期刊刊名：蘭陽學報 　 卷期：16期

篇名出版日期：2017年7月1日

作者：張超琦, Chao-Chi Chang

語言：English

關鍵字：Container Freight Indices, Value-at-Risk (VaR), Long Memory, Fractional Integrated Volatility Models,貨櫃輪運價指數,風險值,長期記憶,分數整合波動模型

被點閱次數：1次

閱讀時間：7sec

摘要：
This study aims to apply Value-at-Risk (VaR) models to evaluate the risk of container freight indices when there is a long memory effect. In this study, we calculate the VaR estimations and expected shortfalls for both short and long trading positions. Moreover, we use the Hyperbolic GARCH and the Fractional Integrated Asymmetric Power-ARCH models to analyse the performance of the VaR models with the normal, Student-t and skewed Student-t distributions. Our results suggest that precise VaR estimates may be acquired from a long memory volatility structure with the Student-t and skewed Student-t distributions. Moreover, for the appropriate risk valuation of container freight indices, the degree of persistence should be examined and modelling that includes volatility clustering, fat-tails and long range dependence should be considered. Therefore, our findings provide a more accurate estimation of VaR for container freight indices.

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