• 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 Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Journal of Korea Port Economic Association
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• v.27 no.2
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• pp.217-240
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• 2011

In this study, vector autoregressive and vector error correction models in the short-run dynamics are considered to analyze the effect of the changes in international crude oil prices on Baltic dry index, Baltic Capesize index and Baltic Panamax index, and the intercorrelations between Capesize and Panamax prices, respectively. First, using the vector autoregressive model, the changes in international crude oil price have a statistically significant positive effect for Capesize at lag 1, for Panamax a significant negative effect at lag 3 and a significant positive effect for Baltic dry index at lag 1. From the impulse response analysis, the international crude oil price causes Baltic dry index to increase in the sort-run and the effect converges on the mean after 3 months. Second, using the vector error correction model, the empirical results for the spillover effects between Capesize and Panamax markets provide that in the case of the deviation from a long-run equilibrium the Panamax price is adjusted toward decreasing. The increases in freight rates of the Capesize market at lag 1 lead to increase the freight rates in Panamax market at present. The Panamax responses from the Capesize shocks increase rapidly for 3 months and the effect converges on the mean after 5 months. The Capesize responses from the Panamax shocks are relatively small, and increase weakly for 3 months and the effect disappears thereafter.

• Korea Trade Review
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• v.44 no.2
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• pp.127-141
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• 2019

In this study, the relationship between Baltic Dry Index(BDI) and maritime trade volume in the dry cargo market was verified using the vector autoregressive (VAR) model. Data was analyzed from 1992 to 2018 for iron ore, steam coal, coking coal, grain, and minor bulks of maritime trade volume and BDI. Granger causality analysis showed that the BDI affects the trade volume of coking coal and minor bulks but the trade volume of iron ore, steam coal and grain do not correlate with the BDI freight index. Impulse response analysis showed that the shock of BDI had the greatest impact on coking coal at the two years lag and the impact was negligible at the ten years lag. In addition, the shock of BDI on minor cargoes was strongest at the three years lag, and were negligible at the ten years lag. This study examined the relationship between maritime trade volume and BDI in the dry bulk shipping market in which uncertainty is high. As a result of this study, there is an economic aspect of sustainability that has helped the risk management of shipping companies. In addition, it is significant from an academic point of view that the long-term relationship between the two time series was analyzed through the causality test between variables. However, it is necessary to develop a forecasting model that will help decision makers in maritime markets using more sophisticated methods such as the Bayesian VAR model.

• Journal of Navigation and Port Research
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• v.41 no.1
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• pp.25-32
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• 2017

The purpose of this investigation is to analyze the synchronization between the representative global freight index, the Baltic Dry bulk Index (BDI) and the China Container Freight Index (CCFI) with monthly data from 2000 to 2016. Using the non-stationarity of the business cycle that is able to include common trends, we employ the Engle-Granger 2 stage co-integration test and found no synchronization. On the contrary, we additionally estimated the causality between the markets and revealed the causality, which implies that the Chinese economy has a significant effect on the global market. The results of this empirical analysis demonstrate that the CCFI of China is appropriate for analyzing the shipping industry. In practice, this means that it is more appropriate to include CCFI in the global market outlook than use it as a substitute for the global freight rate index, the BDI. This is a case study of the synchronization of the economic fluctuations of the shipping industry. It suggests that the economic fluctuations of China need to be considered in the unstable global market forecast. In particular, this case applies to the fluctuations in the shipping industry synchronism and provides important results in scientific terms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.329-330
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• 2021

벌크화물운송은 해상운송시장에서 가장 큰 규모이고 철강 및 에너지 산업을 뒷받침 하는 중요한 시장이다. 또한 운임의 변동성이 가장 큰 시장으로 상당한 수익을 기대할 수 있는 반면에 파산에 이르는 큰 손실이 발생할 수 있기때문에 시장 참여자들은 합리적이고 과학적인 예측을 기반하여 의사결정을 해야 한다. 그러나 해운시장에서는 과학적 의사결정보다는 경험기반의 의사결정에 의존하기 때문에 시황변동성에 취약하다. 본 논문은 벌크운임예측에 신호 분해 방법인 EMD와 인공신경망을 결합한 하이브리드 모델을 적용하여 과학적 예측방법을 제시하고자 한다. 본 논문은 학문적으로 해운시장 운임예측연구에서 거의 시도되지 않았던 시계열분해법과 기계학습기법을 결합한 하이브리드 모델을 제시하였다는데 의미가 있으며 실무적으로는 해운시장에서 빈번이 일어나는 의사결정의 질이 제고되는데 기여할 것으로 기대된다.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.181-190
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• 2018

