This study aims to investigate the lead-lag relationship between the agricultural produce import price in Korea and the corresponding shipping freight rate. Since the Korean economy has pursued an export-driven growth strategy, mainly based on the manufacturing sector, the country has to depend on the vast majority of its agricultural produce consumption after import from foreign countries. Moreover, compared with other high-value products, transportation cost occupies a substantial share of the agricultural commodity price, resulting in changes in the shipping freight rate being a pivotal determinant of agricultural produce import. In this respect, this study explores the possible association between agricultural produce import in Korea and shipping freight rate and the lead-lag relationship. Using a monthly dataset of agricultural produce import prices and freight rates for Handysize and Panamax dry-bulkers for the period between January 2010 and November 2020, this study determines that the shipping freight rate, in general, leads the agricultural commodity price.
The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.
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.
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.
Using data from 2008 to 2017, this study analyzed the financial characteristics of offshore freight shipping companies in Korea, categorized by vessel type, and their impact on business performance. The analysis showed an upward trend in the overall debt ratio of offshore freight transport companies and differences in the financial characteristics of each vessel type as well as the major factors affecting business performance. In particular, the significant factors affecting the ROA performance of the business are the size of business and debt ratio in the LPG line; the growth rate in the bulk line; the size of business, debt ratio, and year in the general freight line; and the size of business, growth rate, entertainment expense rate, debt ratio, and year in the full container line. Therefore, each shipping company needs to recognize differences in key financial factors affecting its performance based on vessel type, implement proactive measures, and diversify its vessel portfolio.
This study analyzes the major factors affecting the freight rates of Very Large Crude-Oil Carriers (VLCC) using co-integration and vector error correction models (VECM). Particularly, we estimate the long-term equilibrium function that determines the VLCC freight rate by conducting difference conversion. In the VECM regression analysis, the error term converges toward long-term balance irrespective of whether the previous period's freight rate is bigger or smaller than the long-term equilibrium rate. Thus, even if the current rate is different from the long-term rate, it eventually converges to the long-term balance irrespective of a boom or recession. This study follows Ko and Ahn (2018), which analyzed the factors affecting the chemical carrier freight rate and was published in the Journal of Shipping and Logistics (Vol. 34, No. 2). It is expected that an academic comparison of the results of each study will be possible if further research is conducted on other vessel types, such as container ships and dry cargo vessels.
The core decisions of bulk shipping businesses can be summarized as the timing and the choice of period for which carrying capacity is traded. In particular, frequent decisions to trade freight either with repeated spot transactions or with a one-off long-term deal critically impact business performance. Even though a variety of freight trading strategies can be employed to facilitate the decisions, chartering practitioners have not been active in utilizing these strategies, and academic research has rarely proposed applicable solutions. The specific properties of freight as a tradable commodity are not properly reflected in existing studies, and limitations have been reported in their application to the real world. This research focused on the establishment of applicable freight trading strategies by taking into account two properties of freight: time perishability and term-dependant pricing. In addition to traditional trading strategies, artificial neural networks were applied for the first time to the test of freight trading strategies. The performances of the trading strategies were measured and compared to produce a remarkable outperformance of the ANN. This research is expected to make a significant contribution to chartering practices by enhancing the quality of chartering decisions and eventually enabling the effective management of freight rate risk. In addition to methodological expansion, the result will propose a way to approach the controversial issue of freight market efficiency.
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.
The aim of this paper is to analyze the mechanics of price formation in the tramp shipping. For the purpose of this study, the main characteristics of tramp freight rates and the market is examined, and a brief examination of the nature ofthe costs of operation is given which are essential for the understanding of the functioning of shipping firms as well as for the understanding of developments in the tramp freight market. The demand and supply relationships in the market is also analysed in detail. Tramp shipping is an industry that has a market which functions under conditions that are not dissimilar to the theoretical model of perfect competition. However, it does notmean that tramp shipping market is a perfectly competitive market. It is apparent that this realworld competitive system has its imperfections, which means that the market for tramp shipping is near to being a perfectly competitive market on an internaitonal scale and it is freight are therefore subjext to the laws of supply and demand. In theory, the minimum freight rate in the short term is that at which the lowest cost vessels will lay-up in preference to operating, and is equal to the variable costs minus lay-up costs; and this would imply that in all times except those of full employment for ships there is a tendency for newer low-cost, and, probably, faster vessels to be driving the older high-cost vessels in the breaker's yards. In this case, shipowners may be reluctant to lay-up their ships becasue of obligations to crews, or because they would lose credibility with shippers or financiers, or simply because of lost prestige. Mainly, however, the decision is made on strictly economic grounds. When, for example, the total operating costs minus the likely freight earnings are greater than the cost of taking the ship out of service, maintaining it, and recommissioning it, then a ship may be considered for laying-up; shipowners will, in other words, run the ships at freight earnings below operating costs by as much as the cost of laying them up. As described above, the freight rates fixed on the tramp shipping market are subject to the laws of supply and demand. In other words, the basic properties of supply and demand are of significance so far as price or rate fluctuations in the tramp freight market are concerned. In connection with the same of the demand for tramp shipping services, the following points should be brone in mind: (a) That the magnitude of demand for sea transport of dry cargoes in general and for tramp shipping services in particular is increasing in the long run. (b) That owning to external factors, the demand for tramp shipping services is capable of varying sharphy at a given going of time. (c) The demad for the industry's services tends to be price inelastic in the short run. On the other hand the demand for the services offered by the individual shipping firm tends as a rule to be infinitely price elastic. In the meantime, the properties of the supply of the tramp shipping facilities are that it cannot expand or contract in the short run. Also, that in the long run there is a time-lag between entrepreneurs' decision to expand their fleets and the actual time of delivery of the new vessels. Thus, supply is inelastic and not capable of responding to demand and price changes at a given period of time. In conclusion, it can be safely stated that short-run changes in freight rates are a direct result of variations in the magnitude of demand for tramp shipping facilities, whilest the average level of freight rates is brought down to relatively low levels over prolonged periods of time.
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.
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