Forecasting the Jordanian stock index: modelling asymmetric volatility and distribution effects within a GARCH framework

H. Al-Hajieh, H. AlNemer, Timothy Rodgers, Jacek Niklewski

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Abstract

The modelling of market returns can be especially problematical in emerging and frontier financial markets given the propensity of their returns to exhibit significant non-normality and volatility asymmetries. This paper attempts to identify which representations within the GARCH family of models can most efficiently deal with these issues. A number of different distributions (normal, Student t, GED and skewed Student) and different volatility of returns asymmetry representations (EGARCH and GJR- -GARCH) are examined. Our data set consists of daily Jordanian stock market returns over the period January 2000 – November 2014. Using both the Superior Predicative Ability (SPA) and Model Confidence Set (MCS) testing frameworks it is found that using GJR-GARCH with a skewed Student distribution most accurately and efficiently forecasts Jordanian market movements. Our findings are consistent with similar research undertaken in respect to developed markets.
Original languageEnglish
Pages (from-to)9-26
JournalCopernican Journal of Finance and Accounting
Volume4
Issue number2
DOIs
Publication statusPublished - 2015

Bibliographical note

This paper is available under a Creative Commons Attribution No-Derivatives license - see http://creativecommons.org/licenses/by-nd/3.0/pl/deed.en for full terms and conditions.

Keywords

  • GARCH
  • asymmetry
  • distributions

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