European equity markets volatility spillover: Destabilizing energy risk is the new normal

While energy risk is increasingly recognized as a systemic risk, there is limited comprehensive analysis of the risk propagation in regional contexts. In this study, we examine oil and natural gas price changes and shocks in relation to equity market returns and volatility for 24 European Economic Area (EEA) countries. In addition to traditional panel regressions, we also deploy the Diebold-Yilmaz (2014) spillover index for a closed network analysis. We differentiate in the cross-section across the core EU block, PIIGS countries, EU enlargement countries joining after 2004, and other non-EU countries, to provide insights into the ongoing debates on the European energy market stability. While we find evidence of the manifestation of energy risk throughout the sample period, we find that until 2019 the primary sources of volatility spillover in the EEA economic network arose from economic or political uncertainty. Energy risks, measured by large crude oil and natural gas price shocks also significantly contributed to equity market volatility, with increasing volatility risk arising from natural gas, a green labelled energy source after 2019. Last, we show that CEEC equity markets are more sensitive to oil and natural gas price shocks when domestic currencies depreciate against the Euro.     

Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method

This paper analyzes dynamic volatility spillovers between four major energy commodities (i.e., crude oil, gasoline, heating oil and natural gas) in the oil-natural gas future markets. We construct a time-varying spillover method by combining the TVP-VAR-SV model and the spillover method of Diebold and Yilmaz (2009, 2012, 2014). We use the spillover method to obtain time-varying total, directional and pairwise volatility spillover indices. Our results summarize as follows: (1) The volatility spillover indices present peaks and troughs during some periods, such as shale gas revolution, financial crisis, and oil price crash; (2) After the U.S. shale gas revolution, the size of volatility spillover from natural gas future market has reduced sharply, but volatility doesn't decouple from the other three oil future markets; (3) The directional spillover is asymmetric. The crude oil and heating oil futures market are main net transmitter of volatility risk information, while the gasoline and natural gas futures markets are the net receiver; (4) For natural gas future market, the pairwise volatility spillover from crude oil future market has the most significant influence.     

Returns and volatilities of energy futures markets: Roles of speculative and hedging sentiments

This study examines how speculative and hedging sentiments influence the returns and volatilities of energy futures markets. We construct speculative and hedging sentiment indices based on the weekly data of fund and commercial positions of four energy futures: crude oil, heating oil, gasoline, and natural gas, traded on the New York Mercantile Exchange (NYMEX) from 15 January 2013 to 5 February 2019. Our study demonstrates that speculative sentiment generates greater market fluctuations in the energy futures markets than hedging sentiment; and, further, speculative sentiment stimulates a reversal effect on the returns of crude oil futures. Moreover, speculative sentiment exerts positive systemic risk compensation on the four futures' returns, whereas hedging sentiment alleviates volatilities in the energy futures markets. Most notably, distinguishing it from the leverage effect in stock markets, the speculative sentiment in the energy futures markets is influenced more by good than by bad news; while hedging sentiment exhibits emotional neutrality, as opposed to its impact on stock markets as reported in the literature. Additionally, the positive hedging sentiment in crude oil futures demonstrates significant systemic risk compensation, whereas the three other futures do not have an influence, confirming the prevalence of speculation in hedging transactions in crude oil futures. Our further analysis shows cross-market volatility spillover effects, among which speculative sentiment inherent in crude oil futures causes volatility spillovers to the three other futures, while hedging sentiment has no such effect. Our study has implications for overseeing international energy futures markets, providing regulators with evidence that will facilitate the development of effective strategies to strengthen market supervision.     

