  Recent literature seek to forecast implied volatility derived from equity,
index, foreign exchange, and interest rate options using latent factor and
parametric frameworks. Motivated by increased public attention borne out of the
financialization of futures markets in the early 2000s, we investigate if these
extant models can uncover predictable patterns in the implied volatility
surfaces of the most actively traded commodity options between 2006 and 2016.
Adopting a rolling out-of-sample forecasting framework that addresses the
common multiple comparisons problem, we establish that, for energy and precious
metals options, explicitly modeling the term structure of implied volatility
using the Nelson-Siegel factors produces the most accurate forecasts.
