An analysis of entry and exit decisions in shipping markets under uncertainty

Objective: The existing literature on the application of real options in maritime economics is rather limited. Bendall & Stent (2003, 2005, 2007) show how investing in a new ship or maritime technology can incorporate an option value. Another application is elaborated by Bjerksund & Ekern (1995), discussing the value of mean-reverting cash flows through contingent claim analysis, applied to time charters. The analysis of the entry and exit decision in the shipping markets can form an interesting extension of this research. Shipping markets are in nature uncertain and cyclical (Stopford, 2009). For this reason, the real options based model of Ruiz-Aliseda & Wu (2012) for stochastically cyclical markets will be applied to the shipping markets, in order to define entry and exit thresholds in this market. Data/Methodology: Data on the freight rates, costs and ship prices are used for estimating the parameters present in the real options based model of Ruiz-Aliseda & Wu (2012) for stochastically cyclical markets. This model incorporates a discrete-time Markov process, an alternative for the traditionally used geometric Brownian motion (GBM) and suited to model the cyclicality of the shipping markets. Investing in a ship and selling it again can respectively be interpreted as entering and exiting the market. Hence, the model can be applied here in order to predict the optimal values for entry and exit in the shipping markets. Results/Findings: The theoretically calculated results will be tested on real cases using data on container time charter prices over different periods, to see how well the theoretically developed model performs in the shipping markets. In that way, the estimation of parameters and the assumptions of the model can be evaluated on their accuracy in these cyclical markets. Implications for Research/Policy: In the first place, this case study can indicate how well the model performs in another market. If the model turns out to successfully predict market entry and exit thresholds, it could help companies to make better predictions on competitor behaviour. Also for policy makers, it could help to have a more accurate insight in the required capacity for ports.