Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis

This paper examines the linkage of crude oil market (WTI) and stock markets of the G-7 countries. We study the mean and volatility spillovers of oil and stock market prices over various time horizons. We propose a new approach incorporating both multivariate GARCH models and wavelet analysis: wavelet-based MGARCH approach. We combine a bivariate GARCH-BEKK model with wavelet multiresolution analysis in order to capture the multiscale features of mean and volatility spillovers between time series. For optimal portfolio allocation decisions, we analyze the multiscale behavior of hedge ratio. Empirical results show strong evidence of significant volatility spillovers between oil and stock markets, as well as time-varying correlations for various market pairs. However, results of wavelet coherence indicate that in most, the WTI market was leading. In addition, it is stated that the decomposed volatility spillovers permit investors to adapt their hedging strategies.     

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.     

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.     

Risk spillovers between oil and stock markets: A VAR for VaR analysis

This article investigates the risk spillover effect between oil and stock markets using a novel multivariate quantile model (i.e., the VAR for VaR approach) and pseudo impulse-response functions. We explore the risk spillover at different quantiles using daily data over the period from January 4, 2000 through August 31, 2018. Our results indicate the asymmetry in spillover effect which is significant at upside quantiles but not significant at downside quantiles. Based on subsample analysis, we find that the risk spillover becomes stronger after 2008 financial crisis, while before the crisis the spillover effect is very weak. The international evidence shows that the asymmetric risk spillover can also be found in other six major stock indices of the G7 group.     

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).     

Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries

This article exploits a new spillover directional measure proposed by Diebold and Yilmaz (2009, 2012) to investigate the dynamic spillover of return and volatility between oil and equities in the Gulf Cooperation Council Countries during the period 2004 to 2012. Our results indicate that return and volatility transmissions are bi-directional, albeit asymmetric. In particular, the oil market gives other markets more than it receives in terms of both returns and volatilities. These trends were more pronounced in the aftermath of the Global Financial Crisis in 2008 as the net contribution of oil has intensified after a burst during the crisis. The empirical evidence from the sample is consistent with a system in which oil is playing the dominant role in the information transmission mechanism between oil and equities in the GCC countries.     

Frequency dynamics of volatility spillovers among crude oil and international stock markets: The role of the interest rate

This paper explores the frequency dynamics of volatility spillovers among crude oil and international stock markets using implied volatility indices. I find evidence of volatility spillovers driven mainly by short-term spillovers. Moreover, low interest rate is the primary driver of volatility spillovers, whose roles mainly stem from its impact on short-term spillovers. The impact of interest rate on long-term spillovers is significantly positive, but relatively limited. The findings highlight that although the low interest rate offers a anticipation of the stability of financial system in the long run, it can be a source of global system risk, especially in the short run.     

Relationship between the oil price volatility and sectoral stock markets in oil-exporting economies: Evidence from wavelet nonlinear denoised based quantile and Granger-causality analysis

This paper examines the extent of volatility between oil price and sectoral indices in the Gulf Cooperation Council (GCC) countries by using quantile regression analysis (QRA) for the return's series and denoised series over the period 2006–2017. Four sectors are found to offer diversification opportunities during a high market (i.e., 90th quantile). All the sectors are found interdependent of oil price volatility; however, the bank and insurance sectors are insusceptible to oil price volatility during the 10th, 25th and 75th quantiles. In addition, QRA results for wavelet nonlinear denoising with a soft-thresholding series indicate that all the sectors are interdependent of oil price volatility but that the aggregate market index, transport and telecommunication sectors are insensitive to oil price volatility during the 75th and 90th quantiles. This highlights the usefulness of denoising the financial returns series when applying regression tools. Moreover, the contagion and interdependence between the oil price and stock returns sectors are estimated by frequency domain causality.     

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.     

The linkages between oil market uncertainty and Islamic stock markets: Evidence from quantile-on-quantile approach

In recent years, Islamic finance has become increasingly influential, especially Islamic stocks, which have created huge investment attractiveness. At the same time, the financialization of oil has made the international oil market increasingly uncertain and more likely to affect other markets. To effectively understand the correlation between the two markets, we empirically analyze the cases of four typical countries from the perspective of oil market uncertainty. This paper utilizes OVX as an accurate measure of oil market uncertainty and applies quantile-on-quantile approach to detect the asymmetric and heterogeneous relations between the variables. Our results show overall negative linkages between OVX changes and Islamic stock returns, and there is indeed asymmetry. Namely, the effects of oil market uncertainty will be more pronounced when it is at higher quantiles. Further comparing the results of the selected four countries, we find some heterogeneities. Oil-importing countries are more sensitive than oil-exporting countries, and Islamic countries are more sensitive than non-Islamic countries. The findings of this article about the linkages between oil market uncertainty and Islamic stock markets is meaningful to the research and financial practice in related fields.     

