Forecasting container shipping freight rates for the Far East – Northern Europe trade lane
Ziaul Haque Munim1, Hans-Joachim Schramm2,3
1School of Business and Law, University of Agder, Norway
2Department of Global Business and Trade, Institute for Transport and Logistics Management, WU Vienna University of Economics and Business, Austria
3Department of Operations Management, Copenhagen Business School, Denmark
This study introduces a state-of-the-art volatility forecasting method for container shipping freight rates. Over the last decade, the container shipping industry has become very unpredictable. The demolition of the shipping conferences system in 2008 for all trades calling a port in the European Union (EU) and the global financial crisis in 2009 have affected the container shipping freight market adversely towards a depressive and non-stable market environment with heavily fluctuating freight rate movements. At the same time, the approaches of forecasting container freight rates using econometric and time series modelling have been rather limited. Therefore, in this paper, we discuss contemporary container freight rate dynamics in an attempt to forecast for the Far East to Northern Europe trade lane. Methodology-wise, we employ autoregressive integrated moving average (ARIMA) as well as the combination of ARIMA and autoregressive conditional heteroscedasticity (ARCH) model, which we call ARIMARCH. We observe that ARIMARCH model provides comparatively better results than the existing freight rate forecasting models while performing short-term forecasts on a weekly as well as monthly level. We also observe remarkable influence of recurrent general rate increases on the container freight rate volatility.
Container shipping, freight rates, forecasting, ARIMA, ARCH, GRI
This paper received the Palgrave-Macmillan Best Paper Award at the IAME 2016 Conference at Hamburg, Germany.
Please cite as:
Munim, Z. H., Schramm, H-J. (2017). Forecasting container shipping freight rates for the Far East – Northern Europe trade lane. Maritime Economics & Logistics, 19(1), pp 106-125. doi.org/10.1057/s41278-016-0051-7