Quantifying the relationship between seaborne trade and shipping freight rates: A Bayesian vector autoregressive approach

Abstract

We employ a Bayesian Vector Autoregressive methodology, to counter the issue of data availability, and explore the relationship between seaborne commodity trade and freight rates. Our results show three important insights: first and foremost, the quantity of seaborne commodity trade has a strong impact on the Baltic Dry Index and the Baltic Dirty Tanker Index, but not on the Baltic Clean Tanker Index, most likely due to the fact that clean tankers can simultaneously operate both in the clean and the dirty sectors. Second, a shock in the price of brent oil has the expected positive response from the Baltic Dry Index, while its relationship with the Baltic Clean Tanker Index and the Baltic Dirty Tanker Index is negative as, in this case, tanker vessels can operate as floating storage units. Third, a relationship between the freight indices appears to hold as a change in one could spill over to the other.