Shipping finance: time to follow a new track?

In this paper we introduce, for the first time, a methodology from the most recent literature of finance to reveal the duration of shipping cycles and then show the benefit of the use of this information to make more successful shipping loans. This is so as banks are willing to finance, during boom periods, shipping loans for new buildings but by this way 'create' oversupply and thus depress the freight market by their own actions. The information about cycles, especially their forecasting, is mostly important as shipping loans are based on project financing/cash flow financing, which means that ship revenue is of utmost importance. The Rescaled Range Analysis is applied here to 379 monthly freight trips—made stationary—between 1971 and 2002 (July), due to Hurst 1 and elaborated and popularized by Mandelbrot 2. The most important effect, however, is that shipping freight series exhibit non-normality and long-run dependence rendering the use of random walk models such as GARCH (Generalized Autoregressive Conditional Heteroscedasticity) problematic. Thus an adequate literature review is carried out with criticism against the models used. The cycles have been calculated as equal to 4.5 years and 2.25 years. This is almost compatible with the most recent paper of Stopford 3. The Hurst exponent was found equal to 0.93, alternating over the periods examined (0.65, 0.73, 0.62, 0.59 and 0.55) and indicating long-term persistence but seriously away from normal/random walk domain. Most studies have said the same using the Jarque--Bera test for normality but provided no alternative.     

Forecasting weekly freight rates for one-year time charter 65 000 dwt bulk carrier, 1989–2008, using nonlinear methods

This paper forecast/predicted the one-year time charter weekly freight rates earned by a 65 000 dwt bulk carrier using 996 weeks of data from 1989 to 2008. First, the need and the importance, but also the futility, of forecasting is discussed in shipping. This is a volatile industry that can be easily likened to the roulette. The introduction is followed by a literature review that has examined the principal recent works in this area and presented a critique of earlier works. Most of the research studied dealt with the shipping industry per se. Since the methods used are considered as a departure from the classical Random Walk, a comprehensive section of the paper is devoted to the methodology of nonlinear, chaotic and deterministic methods. The relevant time series have been transformed into stationary ones, as this is the proper practice (using first logarithmic differences). The time series were tested for randomness (identically and independently distributed) and for long-term correlation using BDS statistic. The methods used were: Rescaled Range Analysis and the related Hurst Exponent; Power Spectrum Analysis; V-statistic and BDS Statistic (using software MATLAB 5.3 and NLTSA V.2.0/2000). The analysis of the data was presented in three separate sections. The relevant ‘attractor’ of the system has been graphically shown. System's dimension has been calculated, which was found to be non-integer, fractal and equal to 3.95. This finding permitted us to proceed to forecasting, as this is a case of a low dimensional chaos (3.95 < 10 dimensions). In order for the predictions to be robust, the prediction horizon allowed was found equal to 8.24 weeks, as indicated by the positive maximum Lyapunov exponent (0.12 rounded). Then NLTSA software was used to make prediction inside- and forecasting outside- the sample, using by selection nonlinear Principal Components and Kernel Density Estimation methods.     

The distribution of the benefits of waterfront reform

In this paper we introduce, for the first time, a methodology from the most recent literature of finance to reveal the duration of shipping cycles and then show the benefit of the use of this information to make more successful shipping loans. This is so as banks are willing to finance, during boom periods, shipping loans for new buildings but by this way ‘create’ oversupply and thus depress the freight market by their own actions. The information about cycles, especially their forecasting, is mostly important as shipping loans are based on project financing/cash flow financing, which means that ship revenue is of utmost importance. The Rescaled Range Analysis is applied here to 379 monthly freight trips—made stationary—between 1971 and 2002 (July), due to Hurst 1 Hurst, HE. 1950. Long-term storage capacity of reservoirs. April1950. pp.770–808. American Society of Civil Engineers. Paper No. 2447 [Google Scholar] and elaborated and popularized by Mandelbrot 2 Mandelbrot, BB. 1975. Stochastic models for the earth's relief, the shape and the fractal dimensions of the coastlines, and the number-area rule for islands. Proceedings of the National Academy of Sciences. 1975, USA 72. pp.3825–3828. [Crossref] , [Google Scholar]. The most important effect, however, is that shipping freight series exhibit non-normality and long-run dependence rendering the use of random walk models such as GARCH (Generalized Autoregressive Conditional Heteroscedasticity) problematic. Thus an adequate literature review is carried out with criticism against the models used. The cycles have been calculated as equal to 4.5 years and 2.25 years. This is almost compatible with the most recent paper of Stopford 3 Stopford, M. 21 September 2001. “Forecasting the dry bulk, tanker and container markets”. In Maritime Cyprus 21 September,  [Google Scholar]. The Hurst exponent was found equal to 0.93, alternating over the periods examined (0.65, 0.73, 0.62, 0.59 and 0.55) and indicating long-term persistence but seriously away from normal/random walk domain. Most studies have said the same using the Jarque--Bera test for normality but provided no alternative.     

Forecasting short-term freight rate cycles: do we have a more appropriate method than a normal distribution?

General economists, as well as Maritime economists, assume that the time series they forecast follow normal distribution, and data is independently and identically distributed around the mean. This paper contests this assumption with the aid of three sets of time series: (1) The Dry Cargo Freight Index, 1741–2005: in this series the data deviate from the mean by more than three standard deviations on no fewer than six occasions, and exhibited “fat tails”; (2) The time charter freight rates for a 10-year-old, 80?000 dwt vessel from 6 January 1989 to 26 December 2008: this series also exhibits skewness and kurtosis in a leptokurtic distribution; and (3) The Dry Cargo Time Charter Index from 1971 to 2004: this series diverged from the normal distribution four times. Next, we searched more recent data for short-term cycles using the V-statistic. One cycle was found to last 4 years (2005 to 2008), which is in accordance with theory. This cycle started on 29 July 2005 and ended when it reached the (lowest) level of $4000 on 1 December 2008. A new 4-year cycle started on 1 December 2008 and is forecast to last until the end of 2010. Short-term forecasting of the cycle using V-statistic is theoretically confirmed by the theory advanced by Hampton [1990, published in 1989, ‘Analysis and shipping cycles I and II’, Seatrade Journal, 19–23. Long and Short Shipping Cycles: The Rhythms and Psychology of Shipping Markets, Monograph, 2nd ed. (Cambridge: Cambridge Academy of Transport), March, p. 66].