A comprehensive look at commodity volatility forecasting

Photo by rawpixel on Unsplash

This research combines recent advances in the Realized Volatility (RV) literature and three specific commodity futures factors to improve the forecasts of commodity volatility. The three forecasting variables are the term structure slope, the time to maturity and a measure of supply and demand uncertainty. I first assess these variables’ empirical contribution to commodity futures volatility, in adding them in RV forecast models. First in the univariate HAR-RV of Corsi (2009) and second in the multivariate VAR-RV of Andersen, Bollerslev, Diebold, and Labys (2003). The long-term memory of assets RV justifies the former, whereas the “financialization” of commodities and the resulting commodity connectedness, supports the latter. I evaluate the out of sample validity of these forecast models and propose one risk management application. Hence, this research is important for both economic understanding and risk management purposes.

Avatar
Loïc Maréchal
PhD candidate in Finance

My research interests include finance, statistical learning and natural science originated data applied to finance.