Non-linear volatility dynamics and risk management of precious metals

• We examine the value-at-risk predictions of four major precious metals.
• We analyze long-memory (volatility persistence) of the precious metals.
• Non-linear long memory GARCH-class models are employed.
• In overall, FIAPARCH model under Student-t innovations provides superior results in terms of serially uncorrelated exceptions.

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with non-linear 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.