Over the past few years, the concept of cluster has been regarded and adopted as a useful policy tool in analyzing maritime industry development. However, there is a lack of studies on the theoretical development of maritime cluster evolution in the existing literature. This paper aims to investigate the dynamic symbiosis derived from maritime cluster evolution. The research leads to a new path to investigate maritime cluster by employing the symbiosis theory in ecology and the Lotka–Volterra model. The paper first develops the concept of maritime cluster classification and evolution. Then, it analyses the compatibility and analogy of biotic community with maritime cluster. In order to study the interaction relationships among maritime sectors, the Lotka–Volterra model is introduced. The model is used to group the revenues of maritime sectors in pairs. These revenues are in turn grouped into a number of comparative pairs accordingly. The model is further advanced to forecast the trend of maritime clusters by studying the existence of an equilibrium point and its stability with the estimated functions. The original approach would deepen the understanding on maritime cluster and stimulate future research. The study also draws insights for policy makers in maritime nations.

An appropriate description of freight rate behaviors is important to maritime forecasting and portfolio diversification in shipping freight markets. We employ general autoregressive conditional heteroscedasticity-copula models to capture the dynamics and interdependencies among shipping freight rates. Using weekly data from 5 January 2002 to 24 March 2018, our main findings are first, Granger causality tests confirm the presence of one-way causality running from the dry bulk and the clean tanker freight rate returns to the container and the dirty tanker freight rate returns, respectively. Second, volatility persistence exists in individual shipping freight market and, in particular, it is much less persistent in the clean tanker freight market. Third, nonlinear dynamic interdependencies among freight rate returns are captured by performing time-varying copulas. The results not only deepen our understanding of freight rate behaviors but also offer new insights into portfolio diversification and risk management in the shipping freight markets.