臺灣博碩士論文加值系統

本研究探討台灣上市散裝航運公司股票報酬波動率與波羅的海綜合
運價指數運費波動率之關聯性、季節性以及變數領先落後之變動情形，
研究樣本涵蓋台灣五家經營散裝船之上市航運公司以及BCI、BPI 與BSI
指數，研究期間自2000 年至2006 年止之日資料，本研究分為兩部份，
第一部份採用AR-GARCH 模型以及季節性ARIMA 模型進行關聯性與季
節性之實證分析，第二部份則以VECM 模型作變數之間互動之影響分
析。研究結果顯示，上市散裝航運公司股價之表現確實受指數波動影響，
影響程度與船隊組合中所持有之船型比例具相當關聯性，且持有現貨船
與短期合約船之航運公司股價與運費之波動較貼近；此外，研究結果亦
發現散裝海運指數與上市散裝航運公司之股價皆存在明顯之季節性現
象，大船型之季節性現象較明顯，至於股價季節性高峰在第一季與第四
季，第三季則表現平緩；最後，研究結果發現散裝海運指數與股價存在
長期均衡關係，變數之間發現指數具有明顯領先股價之效果，但就訊息
反應方面，則以股價之訊息反應速度較為迅度。針對海運市場指數以及
股價存在之特性，將提供海運公司或從事海運投資者作為投資與管理操
作之建議。

This study explores the nature of interaction and seasonality from the volatility of Baltic Dry Index and Taiwanese listed bulk shipping companies stock price, the observation including five of Taiwanese listed bulk shipping companies, Baltic Capesize Index, Baltic Panamax Index, and Baltic Supramax Index is presented. This study makes use of the measures AR-GARCH model and SARIMA model, research is a daily data from January 2000 to December 2006. The result shows that stock return volatility of Taiwanese listed bulk shipping companies influenced by Baltic Dry Index return volatility, and the affection is most significant with the fleet structure,
such as present freighter and short-term contract. In addition, we also found that Baltic Dry Index and stock return exhibit the relationship of seasonal pattern, return of larger vessels exhibit higher seasonal fluctuations compared to smaller vessels, as for stock return seasonal peak in first and fourth quarter but trough in third. Final, the research of VECM model finds that the Baltic
Dry Index returns volatility and stock price volatility exist co-integration; the index volatility is in the lead, furthermore, stock price volatility transfers faster. To comprehend the character between index and stock price in bulk shipping market will help for shipping companies and investor such as operation advice in bulk market.

第一章 緒論
第二章 文獻探討
2.1 AR-GARCH 模型相關文獻
2.2 季節性模型相關文獻
2.3 VAR 或VECH 模型相關文獻
第三章 研究方法
3.1 資料來源與分析
3.2 研究假說
3.3 AR (m)-GARCH (p,q)模型
3.4 季節性分解
3.5 單根檢定
3.5.1 DF 檢定法
3.5.2 ADF 檢定法
3.6 共整合檢定
3.7 向量自我迴歸模型
3.7.1 Granger 因果關係檢定
3.7.2 衝擊反應函數
3.7.3 預測誤差變異數分解
3.7.4 向量誤差修正模型
第四章 實證結果
4.1 資料分析
4.2 AR-GARCH 模型
4.2.1 OLS 估計式
4.2.2 AR 模型
4.2.3 ARCH 效果檢定
4.2.4 GARCH 模型
4.2.5 AR(1)-GARCH(1,1)模型
4.3 季節性ARIMA 模型
4.3.1 長期趨勢
viii
4.3.2 季節變動
4.3.3 不規則變動
4.3.4 季節性分析
4.4 VECM 模型
4.4.1 單根檢定
4.4.2 共整合檢定
4.4.3 因果關係檢定
4.4.4 衝擊反應函數
4.4.5 誤差變異數分解
第五章 結論與建議
5.1 結論
5.2 建議
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