Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This study proposes a novel framework for the joint modelling of commodity forward curves. Its key contribution is twofold. First, we introduce a family of dynamic conditional correlation models based on hierarchical Archimedean copulae (HAC-DCC), which are flexible but parsimonious instruments that capture a wide range of dynamic dependencies. Second, we apply these models in the context of commodity forward curves as part of the framework. An extensive Value-at-Risk analysis shows that certain HAC-DCC models consistently outperform other introduced benchmarks in terms of the preciseness of their out-of-sample distribution forecasts of the returns of various commodity futures portfolios. This shows that the proposed modelling framework, as one of its possible applications, can be a useful and convenient risk management tool.