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This paper is motivated by Bitcoin's rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices' ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers. [ABSTRACT FROM AUTHOR]

In this paper, we contribute to the old debate on the dynamic correlation between gold and stock markets by considering a spectral approach within the framework of portfolio hedging. Specifically, we consider eight MENA stock markets (Tunisia, Egypt, Morocco, Jordan, UAE, Saudi Arabia, Qatar, and Oman) and examine the optimal composition between gold and the stock market index, with a minimum portfolio risk and a high expected return. Based on the spectral approach, we propose seven portfolio structures and evaluate them through a comparison with the conventional DCC-GARCH method and the most best 10 portfolios constructed by using wavelet approach. The main results show that the spectral-based approach outperforms the DCC-GARCH and the wavelet methods. In fact, the optimal gold-stock composition depends on the spectral density of each stock market index, where a stock market index with a stable spectral density requires more investments in gold than a stock market index with an unstable spectral density. [ABSTRACT FROM AUTHOR]

Governments around the world have responded to the COVID-19 outbreak with a mix of policies. The strictest responses of the New Zealand government are notable, given their abilities to contain and limit the spread of the virus. However, their impacts on stock returns remain unclear. This paper investigates the impact of three policies, namely lockdown, the stimulus package, and the travel ban, on the returns of 14 New Zealand industry stock indices. Using daily data from 1 January 2019 to 25 August 2020, evidence points to a heightened level of integration among the various industry stock indices during the early stages of the pandemic. Only lockdown has had a positive impact on aggregate stock returns, suggesting its ability to raise investors' confidence in the overall stock market. At the industry level, the impact of the three response policies is generally positive but heterogeneous across industry stock indices. Notably, none of the three adopted policies significantly impact technology, healthcare, and real estate returns. [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]

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]

This study examines the safe-haven and hedging roles of gold against thirteen Asian stock markets during the COVID-19 outbreak. During the COVID-19 sub-period, gold is shown to be a strong hedge (diversifier) for the majority (minority) of Asian stock markets; it exhibits the property of a strong safe-haven in China, Indonesia, Singapore, and Vietnam, and a weak safe-haven in Pakistan and Thailand. The optimal weights of all stock-gold portfolios are lower during the COVID-19 sub-period than the pre COVID-19 sub-period, suggesting that portfolio investors should increase their investment in gold during the COVID-19 sub-period. The hedging effectiveness for most Asian stock markets is higher during the COVID-19 sub-period. Further analyses show that the hedge portfolio returns in many cases are mostly driven by gold implied volatility and inflation expectations in both sub-periods. Our findings have useful implications for market participants holding investments in Asian stocks during stressful periods. [ABSTRACT FROM AUTHOR]

Although the US transport sector is one of the major users of fossil fuel (e.g., crude oil), the impact of energy price volatility on transport stock sector indexes remains under-researched. The present study addresses this research void by investigating the impact of energy implied volatility on transportation stock returns in the US. Using the crude oil volatility index (OVX), as a proxy of energy price volatility, and three Dow Jones indexes tracking the performance of the airlines, marine and trucking stock subsectors, we employ a GARCH-jump model. The main results show that the oil market sends volatility to the US transport subsector stock indexes, suggesting that oil implied volatility plays a role in pricing US transport stocks. The impact of OVX shocks is asymmetric, indicating that increases and decreases in oil implied volatility have a heterogeneous impact on the transport subsector stock markets. Jumps are significant in the three transport subsector stock indexes, and are time-dependent. Notably, the three transportation subsector stock indexes are more sensitive to OVX shocks than the S&P 500 index. These results have important implications for investors, policymakers, academics, and managers of the US transportation industry. • Study the impact of oil price volatility (OVX) on US transportation stock returns. • The GARCH-jump process is employed. • Find that OVX plays a pivotal role in pricing US transportation stocks. • The impact of OVX is asymmetric. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic. [ABSTRACT FROM AUTHOR]

