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題名：海運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
作者：張超琦
作者(外文)：Chang, Chao-Chi
校院名稱：國立臺灣海洋大學
系所名稱：航運管理學系
指導教授：周恆志
學位類別：博士
出版日期：2015
主題關鍵詞：乾散貨輪運價指數；油輪運價指數；貨櫃輪運價指數；風險值；長期記憶；分數整合波動模型；Dry Bulk Shipping Freight Indices；Tanker Shipping Freight Indices；Container Shipping Freight Indices；Value-at-Risk (VaR)；Long Memory；Fractional Integrated Volatility Models
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本文旨在考量運價指數波動具長期記憶特性下，運用風險值(VaR)模型去評估海運運價指數(包含乾散貨輪運價指數、油輪運價指數及貨櫃輪運價指數)的波動風險。假定投資人無論在損益分配的左尾及右尾皆願意冒險的前提下，以選定具有長期記憶特性的GARCH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。風險值(VaR)就是衡量尾端的損失，損益分配的左尾代表運價指數大跌的最大可能損失，是屬於船東所需面臨的風險；損益分配的右尾則代表運價指數大漲時的最大可能損失，是屬於貨主所需面臨的風險。如果風險值估算錯誤，會造成風險的高估或低估，對船東或貨主會帶來巨大的損失。
對乾散貨航運業而言，研究結果發現乾散貨運價指數報酬率存在長期記憶與厚尾的特性。對於BDI、 BCI、 BHSI 及 BSI而言，FIAPARCH模型的估計結果優於其他長期記憶模型。至於BPI指數，則以HYGARCH模型所得到的估計結果較佳。而根據風險值(VaR)回溯測試的結果，偏態Student-t分配下的模型估計結果表現較佳。
對油輪航運業而言，油輪運價指數報酬率亦存在長期記憶與厚尾的特性。對於BCTI而言，FIEGARCH模型的估計結果優於其他長期記憶模型。至於BDTI指數，則以HYGARCH模型所得到的估計結果較佳。而根據風險值(VaR)回溯測試的結果，偏態Student-t分配下的模型估計結果亦表現較佳。
對貨櫃航運業而言，貨櫃運價指數報酬率同樣存在長期記憶與厚尾的特性。對於HRCI而言，FIAPARCH模型的估計結果優於其他長期記憶模型。至於CCFI指數，則以HYGARCH模型所得到的估計結果較佳。而根據風險值(VaR)回溯測試的結果，對於HRCI而言，偏態Student-t分配下的模型估計結果表現較佳。至於CCFI，則是Student-t分配下的模型估計結果表現較佳。
因此，由本文所得的結果，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，並且可以提供更加準確的運費合約之價格議定，以及運用在遠期運費協議及運費選擇權等衍生性金融商品之交易。
This study aims to apply Value-at-Risk (VaR) models to evaluate the risk of dry bulk shipping freight indices, tanker shipping freight indices, and container shipping freight indices, when there is a long memory volatility process. Assuming that investors in the freight market can venture by holding not only left tail but also right tail risk under the distribution of profit/loss, this study compares the performance of the VaR models with the normal, Student-t and skewed Student-t distributions for both left tail and right tail based on the chosen models. The left tail of the distribution of profit/loss represents the maximum potential loss when the freight indices slump and the shipowners will face the great risk. On the other side, the right tail of the distribution of profit/loss represents the maximum potential loss when the freight indices skyrocket and the shippers will face the great risk. The underestimation or overestimation of VaR will make the shippers or shipowners get huge loss.
For the dry bulk shipping industry, the densities of the BDI, BPI, BCI, BHSI, and BSI exhibit the asymmetric long memory property of volatility and the fat-tail phenomenon. Besides, the asymmetric FIAPARCH model performs better for the BDI, BCI, BHSI and BSI, whereas the HYGARCH model performs better for the BPI. For the backtesting results, the VaR models with the skewed Student-t distributed innovation are preferred for both left tail and right tail.
For the tanker shipping industry, the densities of the BDTI and BCTI also exhibit the asymmetric long memory property of volatility and the fat-tail phenomenon. In addition, the FIEGARCH model performs better for the BCTI, whereas the HYGARCH model performs better for the BDTI. For the backtesting results, the VaR models with the skewed Student-t distributed innovation are preferred for both left tail and right tail.
For the container shipping industry, the densities of the HRCI and CCFI exhibit the long memory property of volatility and the fat-tail phenomenon. Besides, the FIAPARCH model performs better for the HRCI, whereas the HYGARCH model performs better for the CCFI. For the backtesting results, for the HRCI, the skewed Student-t VaR models perform correctly in all of the cases for both left tail and right tail. For the CCFI, the Student-t VaR models perform correctly in all of the cases for both right tail and left tail.
The above results suggest that precise VaR estimates may be acquired from a long memory volatility structure with the Student-t and skewed Student-t distributions. Such models improve the long-term volatility forecast and provide more precise pricing of freight contracts. These findings provide a more accurate estimation of VaR for shipping freight indices and could be applied in trading FFAs and dealing with portfolios of freight derivatives, which includes freight options.
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