• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.47-53
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• 2019

The purpose of the study was to utilize VECM(Vector Error Correction Model) and detect causal relationships among shipping freight rates. Shipping freight rates used in this study were BDI(Baltic Dry Index), HRCI(Howe Robinson Containership Index), WS(World Scale rate) and SCFI(Shanghai Containerized Freight Index). Using weekly data published since August 2nd, 2013 to September 6th, 2019, it was discovered that BDI and WS were heavily influenced by past week's BDI and WS respectively. VECM also found that one percent increase in WS resulted in 0.022% increase in following week's HRCI data. One percent increase in HRCI affects SCFI by 0.77% on the following week. This study believes that finding may help each shipping market of shipping freight rates estimates, thereby encouraging decision markers to exercise discretion and establish best interest decision.

• 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.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.127-132
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• 2021

This paper applies a machine learning model to forecasting freight rates in dry bulk and tanker markets with wavelet decomposition and empirical mode decomposition because they can refect both information scattered in the time and frequency domain. The decomposition with wavelet is outperformed for the dry bulk market, and EMD is the more proper model in the tanker market. This result provides market players with a practical short-term forecasting method. This study contributes to expanding a variety of predictive methodologies for one of the highly volatile markets. Furthermore, the proposed model is expected to improve the quality of decision-making in spot freight trading, which is the most frequent transaction in the shipping industry.

• Journal of Korea Port Economic Association
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• v.36 no.4
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• pp.93-106
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• 2020

Trade is an important economic activity. In particular, since the establishment of the World Trade Organization (WTO), the scope of trade has been expanding due to events such as the entry of China into the WTO in 2001, the establishment of a multilateral trading system, mitigation and integration of trade barriers, and the establishment of the free trade agreement (FTA). Despite the expansion of the trade market, however, extreme events such as the 2008 global financial crisis, the 2016 Brexit, and the 2018 US-China trade war have had a direct negative impact on the trade market. Therefore, the present this study analyzed the dependence structure between the international shipping freight rate index, a variable representing trade activities, and the trade uncertainty between the US and China. The following is a summary of the analysis results. First, the US-Chinese trade policy uncertainty and international shipping freight rate index presented a Frank copula and rotated Clayton copula 270° distribution, respectively, showing the same distribution structure for each country. Second, the Kendall's tau correlation revealed a negative dependence between the international shipping freight rate index and US-Chinese trade policy uncertainty. The degree of dependence was greater in the combination of uncertainty in China's trade policy and international shipping freight rates. In other words, the dependence of global demand and trade policy uncertainty confirmed that China was stronger than the US. Finally, the tail dependence results revealed that the US-Chinese trade policy uncertainty and international shipping freight rates were independent of each other. This means that extreme events related to the trade policy uncertainty or international shipping rate index were not affected by each other.

• Journal of Korea Port Economic Association
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• v.35 no.4
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• pp.107-120
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• 2019

Crude oil is a resource that is being used as a raw material in major industries, representing the price of the raw material market. It is also an important element that affects the shipping market in terms of fuel costs for freight vessels. As a result, crude oil and freight rates are closely related. Therefore, from January 2009 to June 2019, this study analyzed the dependency structure between oil price (WTI) and freight rates (BDI, BCI, BPI, BSI, and BHI) using daily data. The main results are summarized as follows. First, according to the copula results, survival Gumbel copula in WTI-BDI, Clayton copula in WTI-BCI, Survival Joe copula in WTI-BPI, Joe copula in WTI-BSI, and survival Gumbel copula in WTI-BHI were selected as the best-fitted model. Second, looking at Kendall's tau correlation, there is a positive correlation between BDI and oil price. Furthermore, freight rate index (BCI, BPI, BSI) and oil price show positive dependencies. In particular, the strongest dependence was found in BCI and oil price returns. However, BHI and oil price show a negative dependency. Third, looking at the tail-dependency structure, a pair between oil price and BDI, BCI showed a lower tail-dependency. The pair between oil price and BSI showed the upper tail-dependency.

• Journal of Korea Port Economic Association
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• v.31 no.2
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• pp.23-40
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• 2015

This study measures how Korean shipping policies influence the expansion of the country's merchant fleet using system dynamics. It uses various indexes as factors influencing the gross tonnage of the Korean merchant fleet, such as the Baltic Dry Index, Howe Robinson Container Index, China Containerized Freight Index, and Worldscale Index, as well as the US dollar-Korean won exchange rate, world merchant fleet statistics, and the debt ratio of Korean shipping companies. After establishing the simulation model, the mean absolute percentage error is found to be less than 10%, confirming the accuracy of the model. Therefore, a sensitivity analysis is conducted to measure the influence of the selected shipping policies, including the gross tonnage of vessels registered under the Korean second registry system, loans of publicly owned financial institutions to shipping companies, ship investment fund, and the number of shipping companies participating in the tonnage tax scheme. The sensitivity analysis reveals that the influence of vessel tonnage and loans to shipping companies is the most significant, while that of the number of companies participating in the tonnage tax scheme is minimal.

• 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.