IDEAS home Printed from https://ideas.repec.org/a/fau/fauart/v70y2020i1p42-69.html
   My bibliography  Save this article

Measurement of Volatility Spillovers and Asymmetric Connectedness on Commodity and Equity Markets

Author

Listed:
  • Tereza Palanska

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)

Abstract

We study total, directional, and asymmetric connectedness between four commodity futures and S&P 500 Index over the 2002-2015 period by employing a recently developed approach based on realized measures and variance decomposition. We estimate that, on average, volatility transmission accounts for around one-fifth of the volatility forecast error variance. The shocks to the stock markets play the most crucial role. Volatility spillovers were limited before the 2008 financial crisis, and then sharply increased during the crisis. The directional spillovers detect quite low connectedness between soft agricultural commodities and the rest of the assets that we study, which may improve portfolio investors' trading strategies. Finally, we analyze asymmetric connectedness. Our results defy the common perception that adverse shocks impact volatility spillovers more heavily than the positive ones. Overall, we provide new insights into volatility transmission between analyzed markets, which may inform investment decisions and hedging strategies.

Suggested Citation

  • Tereza Palanska, 2020. "Measurement of Volatility Spillovers and Asymmetric Connectedness on Commodity and Equity Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 70(1), pages 42-69, February.
  • Handle: RePEc:fau:fauart:v:70:y:2020:i:1:p:42-69
    as

    Download full text from publisher

    File URL: http://journal.fsv.cuni.cz/mag/article/show/id/1454
    Download Restriction: no
    ---><---

    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. Dejan Živkov & Boris Kuzman & Jonel Subić, 2022. "Measuring the risk-adjusted performance of selected soft agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(3), pages 87-96.
    2. Shah, Adil Ahmad & Dar, Arif Billah, 2021. "Exploring diversification opportunities across commodities and financial markets: Evidence from time-frequency based spillovers," Resources Policy, Elsevier, vol. 74(C).
    3. Zaghum Umar & Oluwasegun Babatunde Adekoya & Mariya Gubareva & Sabri Boubaker, 2024. "Returns and volatility connectedness among the Eurozone equity markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3103-3122, July.
    4. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2022. "Spillover effects between commodity and stock markets: A SDSES approach," Resources Policy, Elsevier, vol. 79(C).
    5. Liu, Pan & Power, Gabriel J. & Vedenov, Dmitry, 2021. "Fair-weather Friends? Sector-specific volatility connectedness and transmission," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 712-736.
    6. Das, Suman & Roy, Saikat Sinha, 2023. "Following the leaders? A study of co-movement and volatility spillover in BRICS currencies," Economic Systems, Elsevier, vol. 47(2).
    7. Dejan Živkov & Suzana Balaban & Marijana Joksimović, 2022. "Making a Markowitz portfolio with agricultural commodity futures," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(6), pages 219-229.
    8. Walid Abass Mohammed, 2021. "Volatility Spillovers among Developed and Developing Countries: The Global Foreign Exchange Markets," JRFM, MDPI, vol. 14(6), pages 1-30, June.

    More about this item

    Keywords

    volatility; spillovers; connectedness; asymmetry; commodity;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

    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:fau:fauart:v:70:y:2020:i:1:p:42-69. 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: Natalie Svarcova (email available below). General contact details of provider: https://edirc.repec.org/data/icunicz.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.