volatility spillovers between pure biodiesel and rapeseed oil prices. Rapeseed stock levels and euro/dollar exchange rates are found to play a significant role in reducing food and biofuel price volatilities.     

Volatility spillovers in China’s crude oil,corn and fuel ethanol markets

Price volatility spillovers among China’s crude oil, corn and fuel ethanol markets are analyzed based on weekly price data from September 5, 2003 to August 31, 2012, employing the univariate EGARCH model and the BEKK-MVGARCH model, respectively. The empirical results indicate a higher interaction among crude oil, corn and fuel ethanol markets after September, 2008. In the overall sample period, the results simultaneously provide strong evidence that there exist unidirectional spillover effects from the crude oil market to the corn and fuel ethanol markets, and double-directional spillovers between the corn market and the fuel ethanol market. However, the spillover effects from the corn and fuel ethanol markets to the crude oil market are not significant.     

The dynamic dependence of fossil energy,investor sentiment and renewable energy stock markets

This study investigates the dynamic directional information spillover of return and volatility between the fossil energy market, investor sentiment towards renewable energy and the renewable energy stock market using the connectedness network approach. Empirical results show that the spillover effects of the volatility system are generally stronger than that of the return system, which suggest that risk transmission among the markets is more obvious. In both systems, the impact of the fossil energy market, especially crude oil, on the renewable energy stock market is greater than the impact of investor sentiment on the renewable energy stock market. This finding shows that the renewable energy stock market is closely related to the fossil energy market. Furthermore, the rolling window approach is adopted to examine the time-varying information spillover among them. The dynamic findings suggest that investor sentiment towards renewable energy can explain the return and volatility of renewable energy stock to a certain degree.     

Investigating dynamic conditional correlation between crude oil and fuels in non-linear framework: The financial and economic role of structural breaks

To understand the crude oil volatility has been a challenge. The non-linear behavior, the skewed and leptokurtic returns, the presence of structural breaks and the constant political instability in suppliers' countries evidence the necessity of complex models to capture the market volatility. At the same time, crude oil is the raw material for several fuels such as jet fuel, gasoline, diesel and others, having a strong influence over their prices. Thus, this study aims to verify the presence of structural breaks in the volatility series and in the correlations between WTI return and the returns of Gasoline, Kerosene Jet Fuel, Diesel, Heating Oil, Propane and Natural Gas. To reach this objective, we identified which model presents the best fit to estimate the conditional mean between WTI and each fuel and we used a Copula–DCC–GARCH model to estimate the conditional volatility avoiding the frequently unrealistic presumptions of normality. Our main results indicate the necessity of a different model for each analyzed pair and the presence of at least one structural break in the conditional volatility and in the correlation between WTI and each fuel, usually preceded by a structural break in WTI return series.