Value-at-Risk Analysis of Container Freight Indices with the Long Memory Volatility Process

貨櫃輪運價指數之風險值分析：考慮運價指數波動率的長期記憶性

張超琦(Chao-Chi Chang)

《蘭陽學報》 16期 (2017/07) Pp. 67-85

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.

關鍵字

Container Freight Indices ； Value-at-Risk (VaR) ； Long Memory ； Fractional IntegratedVolatility Models

並列摘要

本文旨在考量貨櫃輪運價指數波動具長期記憶特性下，運用風險值(VaR)模型去評估貨櫃輪運價指數的波動風險。在多跟空部位下，本研究分別計算其風險值及預期損失。並以選定具有長期記憶特性的HYGARCH及FIAPARCH模型，去比較在三種不同的分配(常態分配、Student-t分配與偏態Student-t分配)特性下的表現結果。本研究建議在Student-t分配與偏態Student-t分配下，藉由具備長期記憶特性的GARCH模型去估計風險值(VaR)，可以得到較為準確的分析結果。亦即當進行運價指數報酬率的風險估計，所採用的估計模型若能同時考量波動叢聚、厚尾、不對稱及長期記憶等特性，將是較為適當的做法，而該模型亦有助於長期的波動預測。

並列關鍵字

貨櫃輪運價指數 ； 風險值 ； 長期記憶 ； 分數整合波動模型

參考文獻

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