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We investigate the asymmetric effects of climate policy uncertainty (CPU), geopolitical risk (GPR), and crude oil prices (WTI) on the realized volatility of the returns of clean energy prices (CEP) in the USA. Using the non-linear autoregressive distributed lags (NARDL) model on data from January 2001 to December 2021, we provide evidence that the effects of CPU, GPR, and WTI on CEP's returns and realized volatility differ in the short and long run and are asymmetric. An increase and decrease in CPU affect CEP's realized volatility more than returns in the long run. Notably, an increase in CPU positively affects the CEP's returns, and a decrease negatively affects CEP's returns in the short run. Moreover, an increase in GPR exerts higher effects on returns in the short run, while both an increase and a decrease in GPR have significant long-run effects. An increase or decrease in WTI shows higher effects on CEP's returns and realized volatility in the long run, while an increase in WTI shows short-run effects. Our findings provide valuable information for making investment decisions while considering the asymmetric effects of climate policy uncertainty, geopolitical risks, and crude oil prices., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Economic policy uncertainty generally tends to induce a pessimistic view of future market behaviour. Furthermore, instabilities in global oil prices have serious implications for the economies of oil exporters and importers, due to their over-dependence on crude oil for revenue and production activities, respectively, and thereby on stock market indices. Against limited empirical evidence, this study examines the spillover effects from global economic policy uncertainty (GEPU) and oil price volatility to the volatility of the stock market indices of oil exporters and importers in both developed and emerging economies. The results show that the spillover effect from GEPU to oil exporters is relatively smaller than to oil importers, for both developed and emerging countries. Conversely, the volatility spillovers from oil prices to oil exporters are relatively larger than to oil importers, for both developed and emerging countries. Specifically, the volatility spillovers from oil prices to oil exporters (importers) in emerging countries are relatively stronger compared to oil exporters (importers) in developed countries. The findings indicate that the volatility of the stock markets of emerging countries is more sensitive to global factors such as GEPU and oil price volatility, and that oil exporters and importers in emerging economies are more sensitive to oil price volatility than oil exporters and importers in developed economies, which is in line with previous studies., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

China is a large oil-consuming country, and sharp fluctuations in oil prices are bound to be detrimental to the stable growth of its economy. Therefore, accurately grasping the impact of the oil market on China's sectors is the key to ensuring its healthy economic development. The article aims to explore the extreme risk transfer link of the oil and China's sectors system, focusing on uncovering the risk spillover mechanism of the oil and providing early warning on it. We apply the TENET method to discuss risk propagation relationships within the oil and sectors system at three levels. The TVP-VAR model is brought in to recognize the factors affecting risk spillover in the oil market from the network correlation perspective. Finally, early warning of oil risk spillover is provided by incorporating the influencing factors into a machine learning model. The outcomes indicate that the risk connectivity of the oil and China's sectors system is highly correlated with extreme events. There are variations in the spillover effects of oil market risk on different sectors, with Telecommunication Services, Utilities, Financials and Major Consumer sectors being the main bearers of the oil risk shocks. Overall, oil risk spillovers are mainly driven by economic policy and geopolitics, but oil price uncertainty is found to have a persistent impact on oil market risk spillovers in the dynamic analysis. Random forest model can provide effective early warning of oil risk spillovers. In addition, the significance analysis shows that oil price uncertainty and inflation are important factors affecting oil risk spillovers and are nonlinearly correlated with them. [ABSTRACT FROM AUTHOR]

This study analyses the spillover in higher-order moments covering volatility, skewness, and kurtosis in the space of green energy, brown energy, and technology stocks in the US from May 8, 2006 to May 18, 2022. The analysis shows four noteworthy findings. Firstly, besides volatility, spillovers of skewness and kurtosis are important. Secondly, spillovers fluctuate with time and intensify during crisis periods, especially around the oil price crash of 2015. Thirdly, clean energy is the major net transmitter of volatility and kurtosis shocks; wind energy and crude oil are the major net transmitters of volatility and skewness shocks, whereas solar energy plays the same role for skewness spillovers. Fourthly, the spillover analysis based on the three moments jointly estimated in the same system indicates that the combined contribution of cross spillovers of volatility, skewness, and kurtosis to the total spillover index constitutes more than 40%. These results provide new insights for investors and policymakers necessary for making comprehensive inferences about asset pricing, portfolio risk management, and market stability under the energy transition. They suggest that if the spillover channels of skewness and kurtosis are overlooked, the risk propagation across these markets will be underestimated, potentially jeopardizing financial stability. • Consider green energy, brown energy, and technology stocks in the US. • Extract higher-order moments and study the system of spillovers. • The joint spillovers of volatility, skewness, and kurtosis are important. • They fluctuate with time and intensify around the oil price crash of 2015. • The combined contribution of cross spillovers of higher-order moments is significant. [ABSTRACT FROM AUTHOR]

