IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v29y2025i1p39-52n1003.html
   My bibliography  Save this article

Investigating the Impact of Consumption Distribution on CRRA Estimation: Quantile-CCAPM-Based Approach

Author

Listed:
  • Ramos Sofia B.

    (ESSEC Business School, Cergy, France)

  • Taamouti Abderrahim

    (Department of Economics, University of Liverpool Management School, University of Liverpool, Chatham St., Liverpool L69 7ZH, UK)

  • Veiga Helena

    (Department of Statistics and Instituto Flores de Lemus, Universidad Carlos III de Madrid, and BRU-UNIDE, Getafe, Spain)

Abstract

Using quantile maximization decision theory, this paper considers a quantile-based Euler equation that states that the asset price is a function of the quantiles of the payoff, consumption growth, the stochastic discount factor, risk aversion, and the distribution of the consumption growth rate. We use a more general distribution assumption (log-elliptical distributions) than the log-normality of the consumption growth rate assumed in the literature. The simulation results show that: (1) the higher the downside risk aversion, the lower the constant relative risk aversion; (2) the heavier the tails of the Student-t distribution, the higher the risk aversion for each level of downside risk aversion; and (3) the curve of the relationship between risk aversion and downside risk aversion shifts upward when the normality assumption is dropped, and the magnitude of this shift is high even for high degrees of freedom of the Student-t distribution. Our results suggest that using normally distributed errors to model stock returns and consumption growth rates could lead to an underestimation of the risk aversion coefficient.

Suggested Citation

  • Ramos Sofia B. & Taamouti Abderrahim & Veiga Helena, 2025. "Investigating the Impact of Consumption Distribution on CRRA Estimation: Quantile-CCAPM-Based Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 29(1), pages 39-52.
    • Handle: RePEc:bpj:sndecm:v:29:y:2025:i:1:p:39-52:n:1003
    DOI: 10.1515/snde-2023-0005
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/snde-2023-0005
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/snde-2023-0005?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.

    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:bpj:sndecm:v:29:y:2025:i:1:p:39-52:n:1003. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.