Freight market information and freight rate indices

2021 ◽  
pp. 121-157
Author(s):  
Manolis G. Kavussanos ◽  
Dimitris A. Tsouknidis ◽  
Ilias D. Visvikis
Keyword(s):  
Freight Rate ◽  
Market Information ◽  
Freight Market

Freight Market Information

2009 ◽  
pp. 107-124
Author(s):  
Amir H. Alizadeh ◽  
Nikos K. Nomikos
Keyword(s):  
Market Information ◽  
Freight Market

scholarly journals PECULIARITIES OF CURRENT WORLD SCIENTIFIC RESEARCHES ON MARITIME ECONOMICS AND SPECIFICALLY SEABORNE TRADE

2021 ◽  
Vol 1 (17) ◽  
pp. 8-22
Author(s):  
G.B. Zaidman ◽  
S.O. Yakubovskiy
Keyword(s):  
Modern Science ◽  
Spillover Effects ◽  
Freight Rate ◽  
Data Availability ◽  
Real Problem ◽  
Shipping Industry ◽  
Trade Data ◽  
Market Participants ◽  
Almost All ◽  
Freight Market

The article analyzes and systemizes current studies of leading world scientists on maritime economics and seaborne trade with the aim to reveal current trends and venues for future researches in this field. Special attention is paid to researches evaluating how the outbreak of coronavirus pandemic impacted shipping industry as a main global supplier of goods. All studies under review are conceptually grouped into two main branches. The first branch comprises papers focused on the world seaborne trade data dynamics, including official maritime reports. As opposed to Ukrainian and Russian maritime economics papers which predominantly describe and portray the statistical data available in official maritime reports issued by international organizations and shipping services providers, leading world scholars use this statistics as a baseline for individualized researches, mainly focused on investigation of correlation between various shipping indicators and prediction of same. The second branch comprises papers investigating trade of certain types of cargo, such as containers, crude oil, dry bulk. Several general peculiarities of both branches of researches are defined. Almost all of them attempt to provide an insight into the nature of a freight rate and to forecast the development of either general freight market or specific cargo related one. The utilized methodology is also identical. Depending on the aim of research and data availability, scholars employ various models of regression analysis, a standard tool of statistical modeling, which estimates the average relationship between two or more variables. No matter which freight market is under investigation, studies usually try to examine the connection of this market with others by evaluating the spillover effects between vessel types and vessel sizes. Distinguishing features of researches lie in the target stakeholders who could benefit from, either the industry in general or particular groups of market participants. In addition, nowcasting trade data is a real problem raised by the industry to modern science, which tries to tackle it by proposing innovative digitalized solutions.

scholarly journals Modeling and Prediction of the Volatility of the Freight Rate in the Roadway Freight Market of China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Wei Xiao ◽  
Mi Gan ◽  
Hongling Liu ◽  
Xiaobo Liu
Keyword(s):  
Moving Average ◽  
Freight Rate ◽  
Autoregressive Integrated Moving Average ◽  
Out Of Sample ◽  
Egarch Model ◽  
Trend Forecasting ◽  
Asymmetric Garch ◽  
Dynamic Time ◽  
Freight Market ◽  
Trucking Sector

The trucking sector is an essential part of the logistic system in China, carrying more than 80% of its goods. The complexity of the trucking market leads to tremendous uncertainty in the market volatility. Hence, in this highly competitive and vital market, trend forecasting is extremely difficult owing to the volatility of the freight rate. Consequently, there is interest in accurately forecasting the freight volatility for truck transportation. In this study, to represent the degree of variation of a freight rate series in the trucking sector over time, we first introduce truck rate volatility (TRV). This investigation utilizes the generalized autoregressive conditional heteroskedasticity (GARCH) family of methods to estimate the dynamic time-varying TRV using the real trucking industry transaction data obtained from an online freight exchange (OFEX) platform. It explores the ability of forecasting with and without reestimation at each step of the conventional GARCH models, a neural network exponential GARCH (NN-EGARCH) model, and a traditional forecasting technique, the autoregressive integrated moving average (ARIMA) approach. The empirical results from the southwest China trucking data indicate that the asymmetric GARCH-type models capture the characteristics of the TRV better than those with Gaussian distributions and that the leverage effects are observed in the TRV. Furthermore, the NN-EGARCH performs better in in-sample forecasting than other methods, whereas ARIMA performs similarly in out-of-sample TRV forecasting with reestimation. However, the Diebold–Mariano test indicates the better forecasting ability of ARIMA than the NN-EGARCH in the out-of-sample periods. The findings of this study can benefit truckers and shippers to capture the tendency change of the market to conduct their business plan, increase their look-to-buy rate, and avoid market risk.