We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants. [ABSTRACT FROM AUTHOR]

We study the tail dependence between crude oil and BRIC stock markets using a time-varying optimal copula (TVOC) approach. We show evidence of multiple tail dependence regimes, suggesting that simple static or dynamic copula specifications do not fully characterize the extreme dependence between oil and BRIC stock markets. The identified combinations of asymmetric and extreme positive lower tail dependence justify the application of the TVOC. Interestingly, the positive lower tail dependence between oil and stock markets and risk spillover from oil is higher for Brazil and Russia (oil exporters) than India and China (oil importers). Finally, we assess the effectiveness of hedging and measure the conditional diversification benefits of investing in oil for BRIC stock indices. Notably, the Chinese and Indian equity markets offer higher conditional diversification benefits when combined with oil in an equally weighted portfolio. [ABSTRACT FROM AUTHOR]

This paper subjects six alternative indicators of global economic activity to empirically examine their relative predictive powers in the forecast of crude oil market volatility. GARCH‐MIDAS approach is constructed to accommodate all the relevant series at their available data frequencies, thereby circumventing information loss and any associated bias. We find evidence in support of global economic activity as a good predictor of energy market volatility. Our forecast evaluation of the various indicators places a higher weight on the newly developed indicator of global economic activity which is based on a set of 16 variables covering multiple dimensions of the global economy, whereas other indicators do not seem to capture. Furthermore, we find that accounting for any inherent asymmetry in the global economic activity proxies improves the forecast accuracy of the GARCH‐MIDAS‐X model for oil volatility. The results leading to these conclusions are robust to multiple forecast horizons and consistent across alternative energy sources. [ABSTRACT FROM AUTHOR]

Unlike previous studies, we examine which of the implied volatilities of US stock and crude oil markets are more suitable and effective hedge for the downside risk of US travel and leisure (T&L) stocks. Using the corrected dynamic conditional correlation process, the results show that the T&L stock index is more negatively and more consistently correlated with the implied volatility of crude oil prices, suggesting that the oil implied volatility is a more suitable hedging asset. Similar results are reported for France, the United Kingdom, and developed markets. They are robust to the frequency of the data and model specification. Furthermore, the hedge ratios vary over time, which requires a regular update of hedged positions. Importantly, the highest hedge effectiveness is associated with the oil implied volatility. [ABSTRACT FROM AUTHOR]

We forecast monthly realized volatility (RV) of the oil price based on an extended heterogenous autoregressive (HAR)-RV model that incorporates the role of the El Niño Southern Oscillation (ENSO), as captured by the Equatorial Southern Oscillation Index (EQSOI). Based on the period covering 1986 January to 2020 December and studying various rolling-estimation windows and forecast horizons, we find that the EQSOI has predictive value for oil-price RV particularly at forecast horizons from 2 to 4 years, and for rolling-estimation windows of length 4 to 6 years. We show that this result holds not only based on standard tests of out-of-sample predictability, but also under an asymmetric loss function. [ABSTRACT FROM AUTHOR]

We contribute to the scarce literature on the oil market volatility index (OVX) by examining the presence of time‐varying jumps in OVX and by assessing the ability of OVX to predict the conditional variance of crude oil returns. Using a GARCH‐jump model, we find evidence that OVX is characterized by jump behaviour that tends to vary over time. Further analysis indicates that accounting for the jump behaviour of OVX helps improve the conditional variance forecasts of crude oil returns. Since the studied features of OVX play a crucial role in asset pricing and risk analyses, our findings have policy implications related to refining volatility prediction models and risk measures. [ABSTRACT FROM AUTHOR]

Stock return predictability has always been one of the central themes of finance literature, given its crucial implications for investment decisions, risk management, and financial and monetary policymaking. This paper evaluates the in-sample and out-of-sample stock return predictive power of the global and Saudi geopolitical risk indices and crude oil returns in the context of six Gulf Cooperation Council (GCC) countries. Monthly data from February 2007 to December 2019 and the feasible generalized least square (FGLS) estimator for predictive modelling by Westerlund and Narayan (2012, 2015) are used. Global and Saudi GPR indices show weak evidence of in-sample predictability of excess stock returns. However, the out-of-sample forecasts show that only the global geopolitical risk index provides superior prediction in the context of Kuwaiti and Omani stock markets, compared to the historical average benchmark model. Crude oil prices are shown to be a better predictor in most cases, in both in-sample and out-of-sample forecast models The results imply that crude oil returns can be used for active prediction of GCC stock market returns, once econometric issues are accounted for. The findings remain mostly unaffected when excess risk adjusted returns are used. [ABSTRACT FROM AUTHOR]

