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Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile‐based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward‐looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision‐making process of risk managers and policymakers. [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]

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. [ABSTRACT FROM AUTHOR]

Unlike previous studies that consider the Chicago Board of Options Exchange (CBOE) implied volatility index (VIX), we examine long memory and fractality in the universe of nine CBOE volatility indices. Using daily data from October 5, 2007, to October 5, 2020, covering calm and crisis periods, we find evidence of long memory and fractality in all indices and a change in the degree of volatility persistence, which points to inefficiency. The long memory of the SKEW index is strong before the onset of three crisis periods, but eases afterwards. The findings provide new insights that matter to investment decisions and trading strategies. [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]

The use of ethanol as a vehicle fuel has reduced greenhouse gas emissions significantly. The introduction of ethanol has also led to a decrease in crude oil prices. Considering the economic and environmental significance of the biofuel markets, a strand of literature investigates the price and volatility dynamics of US ethanol prices. In this paper, in contrast to previous studies, we investigate whether information on structural breaks plays an important role in predicting US ethanol market volatility. Our findings reveal that generalized autoregressive conditional heteroskedasticity (GARCH) models incorporating these breaks improve the prediction of US ethanol market volatility. Furthermore, the persistence of volatility tends to decline when structural breaks are included in the GARCH models. We further note that the influence of good and bad news is properly assessed under such breaks. Our results suggest that ignoring such breaks could mislead the risk assessment procedure for the US biofuel industry. © 2020 Society of Industrial Chemistry and John Wiley & Sons Ltd [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]

Purpose: The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices. Design/methodology/approach: The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH. Findings: The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated. Originality/value: The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks. [ABSTRACT FROM AUTHOR]

We examine spillover and its determinants among Eurozone sector level credit markets using time and frequency domain spillover approaches. Based on network theory and connectedness analysis, we identify the sectors that are major transmitters and receivers of spillover during normal and crisis periods. The rolling window analysis shows that short-run spillover among credit market sectors intensifies during global and Eurozone crisis periods. Further, using Bayesian model averaging, we find that overall financial conditions and stock market volatility are the main drivers of total and sector-level spillover. Our findings have important implications for policymakers and investors interested in Euro-area credit risk at the sector level. [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 study applies a non-parametric causality-in-quantiles test to examine the causal effect of geopolitical risks on return and volatility dynamics of Islamic equity and bond markets. Geopolitical risks are generally found to impact Islamic equity market volatility measures, rather than returns. However, geopolitical risks tend to predict both returns and volatility measures of Islamic bonds. Interestingly, causality, when it exists for returns and/or volatility of Islamic equities and bonds, is found to hold over entire conditional distributions of returns and volatilities, barring the extreme ends of the same. [ABSTRACT FROM AUTHOR]

Abstract We study the dependency structure between option-implied volatilities of gold and silver markets via the application of a copula-based quantile regression. First, we conduct a static analysis and show that the asymptotic lower tail dependence is only pronounced in the low volatility regime of both gold and silver markets. Second, given the existence of a bi-directional causality between the two option-implied volatilities, we consider the lead-lag relationship via non-parametric tail dependence estimators. Results indicate an extreme tail dependency in lower and upper quantiles, with evidence of an asymmetric behavior between/for low and high volatility regimes. Our findings have implications to investors and risk managers. Practically, findings imply evidence of predictability of the probability of gold implied volatility based on the lagged silver implied volatility across different quantiles. Another implication concerns a volatility-based trading strategy, especially during in tandem occurrence of high volatility regimes, which involves the simultaneous selling of an out-of-the money call and put with different strike prices on gold implied volatility. [ABSTRACT FROM AUTHOR]

Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors. [ABSTRACT FROM AUTHOR]

We employ a spectral causality approach to uncover short-, medium-, and long-run causal relations between the US implied volatility index and the five individual implied volatility indexes of BRICS markets from 16th March 2011 to 31st January 2018. We show that the volatility causal relations differ between the short and long run in many cases. Although the results indicate the dominant role played by US uncertainty in shaping uncertainty in all BRICS markets, there is also evidence of a feedback effect from Brazil, Russia, and China to the US that differs across the spectrum. The implications for hedging and risk management practices are explored. [ABSTRACT FROM AUTHOR]

The importance of climate risk as a source of systemic risk for financial markets and the decisions of investors, portfolio managers, and regulators is growing. We examine the directional predictability from two climate risk measures, transition risk and physical risk, to the returns and volatility of European brown and green energy stocks, European carbon emission allowances, and global green bonds. Using daily data, we apply a cross-quantilogram approach in a time-varying setting to measure potential differences in the predictability across quantiles and over various crisis periods. The return predictability results are more pronounced for transition risk than physical risk, especially for brown energy stocks and carbon emission allowances, and they generally vary across periods and markets conditions. The predictability of volatility is also significant at specific time periods and volatility states, especially from transition risk, and the sign of the predictability is positive for brown energy and carbon emission allowances whereas it is negative for green bonds. We show that a lower-than-expected level of discussion about the transition process leads to a heightened volatility of brown energy markets. These findings have important implications regarding climate risks assessment on return and volatility predictability and climate risk and portfolio decarbonization under COP26. • Are asset returns and volatility predictable from climate risks in Europe? • Brown energy and EUA returns are strongly predictable from transition risk. • Asset volatility is predictable from transition risk at strict volatility states. • A lower level of discussion on transition risk increases brown energy volatility. • These findings matter for asset pricing and portfolio decarbonization under COP26. [ABSTRACT FROM AUTHOR]

