IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v83y2019icp180-196.html
   My bibliography  Save this article

Regularization approach for network modeling of German power derivative market

Author

Listed:
  • Chen, Shi
  • Karl Härdle, Wolfgang
  • López Cabrera, Brenda

Abstract

In this paper we propose a regularization approach for network modeling of German power derivative market. To deal with the large portfolio, we combine high-dimensional variable selection techniques with dynamic network analysis. The estimated sparse interconnectedness of the full German power derivative market, clearly identify the significant channels of relevant potential risk spillovers. Our empirical findings show the importance of interdependence between different contract types, and identify the main risk contributors. We further observe strong pairwise interconnections between the neighboring contracts especially for the spot contracts trading in the peak hours, its implications for regulators and investors are also discussed. The network analysis of the full German power derivative market helps us to complement a full picture of system risk, and have a better understanding of the German power market functioning and environment.

Suggested Citation

  • Chen, Shi & Karl Härdle, Wolfgang & López Cabrera, Brenda, 2019. "Regularization approach for network modeling of German power derivative market," Energy Economics, Elsevier, vol. 83(C), pages 180-196.
    • Handle: RePEc:eee:eneeco:v:83:y:2019:i:c:p:180-196
    DOI: 10.1016/j.eneco.2019.06.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988319302087
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2019.06.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. He Jiang, 2023. "Forecasting global solar radiation using a robust regularization approach with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1989-2010, December.
    2. Tadahiro Nakajima & Yuki Toyoshima, 2020. "Examination of the Spillover Effects among Natural Gas and Wholesale Electricity Markets Using Their Futures with Different Maturities and Spot Prices," Energies, MDPI, vol. 13(7), pages 1-14, March.
    3. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2020. "Impact of Solar and Wind Prices on the Integrated Global Electricity Spot and Options Markets: A Time Series Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 337-353.
    4. Zhu, Bo & Deng, Yuanyue & Lin, Renda & Hu, Xin & Chen, Pingshe, 2022. "Energy security: Does systemic risk spillover matter? Evidence from China," Energy Economics, Elsevier, vol. 114(C).

    More about this item

    Keywords

    Regularization; Energy risk transmission; Connectedness; Network; German power derivative market;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    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:eee:eneeco:v:83:y:2019:i:c:p:180-196. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.