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An Attempt to Capture Leptokurtic of Returns and to Model Its Volatility: The Case of Beirut Stock Exchange

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  • Elie BOURI

    () (University of the Holy Spirit of Kaslik – Lebanon, Faculty of Business Administration, P.O.Box : 446 Jounieh – Lebanon)

Abstract

Repeated turmoil in equity indices in developed and emerging markets puts pressures on market participants to deal with the intense volatility of returns. After examining the normality of daily returns in Beirut Stock Exchange (BSE) from June 1999 to May 2011 with Jarque-Berra test (1980), we have compared the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model of Bollerslev (1986) with the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model of Nelson (1991), under three distribution assumptions: the Gaussian, the t-Student and the General Errors Distribution (GED). Empirical results showed that the distribution of daily returns is far from being normally distributed, with fat tails and volatility clustering being persistent. Furthermore, the asymmetric EGARCH-GED model is found to adequately fit the data and incorporate the leverage effect. Surprisingly, good news generates higher volatility than bad news which gives investors in the Lebanese stock market a particular immunity to negative shocks.

Suggested Citation

  • Elie BOURI, 2011. "An Attempt to Capture Leptokurtic of Returns and to Model Its Volatility: The Case of Beirut Stock Exchange," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 8, pages 259-271, December.
  • Handle: RePEc:aic:revebs:y:2011:i:8:bourie
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    References listed on IDEAS

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    Keywords

    volatility; Beirut Stock Exchange; GARCH; EGARCH; Leverage Effect;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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