| Summary: | 博士 === 國立臺灣海洋大學 === 航運管理學系 === 103 === This paper adopts EGARCH-M model to investigate the risk-return trade-off relations in shipping freight market, and analyzes how it was influenced by the 2008 financial tsunami. This research examines the risk-return relation for freight rates of three major shipping markets, i.e. Container, Dry bulk, and Tanker, and compares it with the MSCI World Index to learn more about the difference between shipping market and financial market.
Firstly, this research applies the Chow tests to detect the structural changes of related market. Then divide the sample data into the entire period, the period before the financial tsunami, and the period after the financial tsunami to understand how the risk-return trade-off relations changed in the market, as resulted from the financial tsunami.
Secondly, this research employs the tests developed by Engle and Ng (1993) on volatility asymmetry, and find that the sampled data may be subject to the effect of volatility asymmetry. Therefore, this paper applies the EGARCH-M model to estimate the risk premium parameter β and the degree of asymmetry. Besides that, this research applies a rolling window test in the EGARCH-M model to explore the dynamic characteristic of risk premium parameters, and also adopt the regression analysis to explore the factors that influence of the risk premium parameter.
Empirical results show that the relationship between freight return and risk varies by different types of ships、different shipping markets. The high-risk/high-return trade-off relationship in Dry bulk shipping sectors has changed after the financial tsunami. It means dry bulk shipping market have been changed from high-risk/high- return market into high-risk/low-return market. Furthermore, β value is not only affected by the factor of financial tsunami, but also significantly affected by its former risk premium and its former rate of return positively.
The results of this study can verify the assumptions of financial theory to make up for the lack of empirical literature, and it also helps the shipping company to understand the dynamic risk-return relationship among various ships to secure the optimal fleet allocation.
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