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題名:波羅地海乾散貨運價期貨動態避險比率之估計
書刊名:國立臺灣海洋大學海運學報
作者:周恆志梁金樹吳志淵
作者(外文):Chou, Heng-chihLiang, Gin-shuhWu, Chih-yuan
出版日期:2013
卷期:22:1
頁次:頁73-91
主題關鍵詞:運價期貨避險比率避險績效固定條件相關模型動態條件相關模型Freight rate futuresHedge ratioHedge performanceCCC modelDCC model
原始連結:連回原系統網址new window
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2008年6月18日國際航運期貨交易所(Imarex)開辦以BDI的運價期貨電子交易,吸引更多的航運相關經營者加入交易,因此而增加市場的流動性,使運價期貨市場更具效率。運價期貨的避險績效決定於避險比率的估計效率,良好的避險比率要能準確捕捉現貨與期貨價格相關性的動態變化。本文以BDI指數與BDI指數期貨為研究對象,樣本期間為2008年6月24日到2010年5月7日,比較最小風險避險比率(MVHR)、固定條件相關模型(CCC)、與動態條件相關模型(DCC)三種估計法估計的避險比率之避險績效。實證結果顯示,樣本內測試以CCC模型的避險績效最佳,樣本外的避險績效則是以DCC模型的避險績效表現最好。
The International Maritime Exchange (Imarex) opened the electronic trading to the BDI freight rate futures since 18 June 2008, which attracts more shipping operators to join in the transaction, thus increasing market liquidity, and making the freight rate futures market more efficient. The performance of the freight rate futures is affected by the estimated efficiency of the hedge ratio. An efficient hedge ratio estimation can help capture the dynamic changes of the correlation between the spot and futures prices. In this paper, the BDI and BDI futures are explored, and the sample period is from 24 June 2008 to 7 May 2010. Three alternative hedge ratio estimation models, Minimal Variance Hedge Ratio (MVHR), Constant Conditions Correlation model (CCC) and Dynamic Conditions Correlation model (DCC) are applied and compared The empirical results show that the best performance of in-sample fitting is the hedge ratio estimated by CCC model; but the hedge ratio estimated by DCC model has the best performance for the out-of-sample data.
期刊論文
1.Haigh, M. S.、Holt, M. T.(2002)。Hedging Foreign Currency, Freight and Commodity Futures Portfolios-A Note。The Journal of Futures Markets,22(12),1205-1221。  new window
2.巫春洲、劉炳麟、楊奕農(20090600)。農產品期貨動態避險策略的評價。農業與經濟,42,39-62。new window  延伸查詢new window
3.陳永順、王旭堂(20040300)。國際散裝船市場之時間變動避險比率估計:應用BGARCH及RCAR方法。風險管理學報,6(1),33-56。new window  延伸查詢new window
4.Kavussanos, M. G.、Nomikos, N. K.(2000)。Futures Hedging When the Structure of the Underlying Asset Changes: The Case of the BIFFEX Contract。The Journal of Futures Markets,20(8),775-801。  new window
5.Cullinane, K. P. B.(1992)。A short-term adaptive forecasting model for BIFFEX speculation: a Box-Jenkins approach。Maritime Policy and Management,19(2),91-114。  new window
6.Chou, Ray Y.、Wu, Chun-Chou、Liu, Nathan(2009)。Forecasting time-varying covariance with a range-based dynamic conditional correlation model。Review of Quantitative Finance and Accounting,33(4),327-345。  new window
7.Holmes, P.(1996)。Stock Index Futures Hedging: Hedge Ratio Estimation, Duration Effects, Expiration Effects and Hedge Ratio Stability。Journal of Business Finance and Accounting,23(1),63-77。  new window
8.Tong, Wilson H. S.(1996)。An Examination of Dynamic Hedging。Journal of International Money and Finance,15(1),19-35。  new window
9.Engle, Robert F.(2002)。Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models。Journal of Business & Economic Statistics,20(3),339-350。  new window
10.Bollerslev, Tim(1990)。Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model。The Review of Economics and Statistics,72(3),498-505。  new window
11.Johnson, Leland L.(1960)。The Theory of Hedging and Speculation in Commodity Futures。The Review of Economic Studies,27(3),139-151。  new window
12.Myers, R. J.,(1991)。Estimating Time Varying Optimal Hedging Ratios on Futures Markets。The Journal of Futures Markets,11(1),39-53。  new window
會議論文
1.徐清俊、錢怡成、黃少偉(2002)。台灣股價指數期貨之避險比率及避險效。2002年管理創新與新願景研討會。  延伸查詢new window
 
 
 
 
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