Investigating volatility spillover of energy commodities in the context of the Chinese and European stock markets

This paper investigates spillover from energy commodities to Shanghai stock exchange and European Stock market, and identifies possible risks transmission and portfolio diversification opportunities. The study is conducted on daily spot prices of carbon (CO2) emission, natural gas and crude oil from 16 December 2010 to 29 December 2022, employing Granger causality test, dynamic conditional correlation (DCC), Diebold-Yilmaz (2012) and Barunik-Krehlic (2017) models. Results identify higher volatility and imply greater connectedness in the longer run. Additionally, natural gas is witnessed as the highest contributor of the shocks and crude oil as the highest receiver of the shocks from the network connection. Further results suggest for investment in energy commodities in shorter run rather than long run for efficient portfolio diversification. Results from this study are expected to have practical implications for portfolio managers, investors, and market regulators, given the suggestion of this study to incorporate energy stocks for efficient diversification of risk.     

国际原油价格对我国新能源行业股票价格的波动溢出效应研究

在大气污染日益严峻的情况下,新能源行业受政府大力支持和投资者青睐。新能源与原油一定程度上互为替代品,理论上国际原油价格必然对我国新能源行业股票价格有显著的波动溢出效应,但有些学者却持反对态度,认为我国股票市场对外还没有完全开放,新能源行业发展又很不成熟,所以该溢出效应很难显著。文章运用VAR- Asymmetric- BEKK模型进行比较研究得出：在未去除我国整体股市行情因素时,国际原油价格波动对我国新能源行业股票价格波动溢出效应不显著;而在去除我国整体股行情因素时,国际原油价格波动对我国新能源行业股票价格波动溢出效应在1%显著性水平下显著。表明存在从国际原油价格向我国新能源行业股票价格的波动溢出效应,只是该溢出效应被我国股市总体行情掩盖了。     

Dynamic volatility spillover effects between oil and agricultural products

The strong volatility spillover between crude oil and agricultural commodity markets reduces the diversification benefits and implies costly risk management process faced by portfolio managers and agricultural producers. This paper proposes a comprehensive study of their dynamic implied volatility spillover effects after the global financial crisis 2008–2009, while considering the transition between oil volatility's regimes. By using implied volatility, our analysis emphasizes on the forward-looking information that market traders usually convey in making decisions. We employ the generalized spillover indices within a fractionally integrated VAR model to capture the dynamic patterns of the volatility spillover effects alongside the Markov Switching Autoregressive model to extract the regimes of oil. Our results show new evidence that the net volatility spillover effect from crude oil to all agricultural commodities tends to decrease when crude oil remains in its low volatility regime. Conversely, this effect experiences an increasing trend when crude oil remains in its relatively high volatility regime. A dynamic strategy that combines oil and the most balanced agricultural commodity in terms of volatility transmission with oil (i.e., close-to-zero net volatility spillovers) depending on oil's regimes consistently outperforms the buy and hold strategy in terms of information ratio.     

Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect

Future markets play vital roles in supporting economic activities in modern society. For example, crude oil and electricity futures markets have heavy effects on a nation’s energy operation management. Thus, volatility forecasting of the futures market is an emerging but increasingly influential field of financial research. In this paper, we adopt big data analytics, called Extreme Gradient Boosting (XGBoost) from computer science, in an attempt to improve the forecasting accuracy of futures volatility and to demonstrate the application of big data analytics in the financial spectrum in terms of volatility forecasting. We further unveil that order imbalance estimation might incorporate abundant information to reflect price jumps and other trading information in the futures market. Including order imbalance information helps our model capture underpinned market rules such as supply and demand, which lightens the information loss during the model formation. Our empirical results suggest that the volatility forecasting accuracy of the XGBoost method considerably beats the GARCH-jump and HAR-jump models in both crude oil futures market and electricity futures market. Our results could also produce plentiful research implications for both policy makers and energy futures market participants.     

