IDEAS home Printed from https://ideas.repec.org/a/adr/anecst/y2019i135p57-68.html
   My bibliography  Save this article

Asymmetry, Fat-tail and Autoregressive Conditional Density in Daily Stocks Return Data

Author

Listed:
  • Ali Babikir
  • Mohammed Elamin Hassan
  • Henry Mwambi

Abstract

This paper focuses on the study of unbiasedness and efficiency of the maximum likelihood estimates of the GARCH (1,1) model volatility parameters when the error distribution assumed is Johnson s SU under varying skewness and kurtosis levels. The study is based on a simulation experiment and a real application to daily returns of five stock indices. In general the ML estimates of volatility parameters are found to be unbiased with high efficiency when the true distribution is asymmetric and fat-tailed for all levels of skewness and kurtosis and all parameter levels. Models with time-varying shape parameters are found to give a better fit than models with constant shape parameters.

Suggested Citation

  • Ali Babikir & Mohammed Elamin Hassan & Henry Mwambi, 2019. "Asymmetry, Fat-tail and Autoregressive Conditional Density in Daily Stocks Return Data," Annals of Economics and Statistics, GENES, issue 135, pages 57-68.
  • Handle: RePEc:adr:anecst:y:2019:i:135:p:57-68
    DOI: 10.15609/annaeconstat2009.135.0057
    as

    Download full text from publisher

    File URL: https://www.jstor.org/stable/10.15609/annaeconstat2009.135.0057
    Download Restriction: no

    More about this item

    Keywords

    GARCH; ARCD; Conditional Volatility; Skewness and Kurtosis.;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:adr:anecst:y:2019:i:135:p:57-68. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Laurent Linnemer). General contact details of provider: http://edirc.repec.org/data/ensaefr.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.