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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
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    More about this item

    Keywords

    GARCH; ARCD; Conditional Volatility; Skewness and Kurtosis.;
    All these keywords.

    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

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