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Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models

  • Demiralay, Sercan
  • Ulusoy, Veysel

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with long memory volatility models, namely FIGARCH, FIAPARCH and HYGARCH, under normal and student-t innovations’ distributions. For these analyses, we consider both long and short trading positions. Overall, our results reveal that long memory volatility models under student-t distribution perform well in forecasting a one-day-ahead VaR for both long and short positions. In addition, we find that FIAPARCH model with student-t distribution, which jointly captures long memory and asymmetry, as well as fat-tails, outperforms other models in VaR forecasting. Our results have potential implications for portfolio managers, producers, and policy makers.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 53229.

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Date of creation: 27 Jan 2014
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Handle: RePEc:pra:mprapa:53229
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