Abstract: The growing interest in biofuel as a green energy source has intensified the linkages between corn and ethanol markets, especially in the United States that represents the largest producing and exporting country for ethanol in the world. In this study, we examine the effect of corn market uncertainty on the price changes of US ethanol applying a set of GARCH‐jump models. We find that the US ethanol price changes react positively to the corn market volatility shocks after controlling for the effect of oil price uncertainty. In addition, we document that the impact of corn price volatility on the US ethanol prices appears to be asymmetric. Specifically, only the positive corn market volatility shocks are found to influence the ethanol market returns. Our findings also suggest that time‐varying jumps do exist in the ethanol market. [ABSTRACT FROM AUTHOR]

We examine the predictive power of implied volatility in the commodity and major developed stock markets for the implied volatility in individual BRICS stock markets. We use daily data from March 2011 to October 2016 and employ the newly developed Bayesian Graphical Structural Vector Autoregressive (BGSVAR) model of Ahelegbey et al. (2016). Evidence suggests that the predictability of individual implied volatilities in BRICS is generally a function of both global and within the group stock market implied volatilities, and that the role of commodity market volatility is marginal, except for South Africa. Important implications for policy-makers and portfolio-managers are discussed. [ABSTRACT FROM AUTHOR]

This paper examines the Granger causality from Twitter-based economic uncertainty (TEU) to three safe-haven assets – Bitcoin, gold, and US10 year Treasury notes. Using daily data (June 1, 2011–August 30, 2021) and causality-in-quantiles and wavelets methods, the results indicate variability in the causality between the mean and variance, as well as the market conditions. TEU Granger-causes the returns and volatility of Treasuries, the volatility but not returns of Bitcoin, and neither the volatility nor the returns of gold for the raw series, and the causality is mostly significant at low and middle quantiles for Bitcoin and Treasuries. We include other risk factors and confirm the variability in the causality. Considering the possibility of a hidden causality over various frequency domains due to investors' heterogeneous expectations and perceptions of risk, the wavelet transforms-based causality tests reveal an increase in the predictability of risk indicators under specific investment horizons and market conditions. During the pandemic, TEU strongly predicts future volatility of Treasury and Bitcoin returns, reflecting the importance of social-media posts for safe-haven pricing. These findings highlight the benefits of applying the causality-in-quantiles test to decomposed series to determine the contribution of each scale to the causality over various market conditions. • Study causality from Twitter-based economic uncertainty (TEU) to three safe-havens. • Use daily data from June to August and causality-in-quantiles and wavelets methods. • Show variability in the causality between the mean/variance and market conditions. • Causality is mostly significant at low/middle quantiles for Bitcoin and US Treasuries. • Results reflect the importance of social-media posts for safe-haven pricing. [ABSTRACT FROM AUTHOR]

This paper examines the return and volatility linkages between oil prices and the Lebanese stock market by applying the newly developed VAR-GARCH (Vector Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity) model to weekly data from 30 January 1998 to 30 May 2014. To better understand the impact of the global financial crisis, we divide the data into three sub-periods: the pre-crisis period (02 February 1998–28 December 2007), the crisis period (02 January 2008–30 June 2009), and the post-crisis period (01 July 2009–30 May 2014). Contrary to previous studies showing the two-way transmission of return and volatility from oil prices to the stock markets of oil-exporting countries, our empirical results for the whole period reveal weak unidirectional return and volatility transmissions from oil prices to the Lebanese stock market. While the interrelationship between oil prices and Lebanese stocks increased during the crisis, it eased significantly in the post-crisis period. Our empirical results are important for policymakers involved in shock prevention and for portfolio managers seeking optimal portfolio allocation. [ABSTRACT FROM AUTHOR]

Purpose – This paper aims to examine the dynamic relationship across stock market returns in Morocco, Tunisia, Egypt, Lebanon, Jordan, Kuwait, Bahrain, Qatar, United Arabic Emirates (UAE), Saudi Arabia, and Oman from June 2005 to January 2012. Design/methodology/approach – The paper uses a multivariate model with leptokurtic distribution which allows for both return asymmetry and fat tails. The paper also derives from the model the conditional correlation between stock markets and examines the impact of the global financial crisis of 2008 on the conditional variance and correlation. Findings – The empirical results show that the Middle East and North African (MENA) markets are interconnected by their volatilities and not by their returns. Volatility persists in each market and significant volatility spillovers from small to relatively larger markets. During the crisis, the paper finds that conditional volatilities across markets increase but then during the post-crisis period return to their pre-crisis levels. More importantly, the conditional correlation behaves differently, with a significant evidence of downwards trend in some correlations across the MENA stock markets. Research limitations/implications – One limitation of the study relates to the relatively short-sample period which drives the empirical results. Practical implications – The key results imply that there is still a possibility of benefits from portfolio diversification across specific MENA countries during periods of high volatility. Originality/value – No previous study investigates the transmission of both the first and second moments of the return series across the MENA stock markets allowing for time-varying volatility and correlation and accounts for the 2008 global financial crisis to examine whether the conditional volatilities and correlations have strengthened or weakened during the crisis and afterwards. [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]

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]