Impact of oil price change on airline's stock price and volatility: Evidence from China and South Korea

Generally, the influence of crude oil price on the industries (enterprises) varies because they have different levels of reliance on crude oil. For airlines, the expenditure on fuel accounts for a considerable proportion of their gross costs; thus, airlines are unusually sensitive to changes in the crude oil price. The discussion on the relationship between crude oil price and airlines will help the airlines improve their ability to cope with the crude oil price risk. In addition, the responses of South Korean and Chinese airlines in the event of a price shock, that take, are also very important as the airplane is a basic form of transportation in many countries. This study investigates the impact of three crude oil price (WTI, Brent, Dubai) change on the stock price and volatility of four airlines (Korean Air, Asiana Airlines, Air China, and China Eastern Airlines) using VAR-GARCH-BEKK model. The main findings are as follows. There is return and volatility spillover effect between crude oil price and the stock prices of airlines. The volatility spillover effect between the crude oil price and airlines' stock price is more significant than the return spillover effect. Compared with the transportation industry, the stock prices of smaller airlines of South Korea and China are relatively more sensitive to the change in oil price. In addition, compared with Korea's airlines, China's airlines are influenced more by the oil price change, implying that spillover effects owing to oil price are closely related to the different characteristics of the air transport markets of the two countries.     

The oil stock fluctuations in the United States

The goal of this paper is to examine whether the volatility of the growth in the US oil stocks has changed overtime, and if it has then whether or not this change is real. We find that the growth in volatility of oil stocks has declined overtime. We conduct a Monte Carlo simulation exercise to investigate whether this decline is real or an artefact of the growth definition. Our findings support the fact that the decline in growth volatility of oil stocks is an artefact of the growth definition. This is because a data generating process having a unit root with drift has a tendency to grow and thereby pulls the variance of growth down with time.     

Jump dynamics and volatility: Oil and the stock markets

Our study distinguishes itself from the prior studies within the oil and financial literature by not only examining the asymmetric effects of oil prices on stock returns, but also exploring the importance of structure changes in this dependency relationship. We retrieve daily data on S&P 500 and West Texas Intermediate (WTI) oil transactions covering the period from 1 January 1992 to 7 November 2006, and then transform the available data into daily returns. In contrast to the extant literature, in this study, consideration of expected, unexpected and negative unexpected oil price fluctuations is incorporated into the model of stock returns; we also focus on the ways in which oil price volatility, as opposed to general macroeconomic variables, can influence the stock market. We go on to implement the ARJI (Autoregressive Conditional Jump Intensity) model with structure changes, from which we conclude that high fluctuations in oil prices have asymmetric unexpected impacts on S&P 500 returns.     

Moments-based spillovers across gold and oil markets

In this paper, we use intraday futures market data on gold and oil to compute returns, realized volatility, volatility jumps, realized skewness and realized kurtosis. Using these daily metrics associated with two markets over the period of December 2, 1997 to May 26, 2017, we conduct linear, nonparametric, and time-varying (rolling) tests of causality, with the latter two approaches motivated due to the existence of nonlinearity and structural breaks. While, there is hardly any evidence of spillovers between the returns of these two markets, strong evidence of bidirectional causality is detected for realized volatility, which seems to be resulting from volatility jumps. Evidence of spillovers are also detected for the crash risk variables, i.e., realized skewness, and for realized kurtosis as well, with the effect on the latter being relatively stronger. Based on a moments-based test of causality, evidence of co-volatility is deduced, whereby we find that extreme positive and negative returns of gold and oil tend to drive the volatilities in these markets. In our robustness check, we identify a causal chain in the realized volatility from oil to gold via the financial stress. Our results have important implications for not only investors, but also policymakers.     

