Quantifying the Relationship Between Seaborne Trade and Shipping Freight Rates: A Bayesian Vector Autoregressive Approach

Michail, N.A., Melas, K.D. ,(2020),"Quantifying the relationship between seaborne trade and shipping freight rates: A Bayesian vector autoregressive approach", Martime Transport Research, p.100001, doi: 10.1016/j.martra.2020.100001

10 Pages Posted: 19 Jan 2021

See all articles by Nektarios Michail

Nektarios Michail

Cyprus University of Technology

Konstantinos D. Melas

Metropolitan College, Greece - Faculty of Business and Economics

Date Written: November 10, 2020

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.

Keywords: shipping markets, freight rates, seaborne trade, bayesian vector autoregression

JEL Classification: G11, G12, G17, R41

Suggested Citation

Michail, Nektarios and Melas, Konstantinos, Quantifying the Relationship Between Seaborne Trade and Shipping Freight Rates: A Bayesian Vector Autoregressive Approach (November 10, 2020). Michail, N.A., Melas, K.D. ,(2020),"Quantifying the relationship between seaborne trade and shipping freight rates: A Bayesian vector autoregressive approach", Martime Transport Research, p.100001, doi: 10.1016/j.martra.2020.100001, Available at SSRN: https://ssrn.com/abstract=3725352

Nektarios Michail

Cyprus University of Technology ( email )

Limassol, 3603
Cyprus

Konstantinos Melas (Contact Author)

Metropolitan College, Greece - Faculty of Business and Economics ( email )

74, Sorou St.
Maroussi, 15125
Greece

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
8
Abstract Views
379
PlumX Metrics