• Journal of Korea Port Economic Association
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• v.33 no.1
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• pp.75-87
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• 2017

The purpose of this study is to utilize the system dynamics to carry out a medium and long-term forecasting analysis of the bunker price. In order to secure accurate bunker price forecast, a quantitative analysis was established based on the casual loop diagram between various variables that affects bunker price. Based on various configuration variables such as crude oil price which affects crude oil consumption & production, GDP and exchange rate which influences economic changes and freight rate which is decided by supply and demand in shipping and logistic market were used in accordance with System Dynamics to forecast bunker price and then objectivity was verified through MAPEs. Based on the result of this study, bunker price is expected to rise until 2029 compared to 2016 but it will not be near the surge sighted in 2012. This study holds value in two ways. First, it supports shipping companies to efficiently manage its fleet, offering comprehensive bunker price risk management by presenting structural relationship between various variables affecting bunker price. Second, rational result derived from bunker price forecast by utilizing dynamic casual loop between various variables.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.179-184
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• 2021

In this paper, we propose the deep learning-based neural network model to predict bunker price. In the shipping industry, since fuel oil accounts for the largest portion of ship operation costs and its price is highly volatile, so companies can secure market competitiveness by making fuel oil purchasing decisions based on rational and scientific method. In this paper, short-term predictive analysis of HSFO 380CST in Singapore is conducted by using three recurrent neural network models like RNN, LSTM, and GRU. As a result, first, the forecasting performance of RNN models is better than LSTM and GRUs using long-term memory, and thus the predictive contribution of long-term information is low. Second, since the predictive performance of recurrent neural network models is superior to the previous studies using econometric models, it is confirmed that the recurrent neural network models should consider nonlinear properties of bunker price. The result of this paper will be helpful to improve the decision quality of bunker purchasing.

• Korea Trade Review
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• v.43 no.5
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• pp.159-177
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• 2018

The container shipping sector is an important international logistics operation that connects open economies. Freight rates rapidly change as the market fluctuates, and staff related to the shipping market are interested in factors that determine freight rates in the container market. This study uses the Vector Error Correction Model(VECM) to estimate the impact of factors affecting container freight rates. This study uses data published by Clarksons. The analysis results show a 4.2% increase in freight rates when world container traffic increases at 1.0%, a 4.0% decrease in freight rates when volume of container carriers increases by 1.0%, a 0.07% increase in freight rates when bunker price increases by 1.0%, and a 0.04% increase in freight rates accompanying 1.0% increase in libor interests rates. In addition, if the current freight rate is 1.0% higher than the long-term equilibrium rate, the freight rate will be reduced by 3.2% in the subsequent term. In addition, if the current freight rate is 1.0% lower than the long-term equilibrium rate, the freight rate will decrease by 0.12% in the following term. However, the adjusting power in a period of recession is not statistically significant which means that the pressure of freight rate increase in this case is neglectable. This research is expected to contribute to the utilization of scientific methods in forecasting container freight rates.