Artificial neural networks in freight rate forecasting

Reliable freight rate forecasts are essential to stimulate ocean transportation and ensure stakeholder benefits in a highly volatile shipping market. However, compared to traditional time-series approaches, there are few studies using artificial intelligence techniques (e.g. artificial neural networks, ANNs) to forecast shipping freight rates, and fewer still incorporating forward freight agreement (FFA) information for freight rate forecasts. The aim of this paper is to examine the ability of FFAs to improve forecasting accuracy. We use two different dynamic ANN models, NARNET and NARXNET, and we compare their performance for 1, 2, 3 and 6 months ahead. The accuracy of the forecasting models is evaluated with the use of mean squared error (MSE), based on actual secondary data including historical Baltic Panamax Index (BPI) data (available online), and primary data on Baltic forward assessment (BFA) collected from the Baltic Exchange. The experimental results show that, in general, NARXNET outperforms NARNET in all forecast horizons, revealing the importance of the information contained in FFAs in improving forecasting accuracy. Our findings provide better forecasts and insights into the future movements of freight markets and help rationalise chartering decisions. © 2019, Springer Nature Limited

Volatility Spillovers in Capesize Forward Freight Agreement Markets

This paper is to investigate spillovers in the Capesize forward freight agreements (FFAs) markets before and after the global financial crisis. The paper chooses four Capesize voyage routes FFAs (C3, C4, C5, and C7), two time-charter routes FFAs (BCIT/C average, BPI T/C average), and spot rates as research subjects, covering the periods 3 January 2006 to 24 December 2015. This paper applies Volatility Spillover Multivariate Stochastic Volatility (VS-MSV) model to analyze volatility spillover effects and estimates the parameters via software of Bayesian inference using Gibbs Sampling (BUGS), the deviance information criterion (DIC) used for goodness-of-fit model. The results suggest that there are volatility spillover effects in certain Capesize FFAs routes, and the effects from spot rates to FFAs take place before crisis, yet they are bilateral after crisis. With the development of shipping markets, the correlations between FFAs and spot rate are enhanced, and it seems that the effects depend on market information and traders’ behavior. So practitioners could make decisions according to the spillovers