Risk Transmission from Oil and Natural Gas Futures to Emerging Market Mutual Funds

This study evaluates the impacts of energy markets on emerging market mutual funds (EMMFs). In particular, we investigate the volatility transmission between these funds and the oil and natural gas prices. The findings suggest significant risk spillover from the energy markets to EMMFs. Furthermore, we find a large number of EMMFs’ risk transmitting to oil prices and almost all of the EMMFs’ risk transmitting to natural gas prices. By dividing the sample into two (before and after 2008), we find the EMMFs’ influence on the oil market decreasing after this turbulent period. Our results have important implications for mutual fund managers and investors.     

Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US,Japan, and Germany

We analyzed the return and volatility spillover between the COVID-19 pandemic in 2020, the crude oil market, and the stock market by employing two empirical methods for connectedness: the time-domain approach developed by Diebold and Yilmaz (2012) and the method based on frequency dynamics developed by Barunik and Krehlik (2018). We find that the return spillover mainly occurs in the short term; however, the volatility spillover mainly occurs in the long term. From the moving window analysis results, the impact of COVID-19 created an unprecedented level of risk, such as plummeting oil prices and triggering the US stock market circuit breaker four times, which caused investors to suffer heavy losses in a short period. Furthermore, the impact of COVID-19 on the volatility of the oil and stock markets exceeds that caused by the 2008 global financial crisis, and continues to have an effect. The impact of the COVID-19 pandemic on financial markets is uncertain in both the short and long terms. Our research provides some urgent and prominent insights to help investors and policymakers avoid the risks in the crude oil and stock markets because of the COVID-19 pandemic and reestablish economic development policy strategies.     

The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach

Investor sentiment has become an important factor affecting oil price volatility and extreme risk. Therefore, we utilise a VaR-GARCH model to detect the extreme risk of the crude oil market during 2007–2017, and then explore the causality between investor sentiment and extreme risk in the crude oil market, and their lead-lag and co-movement relationships in the time-frequency domain. The empirical results show that: firstly, investor sentiment leads downside risk but lags the upside risk in the crude oil market; secondly, in the time domain, there is a co-movement between investor sentiment and extreme risk in the crude oil market, in particular, investor sentiment may Granger cause extreme risk in the crude oil market at the 1% significance level but not vice versa; thirdly, in the frequency domain, weak coherence can be found in high-frequency bands but increases in low-frequency bands during the whole sample period, which indicates that the impact of investor sentiment on extreme risk in the crude oil market will last for a long time, although the affected period tends to decrease.     

The impact of institutional analyst forecast divergence on crude oil market: Evidence from the mixed frequency models

As important information intermediaries, analysts play a non-negligible role in the crude oil market. Existing research often focuses on analysts' collection and interpretation of firm-specific information, but neglects the impact of analysts' forecasts on specific markets such as the crude oil market, which is crucial to the safe and stable development of the crude oil market. Therefore, this study uses historical data from January 2011 to December 2020 as a sample to construct analysts' forecast divergence indicators from 15 institutional analysts' forecast data on international crude oil futures prices. It then explores the impact of institutional analysts' forecast divergence on oil price return volatility, crude oil market jumps and crude oil futures trading volume, based on various mixed-frequency models. The results are as follows: First, volatility in oil price returns increases with a growing divergence in analysts' forecasts. Second, analysts' forecasts do not trigger jump in the crude oil market on the first six days after the information is released, but trigger a significant positive jump in the market on the seventh day. Third, the impact of analysts' forecast divergence on trading volume is weak; however, the reverse effect is significant, while the static and dynamic spillover results are consistent.     

Have the predictability of oil changed during the COVID-19 pandemic: Evidence from international stock markets

The COVID-19 has undoubtfully brought fierce shocks to the real economic activities, financial market and public lives. Under this special condition, this study explores whether the predictability of crude oil futures information has changed before and during the COVID-19 pandemic for 19 international stock markets. From an in-sample perspective, we find that the crude oil futures RV can significantly affect future stock volatility for each equity index except SSEC. Moreover, the out-of-sample results from statistic and economic perspective reveal that crude oil futures RV is a more efficient predictor during the COVID-19 pandemic compared with the pre-crisis period. Furthermore, we find that the predictability of crude oil futures information is stronger from March to May 2020, when the epidemic is seriously prevailing. The empirical results from alternative evaluation method, recursive window method, alternative realized measures, controlling VIX and the seasonal effect, asymmetric forecasting window and different testing windows are robust and consistent. Our findings could offer novel and significant policy and practical implications.     

