IDEAS home Printed from https://ideas.repec.org/p/hhs/lunewp/2016_002.html
   My bibliography  Save this paper

Cross-Commodity News Transmission and Volatility Spillovers in the German Energy Markets

Author

Listed:

Abstract

This study investigates volatility spillovers to electric power from large exogenous shocks in the prices of gas, coal, and carbon emission allowances in the German energy market. Our sample ranges from 2008 to 2016 and covers periods of different market conditions. We use a general VAR-BEKK model and the volatility impulse response function methodology to analyze and evaluate the spillover effects. Special attention is paid to selecting an appropriate econometric volatility model. Our results show that the spillover effects often are of a significant magnitude and display considerable variation over time and across commodities. Coal and gas generate non-negligible spillovers during almost the entire sample period. Carbon has very little impact during the early and late parts of the sample, but generates significant, and highly variable, spillovers during the period from 2011 to the end of 2014.

Suggested Citation

  • Green, Rikard & Larsson, Karl & Lunina, Veronika & Nilsson, Birger, 2016. "Cross-Commodity News Transmission and Volatility Spillovers in the German Energy Markets," Working Papers 2016:2, Lund University, Department of Economics, revised 11 Oct 2017.
    • Handle: RePEc:hhs:lunewp:2016_002
    as

    Download full text from publisher

    File URL: https://lucris.lub.lu.se/ws/portalfiles/portal/194592926/WP16_2
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Karali, Berna & Ramirez, Octavio A., 2014. "Macro determinants of volatility and volatility spillover in energy markets," Energy Economics, Elsevier, vol. 46(C), pages 413-421.
    4. Bauwens, Luc & Laurent, Sebastien, 2005. "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 346-354, July.
    5. Reboredo, Juan C., 2014. "Volatility spillovers between the oil market and the European Union carbon emission market," Economic Modelling, Elsevier, vol. 36(C), pages 229-234.
    6. repec:dau:papers:123456789/5450 is not listed on IDEAS
    7. Koenig, P., 2011. "Modelling Correlation in Carbon and Energy Markets," Cambridge Working Papers in Economics 1123, Faculty of Economics, University of Cambridge.
    8. Le Pen, Yannick & Sévi, Benoît, 2010. "Volatility transmission and volatility impulse response functions in European electricity forward markets," Energy Economics, Elsevier, vol. 32(4), pages 758-770, July.
    9. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    10. Angus Deaton & Guy Laroque, 1992. "On the Behaviour of Commodity Prices," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 59(1), pages 1-23.
    11. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    12. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    13. Gjolberg, Ole & Johnsen, Thore, 1999. "Risk management in the oil industry: can information on long-run equilibrium prices be utilized?," Energy Economics, Elsevier, vol. 21(6), pages 517-527, December.
    14. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    15. Philipp Koenig, 2011. "Modelling Correlation in Carbon and Energy Markets," Working Papers EPRG 1107, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    16. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    17. Hafner, Christian M. & Herwartz, Helmut, 2006. "Volatility impulse responses for multivariate GARCH models: An exchange rate illustration," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 719-740, August.
    18. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," The Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    19. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    20. Liu, Hsiang-Hsi & Chen, Yi-Chun, 2013. "A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: The impacts of extreme weather," Economic Modelling, Elsevier, vol. 35(C), pages 840-855.
    21. De Vany, Arthur S. & Walls, W. David, 1999. "Cointegration analysis of spot electricity prices: insights on transmission efficiency in the western US," Energy Economics, Elsevier, vol. 21(5), pages 435-448, October.
    22. Luc Bauwens & Sébastien Laurent, 2002. "A New Class of Multivariate skew Densities, with Application to GARCH Models," Computing in Economics and Finance 2002 5, Society for Computational Economics.
    23. Comte, F. & Lieberman, O., 2003. "Asymptotic theory for multivariate GARCH processes," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 61-84, January.
    24. Cheng, Wan-Hsiu & Hung, Jui-Cheng, 2011. "Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 160-173, January.
    25. Jaime Casassus & Peng Liu & Ke Tang, 2013. "Economic Linkages, Relative Scarcity, and Commodity Futures Returns," The Review of Financial Studies, Society for Financial Studies, vol. 26(5), pages 1324-1362.
    26. Lin, Sharon Xiaowen & Tamvakis, Michael N., 2001. "Spillover effects in energy futures markets," Energy Economics, Elsevier, vol. 23(1), pages 43-56, January.
    27. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-359, October.
    28. Engle, Robert F, 2000. "Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models," University of California at San Diego, Economics Working Paper Series qt56j4143f, Department of Economics, UC San Diego.
    29. McAleer, Michael & Chan, Felix & Hoti, Suhejla & Lieberman, Offer, 2008. "Generalized Autoregressive Conditional Correlation," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1554-1583, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Zongyuan & Luo, Qingtian, 2023. "Inter-industry risk spillover, role reversal, and economic stability," Finance Research Letters, Elsevier, vol. 57(C).
    2. Tsuji, Chikashi, 2020. "Correlation and spillover effects between the US and international banking sectors: New evidence and implications for risk management," International Review of Financial Analysis, Elsevier, vol. 70(C).
