How a Company Could Benefit from the Volatility of Prices: The Shipping Industry as a Case Study

Abstract

Statisticians, almost 190 years ago, established the law of the “large numbers” and the “central limit theorem”-CLT. The CLT led to “Normal Distribution”, which determines the 99.6% probability for something to happen, provided the number of repetitions → ∞. This tool, despite its formidable equation, is based on two simple parameters: the mean μ and the variance σ2. “Normality” was so strong1 that risk is defined as the deviation from μ. Surprises came mainly in 1894, 1929, 1987, and in 2008, when the deviations reached an unbelievable 22σ (Black Monday)! Research thereafter focused on cases where σ > 3, known as the “fat tails”. The fat-tailed distributions were specified by Cauchy (in 1824) and identified in financial markets by Mandelbrot B and in shipping markets by Goulielmos A. The yardstick of risk σ, replaced by α, and the shape of the distribution allowed for longer tails, etc., i.e., a departure from normality, called leptokurtosis. The shipping industry suffered, from 1741, from 22 cycles, till 2022 (May). But this was not really a surprise. The Surprise was the decreasing duration of the cycles over time, from 15 years to 8, the reduction of good times, and the increase of bad years, which emerge now more frequently! Any symmetry assumed between peaks and troughs in freight rate markets proved to be dangerous! History is useful, but it depends on the assumption that it is repeated2. Models to forecast freight rates and α in the shipping industry failed, despite the use of nonlinear models and computer software. The appearance of the “Joker” destroyed all forecasting attempts! The purpose of this work was to indicate chronologically the opportunities that have been created by volatility, for shipowners, mainly from the very volatile prices of the newly-built, but also of the second-hand ships in liquid and dry shipping sectors. As shown the 30% to 51% of the total cost is determined by these prices, and thus a competitive advantage can be sought after the ship has been built or bought. We used “suitable” diagrams of historical time series related to shipping prices and showed how a bad economic phenomenon like wild volatility could be used to achieve an excellent performance. The whole analysis is based on the principle: “how to exploit opportunities in times—which become now frequent—of wild economic variations?”