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

Yadav, Miklesh Prasad; Sharif, Taimur; Ashok, Shruti; Dhingra, Deepika; Abedin, Mohammad Zoynul

doi:10.1016/j.ribaf.2023.101948

Cited by 40 publications

(5 citation statements)

References 43 publications

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“…Wang and Wang (2019) contend that frequency spillover plays a pivotal role in predicting equity market volatility, the dynamics of which between oil and equity markets are contingent upon distinct temporal horizons, either short-term or long-term. Yadav et al (2023) examine volatility spillover from energy commodities to the Shanghai Stock Exchange and European equity markets, identifying persistent volatility in the long-term but not in the short-term duration. Liu et al (2017) disclose that the volatility linkage between oil and the US equity market is increasingly oriented towards short-term intervals, whereas the connection with the Russian equity market is evolving across all temporal scales.…”

Section: Volatility Spillovermentioning

confidence: 99%

“…To evaluate the ramifications of carbon emission trading, the extant literature predominantly investigates the spillover of prices or returns in Chinese pilot ETS markets and global ETS platforms. Yadav et al (2023) identify a substantial association between carbon emissions indices and equity indices within both the Chinese and European stock markets, incorporating the variables of crude oil and natural gas indices. Aslan and Posch (2022) utilize connectedness network analysis and ascertain that carbon emission allowance futures predominantly function as net recipients of volatility connectedness emanating from European stock market sector indices.…”

Section: Introductionmentioning

confidence: 99%

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Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets

Ma,

Feng,

Chen

et al. 2024

JRFM

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The carbon emission trading markets represent an emerging domain within China. The primary objective of this study is to explore whether carbon price volatility influences stock market volatility among companies subject to these emission trading regulations. Employing daily returns data from 293 publicly traded companies regulated by these emission trading markets, this study encompasses the national carbon market and eight pilot regional carbon markets spanning from August 2013 to October 2023. The results demonstrate that volatility in regional carbon prices positively impacts the stock volatility of companies in the corresponding emission trading region, indicating a volatility spillover effect. Moreover, this spillover effect is more pronounced in sectors marked by lesser carbon intensity than those with greater carbon intensity. The volatility transmission is more pronounced in coastal areas than in inland regions. However, no notable distinctions in volatility transmission are discerned between the periods before and throughout the COVID-19 pandemic. Vector autoregression analyses substantiate that lagged carbon price fluctuations possess limited predictive capacity for contemporaneous equity market volatility and vice versa. The robustness of these outcomes is fortified by applying the E-GARCH model, which accounts for the volatility clustering phenomenon. As the first investigation into the volatility spillover effect between China’s emission trading market and corresponding stock markets, this study offers valuable insights into the investment strategies of retail investors, the formulation of carbon regulations by policymakers, and the carbon emission strategies of corporate managers.

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Section: Volatility Spillovermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets

Ma,

Feng,

Chen

et al. 2024

JRFM

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“…Their findings showed that the energy market acted as a risk receiver and experienced an increased level of risk absorption after the pandemic. Yadav et al (2023) examined the volatility spillover characteristics of energy commodities using various methods, such as the Granger causality test and the Diebold and Yilmaz spillover index model. They found that volatility exhibits stronger persistence in the long run.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Financial Risk Management in China Using Complex Network Analysis

Fang,

Deng,

et al. 2023

Journal of Organizational and End User Computing

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Effective energy financial risk management is crucial to ensure that China's economic system can remain stable. This article utilizes the quantile vector autoregressive spillover index model, complex networks, and deep learning methods to simultaneously assess both the internal and external energy financial market risks in China. Spillover effects under different market conditions are also examined. The research findings indicate that: (1) Under extreme market conditions, static total spillover values between internal and external markets exceed 70%, while under normal market conditions, they are only around 53% and 13%, respectively; (2) Crude oil and fuel oil as well as energy and stocks are important nodes in both internal and external markets; and (3) The attention-convolutional neural network-long short-term memory model outperforms the second-best performing model, and achieves an improvement of 12.9% and 21.4% in terms of mean absolute error and root mean square error, respectively; inclusion of early warning indicators leads to further improvements of 19.8% and 31.9%, respectively.

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“…Electricity-generating plants and other carbon-intensive establishments were allowed to trade carbon permits within the European Union. The European Union Emission Trading System (EU-ETS) cap-and-trade system was designed to simultaneously lower emissions and costs for members in the carbon trading system (Tan et al 2020;Yadav et al 2023). Participants in the carbon tradable allowance program receive annual market-tradable allowances at the commencement of each trading cycle.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Nexus between European Carbon Emission Trading and Financial Market Returns: Practical Implications for Carbon Risk Reduction and Hedging

Tabash,

Al-Absy,

Hannoon

2024

JRFM

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The carbon–financial nexus helps firms evaluate susceptibility to carbon risk more effectively. This is the first research article to model the short- and long-run co-integrating association between European financial markets, the CBOE oil price volatility index (OVZ) and the European carbon emission trading system (EU-ETS) by using the daily returns from 1 October 2013 to 1 October 2023. We utilize co-integration test followed by the ARDL framework with an error correction mechanism (ECM). Moreover, we utilize the DCC-GARCH-t copula framework to estimate the hedge ratio and to select an optimal portfolio weight for carbon risk hedging. Overall, the findings suggested that EU-ETS (OVZ) has a consistent positive (negative) short-term influence on all the equity returns of Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the stock indices of the whole Eurozone. However, in the long term, EU-ETS has a positive (negative) effect on the stock returns of France and the Eurozone (Belgium and Spain). Belgian and Spanish companies could implement long-term carbon reduction policies. Belgian and Spanish firms should focus on the utilization of green energy resources and the internalization of carbon emission-free mechanical processes as this may offer a safeguard against the additional pressure arising from escalating carbon prices. Finally, an optimal portfolio weight selection strategy based upon the DCC-GARCH-t copula approach aims for higher hedging effectiveness (HE) than the hedge ratio strategy when adopting short-term positions in Italian and Danish equity markets to reduce the risk of long-term EU-ETS volatility.

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

Cited by 40 publications

References 43 publications

Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets

Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets

Energy Financial Risk Management in China Using Complex Network Analysis

Modeling the Nexus between European Carbon Emission Trading and Financial Market Returns: Practical Implications for Carbon Risk Reduction and Hedging

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