• Korea Trade Review
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• v.48 no.1
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• pp.127-163
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• 2023

The objective of this study is to investigate the volatility spillover effects among BDI, CCFI and SCFI. This paper will divide the empirical analysis section into two periods to analyze and compare the differences in volatility spillover effect between shipping freight indices before and after the outbreak of COVID-19 separately. First, in order to compare the mean spillover impact and index lead-lag correlations in BDI and CCFI indices, along with BDI and SCFI indices before and after COVID-19, the co-integration analysis and the test of Granger causality built on the VAR model were utilized. Second, the impulse response and variance decomposition are employed in this work to investigate how the shipping freight index responds to shocks experienced by itself and other freight indices in a short period. Before the COVID-19 epidemic, the results demonstrated that the BDI freight index is the Granger cause of the variable CCFI freight index. But the BDI and CCFI freight indices have no apparent lead-lag relationships after COVID-19, and this empirical result echoes the cointegration test result. After the COVID-19 epidemic, the SCFI index leads the BDI index. This study employs the VAR-BEKK-GARCH joint model to explore the volatility spillover results between dry bulk and container transport markets before and after COVID-19. The empirical results demonstrate that after COVID-19, fluctuations in the BDI index still affect the CCFI index in the maritime market. However, there is no proof of a volatility spillover relationship between the BDI and SCFI after the COVID-19 epidemic. This study will provide an insight into the volatility relationship among BDI, CCFI and SCFI before and after the the COVID-19 epidemic occurred.

• Korea Trade Review
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• v.47 no.1
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• pp.13-24
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• 2022

This study was conducted because the global economic downturn caused by COVID-19 caused a surge in logistics costs and it was no longer possible to predict logistics costs using existing methods. For this study, we made the assumption that economic indicators affect logistics cost. Chapter 2 examines the current status of the liner market and factors affecting logistics costs. Based on this, Chapter 3 collects independent and dependent variables to determine the analysis model. As the independent variable, economic indicators of major countries constituting the SCFI were selected, and the dependent variables were the SCFI Europe Index and the SCFI USA Index. In Chapter 4, a panel analysis was conducted based on this, and it was confirmed that major economic indicators had a negative (-) effect on SCFI. This is contrary to the existing research results, which can be attributed to the special situation caused by COVID-19 and the imbalance of demand and supply by region. The results of this study are meaningful in that they can predict long-term logistics cost volatility without analyzing supply and demand, and can be applied to other studies as well.

• East Asian Economic Review
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• v.28 no.3
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• pp.359-388
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• 2024

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry's health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study's findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.5
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• pp.1159-1168
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• 2012

Control Flow Integrity(CFI) and Control Flow Locking(CFL) prevent unintended execution of software and provide integrity in control flow. Attackers, however, can still hijack program controls since CFI and CFL does not support fine-granularity, context-sensitive protection. In this paper, we propose a new CFI scheme, Source-code CFI(SCFI), to overcome the problems. SCFI provides context-sensitive locking for control flow. Thus, the proposed approach protects software against the attacks on the previous CFI and CFL schemes and improves safety.

• 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 Digital Convergence
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• v.17 no.4
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• pp.65-73
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• 2019

This study performed Panel Regression Analysis (PRA) with the debt ratio as a dependent variable and the ROE (return on equity), sales volume, current ratio, total capital, and Shanghai Containerized Freight Index (SCFI) as an independent variable. According to the GLS analysis, the current ratio to liabilities ratio was negative, and for sales, the ratio of liabilities was positive. Capital totals also had a negative impact on the debt ratio. However, ROE, unlike the hypothesis, had negative effects on the liability ratio, and the SCFI index was not significant. As implications of this research, the company confirmed that its sales increased as the debt ratio of global shipping companies rose, achieving economies of scale. However, it was confirmed that the actual size of the economy through the injection of other capital would help increase sales but not affect net profit. Shipping companies should expand their business power and secure large container vessels to secure credibility of shippers. In the future research, an analysis considering exchange rate, global economic growth rate, and manufacturing production index is needed.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.25-39
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• 2023

As the scope of supply chains expands globally, unpredictable risks continue to arise. The occurrence of these supply chain risks affects port cargo throughput and hinders port operation. In order to examine the impact of global supply chain risks on port container throughput, this study conducted an empirical analysis on the impact of variables such as the Global Supply Chain Pressure Index (GSCPI), Shanghai Container Freight Index (SCFI), Industrial Production Index, and Retail Sales Index on port traffic using the vector autoregressive(VAR) model. As a result of the analysis, the rise in GSCPI causes a short-term decrease in the throughput of Busan Port, but after a certain point, it acts as a factor increasing the throughput and affects it in the form of a wave. In addition, the industrial production index and the retail sales index were found to have no statistically significant effect on the throughput of Busan Port. In the case of SCFI, the effect was almost similar to that of GSCPI. The results of this study reveal how risks affect port cargo throughput in a situation where supply chain risks are gradually increasing, providing many implications for establishing port operation policies for future supply chain risks.