• Korea Trade Review
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• v.44 no.1
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• pp.75-86
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• 2019

This study performs a factor analysis that affects the bunker oil price using the Co-integration model and Vector Error Correction Model (VECM). For this purpose, we use data from Clarkson and the analysis results show 17.6% decrease in bunker oil price when the amount of crude oil production increases at 1.0%, 10.3% increase in bunker oil price when the seaborne trade volume increases at 1.0%, 1.0% decrease in bunker oil price when total volume of vessels increases at 1.0%, and 0.003% increase in bunker oil price when 1.0% increase in world GDP, respectively. This study is meaningful in that this study estimates the speed of convergence to long-term equilibrium and identifies the price adjust mechanism which naturally exists in bunker oil market. And it is expected that the future study can provide statistically more meaningful econometric results if it can obtain data during more long-periods and use more various kinds of explanatory variables.

• 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 Korea Port Economic Association
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• v.29 no.2
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• pp.63-79
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• 2013

The fundamental endeavor of this study is to investigate the asymmetric relationship between bunker price and Baltic Dry-bulk Index (hereafter BDI). Previous investigations employ linear form based analysis between oil price and BDI but we develop nonlinear and asymmetric cointegration method, which is properly able to capture the decreasing and increasing periods differently. The empirical results show there is no relationships in linear model (e.g. Engle and Granger's methods). On the contrary, our estimate reveals there is significant long-run relationship with asymmetric framework, which implies the necessity of nonlinear and asymmetric consideration to the bunker price analysis.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.99-110
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• 2018

This study empirically examines simple methodology to quantify the risk resulted from the uncertainty of bunker price and foreign exchange rate, which cause main resources of the cost in shipping industry during the periods between $1^{st}$ of January 2010 and $31^{st}$ of January 2018. To shed light on the risk measurement in cash flows we tested GBM(Geometric Brownian Motion) frameworks such as the model with conditional heteroskedasticity and jump diffusion process. The main contribution based on empirical results are summarized as following three: first, the risk analysis, which is dependent on a single variable such as freight yield, is extended to analyze the effects of multiple factors such as bunker price and exchange rate return volatility. Second, at the individual firm level, the need for risk management in bunker price and exchange rate is presented as cash flow. Finally, based on the scale of the risk presented by the analysis results, the shipping companies are required that there is a need to consider what is appropriate as a means of risk management.

• 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.

• Environmental and Resource Economics Review
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• v.16 no.2
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• pp.239-273
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• 2007

Bunker-C is sold at the different price in the market for international bunkering and for export, though the quality of bunker-c is not much different in two markets. The price difference in two markets tends to increase since 2002 in Korea. This study shows that there is a possibility for a structural change in the price difference in two markets in Korea around June, 2002. In the search for possible explanations for this structural change, empirical analyses found that the price difference in Singapore, which had not have any explanatory power before June, 2002, has explained the price difference in Korea after July, 2002. Other explanatory variable for the price difference was the growth rate of crude oil price in the previous period. The empirical results suggest that the price difference in bunkering market and export market might be explained by the price discrimination which is adopted as a competitive strategy by oil companies in competing with Singapore.

• Journal of Navigation and Port Research
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• v.43 no.2
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• pp.134-141
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• 2019

The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.

• Journal of Korea Port Economic Association
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• v.38 no.3
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• pp.15-28
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• 2022

Bunkering refers to the supply of bunker fuel necessary for the ship operation, as well as minimizing the price and supply cost of fuel itself, and includes supplying good quality fuel oil in a timely manner and at the optimal port. Bunkering is an important criterion in terms of cost for shipping companies because bunkering involves a significant cost to the purchaser of bunkering from the time of initial purchase. This study aims to prioritize selection criteria for tramper companies to call port for bunkering. For this study, the variables were selected by analyzing the common criteria such as price, location, bunker quality and service and infrastructure etc. employed in previous studies. The AHP method was employed to prioritize the criteria in order. As a result of the analysis, the high level factors appeared in the order of price, location, bunkering quality and port service and infrastructure factors. The importance of price criterion and location criterion was found to be high. In the low level criterion of price, the bunker price per MT was ranked first in importance. In terms of location criteria, the location on the main trade route was high. In the low criteria of bunker quality and port service, the bunkering available types and bunker quality were found to be important factors, and in the low level criteria of infrastructure, anchorage and availability of bunkering during loading and discharging and port security factors were found to be important criteria. This study provides the guidelines for research designed to compare the bunkering port selection factors and to derive their importance suggesting the ways to enhance competitiveness as a bunkering port.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.73-89
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• 2021

The handysize bulk carriers are capable of transporting a variety of cargo that cannot be transported by mid-large size ship, and the spot chartering market is active, and it is a market that is independent of mid-large size market, and is more risky due to market conditions and charterage variability. In this study, Granger causality test, the Impulse Response Function(IRF) and Forecast Error Variance Decomposition(FEVD) were performed using monthly time series data. As a result of Granger causality test, coal price for coke making, Japan steel plate commodity price, hot rolled steel sheet price, fleet volume and bunker price have causality to Baltic Handysize Index(BHSI) and charterage. After confirming the appropriate lag and stability of the Vector Autoregressive model(VAR), IRF and FEVD were analyzed. As a result of IRF, the three variables of coal price for coke making, hot rolled steel sheet price and bunker price were found to have significant at both upper and lower limit of the confidence interval. Among them, the impulse of hot rolled steel sheet price was found to have the most significant effect. As a result of FEVD, the explanatory power that affects BHSI and charterage is the same in the order of hot rolled steel sheet price, coal price for coke making, bunker price, Japan steel plate price, and fleet volume. It was found that it gradually increased, affecting BHSI by 30% and charterage by 26%. In order to differentiate from previous studies and to find out the effect of short term lag, analysis was performed using monthly price data of major cargoes for Handysize bulk carriers, and meaningful results were derived that can predict monthly market conditions. This study can be helpful in predicting the short term market conditions for shipping companies that operate Handysize bulk carriers and concerned parties in the handysize chartering market.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.669-675
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• 2003

This paper examines structural breaks and asymmetries of prices of four domestic petroleum products, i.e., gasoline, kerosene, diesel, and bunker-C, following the changes in the pricing policies pertaining to petroleum products in Korea from the government-controlled pricing system to the market pricing system. We use the monthly wholesale market price data for the sample period between July 1988 and December 2001. Using the four methods: the Chow test, the CUSUM/CUSUMQ tests, the Bayesian approach and the Dufour test, the structural behaviors of the petroleum product prices are examined. We found that structural change occurred in all petroleum products, with the exception of Kerosene, at the point of pricing policy change from government-controlled to the spot-price related pricing system. We, also conducted asymmetric analysis using the Borenstein, Cameron, and Gilbert (1997)'s model and found evidences of price asymmetry for all four product types, but in different pattern for each product.