IDEAS home Printed from https://ideas.repec.org/a/eme/jespps/jes-08-2023-0442.html
   My bibliography  Save this article

Connectivity among the returns of sectoral indices of the Brazilian capital market

Author

Listed:
  • Mathias Schneid Tessmann
  • Marcelo De Oliveira Passos
  • Omar Barroso Khodr
  • Alexandre Vasconcelos Lima
  • Vinícius Braga

Abstract

Purpose - As specific objectives, we intend to: (1) measure the connectivity between the spillovers of returns from the financial and nonfinancial sectors of the Brazilian stock market; (2) estimate the spillovers of individual returns for each sector to identify periods of higher and lower profits over a period of around eight years; (3) investigate the existence of relationships between these repercussions between pairs of sectoral indices, evaluating how much each specific sector transfers to each other and the market as a whole and (4) examine whether the connectivity of the Brazilian stock market itself and future interest rates in the USA and Brazil as well as the risk of the Brazilian economy, were explanatory variables of the dynamics of interdependence in the returns of these indices. Design/methodology/approach - With a daily series of closing prices of sectoral indices from March 3, 2015, until June 21, 2023, we researched eight of the most relevant sectoral indices on the São Paulo Stock Exchange (B3). With this data, we estimate the Diebold–Yilmaz spillover index and frequency decompositions of Barunik–Krehlik. Findings - The conclusions indicate that there is an overall connection of 66% in the financial and nonfinancial sectoral indices, with a peak of 83%. The consumer, energy and public services sectors stand out as significant sources of primary spillovers. When we classified secondary effects into periods, we saw that the shocks dissipated as time passed and the returns of the commodity index remained resilient across all periods. Originality/value - Our conclusions highlight the influence of three main factors in sectors with a high degree of connectivity: periods of increased uncertainty; negative externalities in post-crisis periods and the impact of financial news on market sentiment. We think this study provides information that can be useful for policymakers, investors, investment portfolio managers, economists (financial, monetary and industrial), investment consultants and researchers who are interested in the complex interconnection among emerging market stock indices.

Suggested Citation

  • Mathias Schneid Tessmann & Marcelo De Oliveira Passos & Omar Barroso Khodr & Alexandre Vasconcelos Lima & Vinícius Braga, 2024. "Connectivity among the returns of sectoral indices of the Brazilian capital market," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 52(4), pages 655-672, July.
  • Handle: RePEc:eme:jespps:jes-08-2023-0442
    DOI: 10.1108/JES-08-2023-0442
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JES-08-2023-0442/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JES-08-2023-0442/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1108/JES-08-2023-0442?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.

    More about this item

    Keywords

    Spillover index; Frequency decomposition; Emerging markets; Sectoral stock indices; C22; C51; C58; G11;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:eme:jespps:jes-08-2023-0442. 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: Emerald Support (email available below). General contact details of provider: .

    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.