IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/2010705.html
   My bibliography  Save this article

Dependence and Risk Spillover among Hedging Assets: Evidence from Bitcoin, Gold, and USD

Author

Listed:
  • Jiang Yu
  • Yue Shang
  • Xiafei Li
  • Dehua Shen

Abstract

Understanding the dependence and risk spillover among hedging assets is crucial for portfolio allocation and regulatory decision making. Using various copula and conditional Value-at-Risk (CoVaR) measures, this paper quantifies the dependence and risk spillover effects between three traditional and emerging hedging assets: Bitcoin, gold, and USD. Furthermore, we investigate these effects at various short- and long-term horizons using a variational model decomposition (VMD) method. The empirical results show that there is strong negative dependence between gold and USD, but Bitcoin and gold are weakly and positively connected. Secondly, risk spillovers exist only between Bitcoin and gold and between gold and USD. The risk spillover effect between Bitcoin and gold are not stable, that is, if Bitcoin or gold faces the downward or upward risk, both the downward and upward risk of another asset have the chance to increase. The negative risk spillover between gold and USD is stable, especially in long-term horizons. Finally, the risk spillover between Bitcoin and gold as well as between gold and USD are asymmetric at downward and upward market environment.

Suggested Citation

  • Jiang Yu & Yue Shang & Xiafei Li & Dehua Shen, 2021. "Dependence and Risk Spillover among Hedging Assets: Evidence from Bitcoin, Gold, and USD," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-20, September.
    • Handle: RePEc:hin:jnddns:2010705
    DOI: 10.1155/2021/2010705
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/ddns/2021/2010705.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/ddns/2021/2010705.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/2010705?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
    ---><---

    Citations

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


    Cited by:

    1. Jingjing Li & Xinge Rao & Xianyi Li & Sihai Guan, 2022. "Gold and Bitcoin Optimal Portfolio Research and Analysis Based on Machine-Learning Methods," Sustainability, MDPI, vol. 14(21), pages 1-12, November.

    More about this item

    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:hin:jnddns:2010705. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.