    3. Yan Lu & Xu Yang & Yixiang Ma & Lean Yu, 2022. "Rebound Effect of China’s Electric Power Demand in the Context of Technological Innovation," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    4. Mehdi Mili & Jean‐Michel Sahut & Frédéric Teulon, 2020. "Shift‐contagion in energy markets and global crisis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 725-736, August.
    5. Samitas, Aristeidis & Kampouris, Elias & Polyzos, Stathis, 2022. "Covid-19 pandemic and spillover effects in stock markets: A financial network approach," International Review of Financial Analysis, Elsevier, vol. 80(C).
    6. Saleh Mothana Obadi & Matej Korcek, 2020. "Driving Fundamentals of Natural Gas Price in Europe," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 318-324.
    7. Chen, Hao & Xu, Chao & Peng, Yun, 2022. "Time-frequency connectedness between energy and nonenergy commodity markets during COVID-19: Evidence from China," Resources Policy, Elsevier, vol. 78(C).
    8. Coskun, Merve & Taspinar, Nigar, 2022. "Volatility spillovers between Turkish energy stocks and fossil fuel energy commodities based on time and frequency domain approaches," Resources Policy, Elsevier, vol. 79(C).
    9. Vellachami, Sanggetha & Hasanov, Akram Shavkatovich & Brooks, Robert, 2023. "Risk transmission from the energy markets to the carbon market: Evidence from the recursive window approach," International Review of Financial Analysis, Elsevier, vol. 89(C).
    10. Tanin, Tauhidul Islam & Hasanov, Akram Shavkatovich & Shaiban, Mohammed Sharaf Mohsen & Brooks, Robert, 2022. "Risk transmission from the oil market to Islamic and conventional banks in oil-exporting and oil-importing countries," Energy Economics, Elsevier, vol. 115(C).
    11. Gao, Wang & Wei, Jiajia & Zhang, Hongwei & Zhang, Haizhen, 2024. "Does climate policy uncertainty exacerbate extreme risk spillovers between green economy and energy metals?," Resources Policy, Elsevier, vol. 91(C).
    12. Zhao, Lili & Wen, Fenghua & Wang, Xiong, 2020. "Interaction among China carbon emission trading markets: Nonlinear Granger causality and time-varying effect," Energy Economics, Elsevier, vol. 91(C).
    13. Ben Amar, Amine & Goutte, Stéphane & Isleimeyyeh, Mohammad, 2022. "Asymmetric cyclical connectedness on the commodity markets: Further insights from bull and bear markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 386-400.
    14. Lai T. Hoang & Dirk G. Baur, 2021. "Spillovers and Asset Allocation," JRFM, MDPI, vol. 14(8), pages 1-31, July.
    15. Tiantian Liu & Xie He & Tadahiro Nakajima & Shigeyuki Hamori, 2020. "Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe," Energies, MDPI, vol. 13(8), pages 1-20, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    2. Themistoclis Pantos & Stathis Polyzos & Aggelos Armenatzoglou & Ilias Kampouris, 2019. "Volatility Spillovers in Electricity Markets: Evidence from the United States," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 131-143.
    3. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    4. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    5. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    6. Chang, Chia-Lin & McAleer, Michael & Wang, Yanghuiting, 2018. "Testing Co-Volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances," Energy, Elsevier, vol. 151(C), pages 984-997.
    7. David Gabauer, 2020. "Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 788-796, August.
    8. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    9. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    10. Massimiliano Caporin & Michael McAleer, 2011. "Thresholds, news impact surfaces and dynamic asymmetric multivariate GARCH," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(2), pages 125-163, May.
    11. Massimiliano Caporin & Michael McAleer, 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Working Papers in Economics 11/23, University of Canterbury, Department of Economics and Finance.
    12. Caporin, Massimiliano & McAleer, Michael, 2014. "Robust ranking of multivariate GARCH models by problem dimension," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
    13. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    14. Eric Jondeau & Michael Rockinger, 2002. "Conditional Dependency of Financial Series: The Copula-GARCH Model," FAME Research Paper Series rp69, International Center for Financial Asset Management and Engineering.
    15. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    16. David E. Allen & Michael McAleer & Robert Powell & Abhay K. Singh, 2017. "Volatility spillover and multivariate volatility impulse response analysis of GFC news events," Applied Economics, Taylor & Francis Journals, vol. 49(33), pages 3246-3262, July.
    17. Hagströmer, Björn & Hansson, Björn & Nilsson, Birger, 2013. "The components of the illiquidity premium: An empirical analysis of US stocks 1927–2010," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4476-4487.
    18. Vellachami, Sanggetha & Hasanov, Akram Shavkatovich & Brooks, Robert, 2023. "Risk transmission from the energy markets to the carbon market: Evidence from the recursive window approach," International Review of Financial Analysis, Elsevier, vol. 89(C).
    19. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    20. Wu, Ruirui & Qin, Zhongfeng, 2024. "Asymmetric volatility spillovers among new energy, ESG, green bond and carbon markets," Energy, Elsevier, vol. 292(C).

    More about this item

    Keywords

    energy markets; time-varying volatility spillovers; volatility impulse response function; skew-Student asymmetric BEKK;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:lunewp:2016_002. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Iker Arregui Alegria (email available below). General contact details of provider: https://edirc.repec.org/data/delunse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.