Asymmetric effects and volatility transmission from metals markets to solar energy stocks: Evidence from DCC, ADCC, and quantile regression approach

Detrimental environmental repercussions have recently given rise to an interest in green investments. Although solar energy stocks are appealing assets for ethical investors, little is known about their dynamic correlations and linkages with metal (silicon, lithium, and rare earth) markets, particularly during economic events which is essential for hedging effectiveness and asset allocation. This study investigates the nexus between metal markets, oil price volatility (OVX), market sentiments (VIX), and solar energy markets using DCC, ADCC models, and the quantile regression approach. The results show both symmetric and asymmetric shock spillover between metals markets, VIX, OVX, and solar energy markets which are more prominent during COVID-19 pandemic, US-China trade frictions, and Russian invasion of Ukraine. For portfolio management, the hedging effectiveness of lithium stocks is highest, followed by silicon and rare earth metals. However, the hedge ratios are time-varying, and the variability is highest during US-China trade frictions. The quantile regression estimates reveal that lithium market is the most persistent determinant of solar energy stocks followed by silicon market even after segregating the periods into Paris Agreement and COVID-19 pandemic. Thus, lithium and silicon are driving markets of solar energy markets and can be a cause of omitted variable bias if stay unobserved. Nonetheless, there is little influence of VIX, rare earth metals, and OVX on solar energy stocks. Lastly, the estimations of threshold regression suggest that market sentiments change the association between metal markets and solar energy markets after the VIX reaches a certain threshold level. © 2023

How do carbon, stock, and renewable energy markets interact: Evidence from Europe

In a low-carbon context, the connectedness among carbon, stock, and renewable energy markets has been strengthening. This study examines the effect of Brexit, the launch of the European Green Deal and the COVID-19 pandemic on the connectedness among carbon, stock, and renewable energy markets by employing Time Varying Parameter -Vector Auto Regression (TVP-VAR). First, equal interval impulse response analysis shows that in the short term, the renewable energy market suffers from a positive shock from the carbon market and this shock gradually decreases from the initial 1.6×10−3. In the long run, the connectivity between the carbon market and the stock market, and between the carbon market and the renewable energy market is almost 0. Second, we can conclude that the positive connectivity between stock market to carbon market and renewable energy market to carbon market is enhanced by COVID-19 in the short term, with values of 7.5×10−3 and 3.6×10−3 respectively. Finally, renewable energy market received a greater negative impact from the carbon market during COVID-19 than during the release of the European Green Deal, while Brexit allowed positive carbon price spillover to renewable energy price. © 2023 Elsevier Ltd

Dynamic spillover between traditional energy markets and emerging green markets: Implications for sustainable development

This study employs the time-varying vector parameter autoregression model and Diebold-Yilmaz (2012, 2014) spillover approach to explore the static, net, dynamic and directional spillover effects between China's traditional energy and emerging green markets and the impact of the COVID-19 outbreak on spillover effects. Spillover networks are constructed to observe structural changes in the directional spillover of each target financial market before and after the pandemic's outbreak. Changes in hedging indicators of portfolios composed of two types of markets before and after the outbreak of COVID-19 are compared to provide directional guidance for investors to choose portfolios in the post-pandemic era. We found that the outbreak of the pandemic had a considerable impact on the volatility of various spillover effects of the studied markets. The total spillover level of the system increased rapidly by 18% in the early stages of the pandemic. Green bond was the largest net recipient of volatility spillovers in the whole system, followed by crude oil, while new energy was the largest net contributor of volatility spillovers in the whole system, followed by clean energy. After the outbreak, the hedging effectiveness of portfolios with long positions in traditional energy markets and short positions in emerging green markets improved significantly. In particular, a portfolio with long positions in the crude oil market and short positions in the green bond market is the best risk-hedging portfolio. © 2023 Elsevier Ltd

Analysis of Dynamic Connectedness Relationships among Clean Energy, Carbon Emission Allowance, and BIST Indexes

