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

Sercan Demiralay and Veysel Ulusoy

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: Long memory; value-at-risk; volatility modeling; precious metals prices (search for similar items in EconPapers)
JEL-codes: C53 C58 G17 (search for similar items in EconPapers)
Date: 2014-01-27
New Economics Papers: this item is included in nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/53229/1/MPRA_paper_53229.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:53229

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter (winter@lmu.de).