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Forecasting the Volatility of the Dow Jones Islamic Stock Market Index: Long Memory vs. Regime Switching

  • Adnen Ben Nasr

    ()

    (Laboratoire BESTMOD, ISG de Tunis, Universite de Tunis, Tunisia)

  • Thomas Lux

    ()

    (Department of Economics, University of Kiel, Germany and Banco de Espana Chair in Computational Economics, University Jaume I, Castellon, Spain)

  • Ahdi N. Ajmi

    ()

    (College of Science and Humanities in Slayel, Salman bin Abdulaziz University, Kingdom of Saudi Arabia)

  • Rangan Gupta

    ()

    (Department of Economics, University of Pretoria)

The financial crisis has fueled interest in alternatives to traditional asset classes that might be less affected by large market gyrations and, thus, provide for a less volatile development of a portfolio. One attempt at selecting stocks that are less prone to extreme risks, is obeyance of Islamic Sharia rules. In this light, we investigate the statistical properties of the Dow Jones Islamic Finance (DJIM) index and explore its volatility dynamics using a number of up-to-date statistical models allowing for long memory and regime-switching dynamics. We find that the DJIM shares all stylized facts of traditional asset classes, and estimation results and forecasting performance for various volatility models are also in line with prevalent ndings in the literature. Overall, the relatively new Markov-switching multifractal model performs best under the majority of time horizons and loss criteria. Long memory GARCH-type models always improve upon the short-memory GARCH specification and additionally allowing for regime changes can further improve their performance.

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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201412.

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Length: 27 pages
Date of creation: Mar 2014
Date of revision:
Handle: RePEc:pre:wpaper:201412
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