Forecasting container shipping freight rates for the Far East – Northern Europe trade lane
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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.
Keywordscontainer shipping freight rates forecasting ARIMA ARCH GRI
The paper is the recipient of the Palgrave Macmillan prize for best conference paper at the International Association of Maritime Economists (IAME) conference, Hamburg, August 2016. The authors are indebted to Sebastian Kummer for constructive comments and suggestions on an earlier version of the manuscript. Authors are also thankful to Jann Goedecke for useful suggestions.
- Alixpartners (2015) Container Shipping Outlook [Online]. http://www.alixpartners.com/en/Publications/AllArticles/tabid/635/articleType/ArticleView/articleId/1590/Finding-Focus.aspx#sthash.Gxl2chd8.dpbs, accessed 15 December 2015.
- Alphaliner (2013a) Alphaliner Weekly Newsletter, 2013(21), 14/05–20/05/2013.Google Scholar
- Alphaliner (2013b) Alphaliner Weekly Newsletter, 2013(42), 08/10–14/10/2013.Google Scholar
- Alphaliner (2013c) Alphaliner Weekly Newsletter, 2013(48), 19/11–25/11/2013.Google Scholar
- Alphaliner (2014) Alphaliner Weekly Newsletter, 2014(51), 16/12–22/12/2014.Google Scholar
- Alphaliner (2015) Alphaliner Weekly Newsletter, 2015(24), 10/06–16/06/2015.Google Scholar
- Balticexchange (n.d.) Ningbo Containerised Freight Index. http://www.balticexchange.com/market-information/containers/
- Beenstock, M. and Vergottis, A. (1993) Econometric Modelling of World Shipping. London: Chapman & Hall.Google Scholar
- Box, G.E. and Jenkins, G.M. (1976) Time series analysis: forecasting and control, revised ed., San Francisco: Holden-Day.Google Scholar
- CI (2009) Freight Rate Data. Containerisation International. London: Informa.Google Scholar
- CTS (n.d.) Container Trade Statistics. Woking: Container Trades Statistics Ltd.Google Scholar
- Dixon, M. (2010) Hedging your bets. Containerisation International 43(8): 26–29.Google Scholar
- Drewry Maritime Research (2011) Unmasking Freight Rates. London: Drewry Maritime Research.Google Scholar
- Drewry Shipping Consultants (2012) Index-Linked Container Contracts. London: Drewry Shipping Consultants Ltd.Google Scholar
- Dupin, C. (2010) Hedging your box bets, American Shipper 52(3): 34–37.Google Scholar
- EC (2008) Guidelines on the application of Article 81 of the EC Treaty to maritime transport services 2008/C 245/02. Brussels: European Commission.Google Scholar
- EC (2016) Press Release IP/16/317. Brussels: European Commission.Google Scholar
- Fan, L. (2011) Econometric analyses of container shipping market and capacity development. PhD Thesis, The Hong Kong Polytechnic University, Hong Kong.Google Scholar
- Gujarati, D.N. (2003) Basic Econometrics (4th ed.). New York: McGraw-Hill.Google Scholar
- Hoffmann, J. (2010) Shipping Out of the Economic Crisis. Brown Journal of World Affairs 16(2): 121–130.Google Scholar
- Kavussanos, M.G. (1996) Price risk modelling of different size vessels in the tanker industry using autoregressive conditional heteroskedastic (ARCH) models. Transportation Research Part E: Logistics and Transportation Review 32(2): 161–176.Google Scholar
- Kavussanos, M.G., Visvikis, I.D., and Dimitrakopoulos, D.N. (2015) Freight Markets and Products. In: A. Roncoroni, G. Fusai and M. Cumrins (eds.) Handbook of Multi-Commodity Markets and Products: Structuring, Trading and Risk Management. Wiley, pp. 355–398.Google Scholar
- Miller, K., Ward, R., Craig, A.W., Lowe, E.K., Kroll, C.A., Hale, T., Miller, E., Cassels, J., Schöne, S. and Höth, A. (2015) Risk Management and Applications. In: O. Schinas, C. Grau and M. Johns (eds.) HSBA Handbook on Ship Finance. Berlin: Springer, pp. 297–321.Google Scholar
- Murphy, A. (2016) The three final alliances, Lloyds List – Containers. https://www.lloydslist.com/ll/sector/containers/article528140.ece, accessed 17 June 2016.
- SSE (n.d.) Shanghai Shipping Exchange. http://en.sse.net.cn/index.jsp.
- SSE (2016a) About CCFI. http://en.sse.net.cn/indices/introduction_ccfi_new.jsp, accessed 15 January 2016.
- SSE (2016b) About SCFI. http://en.sse.net.cn/indices/introduction_scfi_new.jsp, accessed 15 January 2016
- Strandenes, S.P. and Wergeland, T. (1982) Freight Markets and Bulk Demand Efficiency. Norwegian School of Economics and Business Administration.Google Scholar
- Tinbergen, N. (1959) Behaviour, systematics, and natural selection. Ibis 101(3–4): 318–330.Google Scholar
- UNCTAD (2015) Review of Maritime Transport. Geneva: United Nations Conference on Trade and Development.Google Scholar
- Verbeek, M. (2008) A Guide to Modern Econometrics. Hoboken: Wiley.Google Scholar
- WCI (n.d.) The World Container Index. http://www.worldcontainerindex.com.
- Zannetos, Z.S. (1966) The Theory of Oil Tankship Rates. Cambridge: MIT Press.Google Scholar