Understanding and examining energy markets correctly is crucial for stakeholders to attain maximum benefit and avoid risks. As a matter of fact, the volatility that occurred in energy markets and recent crises had major impacts on national economies. Dynamic connectedness relationships (DCRs) can make quite powerful predictions for both low-frequency data and limited time-series data. The objective of this study is to explicate the dynamic connectedness relationships among the BIST sustainability index, BIST 100 index, S&P Global Clean Energy index (S&P GCEI), and S&P GSCI carbon emission allowances (EUA). The daily data obtained over the period 11 April 2014–11 November 2022 were used for the research study. The DCRs among the variables used in the study were investigated by employing the time-varying parameter vector autoregressive (TVP-VAR) model. As a result of the study, the volatility from carbon emission allowances was determined to spill over to S&P GCEI, BIST 100, and BIST sustainability indexes. During the COVID-19 pandemic, significant reductions were detected in the volatility spillover (VS) from carbon emission allowances to S&P GCEI, BIST 100, and BIST sustainability indexes. Moreover, it was revealed that a weak VS existed from S&P GCEI to BIST sustainability and BIST 100 indexes. The findings reveal the importance of policymakers taking some incentive measures in EUA prices and also its role in portfolio diversification. © 2023 by the authors.

Superhighways and roads of multivariate time series shock transmission: Application to cryptocurrency, carbon emission and energy prices

Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate;(b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets. © 2023 Elsevier B.V.

Markov-Regime Switches in Oil Markets: The Fear Factor Dynamics

This paper is an attempt to examine regime switches in the empirical relation between return dynamics and implied volatility in energy markets. The time-varying properties of the return-generating process are defined as a function of several risk factors, including oil market volatility and changes in stock prices and currency rates. The empirical evidence is based on Markov-regime switching models, which have the capacity to capture, in particular, the stochastic behavior of the OVX oil volatility index as a benchmark for investors' fear. The results suggest that the dynamics of oil market returns are governed by two distinct regimes, a state driven by a negative relationship between returns and implied volatility and another state characterized by a more pronounced negative correlation. It is the latter regime with a stronger correlation that tends to prevail over the sample period from 2008 to 2021, but the frequency of regime shifts also seems to increase under more volatile oil price dynamics in association with significant events such as the COVID-19 pandemic. Thus, the evidence of a negative correlation structure is found to be robust to changes in the estimation period, which suggests that the oil volatility index remains a reliable gauge of market sentiment in the energy markets. © 2023 by the author.

An Wavelet analysis of connectedness between volatility of the energy and other markets during the COVID-19 crisis

By applying novel partial wavelet coherency, this paper investigates the transmission mechanism of the volatility from the oil, gold, and silver sector to the energy sector in the time and frequency dimensions as well as the influence of the COVID-19 health crisis on this linkage. The multiple coherencies suggest at least five multiple cycles, which are located at high frequencies (the 52 – 132-day frequency band). Among these cycles, the largest one occurs at the low frequency (the 120 – 132-day frequency band), and this cycle is persistently prolonged. Notably, the four sectors' remarkable interlinkages of the volatility are presented more clearly since the COVID-19 pandemic first appeared and hit the globe (from the end of 2019 to the middle of 2020). The partial coherency between the volatility of the energy sector and the volatility of the oil sector reveals that the relations between two sectors are relatively persistent, which changes in the energy sector's volatility cause the oil sector to become more volatile. The partial coherency between the volatility of the energy sector and the volatility of the gold and silver sector suggests their interlinkages are time-varying and can be divided into four phases. The relationships are either positive or negative, and the energy sector or the silver or gold sector could be an attendant of other market's rising volatility. During the time of the COVID-19 pandemic, the energy sector's volatility is in-phase with the oil and silver sector's volatility leading, whilst the gold sector's volatility leads to the energy sector's volatility, and the relation is negative. © 2023

Could volatile cryptocurrency stimulate systemic risks in the energy sector? Evidence from novel connectedness models

