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Long memory and structural breaks in modeling the return and volatility dynamics of precious metals

  • Mohamed El Hedi Arouri

    (CRCGM - Centre de Recherche Clermontois en Gestion et Management - UBP - Université Blaise Pascal - Clermont-Ferrand 2 - Université d'Auvergne - Clermont-Ferrand I - École Supérieure de Commerce (ESC) - Clermont-Ferrand)

  • Shawkat Hammoudeh

    (Lebow College of Business, Drexel University - CERAG - Centre d'études et de recherches appliquées à la gestion - Grenoble 2 UPMF - Université Pierre Mendès France - CNRS)

  • Amine Lahiani

    (LEO - Laboratoire d'économie d'Orleans - CNRS - UO - Université d'Orléans)

  • Duc Khuong Nguyen

    (IPAG - CERAG - Centre d'études et de recherches appliquées à la gestion - Grenoble 2 UPMF - Université Pierre Mendès France - CNRS)

We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semiparametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA-FIGARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.

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Date of creation: 07 Mar 2013
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Handle: RePEc:hal:wpaper:hal-00798033
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