This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the period from 1/16/2008 to 7/29/2011. The statistical approach employs dynamic log-returns quantile regression models to forecast price densities using implied volatilities (IVs) and factors estimated through principal component analysis (PCA) from the IVs, pooled IVs and lagged returns. Extensive in-sample and out-of-sample analyses are conducted, including assessment of excess trading returns, and evaluations of several combinations of quantiles, model specifications, and NCFC's. The results suggest that the IV-PCA-factors, particularly pooled return-IV-PCA-factors, improve quantile forecasting power relative to models using only individual IV information. The ratio of the put-IV to the call-IV is also found to improve quantile forecasting performance of log returns. Improvements in quantile forecasting performance are found to be better in the tails of the distribution than in the center. Trading performance based on quantile forecasts from the models above generated significant excess returns. Finally, the fact that the single IV forecasts were outperformed by their quantile regression (QR) counterparts suggests that the conditional distribution of the log-returns is not normal.

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