• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.41-57
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• 2023

The drybulk shipping market has high freight rate volatility in the chartering market and various and complex factors affecting the market. In the unstable economic situation caused by the COVID-19 pandemic in 2020, the BDI plunged due to a decrease in trade volume, but turned from the end of 2020 and maintained a booming period until the end of 2022. The main reason for the market change is the decrease in the available fleet that can actually be operated for cargo transport due to port congestion by the COVID-19 pandemic, regardless of the fleet and trade volume volatility that have affected the drybulk shipping market in the past. A decrease in the actual usable fleet due to vessel waiting at port by congestion led to freight increase, and the freight increase in charting market led to an increase in second-hand ship and new-building ship price in long-term equilibrium relationship. In the past, the drybulk shipping market was determined by the volatility of fleet and trade volume. but, in the future, available fleet volume volatility by pandemics, environmental regulations and climate will be the important factors affecting BDI. To response to the IMO carbon emission reduction in 2023, it is expected that ship speed will be slowed down and more ships are expected to be needed to transport the same trade volume. This slowdown is expected to have an impact on drybulk shipping market, such as a increase in freight and second-hand ship and new-building ship price due to a decrease in available fleet volume.

• Journal of Navigation and Port Research
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• v.48 no.1
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• pp.62-77
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• 2024

In the maritime shipping industry, imbalance between supply and demand has persistently increased, leading to the utilization of blank sailings by major shipping companies worldwide as a key means of flexibly adjusting vessel capacity in response to shipping market conditions. Traditionally, blank sailings have been frequently implemented around the Chinese New Year period. However, due to unique circumstances such as the global pandemic starting in 2020 and trade tensions between the United States and China, shipping companies have recently conducted larger-scale blank sailings compared to the past. As blank sailings directly impact freight transport delays, they can have negative repercussions from perspectives of both businesses and consumers. Therefore, this study employed Poisson regression models and negative binomial regression models to analyze the influence of maritime freight rate determinants on shipping companies' decisions regarding blank sailings, aiming to proactively address potential consequences. Results of the analysis indicated that, in Poisson regression analysis for 2M, significant variables included global container shipping volume, container vessel capacity, container ship scrapping volume, container ship newbuilding index, and OECD inflation. In negative binomial regression analysis, ocean alliance showed significance with global container shipping volume and container ship order volume, the alliance with container ship capacity and interest rates, non-alliance with international oil prices, global supply chain pressure index, container ship capacity, OECD inflation, and total alliance with container ship capacity and interest rates.

• Korea Trade Review
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• v.43 no.6
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• pp.51-70
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• 2018

This study is to define the relationship between capital structure and the market share in the global shipping market, estimating the debt-equity ratio. To analyze the impact of the debt-equity ratio on market share, this study collected data from the 100 largest shipping companies from 2010 to 2017. Results identified that global shipping lines moderate their debt-equity rates to 62%, and all of them strategically utilize debt in order to increase market share in global shipping market. In comparison between the group focused on cargo volume and another group focused on freight rates, it is found that the group focused on cargo volume increase their handling cargo volume through increasing the debt rates. Another group used debt rate for reducing the freight rate and enhancing market power. Furthermore, after classifying the samples into high-growth and low-growth companies, this study compared the group focused on cargo volume and another group focused on freight rates. As a result, the low-growth group showed more significant impacts of the debt rate on market share than the high-growth group. The results of this study provide useful insight for future strategic decision making of shipping lines in the global shipping market.

• Asia-Pacific Journal of Business
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• v.9 no.3
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• pp.101-115
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• 2018

The purpose of this study is to examine the growth status and future prospects of the shipping industry over the past decade through a review of the statistical database and related literature. We classify the shipping industry and survey a number of companies, number of employees, sales trends and shipping logistics market outlook, port cargo volume, and container throughput trends with regard to the sea cargo shipping business, freezing cold warehousing business, harbor cargo unloading business, and international freight forwarding business. We will also look at the overall trends, scale, cargo volume, and harbor automation status of the global shipping market.

