IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-04250255.html
   My bibliography  Save this paper

COVID-19 Pandemic and Financial Contagion

Author

Listed:
  • Julien Chevallier

    (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8 Vincennes-Saint-Denis)

Abstract

The original contribution of this paper is to empirically document the contagion of the Covid-19 on financial markets. We merge databases from Johns Hopkins Coronavirus Center, Oxford-Man Institute Realized Library, NYU Volatility Lab, and St-Louis Federal Reserve Board. We deploy three types of models throughout our experiments: (i) the Susceptible-Infective-Removed (SIR) that predicts the infections' peak on 2020-03-27; (ii) volatility (GARCH), correlation (DCC), and risk-management (Value-at-Risk (VaR)) models that relate how bears painted Wall Street red; and, (iii) data-science trees algorithms with forward prunning, mosaic plots, and Pythagorean forests that crunch the data on confirmed, deaths, and recovered Covid-19 cases and then tie them to high-frequency data for 31 stock markets.

Suggested Citation

  • Julien Chevallier, 2020. "COVID-19 Pandemic and Financial Contagion," Post-Print halshs-04250255, HAL.
  • Handle: RePEc:hal:journl:halshs-04250255
    DOI: 10.3390/jrfm13120309
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Prelorentzos, Arsenios-Georgios N. & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Xidonas, Panos & Goutte, Stephane & Thomakos, Dimitrios D., 2024. "Introducing the GVAR-GARCH model: Evidence from financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    2. Zhang, Yi & Zhou, Long & Wu, Baoxiu & Liu, Fang, 2024. "Tail risk transmission from the United States to emerging stock Markets: Empirical evidence from multivariate quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    3. Lin, Weinan & Ouyang, Ruolan & Zhang, Xuan & Zhuang, Chengkai, 2023. "Network analysis of international financial markets contagion based on volatility indexes," Finance Research Letters, Elsevier, vol. 56(C).

    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:hal:journl:halshs-04250255. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.