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Models assuming the normality of real data are widely used in many disciplines, e.g. in finances, where the price processes are represented by the geometrical Brownian motion (Black—Scholes model). However, in last years this approach has been criticised because of the heavy tails of empirical data distributions which contradict the normality. The researchers have tried to overcome this problem creating models based on the so called jump-diffusion processes. Unfortunately, it has not helped either because the empirical data distributions, even after extraction of jumps, had significantly higher kurtosis than in the normal case. In this paper we consider the normality of the average net freight tics from the s.c. Shanghai indeces and from one of the leading shipping lines and we have found out that also in this instance the empirical distributions differ from the gaussian one. But they are usually virtually normal except for only few large outliers. Therefore we applied the so-called threshold method for the identification of outliers and decided to treat them as jumps. It turned out that for remaining data the normality had been confirmed by all basic statistical tests, where even in the worst case the p-value had exceeded 10%. Besides, the net freight tics were symetrical and centralized. This result opens the way for a proper application of gaussian models for the net freight behavior. Such models, similarly to the case of the financial market, could be used for predicting purposes and for the construction of hedging tools.

The Normality of Weekly Relative Changes of the Freight Rate in Container Shipping