The goal of this study is empirically to investigate the asymmetric relationship between two variables using the dry cargo freight rates and raw material price data from January 2012 to May 2018. First, we estimate the asymmetry of macroeconomic indicators of commodity prices by using a two - step threshold cointegration test. Second, the asymmetric relation test of the trade balance of existing commodity price changes is tested by bypassing to the high frequency dry cargo freight rate index. As a result of the estimation, in contrast to the existing linear analysis, each boundary value for the lower limit and the upper limit has different asymmetry. This implies that the period of fluctuation of the sudden residual that causes irregular rate of return fluctuations does not establish a long term equilibrium relationship between the raw material price and the dry cargo freight rate. Therefore, in order to consider the sudden price change in the analysis, it is necessary to include the band of inaction that controls the irregular volatility, which is consistent with the asymmetry hypothesis.

• Korea Trade Review
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• v.43 no.3
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• pp.125-145
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• 2018

The Baltic Shipping Exchange is reporting the Baltic Dry Index (BDI) which represents the average charter rate for bulk carriers transporting major cargoes such as iron ore, coal, grain, and so on. And the current BDI index is reflected in the proportion of capesize 40%, panamax 30% and spramax 30%. Like mentioned above, the capesize plays a major role among the various sizes of bulk carriers and this study is to analyze the influence of the factors influencing on charter rate of capesize carriers which transport iron ore and coal as the major cargoes. For this purpose, this study verified causality between variables using Vector Error Correction Model (VECM) and tried to derive a long-run equilibrium model between the dependent variable and independent variables. Regression analysis showed that every six independent variable has a significant effect on the capesize charter rate, even at the 1% level of significance. Charter rate decreases by 0.08% when capesize total fleet increases by 1%, charter rate increases by 0.04% when bunker oil price increases by 1%, and charter rate decreases by 0.01% when Yen/Dollar rate increases by 1%. And charter rate increases by 0.02% when global GDP increases by one unit (1%). In addition, the increase in cargo volume of iron ore and coal which are major transportation items of capesize carriers has also been shown to increase charter rates. Charter rate increases by 0.11% in case of 1% increase in iron ore cargo volume, and 0.09% in case of 1% increase in coal cargo volume. Although there have been some studies to analyze the influence of factors affecting the charterage of bulk carriers in the past, there have been few studies on the analysis of specific size vessels. At present moment when ship size is getting bigger, this study carried out research on capesize vessels, which are biggest among bulk carriers, and whose utilization is continuously increasing. This study is also expected to contribute to the establishment of trade policies for specific cargoes such as iron ore and coal.

• Korea Trade Review
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• v.48 no.2
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• pp.27-43
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• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• Journal of Navigation and Port Research
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• v.42 no.1
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• pp.57-66
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• 2018

Maritime industry is affected by outside factors significantly due to its derivative demand characteristics. However, the supply side can not react to these changes immediately and due to this uniqueness, maritime industry repeats the boom-bust cycle. Therefore the government itself needs to operate early warning system in order to monitor the market and notice the upcoming risks by setting up a system to prepare for the situations. In this research, signal approach is used to establish early warning system. Overall leading index is composed of crisis index that is based on BDI(Baltic Dry Index) and various leading indexes such as finance, economy, shipping and the others. As a result of computing overall leading index which is early warning system in maritime through signal approach, the index showed a high correlation coefficient with actual maritime risk index by difference of 4 months. Also, the result was highly accurate with overall leading index's QPS(Quadratic Probability Score) at 0.37.