• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.63-65
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• 2023

한국 경제에 근간이 되는 산업은 제조업이고, 그중 석유화학산업은 전량 원유를 수입하여 우리나라의 기술력으로 가공하여 재수출하는 전략적 성장 산업이다. 수많은 제조업의 원료가 되는 원유를 전량 해상운송을 통해 수입하는 우리나라는 변동성이 심한 유조선 운임 시장에 대해 기민하게 대응해야 한다. 유조선 운임 시장의 위기는 관련 해운회사의 위기에서 끝나지 않고 원유를 사용하는 산업에서부터 국민의 생활까지 영향을 미칠 수 있으므로, 본 연구에서 신호접근법을 활용한 조기경보모형을 제시했다. BDTI 운임지수를 활용하여 유조선 해운시장 위기를 정의하고, 38개의 거시경제, 금융, 원자재 지표 그리고 해운시장 데이터를 활용해 시차상관관계를 분석하여 유조선 해운시장 위기에 선행적으로 반응하는 종합선행지수를 도출했다. 연구 결과, 종합선행지수는 두 달 전 가장 높은 0.499의 시차상관계수 값을 가졌으며, 5개월 전부터 유의미한 상관계수 값을 나타냈다. 더불어 QPS 값은 0.13으로 위기 예측에 대해 높은 정확성을 지니는 것으로 검증됐다.

• 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.38 no.2
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• pp.49-67
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• 2022

This study applied the method of Diebold and Yilmaz (2012, 2014, 2016) to analyze the connectedness between the Freight Index (BDI, BDTI, BCTI), energy price(oil, natural gas, coal), and grain price(soybean, corn, wheat) from July 19, 2007 to March 31, 2022. The main analysis results of this paper are as follows. First, according to the network analysis results, the total connectedness was measured to be 20.43% for the entire analysis period, indicating that there was a low correlation between the freight index and the commodity price. In addition, looking at the directional results, the variable with the greatest effects was corn, and conversely, the variable with the lowest effects BDI. When classified by events, BCTI was found to play a major role only during the COVID-19 period. Second, according to the results of the rolling-sample analysis, the total connectedness be found to be highly correlated with changes in economic conditions such as the financial crisis, trade war, and COVID-19 when specific events occurred.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.223-242
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• 2023

This study analyzes the spillover effects of returns and volatility between the commodity market and the maritime freight market across various frequency domains (short-term, medium-term, long-term). The key findings of the study can be summarized as follows. First, from the perspective of returns, a high linkage is observed in the short-term between the commodity and maritime freight markets, with the metal commodities market playing a particularly significant role in information transmission effect of return series. Second, in terms of volatility, the total connectedness increases from the short- to the long-term, with substantial long-term risk transmission effects observed especially in the BDI, BDTI, agricultural, and energy commodity markets. Notably, during major global events such as the U.S.-China trade war, COVID-19, and the Russia-Ukraine conflicts, a marked increase in the risk transmission effect in the energy commodities market was identified.

• 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.