Do the crude oil futures of the Shanghai International Energy Exchange improve asset allocation of Chinese petrochemical-related stocks?

The Shanghai International Energy Exchange (INE) facilitates both local and international investment in Chinese petrochemical-related stocks through local crude oil futures. This study investigates whether the Chinese emerging market can better aid investors' risk hedging and asset allocation compared to two major international developed markets–the Brent and West Texas Intermediate (WTI) crude oil futures markets—and examines the pairwise risk hedging effects and multi-asset allocation performance of INE and petrochemical-related stocks. The results show that INE has higher hedge effectiveness than Brent and WTI under pairwise hedging. Further, in multi-asset allocation, the portfolios containing INE outperform other portfolios. Overall, INE results in a better diversification effect and volatility reduction than the use of WTI crude oil futures to construct multi-asset allocation with Chinese petrochemical-related stocks. However, INE performance is inferior to Brent's in terms of constructing portfolios with oil or energy stocks. Finally, our results are robust to the five factors proposed by Fama and French (2015) in asset pricing.     

Sentiment and stock prices: The case of aviation disasters

Behavioral economic studies reveal that negative sentiment driven by bad mood and anxiety affects investment decisions and may hence affect asset pricing. In this study we examine the effect of aviation disasters on stock prices. We find evidence of a significant negative event effect with an average market loss of more than $60 billion per aviation disaster, whereas the estimated actual loss is no more than $1 billion. In two days a price reversal occurs. We find the effect to be greater in small and riskier stocks and in firms belonging to less stable industries. This event effect is also accompanied by an increase in the perceived risk: implied volatility increases after aviation disasters without an increase in actual volatility.     

Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis

The response of renewable energy stock returns to the dynamics of fossil energy markets is a vital concern of low-carbon transitions. There is still sparse literature documenting the directional dependence of renewable energy stock returns on the connectedness among fossil energy returns, even though previous studies have examined the relationship among renewable energy stocks and fossil energy markets. Additionally, the conclusions of prior studies are quite far from reaching a consensus regarding the relationship between the renewable energy stock and the fossil energy markets. To this end, by using the TVP-VAR based connectedness approach and Cross-Quantilogram techniques, this study does the first attempt to unpack the complicated and controversial directional dependence of renewable energy stock returns on the returns and connectedness of fossil energy markets, considering various market conditions and time horizons. The empirical analysis demonstrates that, first, the directional dependence of renewable energy stock returns on fossil energy returns is pronounced during extreme market conditions, whereas they appear to be decoupled from fossil energy returns during normal market conditions. Second, the total connectedness between fossil energy returns transmits a substantial shock to renewable energy stock returns during most market conditions, which is in stark contrast to the information transmission directly originating from fossil energy markets. The performance of renewable energy stock markets improves with stronger fossil energy return connectedness, whereas weaker fossil energy return connectedness hinders it. Additionally, further study reveals that the directional dependence of renewable energy stock returns on the net connectedness of the crude oil market is dominated by negative dependence when the net connectedness of the crude oil market is low, whereas it displays positive dependence when the net connectedness of the crude oil market is high. This directional dependence pattern on the net connectedness of the crude oil market is opposite to that exhibited in the net connectedness of the coal and natural gas markets. Third, in general, the directional dependence of renewable energy stock returns on fossil energy returns is more pronounced in the short term but diminishes over the medium and long terms. Conversely, the directional dependence of renewable energy stock returns on fossil energy return connectedness persists over the medium and long terms. Final, with the outbreak of the Global Financial Crisis during 2007–2008, we notice an abrupt jump in the directional dependence of renewable energy stock returns on fossil energy returns and their connectedness, particularly during extreme market conditions. Our findings provide noteworthy implications for energy transformation, energy security, and climate mitigation.     

