• Korea Trade Review
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• v.47 no.2
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• pp.133-154
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• 2022

The Baltic Capesize Index (BCI), which is used as an indicator for marine transportation of steel raw materials, is one of the key economic indexes for managing the risk of loss due to rapid market fluctuations when steel companies establish business strategies and procuring plans for raw materials. Still, the conditions of supply and demand of steel raw materials has been extremely affected by volatility shocks from drastic events like the financial crisis such as the Lehman Brothers incident and changes in the external environment such as COVID-19. And, especially since the 2008 financial crisis, endeavors to predict the market conditions of the steel raw material is becoming more and more arduous for the deepening uncertainty and increased volatility of BCI, which has been used as a leading indicator of the real economy. This study investigates the correlation between the steel raw material market and the marine transportation market by estimating the spillover effect of information between markets. The vector error correction model (VECM) was used to analyze information transfer based on the correlation between the BCI and crude steel production, capesize fleet supply, raw material price, and cargo volume.

• Journal of Korea Port Economic Association
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• v.29 no.1
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• pp.67-76
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• 2013

It has been more than four years since the outbreak of global financial crisis. However, the world economy continues to be challenged with new crisis such as the European debt crisis and the fiscal cliff issue of the U.S. The global economic environment remains fragile and prone to further disappointment, although the balance of risks is now less skewed to the downside than it has been in recent years. It's no wonder that maritime business will be bearish since the global business affects the maritime business directly as well as indirectly. This paper, hence, aims to predict the Baltic Dry Index representing the shipping business using the ARIMA-type models and Hodrick-Prescott filtering technique. The monthly data cover the period January 2000 through January 2013. The out-of-sample forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. These forecasting performances are also compared with those of the random walk model. This study shows that the ARIMA models including Intervention-ARIMA have lower rmse than random walk model. This means that it's appropriate to forecast BDI using the ARIMA models. This paper predicts that the shipping market will be more bearish in 2013 than the year 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

• 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 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.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.157-165
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• 2016

The purpose of this study was to analyze the effect of the shipping industry real economy index on the stock prices of domestic shipping companies. The parameters used in this analysis were the stock price of H Company in South Korea and shipping industry real economy indices including BDI, CCFI and HRCI. The period analysis was from 2012 to 2015. The weekly data for four years of the stock price index of shipping companies, BDI, CCFI, and HRCI were used. The effects of CCFI and HRCI on the stock price index of domestic shipping companies were analyzed using the VAR model, and the effects of BDI on the stock price index of domestic shipping companies were analyzed using the VECM model. The VAR model analysis results showed that CCFI and HRCI had negative effects on the stock price index, and the VECM model analysis results showed that BDI also had a negative effect on the stock price index.

• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.167-173
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• 2023

The manufacturing industry is the backbone of the Korean economy. Among them, the petrochemical industry is a strategic growth industry, which makes a profit through reexports based on eminent technology in South Korea which imports all of its crude oil. South Korea imports whole amount of crude oil, which is the raw material for many manufacturing industries, by sea transportation. Therefore, it must respond swiftly to a highly volatile tanker freight market. This study aimed to make an early warning model of crude oil shipping market using a signal approach. The crisis of crude oil shipping market is defined by BDTI. The overall leading index is made of 38 factors from macro economy, financial data, and shipping market data. Only leading correlation factors were chosen to be used for the overall leading index. The overall leading index had the highest correlation coefficient factor of 0.499 two months ago. It showed a significant correlation coefficient five months ago. The QPS value was 0.13, which was found to have high accuracy for crisis prediction. Furthermore, unlike other previous time series forecasting model studies, this study quantitatively approached the time lag between economic crisis and the crisis of the tanker ship market, providing workers and policy makers in the shipping industry with an framework for strategies that could effectively deal with the crisis.

• Journal of Korea Port Economic Association
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• v.30 no.3
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• pp.1-14
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• 2014

This paper aims at measuring how new information is incorporated into volatility estimates. Various GARCH models are compared and estimated with daily BDI(Baltic Dry Index) data. While most researchers agree that volatility is predictable, they differ on how this volatility predictability should be modelled. This study, hence, introduces the asymmetric or leverage volatility models, in which good news and bad news have different predictability for future. We provide the systematic comparison of volatility models focusing on the asymmetric effect of news on volatility. Specifically, three diagnostic tests are provided: the sign bias test, the negative size bias test, and the positive size bias test. From the Ljung-Box test statistic for twelfth-order serial correlation for the level we do not find any significant serial correlation in the unpredictable BDI. The coefficients of skewness and kurtosis both indicate that the unpredictable BDI has a distribution which is skewed to the left and significantly flat tailed. Furthermore, the Ljung-Box test statistic for twelfth-order serial correlations in the squares strongly suggests the presence of time-varying volatility. The sign bias test, the negative size bias test, and the positive size bias test strongly indicate that large positive(negative) BDI shocks cause more volatility than small ones. This paper, also, shows that three leverage models have problems in capturing the correct impact of news on volatility and that negative shocks do not cause higher volatility than positive shocks. Specifically, the GARCH model successfully reveals the shape of the news impact curve and is a useful approach to modeling conditional heteroscedasticity of daily BDI.

• 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 Life Science
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• v.17 no.2 s.82
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• pp.248-253
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• 2007

This research investigated whether exposure of diesel exhaust particulate (DEP) and particulate matter (PM) effects on airway remodeling in asthma induced Balb/c and IL-10 knock out (KO) mouse. Mice were sensitized with intraperitoneal injection with ovalbumin, followed by challenges with intranasal ovalbumin. After that mice placed in inhalation chamber and exposed to DEP and $PM(10\;mg/m^3)$. The evidence of airway remodeling was assessed by masson's trichrome staining and PAS staining. The stainability of masson's trichrome and PAS reaction were increased in asthma-induced Baltic mice groups compared with control mice groups. More intensive stainability for masson's trichrome and PAS were appeared in the asthma-induced DEP and PM-exposed groups than asthama-induced groups. But, not significantly increased subepithelial fibrosis and the nember of goblet cell hyperplasia in asthma-induced IL-10 KO mice groups and asthma-induced+DEP and PM-exposed IL-10 KO mice than IL-10 KO mice groups. These results indirectly suggesting that exposure to DEP and PM in asthmatic patients might be aggravate clinical symptoms and IL-10 which seems to play a central role in allergic asthma. In conclusion, DEP and PM exposure might have additive effects on the ovalbumin- induced asthma in a murine model.

• 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.