Studies on Factors and Trend Prediction on Bulk Shipping Freight Rates

碩士 === 國立交通大學 === 運輸科技與管理學系 === 95 === The growth rate of marine market in G8 and China will still lead other countries in the next ten years. The territory all over the world is vast but natural resources are scarce and imbalanced. Raw materials and grains must to be delivered to developing and les...

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Bibliographic Details
Main Authors: Lee, Szu-Hui, 李思慧
Other Authors: Hsieh, Shang-Hsing
Format: Others
Language:zh-TW
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/52456008637298431012
Description
Summary:碩士 === 國立交通大學 === 運輸科技與管理學系 === 95 === The growth rate of marine market in G8 and China will still lead other countries in the next ten years. The territory all over the world is vast but natural resources are scarce and imbalanced. Raw materials and grains must to be delivered to developing and less developed countries by ships. Some commentators indicate that in the global container flow, one out of five is relevant with China. China’s economic development adds huge demands in marine market and leads the tariff fluctuating. China has become one of the most important economies in Asia-Pacific area, but only very few papers discussed maritime freight rate in terms of China economic development. In recent decades, the growth of China maritime industry brings many changes for marine market. This paper tries to discuss the shocks for maritime freight rate from G8 and China’s economic development, predicting the trends of freight rate changes in the next five years and hopes the results will be useful for Taiwanese marine companies to plan the worldwide deployment. Regression model and time series model were the main tools in most of previous researches about marine freight rate prediction. They needed large amount of data for testing the probability distribution and curve fitting. In this study, the historical data of the major factors causing the increase in marine freight rate only about ten-year period. Therefore, we will also use Gray Theory to make the analysis and prediction, because it does not need large amount of data to formulate the model. We will also try ARIMA time series, and compare the results.