• Korea Trade Review
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• v.48 no.3
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• pp.69-88
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• 2023

This study analyzes the dynamic characteristics of cargo volume (demand), ship fleet (supply), and freight rate (price) of container, dry bulk, and tanker shipping markets by using the VAR and VECM models. This analysis is expected to enhance the statistical understanding of market dynamics, which is perceived by the actual experiences of market participants. The common statistical patterns, which are all shown in the three shipping markets, are as follows: 1) The Granger-causality test reveals that the past increase of fleet variable induces the present decrease of freight rate variable. 2) The impulse-response analysis shows that cargo shock increases the freight rate but fleet shock decreases the freight rate. 3) Among the three cargo, fleet, and freight rate shocks, the freight rate shock is overwhelmingly largest. 4) The comparison of adjR² reveals that the fleet variable is most explained by the endogenous variables, i.e., cargo, fleet, and freight rate in each of shipping markets. 5) The estimation of co-integrating vectors shows that the increase of cargo increases the freight rate but the increase of fleet decreases the freight rate. 6) The estimation of adjustment speed demonstrates that the past-period positive deviation from the long-run equilibrium freight rate induces the decrease of present freight rate.

• Journal of Korea Port Economic Association
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• v.36 no.4
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• pp.17-30
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• 2020

In the shipping industry, it is essential to engage in the preemptive prediction of freight rate volatility through market monitoring. Considering that freight rates have already started to fall, the loss of shipping companies will soon be uncontrollable. Therefore, in this study, factors affecting the freight rates of bulk carriers, which have relatively large freight rate volatility as compared to container freight rates, were quantified and analyzed. In doing so, we intended to contribute to future shipping market monitoring. We performed an analysis using a vector error correction model and estimated the influence of six independent variables on the charter rates of bulk carriers by Handy Size, Supramax, Panamax, and Cape Size. The six independent variables included the bulk carrier fleet volume, iron ore traffic volume, ribo interest rate, bunker oil price, and Euro-Dollar exchange rate. The dependent variables were handy size (32,000 DWT) spot charter rates, Supramax 6 T/C average charter rates, Pana Max (75,000 DWT) spot charter, and Cape Size (170,000 DWT) spot charter. The study examined charter rates by size of bulk carriers, which was different from studies on existing specific types of ships or fares in oil tankers and chemical carriers other than bulk carriers. Findings revealed that influencing factors differed for each ship size. The Libo interest rate had a significant effect on all four ship types, and the iron ore traffic volume had a significant effect on three ship types. The Ribo rate showed a negative (-) relationship with Handy Size, Supramax, Panamax, and Cape Size. Iron ore traffic influenced three types of linearity, except for Panamax. The size of shipping companies differed depending on their characteristics. These findings are expected to contribute to the establishment of a management strategy for shipping companies by analyzing the factors influencing changes in the freight rates of charterers, which have a profound effect on the management performance of shipping companies.

• Journal of Korea Port Economic Association
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• v.35 no.3
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• pp.61-72
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• 2019

In order to enhance the competitiveness of the container shipping industry and promote its development, based on the empirical analyses using multivariate time series models, this study aims to suggest a few strategies related to the dynamics of the container shipping market. It uses the vector autoregressive (VAR) and vector error correction (VEC) models as analytical methodologies. Additionally, it uses the annual trade volumes, fleets, and freight rates as the dataset. According to the empirical results, we can infer that the most exogenous variable, the trade volume, exerted the highest influence on the total dynamics of the container shipping market. Based on these empirical results, this study suggests some implications for ship investment, freight rate forecasting, and the strategies of shipping firms. Concerning ship investment, since the exogenous trade volume variable contributes most to the uncertainty of freight rates, corporate finance can be considered more appropriate for container ship investment than project finance. Concerning the freight rate forecasting, the VAR and VEC models use the past information and the cointegrating regression model assumes future information, and hence the former models are found better than the latter model. Finally, concerning the strategies of shipping firms, this study recommends the use of cycle-linked repayment scheme and services contract.

• 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.

• 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.40 no.5
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• pp.345-351
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• 2016

This study presents a system dynamics methodology to evaluate quantitatively the effect of the Korean government's development policy, such as tax reductions, on the industrial economy. System dynamics is often perceived as an optimized means to identify the dynamic inter-relationships among various factors of development policies, and in particular the industrial characteristics and uncertainties of the coastal shipping industry. The results of simulations used in this study shows that the impact of development policies such as tax reductions would increase shipping demand for about 4 years, and that tax incentives could raise the demand volume for cabotage cargo from 5.26 to 11.11%, through the available freight-down by 90~95% points. The system dynamics approach used in this paper represents an initial attempt to use this methodology in studies of the coastal shipping industry. On the basis of our simulations, the industrial effects of other development policies, such as ship financing support, investment of social overhead, or crew supply, could also be analyzed effectively. Additionally, it should be possible to extend these results by developing a comprehensive model encompassing these various analyses.