The Dynamics of Dry Bulk Shipping Market Under the Shipping Cycle Perspective: Market Relationships and Volatility

2018 ◽  
Vol 2672 (11) ◽  
pp. 1-9 ◽  
Author(s):  
Yijie Wu ◽  
Jingbo Yin ◽  
Pan Sheng
Keyword(s):  
Financial Crisis ◽  
Recovery Period ◽  
Granger Causality Test ◽  
Freight Rate ◽  
Shipping Industry ◽  
Causality Test ◽  
Bulk Shipping ◽  
Before And After ◽  
Causality Relationship ◽  
Freight Market

The shipping industry plays an essential role in world trade. For shipping companies, having an accurate view of the markets and grasp of the interactions between the freight market, second-hand ship market, and the newbuild ship markets is essential. The shipping market cycles are divided into four periods (trough, recovery, peak, and recession) based upon shipping cycle theory. The current shipping markets have been stuck in the trough period since the financial crisis in 2008. This paper investigates the recovery period and causality relationship between the freight rate, second-hand price, and new-build ship price, in the dry bulk shipping market and applies the Granger causality test at each stage of the cycle based on quantitative analysis. The results show that the recovery period and causality relationship can be identified only during the trough and peak periods. When comparing the results for the trough period before and after the financial crisis, we find similarities between the two periods, leading us to conclude that the shipping cycle rules still apply.

Changing the Crow’s Nest Freight Rate

1988 ◽  
Vol 22 (4) ◽  
pp. 93-114
Author(s):  
Gary Munro
Keyword(s):  
Freight Rate

PEMASARAN SELEDRI DI DESA OEPAHA KECAMATAN NEKAMESE KABUPATEN KUPANG

2020 ◽  
Vol 21 (2) ◽  
pp. 122-129
Author(s):  
Maria C K Nadjib ◽  
Alfetri N.P Lango ◽  
Paulus Un
Keyword(s):  
Sampling Methods ◽  
Descriptive Analysis ◽  
Marketing Channel ◽  
Simple Random Sampling ◽  
Market Information ◽  
Snowball Sampling ◽  
Zero Level ◽  
Farming Community ◽  
Profit Analysis ◽  
The Village

The research, which was conducted in the village of Oepaha, Nekamese District, Kupang Regency, from June to July 2019, aims to identify marketing channels, capabilities and margins, and the share and benefits of celery marketing for farmers. The location of the research was determined using purposive sampling methods, considering that the location was the most important supplier area for celery in Kupang Regency. The population in this study was the farming community in Oepaha Village, Nekamese district, Kupang Regency,in the amount of 96 celery farmers. The sampling methods is carried out by simple random sampling according to the Slovenian formula, so that a sample of 49 celery farmers is obtained. Sampling for marketing institutions was determined by selecting the marketing institutions which directly involved in celery marketing using snowball sampling methods. In the end, respondents selected marketing institutes consisting of village collectors amounted to 4 people and retailers amounted to 4 people. The analysis of the data used in this study includes descriptive analysis, marketing margin analysis, farmer's share analysis and marketing profit analysis. The results showed that the marketing channel for celery carried out by farmers consisted of two channels, namely farmers directly to consumers and farmers to consumers through intermediaries, namely village collectors and retailers. Celery marketing functions that arise are the functions of sales, purchases, transport, standardization and financing and market information on the zero level channel,while sales, purchasing, transportation, storage, standardization, and financing, as well as market information, run on the second level channel. The marketing margin at zero level is Rp. 51.000,-, while the second level marketing channel is Rp. 17,000 at the collector and Rp. 34,000 at the retailer. The percentage of farmer’s share received by farmers is 25% at zero level and 25% at second level channel. The profit from celery marketing in the zero level marketing channel is Rp. 8.261 (farmers), the second level marketing channel is Rp. 16,688 (farmers), Rp. 15.267 (collectors' traders), Rp. 28,029 (retailers).

Cash, Food or Vouchers in Urban and Rural Kenya? An Application of the Market Information and Food Insecurity Response Analysis Framework

2011 ◽  
Author(s):  
Hope C. Michelson ◽  
Erin Lentz ◽  
Rich Mulwa ◽  
Mitchell Morey ◽  
Laura Cramer ◽  
...  
Keyword(s):  
Food Insecurity ◽  
Response Analysis ◽  
Market Information ◽  
Analysis Framework ◽  
Rural Kenya
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