Munich Personal RePEc Archive

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

Demiralay, Sercan and Ulusoy, Veysel (2014): Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models.

[img]
Preview
PDF
MPRA_paper_53229.pdf

Download (1MB) | Preview

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.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.