国际金融市场与国际原油期货市场溢出效应实证检验——基于VAR-BEKK模型的分析

文章通过构建VAR模型和BEKK模型对道琼斯股票市场、美元/欧元汇率市场与国际原油期货市场的动态关系进行了实证检验。结果表明：道琼斯股票市场与WTI原油期货市场存在双向的价格溢出效应,以及前者向后者的单向波动溢出效应;美元/欧元汇率市场存在向WTI原油期货市场单向的价格溢出效应和波动溢出效应。所以,国际原油期货市场与国际金融市场联系紧密,国际原油的金融属性日益体现,其价格变动更多受外部国际金融市场风险影响。     

Time-frequency spillovers among carbon,fossil energy and clean energy markets: The effects of attention to climate change

We explore the time-frequency spillovers among carbon, fossil energy and clean energy markets, and consider the casual effects of climate change attention. The spillover effects among carbon, fossil energy and clean energy markets are time-varying. Carbon market is a net receiver of spillovers from the oil market and clean energy markets in the short term, but it becomes a net transmitter of spillovers to the coal and gas markets in the long term. Our marginal spillover effects analysis suggests that the COVID-19 pandemic has increased cross-market risk contagion in the long term and that carbon market bears larger input risks. Investors' attention to climate change has significant causal effects on the spillovers, and the causal impact of climate change attention on total spillover has significantly increased during the COVID-19 pandemic. Our findings provide important guidelines for investment in environmental protection and demonstrate the importance of formulating differentiated policies for environmental protection in different time horizons.     

The Effects of Purchases of Mortgages and Securitization by Government Sponsored Enterprises on Mortgage Yield Spreads and Volatility

In this paper, we investigate the effects of GSE (government sponsored enterprise) activities on mortgage yield spreads and volatility. Using various regression procedures (i.e., vector error correction (VEC) and GARCH models) and controlling for default and prepayment risk, we find that securitizations and purchases of mortgages by GSEs reduce mortgage yield spreads and volatility. In particular, we find that the yield spread between conforming and 10-year constant maturity treasury (CMT) rates decreases by 8.0 bp per $1billion increase in the level of GSE securitizations. Similarly, if GSEs increase mortgage purchases, the yield spread decreases 10.5 bp per $1billion increase of purchases. In addition, we hypothesize and find that GSE activities have a spillover effect to the non-conforming mortgage market; via investor substitutions, GSE purchases and securitizations of conforming loans reduce non-conforming loan rates. Thus, the measured influence of GSE activities is biased downward when measured using the spread of non-conforming loans over conforming loan rates. We also find that purchases of mortgages by GSEs significantly reduce mortgage yield volatility. In sum, our findings show that GSE activities reduce and stabilize mortgage market rates.     

Can energy predict the regional prices of carbon emission allowances in China?

The carbon emission trading is an important market-oriented tool in the process of China's carbon neutrality, which makes companies face tremendous pressure to reduce emissions while having strong energy demands. In order to evaluate whether energy prices can be robust predictors of the prices of emission allowances, this study perform extreme bounds analysis (EBA) in four representative markets. The empirical results reveal that energy prices can indeed predict the prices of emission allowances, but the robustly predictive capabilities of different energy prices vary with regions. Among them, thermal coal is the robustly positive predictor for Guangdong, Hubei and Shanghai market; natural gas is the robustly negative predictor for all the four chosen regions; and crude oil can only positively predict Hubei market with robustness. Meanwhile, the horizons that predictions from energy to emission allowance can be performed as well as the predictive coefficients also vary with energy types and regions. And some trading implications are also provided alongside.     

Computing the market price of volatility risk in the energy commodity markets

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.