Modelling and Forecasting the Capsized Market Spot Freight Rate

Abstract

Capesize market is one of the shipping markets which is characterized with high volatility. An investor in this volatile market will find it very difficult for him to succeed by making a good decision. Most of the companies are faced with high risk of collapse if the managers are uncertain about the future. The study employed two econometrics models; Error Correction (EC) model and ARMA (Auto Regressive Moving Average) model for analyzing the dry bulk capesize shipping market spot freight rates that gives reliable results from which economic inferences can be made confidently. Root Mean Squared Error (RMSE), Theil Inequality Coefficient (U) and the Bias proportion are the forecasting techniques employed by this study as criteria for assessing the accuracy of the forecasting results of the two models. It was found out that the ARMA model has a stronger predictive power as compared to EC model as the ARMA model exhibits a smaller forecasting error. This implies that, in principle, ARMA model is to be preferred if one has to predict the capesize spot freight rate.

Keywords: Spot freight rate, Capesize dry bulk market, Econometrics model, Forecasting