We employ a large dataset of physical inventory data on 21 different commodities for the period 1993-2011 to empirically analyze the behaviour of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we explore the relationship between...

We employ a large dataset of physical inventory data on 21 different commodities for the period 1993-2011 to empirically analyze the behaviour of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we explore the relationship between...

We employ a large dataset of physical inventory data on 21 different commodities for the period 1993-2011 to empirically analyze the behaviour of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we analyze the relationship between...

The recent sharp increase in the prices of primary food commodities has raised serious concerns of policy makers on the role of index funds in these food markets. In this paper, we employ a dataset on trading positions of index fund investors from the US Commodity Futures Trading Commission...

This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong...

We analyze the variance risk of commodity markets. We construct synthetic variance swaps and find significantly negative realized and expected variance swap payoffs in most markets. We find evidence of commonalities among the realized payoffs of commodity variance swaps. We also document...

We analyze the relationship between economic uncertainty and commodity market volatility. We find that commodity market volatility comoves strongly with economic and financial uncertainty, especially during recessions. Variables associated with credit risk, financial market stress, and...

We develop a framework to quantify the convenience yield risk (CYR) inherent to each commodity futures market. Implementing our approach, we document that our novel CYR measure is informative about future commodity returns. In panel regressions, the CYR predicts future returns with a positive...

We contribute to the literature by analyzing forecast combination methods in the context of machine learning to predict equity returns. Whilst individual models lack robustness, forecast combinations display stability and are able to produce improved results with Sharpe ratios up to 3.16. We use...