By identifying the connectedness of seven indicators from January 1, 2019, to June 13, 2022, we choose an extended joint connectedness approach to a vector autoregression model with time-varying parameter (TVP-VAR) to analyze interlinkages between Crypto Volatility (CV) and Energy Volatility (EV). Our findings show that the COVID-19 outbreak seems to have an impact on the dynamic connectedness of the whole system, which peaks at about 60% toward the end of 2019. According to net total directional connectedness over a quantile, throughout the 2020–2022 timeframe, natural gas and crude oil are net shock transmitters, while the CV, clean energy, solar energy, and green bonds consistently receive all other indicators. Specifically, pairwise connectedness indicates that the CV appears to be a net transmitter of shocks to all energy indicators before the COVID-19 outbreak but acts as a net receiver of shocks from clean energy, wind energy, and green bonds in late 2020. The CV mostly has spillover effects on green bonds. The primary net transmitter of shocks to the Crypto market is crude oil. Our findings are critical in helping investors and authorities design the most effective policies to lessen the vulnerabilities of these indicators and reduce the spread of risk or uncertainty. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Futures hedging in crude oil markets: A trade-off between risk and return

Risk and return are two fundamentals that have an impact on an investor's or hedger's investing choices. Based on the proposed synchronous movement intensity index, this paper aims to improve the hedging performance by adjusting the model-driven hedge ratio and realize the trade-off between return and risk in futures hedging. First, without loss of generality, we forecast crude oil spot and futures volatility using 10 GARCH-type models, including three linear models and seven nonlinear models, to obtain the ex-ante hedging ratio under the minimum variance framework. Then, we develop a novel and tractable method to identify the market state based on the index of consistency intensity, in which the index portrays the synchronous degree of stock price movements in the energy sector. Last but not least, we propose the hedge ratio adjustment criteria based on the identified state, and adjust the ratio driven by GARCH-type models of futures in accordance with the market state. Empirical results of crude oil futures markets indicate that the proposed state-dependent hedging model is superior to the commonly used models in terms of three criteria including mean of returns, variance, and ratio of mean to variance of returns for measuring hedging effect. We apply the DM test to make a statistical inference and discover that while the mean and the ratio of mean to variance of returns are increasing, the variance and hedging effectiveness of the hedged portfolio based on the modified methods are not significantly affected. Furthermore, the superiority of the proposed method is robust to different market conditions, including significant rising or falling trends, large basis, and COVID-19 pandemic. We also test the robustness of the proposed method with respect to the baseline model, quantile, and evaluation window. Overall, this paper provides a more realistic approach for crude oil risk managers to hedge crude oil price risk, some corresponding implications are also concluded. © 2022 Elsevier Ltd

Connectedness among El Niño-Southern Oscillation, carbon emission allowance, crude oil and renewable energy stock markets: Time- and frequency-domain evidence based on TVP-VAR model

Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets. © 2022 Elsevier Ltd

Do oil shocks affect the green bond market?

This study examines the predictive power of oil shocks for the green bond markets. In line with this aim, we investigated the extent to which oil shocks could be used to accurately make in- and out-of-sample forecasts for green bond returns. Three striking findings emanated from our results: First, the three types of oil shock are reliable predictors for green bond indices. Second, the performances of the predictive models were consistent across the different forecasting horizons (i.e. H = 1 to H = 24). Third, our findings were sensitive to classifying the dataset into pre-COVID and COVID eras. For instance, the results confirmed that the predictive power of oil shocks declined during the crisis period. We also discuss some policy implications of this study's findings. © 2022 The Author(s)

Stochastic ordering of systemic risk in commodity markets

The contribution of commodity risks to the systemic risk is assessed in this paper through a novel approach that relies on the stochastic property of concordance ordering of CoVaR. Considering the period that spans from 2005 to 2022 and the VIX as the proxy for the stability of the financial system, we build the stochastic ordering of systemic risk for 35 commodities belonging to four sectors: Agriculture, Energy, Industrial Metals, and Precious Metals. The estimates of the ΔCoVaR signal that contagion effects from commodity markets to the financial system have been stronger during the years 2017–2019. Backtests validate CoVaR as a more resilient risk measure than the VaR, especially during periods of market turmoils. The stochastic ordering of CoVaR shows that severe losses (downside risk) in commodity markets tend to exacerbate systemic financial distress more than gains (upside risk). Commodity risks arising from WTI and EUA are threatening triggers for systemic risk. In contrast, the financial system is less vulnerable to a broader range of scenarios arising from fluctuations in Gold prices. As top contributors to the systemic risk, among the sectors we find Energy and Precious Metals with respect to upside risk and downside risk. The Covid-19 crisis has deeply amplified the systemic influence arising from the downside risk of WTI, Gasoline, and Natural Gas UK and has confirmed the safe-haven role of Gold. © 2022 Elsevier B.V.

Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

Our article employs a quantile vector autoregression (QVAR) to identify the connectedness of seven variables from April 1, 2019, to June 13, 2022, in order to examine the relationships between crypto volatility and energy volatility. Our findings reveal that the dynamic connectedness is approximately 25% in the short term and approximately 9% in the long term. The 50% quantile equates to the overall average connectedness of the entire period, according to dynamic net total directional connectedness over a quantile, which also indicates that connectedness is very intense for both highly positive changes (above the 80% quantile) and crypto and energy volatility (below the 20% quantile). With the exception of the early 2022 period when the Crypto Volatility Index transmits a net of shocks because of the Ukraine-Russia Conflict, dynamic net total directional connectedness implies that in the short term, the Crypto Volatility Index acts as a net shock receiver across time. While this indicator is a net shock receiver for long-term dynamics, wind energy is a net shock transmitter during the short term. Green bonds are a short-term net shock receiver. This role is valid in the long term. Clean energy and solar energy are the long-term net transmitters of shocks;nevertheless, the series is always and only momentarily a net receiver of shocks because of the short-term dynamics. Natural gas and crude oil play roles in both two quantiles. Dynamic net pairwise directional connectedness over a quantile suggests that uncertain events like the COVID-19 epidemic or Ukraine-Russia Conflict influence cryptocurrency volatility and renewable energy volatility. © 2022 Elsevier Ltd

The nexus between oil and airline stock returns: Does time frequency matter?

This paper investigates the relationship between oil and airline stock returns under different time frequencies. First, we propose an Autoregressive moving average model with mixed frequency exogenous variable to analyse the different impacts of oil on airline stock returns on daily, weekly, and monthly basis. We consistently find a negative oil-airline stock return nexus on a daily basis, but a positive relationship on a weekly basis. While the former supports the economic-based channel, the latter is in line with the market inertia channel. Our findings help explain mixed results reported in the literature. Further, our time frequency connectedness analysis shows that the economic-based channel dominates the market inertia channel since the connectedness is more pronounced in the short-run compared to the medium- and long-run. Our block connectedness results highlight that business models of airline firms can play a significant role in affecting the connectedness, in which the low-cost airlines are more sensitive to the oil price changes. It is worth noting that there are distinguished drivers of the oil-airline stock return nexus in different time frequencies. The drivers also vary between the Global Financial Crisis and the COVID-19 pandemic. Our results are consistent under a battery of robustness checks and deliver important implications to investors, portfolio managers, and executives of airline firms. © 2022 Elsevier B.V.

Return and volatility connectedness between gold and energy markets: Evidence from the pre- and post-COVID vaccination phases

Using a two-step VAR asymmetric BEKK GARCH model, this research explores the asymmetric return and volatility connectedness between gold and several energy markets during three subperiods: pre-COVID, before vaccination, and after vaccination. Gold's returns and volatility spillover are generally found to be time- and energy-dependent. In addition, the optimal weights, hedge ratios, and hedging effectiveness of energy commodity and gold pairs are calculated during the three subperiods. The results of optimal weights show that investors should increase their investment in energy commodities more than gold (energy commodities) during the after-vaccination period (the pre-vaccination period). Moreover, the hedging strategy would only be effective within the COVID-19 vaccination period, which could have implications for the strategic asset allocation of policy-makers and international investors. Finally, we examine the potential determinants of conditional correlations between gold and energy markets. VIX, EPU, and new confirmed cases are found to be the main predictors of correlations for most energy commodity–gold pairs during the examined period. © 2022 Economic Society of Australia, Queensland

Network evolution underneath the volatility spillover in traditional and clean energy markets

