IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v28y2021i9p755-759.html

Corona, crisis and conditional heteroscedasticity

Author

Listed:
  • Tamás Kiss
  • Pär Österholm

Abstract

In this paper, we illustrate the macroeconomic risk associated with the early stage of the corona-virus outbreak. Using monthly data ranging from July 1991 to March 2020 on a recently developed coincidence indicator of global output growth, we estimate an autoregressive model with GARCH effects and non-Gaussian disturbances. Our results indicate that i) accounting for conditional heteroscedasticity is important and ii) risk, measured as the volatility of the shocks to the process, is at a very high level – largely on par with that experienced around the financial crisis of 2008–2009.

Suggested Citation

  • Tamás Kiss & Pär Österholm, 2021. "Corona, crisis and conditional heteroscedasticity," Applied Economics Letters, Taylor & Francis Journals, vol. 28(9), pages 755-759, May.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:9:p:755-759
    DOI: 10.1080/13504851.2020.1776829
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2020.1776829
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2020.1776829?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 look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Dimitrios Vortelinos & Angeliki Menegaki & Ioannis Passas & Alexandros Garefalakis & Georgios Viskadouros, 2024. "Heterogeneous Responses of Energy and Non-Energy Assets to Crises in Commodity Markets," Energies, MDPI, vol. 17(21), pages 1-25, October.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:taf:apeclt:v:28:y:2021:i:9:p:755-759. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.