We examine the effects of three monthly climate risk factors, climate policy uncertainty (CPU), climate change news (CCN), and negative climate change news (NCCN), on the long-run volatilities and correlation of daily green and brown energy stock returns, and perform a hedging analysis. Given that our dataset combines daily and monthly data, we apply mixed data sampling models such as GARCH-MIDAS and DCC-MIDAS. To deal with volatility clustering, asymmetric effects, and negative skewness in innovations, which characterize our dataset, we use those models in asymmetric form with a bivariate skew-t distribution. Firstly, the GARCH-MIDAS models indicate that climate risk has a significant impact on the long-run volatility of brown energy stocks. Secondly, the DCC-MIDAS models reveal that the long-run correlation of green-brown stock returns decreases with the climate risk, suggesting a negative effect and hedging opportunities. Thirdly, the hedging analysis shows that incorporating a climate risk factor, especially NCCN, into the long-run component of dynamic correlation significantly improves the hedging performance between green and brown energy stock indices. The results are robust to an out-of-sample analysis under various refitting window sizes. They matter to portfolio and risk managers for energy transition and portfolio decarbonization. • We extend the symmetric DCC-MIDAS model to an asymmetric DCC-MIDAS model with bivariate skew-t. • The long-run volatilities of brown stock significantly increase with the climate risk factors. • The negative climate change news index has a larger effect on the long-run volatilities of brown stocks. • The long-run correlation between brown and green stocks decreases with an increase in climate risk factors. • The hedging is significantly improved by inserting climate risk into the long-run correlation. [ABSTRACT FROM AUTHOR]

We examine the multifractal scaling behaviour and weak form market efficiency of clean energy stock indices using an asymmetric MF-DFA. We find asymmetric multifractality in the US, European, and global clean energy stock indices. Asymmetric multifractality in the clean energy stock index of the US is due to fat-tails and long-range correlation. However, for European and global clean stocks, multifractality is due only to fat-tailed distribution. We find higher efficiency in the upward trend of the European and global clean stock markets whereas, for the US, the market is less efficient when the market is upward trending. The time-varying market deficiency measure shows that US clean energy stocks are becoming relatively more efficient over time. • Asymmetric multifractal behaviour of clean stocks. • Markets are weak form efficient. • Multifractality of US due fat-tail distribution and long-range correlation. • Higher efficiency in the upward trend for European and global stocks. • US clean stocks are becoming relatively efficient over time. [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]

Unlike previous studies examining the association between crude oil and renewable energy stock prices under average conditions, we employ a quantile-based regression approach offering a more comprehensive dependence structure under diverse market conditions. Using weekly data covering crude oil prices (WTI market) and three clean energy stock indices (the Wilderhill Energy Index, MAC Global Solar Energy Index, and S&P Global Clean Energy Index), quantile regression analyses provide solid evidence of the decreasing dependence of clean energy stock returns on crude oil returns. The lagged effect of WTI oil returns on clean energy stock returns is generally significant, which indicates that clean energy stock returns react differently to new information on oil returns under different market conditions. We further check for asymmetrical effects of oil returns on clean energy stock returns in various market conditions and find a strong effect of negative oil returns during bearish periods and an insignificant effect during bullish episodes. • Study crude oil prices and clean energy stock indices with quantile regression. • Show decreasing dependence of clean energy stock returns on oil returns. • Indicate a significant lagged effect of oil returns on clean energy stock returns. • Find a strong effect of negative oil returns during bearish periods. • Show an insignificant effect during bullish episodes. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between clean energy stock indices and energy metals that are sensitive to the growth in demand for clean energy solutions, and makes inferences about whether energy metals can act as hedges or safe havens for clean energy stock indices. The sample period is April 2011 to April 2021, a period which saw substantial investments into the clean energy industry as the capital deployed to clean energy generation during this period was about three times higher than the preceding decade. The main results indicate statistically significant non-linear relationships among the markets under study. All energy metals, except cobalt, have a significant positive linkage with clean energy stock indices and such associations do hold during episodes of high volatility. While none of the energy metals under study acts as a hedge for clean energy stock markets, the results support previous evidence on the hedging properties of precious metals, showing that gold and silver serve as hedges for certain clean energy stock indices. These results have important implications amid tremendous growth in clean energy stock investments and the repeating occurrence of periods of uncertainty. • We investigate if energy metals can hedge clean energy equity risk. • None of the energy metals acts as a hedge. • Gold and silver act as hedges for certain clean energy stocks. [ABSTRACT FROM AUTHOR]