Global Advanced Research Journal of Social Science (GARJSS)
December 2012 Special Anniversary Review Issue Vol. 1(7), pp. 118-135
Copyright © 2012 Global Advanced Research Journals

Review

Long-term forecasting of BFI using chaos cycle theory and maritime technical analysis

Alexandros M. Goulielmos

Ex Professor Marine Economics Department of Maritime Studies, University of Piraeus, 80 Karaoli and Dimitriou St., Piraeus 18534, Greece.

E-mail: ag@unipi.gr and am.goulielmos@hotmail.com

Accepted 04 December, 2012

Abstract

We tackle the problem to produce reliable long-run freight rates forecasts in maritime economy. Available tools developed in academia aimed at predicting stock prices with short term correlation, using models like GARCH. Moreover, Chaos theory models developed since 1963 by Mandelbrot, using Rescaled Range Analysis, provide short-run forecasts, the range of which depends on Lyapunov’s exponent. Moreover, “Maritime” Technical Analysis, due to Hampton, supports the existence of short-run (3-4 years) and long-run (16-24 years) shipping cycles. So, the paper applies rescaled range analysis and maritime technical analysis to produce long-term forecasts through cycles. Since 1981, and in 1973, and at the end of 2008, shipping has experienced dramatic drops in freight markets, which Mandelbrot, in another context, described as the “joker” in the pack and “Noah” effect. Applying BDS and other tests on BPI time series of daily and weekly rates (1999-2011), we found: non-normality, long term correlation and chaos. The Hurst exponent found 0.93<1.00, indicating a very strong ‘black’ noise. The ‘Lyapunov’ exponent allowed forecasting up to 6 days/weeks. In such a case, to obtain long term forecasting we calculated cycles on the principle that those persistent cycles will be repeated. Cycles identified with Chaos theory were: 28 months to 35 and 4 to 9 years -using Vn statistic. In addition, Maritime technical analysis showed the short term cycle, which ended in May 2011, and the long term cycle, to end in 2017.

Keywords: forecasting freight rates index, chaos and Vn statistic/H exponent, maritime technical analysis, BPI 1999-2011, tests for normality (JB) and iid (BDS)