Forecasting Commodity Markets Volatility: HAR or Rough?

Abstract

Commodity is one of the most volatile markets and forecasting its volatility is an issue of paramount importance. We study the dynamics of the commodity markets volatility by employing fractional stochastic volatility 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 fractional volatility models.

Keywords: commodity markets, realized volatility, fractional Brownian motion, HAR, volatility forecast

JEL Classification: C20, C53, C58, G13, Q02