Jumps in the price process of assets represent a sort of tail risk and are found to affect many aspects of asset pricing, volatility modelling and asset allocation. In this paper, we detect price jumps in the realised volatility series of a wide set of commodity futures and find evidence of jumpy behaviour, especially in energy and agricultural commodities. We examine whether the realised volatilities of commodity futures jump together and find evidence that cojumping is significant and generally clustered within the commodity groups, suggesting some sort of segmentation regarding the tail risk behaviour across energy, agricultural and metals commodities. Additional analysis shows that price jumps and macroeconomic news surprises tend to occur together in specific commodities such as crude oil, which confirms earlier findings about the sensitivity of crude oil to news about the economy. [ABSTRACT FROM AUTHOR]

This study explores the dynamic effects of different oil shocks on real exchange rates in net oil importers and exporters. Specifically, the connectedness measures are combined with the structural vector autoregressive model. The findings show that oil supply shocks have a larger depreciating influence on exchange rates in oil exporters than in importers. All countries are generally more sensitive to oil-specific demand shocks, and this sensitivity can lead to a significant appreciation in real exchange rates, except in Japan and the United Kingdom. Further, the spillover effect between oil shocks and exchange rates has strengthened after the global financial crisis of 2007–08. Our findings provide useful implications for the policy-makers and market risk managers to effectively avoid exchange rate risk induced by oil shocks. [ABSTRACT FROM AUTHOR]

This article uses dependence-switching copulas and time-varying single copulas to characterize the world oil-stock market dependence in a broad range of emerging economies, and it then conducts a regression analysis to explore the determinants of the market dependence patterns. Our results support a positive crude oil-emerging stock market link overall. The regression results show that oil return volatility, country-specific variables (i.e., stock market volatility, petroleum production growth), and US economic policy uncertainty have positive effects on the oil-stock dependence. However, a strong US economy tends to decrease the oil-stock dependence. The robustness of these findings is confirmed. [ABSTRACT FROM AUTHOR]

This paper investigates connectedness in option implied moments, such as volatility, skewness, and kurtosis across precious metals (gold, silver) and energy (crude oil and natural gas) markets in both time and frequency domains. Using daily option data from January 4, 2010, to December 31, 2020, we first construct implied moments and then examine their static and dynamic connectedness via time-frequency spillover methods and network analysis. The results show that system-wide connectedness weakens as the moment order becomes higher, and the level of spillovers in all implied moments is much higher at a lower frequency. The spillovers show substantial time variation and pronounced intensity during turbulent periods. Gold is the net transmitter in precious metals and energy markets. However, if excluding the crisis period in 2020, crude oil and gold play dominant roles. Further analysis shows the main drivers of the spillover indices for the higher-order moments. Our findings have important implications for commodity traders and investors, risk managers, and policymakers. • Construct implied higher-order moments for gold, silver, oil, and natural gas. • Study dynamic spillovers via time-frequency methods and network analysis. • Spillovers are higher at lower frequency and weaken as the moment order increases. • Gold is the net transmitter in all three estimators in this system. • Crude oil shifts from net transmitter to net recipient during crisis period. [ABSTRACT FROM AUTHOR]

Although the global crude oil market plays a significant role in pricing edible oils, the association between energy price uncertainty and the Malaysian palm oil industry remains understudied. Given that palm oil is widely used as a cheap feedstock for biodiesel, it is important to investigate whether risk transmits from the oil market to the Malaysian palm oil industry. Employing GARCH-jump models, this study extends the scant literature. The results reveal that the crude oil volatility index (OVX) significantly influences palm oil prices suggesting that an upturn in oil market volatility negatively impacts palm oil prices. Subsample analyses show that the negative impact of OVX intensified during the 2014 oil price decline and the COVID-19 outbreak. The effect of OVX is asymmetric, implying that changes (upward and downward shifts) in oil price variance exert a heterogeneous impact on the price levels of this edible oil. It is also observed that palm oil prices experience time-dependent jumps. We show that volatility significantly transfers from the crude oil to the palm oil market during periods of high uncertainty. Hence, investors and policymakers could use the information content of OVX for forecasting future trends in palm oil prices. [ABSTRACT FROM AUTHOR]

Previous studies indicate that the US equity market implied volatility index (VIX) impacts the crude oil market volatility. However, the VIX typically reflects macroeconomic fluctuations, little affected by social media or investor perception changes. In this paper, we use recently introduced news-based equity market volatility (EMV) trackers to examine their impacts on crude oil volatility in its various states and their ability to predict oil volatility relative to that of the VIX. Applying quantile regressions, the results indicate a significant impact of EMV trackers on the oil market volatility during periods of high oil volatility, whereas the impact is mostly insignificant when the oil market is less volatile, which points to an asymmetry. Further analysis shows that various EMV trackers (EMV-overall, EMV-commodity, EMV-crises) have better forecasting power than VIX, economic policy uncertainty (EPU) and geopolitical risk (GPR) indexes. Our findings are relevant to asset pricing, oil portfolio formation, and risk management. • We explore the impact of news-based volatility trackers (EMV) on oil volatility. • The quantile regression model is employed. • The results are compared with VIX, EPU and GPR indexes. • EMV trackers have better forecasting power than other uncertainty indexes. [ABSTRACT FROM AUTHOR]

