IDEAS home Printed from https://ideas.repec.org/a/taf/macfem/v16y2023i2p264-284.html
   My bibliography  Save this article

Risk vs Upside uncertainty: application of quantile regression in investment analysis

Author

Listed:
  • Seema Rehman
  • Jameel Ahmed Khilji
  • Saqib Sharif

Abstract

This paper examines the implications for risk taking in an emerging stock market, namely, Pakistan Stock Exchange (PSX), using tools that specifically account for the asymmetries. We perform sectoral level price data analysis to infer how investors behaved during various states of stock market such as bullish, bearish, stable etc. Using monthly data over 2005–2020, we estimate the Capital Asset Pricing Model (CAPM) using quantile regression framework, which is robust to distributional assumptions and can estimate the elasticities across the risk spectrum. The empirical findings suggest that the elasticities, namely, betas, are significant across quantiles. It implies that the risk-return relationship behaves differently across the market states and that the investors and policymakers, therefore, should calibrate their decisions accordingly.

Suggested Citation

  • Seema Rehman & Jameel Ahmed Khilji & Saqib Sharif, 2023. "Risk vs Upside uncertainty: application of quantile regression in investment analysis," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 16(2), pages 264-284, May.
    • Handle: RePEc:taf:macfem:v:16:y:2023:i:2:p:264-284
    DOI: 10.1080/17520843.2021.1952639
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/17520843.2021.1952639
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/17520843.2021.1952639?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

    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:macfem:v:16:y:2023:i:2:p:264-284. 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/REME20 .

    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.