Forecasting volatility in commodity markets with long-memory models

Commodities are the most volatile markets, and forecasting their volatility is an issue of paramount importance. We examine the dynamics of commodity markets volatility by employing three typical long-memory models: fractional integrated generalized autoregressive conditional heteroscedastic (FIGARCH), fractional stochastic volatility (FSV), and heterogeneous autoregressive (HAR) models. Based on a high-frequency futures price dataset of 22 commodities, we confirm that the volatility of commodity markets is rough, and volatility components over different horizons are economically and statistically significant. Long memory with anti-persistence is evident across all commodities, with weekly volatility dominating in most commodity markets and daily volatility for oil and gold markets. HAR models display a clear advantage in forecasting performance compared to the two other models for short horizons, while fractional volatility models yield comparative better forecasts for longer horizons.