Being an environmentally friendly fuel obtained from rapeseed oil, biodiesel is used extensively in Europe. However, the dependence structure between global crude oil prices and the European prices of biodiesel and rapeseed oil is understudied and unclear. In this paper, we address this gap by utilizing asymmetric copulas and cross-quantilogram approaches on daily data. The results of the DCC-Student-t copula indicate that during bearish periods the conditional connectedness between crude oil prices and biodiesel (rapeseed oil) prices are stronger than during bullish periods, indicating increased co-movement with a decline in crude oil prices. The application of cross-quantilogram indicates that an increase in crude oil price positively influences biodiesel prices reflecting an asymmetric dependence structure among the assets. There is evidence of shifts in the dynamics of quantile dependency during periods of financial and economic turmoil. Overall, the results show a significant dependence between the global crude oil market and the European markets of biodiesel and rapeseed oil in specific periods and under specific market conditions, which have important implications for policymakers and investors. [ABSTRACT FROM AUTHOR]

We study the effect of uncertainty in energy sector firms, as reflected in the information in the energy sector implied volatility index (VXXLE), on clean energy exchange traded funds (ETFs). Unlike the existing literature that mainly employs linear models to study the association between clean energy and financial markets, we apply the Markov regime switching process to uncover the impact of the VXXLE on clean energy ETFs during high and low volatility regimes. Our findings reveal a negative impact, suggesting that, when implied volatility levels are high for energy sector firms, a drop is likely in clean energy asset returns. The results also show evidence of an asymmetric effect. During high volatility regimes, the association between the VXXLE and clean energy ETFs holds stronger than in low volatility regimes. These results remain unchanged controlling for the effects of oil prices, crude oil volatility, and technology stock prices. We further document that changes in levels of the VXXLE substantially impact the realized volatility of clean energy ETFs. Our findings have significant implications for investors and policymakers. • Study the impact of energy sector volatility (VXXLE) on clean energy ETFs. • Use regime switching process to differ between high and low volatility regimes. • Find a negative impact and evidence of an asymmetric effect. • Results remain unchanged controlling for the effects of several variables. • Changes in VXXLE impact the realized volatility of clean energy ETFs. [ABSTRACT FROM AUTHOR]

Although clean energy equities have emerged as a new asset class for market participants, especially environmentally concerned investors, existing and previous studies pay very little attention to how equity investors in clean energy markets can reduce their downside risk. The authors of this paper address this void by considering the roles of the commodity market volatility indexes of crude oil, gold and silver. The results of the dynamic conditional correlation model show that commodity volatilities and clean energy equity prices move in opposite directions. Based on the hedging effectiveness, each of the three volatility indexes acts as an effective tool for reducing the risk of clean energy equity indexes. The implied volatility index of crude oil is the most effective tool, followed by that of gold and silver. Further analysis indicates the robustness of the results to the application of an asymmetric conditional correlation model. The findings extend the limited understanding of how to hedge the downside risk of clean energy stock indexes, and provide useful implications to market participants on the ability of implied volatility indexes of major commodities to hedge that risk. Image 1 • Hedging roles of commodity volatility indexes for clean energy stocks are examined. • Dynamic conditional correlation model and hedging effectiveness index are applied. • Commodity volatilities and clean energy stock prices move in opposite directions. • Each of the volatility indexes of oil, gold, and silver is an effective hedge. • Crude oil volatility index is the most effective hedge, followed by that of metals. [ABSTRACT FROM AUTHOR]

We study the jump behaviour in the sovereign risks of major oil-exporting countries and examine whether it is affected by jumps in the price and volatility of crude oil. Data used are daily from 14 February 2011, to 31 July 2019. We detect the presence of jumps in many oil exporters and find evidence of less frequent jumps in the crude oil market. The jumps in sovereign risks of oil-exporters are significantly affected by oil volatility jumps, not by oil price jumps. These findings suggest that the sovereign risks of oil-exporters are affected by abrupt movements in oil implied volatility, which points to a contagion effect. The findings are useful not only for refining the prediction of the sovereign risks of oil-exporters but also in reducing the potential effect of price discontinuity of sovereign risks on public finances sustainability and financial health. [ABSTRACT FROM AUTHOR]