When facing volatility spillovers in energy markets, all players require risk mitigation strategies to insulate themselves from the same. To prevent energy markets from being strongly crashed by volatility spillovers, which even trigger financial crises, in this paper, we use network analysis as an aid to identify spillovers among the main nine energy markets. Specifically, we first measure the volatility spillovers among the main energy markets through a BEKK model. Based on this, influential markets are identified by using network analysis. The coal, wind and water energy markets should be paid close attention as they occupy vital roles in the volatility spillover network. Even though clean energy markets contribute more in terms of market stability, traditional energy markets are still important to ensure energy supply when experiencing extreme crashes caused by COVID-19. In this paper, we make the contributions to analysing volatility spillovers in multiple energy markets and identifying crucial energy markets in volatility spillover networks, then provide more market information that helps the government and policymakers effectively manage systemic risks caused by volatility spillovers. The effective risk management of crucial energy markets enhances economic recovery and stability, especially in the post-COVID-19 era.

Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network

This paper aims to develop an artificial neural network-based forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007-2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural network-based models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.

Dynamic connectedness among climate change index, green financial assets and renewable energy markets: Novel evidence from sustainable development perspective

This paper investigates the connectedness among the climate change index, green financial assets, renewable energy markets, and geopolitical risk index from June 1, 2012 to June 13, 2022, using Quantile Vector Autoregressive (QVAR) and wavelet coherence (WC). The Total connectedness index (TCI) varies as long as the highest TCI originates in the upper quantile. We also note that the higher TCI decreases after the second wave of COVID19 and increases during the first 100 days of the Russia-Ukraine conflict. Moreover, the results show that Geopolitical risk (GPR) is a net transmitter of the climate change index during the Russian invasion of Ukraine. The green bond and clean energy markets are negatively connected to the GPR at extreme 10 th and 90 th quantiles. The wavelet coherence confirms the QVAR results that the climate change market can be a safe haven against GPR during the Russian invasion. The climate change index, green financial assets, and clean energy are strong influencers in the financial markets and are vital to international peace, reducing geopolitical risk. The study reports a few novel conclusions and implications from a sustainable development perspective.

Do green bonds offer a diversification opportunity during COVID-19?-an empirical evidence from energy, crypto, and carbon markets.

The present study is a novel attempt to unravel the connectedness of the green bond with energy, crypto, and carbon markets using the S&P green bond index (RSPGB). We consider MAC global solar energy index (RMGS) and ISE global wind energy index (RIGW) as proxies of the energy market and use bitcoin and the European energy exchange carbon index (REEX) for the cryptocurrency and carbon market. Employing the Diebold and Yilmaz (2012), Baruník and Krehlík (2018), and wavelet coherence econometric techniques, we find that the energy market (RMGS) has the highest connectedness derived from other asset classes, and bitcoin (RBTC) has the least connectedness. Concurrently, we find that the risk transmission is heterogeneous in different scales as the short period has less connectedness than the medium and long run. We conclude that the overall diversification opportunity among green bonds, energy stock, bitcoin, and the carbon market is more in the short-run than in the medium and long-run. In summary, our findings on the green bond market will provide investors, portfolio managers, and policymakers with critical insight into ensuring a sustainable financial market.

Hedging strategies among financial markets: the case of green and brown assets.

Recognizing the growing importance of the green energy market-renewable energy stocks and bonds-and its classification as a viable financial asset, this paper examines hedging strategies with brown market instruments-gold, oil, bond and the composite S&P500-on the green energy markets. That is, we examine whether, and to what extent brown assets can provide a hedge for green assets, using variants of the multivariate GARCH framework (DCC, ADCC and GO-GARCH). Our dataset spans the period 01/12/2008 to 30/09/2021. To account for the influence of the COVID-19 pandemic, we split the dataset into two-pre-covid (1/12/2008-10/03/2020) and covid-era (11/03/202-30/09/2021). Two key findings emanate from our results: first, conventional bonds and stocks provide the most consistent hedge for investment in the green markets. Second, the results are sensitive to the state of the market-hedging effectiveness declined during the covid period in the green stock market. Among other things, it is recommended that investors include instruments of the green market in portfolio allocation.