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題名：價格波動與最適避險模型之研究-以散裝乾貨船市場為例
作者：陳永順
作者(外文)：Yung-Shum Chen
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：王旭堂
學位類別：博士
出版日期：2004
主題關鍵詞：價格波動；波動叢聚；槓桿效應；風險係數；避險比率；避險效率；pricing volatility；volatility clustering；leverage effect；coefficient of risk；hedging ratio；hedging efficiency
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摘要
基於無法正確預測諸多影響海運市場價格因素，確實形成投資散裝不定期船產業的一大難題。海運產業為國際性事業，存有相當多國家投資人間相互強烈競爭，且海運產業對經濟因素具有高度敏感性、資本密集性和高度競爭性等因素，導致海運價格形成劇烈漲跌振盪。故海運產業具有隱含高度投機等級之基本風險特性。投資散裝海運市場無法避免須面對高度不確定性風險，然而投資人唯一生存之道有必要進行適當避險之風險管理策略，以減少暴露高度市場不確定性風險，此課題已引起投資人高度重視。
投資人不僅應正確認知海運市場價格波動的特性，而且各船舶產業報酬間相關程度對投資人也極端重要，透過投資多樣化船隊策略是否能有效達到降低海運市場風險。現階段普遍受歡迎且公認效果不錯的GARCH模型方法，容許使用於分析散裝乾貨海運市場價格波動隨時間變動與投資避險策略的選擇。本研究結果可提出以下幾點散裝海運市場特性：
(1)愈大型散裝船承受比愈小型散裝船更大的價格波動之環境包括波動持續性與槓桿效應。相反地，較小型散裝船比較大型散裝船在價格移動呈現更明顯波動叢聚效應。
(2)各船舶產業報酬間相關程度是時變的且在固定平均值作上下浮動。當市場處於下跌期間，各船舶產業報酬間出現較高相關性，假如投資人試圖藉由投資多樣化船隊組合來對抗低迷市場行情，以確保投資利益，似乎效果有限。本研究結果可提供海運投資人如何妥善安排最佳船舶資產配置以對抗暴露在高度不確定市場風險。
(3)各船舶產業系統風險呈現顯著正相關，不同型船在波動增大期間，他們系統風險傾向以不同方向移動。當各市場具有系統風險小於1時，其市場系統風險傾向呈現負的時間變動；反之，大於1時，一般呈現正的時間變動，並指出散裝海運市場價格波動與個別市場的系統風險間呈現正的關係。結果，散裝海運市場價格波動增加，對較安全與較高風險市場將受到不同程度影響。
(4)當三型散裝乾貨船市場價格受到相同正面與負面衝擊效果時，下跌效應較上漲效應表現更加強烈。另一結果顯示即期市場價格領先遠期價格，並指出投資人預期心理深受即期價格表現影響，以及預期心理也在同時間隨市場情況加以修正，因此影響因素將修正投資人在未來預期心理。
(5)發現散裝海運市場最佳避險比率的時間路徑具有相當大上下變動。在報酬變異降低幅度的條件下，比較各選擇避險模型執行避險績效，結果發現以GARCH模型正定對角線展現報酬組合變異最大降幅。故本研究建議投資人可嘗試採用BGARCH模型當作量測時間避險比率之方法，應可在不影響獲利前提下，俾能達成具有顯著地降低風險之最佳投資組合。
最後，本研究採用GARCH各類型之模型以探討散裝船產業價格波動風險特性，以及試圖尋求最佳避險績效的方法，以提供相關投資人或研究者參考，並期望吸引有興趣學者共同加入更廣泛與深度相關課題；諸如遠期合約價格定價、套利與船舶資產最佳投資組合等研究。
關鍵詞：價格波動、波動叢聚、槓桿效應、風險係數、避險比率、避險效率
Abstract
There are some of factors not to be forecasted with sufficient accuracy, which poses a big problem for investors planning to invest in tramp shipping market. In essence, the shipping industry is an international business, which means that there is strong competition between investors of a multitude of countries. Because of economic sensitivity, capital intensity, and competitive factors, which leads to extremely volatile price swing. The fundamental risk characteristics of the shipping industry imply speculative-grade rating.
It is well known that investment in bulk shipping market inevitably faced extreme uncertain risk. However, the investor can only survive on condition that they shall launch out appropriate strategy of risk management to mitigate exposure of high market uncertain risk. This fact have stimulated that most investors begin emphasizing on these critical issues.
The investors shall not exactly recognize characteristics of volatility, but also the correlation of returns across shipping sectors is of great practical importance to investors, as it determines the degree, which they can reduce their exposure to shipping market risk by diversifying their feet. A prevailing GARCH-type model have been recognized a better method to allow us for analyzing both time-varying volatility and hedging option. The result of study can present some following characteristics in bulk shipping market.
(1) Larger bulk carriers face a more volatile rate environment than the small carriers including persistent effect and leverage effect. Conversely, the smaller bulk vessel appears much more reaction or spiky effect than larger bulk vessel in the shipping price movement.
(2) The correlation of return is time-varying and shipping price fluctuates around a constant positive average value. The correlation appears to be getting higher during market downturns, if investors intend to secure the benefit of having a diversified fleet as a protective means against severe sluggish market is negligible. The result of this study may provide shipping market investors how to well pre-arrange optimal allocation of vessel assets preventing from exposure of extreme uncertain risk.
(3) Its systematic risk is significantly positively related, the systematic risks of different types tend to move in different directions during periods of increasing price volatility. While the market segments with systematic risk less than one tend to show negative time variability, while market segments with systematic risk greater than one generally show positive time variability, indicating a positive relationship between the volatility of the dry bulk freight market and the systematic risk of individual market segments. Consequently，safer and riskier market segments are affected differently by increases in dry bulk freight market volatility.
(4) The downward effects are stronger than upward effects among three types of bulk carrier while they are impacted by same magnitude of positive and negative shock. Another result shows that spot rate leads to forward rate. It indicated that the expectations of investors would be deeply influenced by the spot rate, and these expectations would be revised by market situation at that time, then, influencing factors will correct the expectation of investors in the future.
(5)Finding that the bulk shipping market possessed substantial fluctuations in the time path of optimal hedge ratios. Hedging performance in terms of variance reduction of returns from alternative models have been conducted, the result of the finding that diagonal Vech presentation of GARCH model provides the largest reduction in the variance of the return portfolio. This study may recommend investors to use the BGARCH as method of measuring time hedging ratio, enabling to achieve the optimal portfolio with the high reduction of risk under no influence of profit gain.
Finally, this study have taken the initiative in adopting GARCH-type model to investigate characteristics of risk of price volatility in the bulk shipping industry, and intend to look for the method of optimal hedging performance for providing relevant investors or researchers with guidance, and expecting to attract interesting participants to partake in studying wider and deeper relevant issues such as pricing freight rate, arbitrage and portfolio etc.
Keywords：pricing volatility, volatility clustering, leverage effect, coefficient of risk, hedging ratio, hedging efficiency
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