Oil volatility risk and stock market volatility predictability: Evidence from G7 countries

Academic research relies extensively on stock market information to forecast oil volatility, with relatively little attention paid to the reverse evidence. Our paper fills this gap by investigating the predictive ability of oil volatility risk to forecast stock market volatility. Using oil volatility risk premium (oil VRP) as the predictor, we find that oil VRP does exhibit statistically and economically significant in-sample and out-of-sample forecasting power for G7 countries, even controlling for some popular macroeconomic variables. These findings are robust when using alternative proxies for volatilities of stock and oil. Furthermore, the strength of the predictive evidence is substantial during relatively high and low level of stock market, while is substantially higher for recessions vis-á-vis expansions. Oil VRP can also contains additional information for predicting a series of macroeconomic variables, which serves as an available explanation for its forecasting ability.     

Price and volatility linkages between international REITs and oil markets

This study analyzes price and volatility transmissions between nineteen real estate investment trusts (REITs) and the oil markets. The REITs data represents a variety of countries at different stages of their development and the expanded analytical approach includes accounting for structural shifts as gradual processes – as opposed to strictly abrupt processes typically assumed in the literature. Oil prices are found to primarily predict REITs prices in mature REITs markets, but the feedback from REITs to oil prices is weak. From the perspective of volatility, strong evidence of bidirectional transmission in majority of the markets is observed. Our results are in general robust to a shorter common sample period of the various countries. This study further demonstrates the importance of accounting for gradual (smooth) structural shifts for price transmission analysis.     

Good,bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets

In this article, we investigate the impacts of jumps, cojumps and their signed components on forecasting oil futures price volatility in the framework of the heterogeneous autoregressive realized volatility model. Our empirical results reveal several noteworthy findings. First, the effects of signed jumps and cojumps based on the daily and intraday jump tests on future volatility are asymmetric, and the negative components are much more powerful in forecasting volatility. Moreover, our proposed models, including the signed jump and cojump components, are able to generate higher forecasting accuracy, and we find that disentangling the effects of positive and negative jumps and cojumps can significantly improve forecasts of future volatility. Lastly, our findings are reliable for various robustness checks and our study provides some new insights into forecasting oil price realized volatility, which are useful for researchers, market participants, and policymakers.     

Regime-switching stochastic volatility: Evidence from the crude oil market

This paper incorporates regime-switching into the stochastic volatility (SV) framework in an attempt to explain the behavior of crude oil prices in order to forecast their volatility. More specifically, it models the volatility of oil return as a stochastic volatility process whose mean is subject to shifts in regime. The shift is governed by a two-state first-order Markov process. The Bayesian Markov Chain Monte Carlo method is used to estimate the models. The main findings are: first, there is clear evidence of regime-switching in the oil market. Ignoring it will lead to a false impression that the volatility is highly persistent and therefore highly predictable. Second, incorporating regime-switching into the SV framework significantly enhances the forecasting power of the SV model. Third, the regime-switching stochastic volatility model does a good job in capturing major events affecting the oil market.     

Risk spillovers in oil-related CDS,stock and credit markets

This paper examines risk transmission and migration among six US measures of credit and market risk during the full period 2004–2011 period and the 2009–2011 recovery subperiod, with a focus on four sectors related to the highly volatile oil price. There are more long-run equilibrium risk relationships and short-run causal relationships among the four oil-related Credit Default Swaps (CDS) indexes, the (expected equity volatility) VIX index and the (swaption expected volatility) SMOVE index for the full period than for the recovery subperiod. The auto sector CDS spread is the most error-correcting in the long run and also leads in the risk discovery process in the short run. On the other hand, the CDS spread of the highly regulated, natural monopoly utility sector does not error correct. The four oil-related CDS spread indexes are responsive to VIX in the short- and long-run, while no index is sensitive to SMOVE which, in turn, unilaterally assembles risk migration from VIX. The 2007–2008 Great Recession seems to have led to “localization” and less migration of credit and market risk in the oil-related sectors.     

Energy prices,volatility, and the stock market: Evidence from the Eurozone

This paper constitutes a first analysis on stock returns of energy corporations from the Eurozone. It focuses on the relationship between energy market developments and the pricing of European energy stocks. According to our results, oil price hikes negatively impact on stock returns of European utilities. However, they lead to an appreciation of oil and gas stocks. Interestingly, forecastable oil market volatility negatively affects European oil and gas stocks, implying profit opportunities for strategic investors. In contrast, the gas market does not play a role for the pricing of Eurozone energy stocks. Coal price developments affect the stock returns of European utilities. However, this effect is small compared to oil price impacts, although oil is barely used for electricity generation in Europe. This suggests that for the European stock market, the oil price is the main indicator for energy price developments as a whole.