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研究生:趙婉伶
研究生(外文):Wan-Ling Chao
論文名稱:非對稱多變量模型於能源期貨最適動態避險策略之比較與應用
論文名稱(外文):Optimal Dynamic Hedging Strategy of Energy Futures Markets: Application of Asymmetric Multivariate Models
指導教授:涂登才涂登才引用關係巫春洲巫春洲引用關係
指導教授(外文):Teng-Tsai TuChun-Chou Wu
學位類別:碩士
校院名稱:銘傳大學
系所名稱:財務金融學系碩士班
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:112
中文關鍵詞:動態避險非對稱波動CopulaCARR多變量GARCH
外文關鍵詞:Multivariate GARCHAsymmetric CARRCopulaAsymmetric VolatilityDynamic Hedge
相關次數:
  • 被引用被引用:3
  • 點閱點閱:186
  • 評分評分:
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  • 收藏至我的研究室書目清單書目收藏:0
有鑑於能源價格波動幅度頗為劇烈,而利用期貨工具來進行避險是頗為直觀而且可行的方法之一。本研究嘗試應用非對稱多變量模型建構最適動態避險策略,並與對稱結構模型進行避險績效的比較與分析。研究方法上,為建構最適動態避險比率,我們引入非對稱條件相關係數模型和混合Copula-GARCH模型兩種方法。就非對稱條件相關係數模型而言,分別探討靜態與動態的相關性模型,亦即應用具結構轉變之固定條件相關係數模型(A-CCC)與非對稱一般化動態條件相關係數模型(AG-DCC)衡量現貨和期貨之非對稱相關性行程。同時為考量現貨與期貨波動性亦可能存在非對稱性,值得進一步應用GJR-GARCH模型、非對稱條件動態變幅(A-CARR)模型與非對稱實現波動性之乘數誤差模型(AMEM-RV)估計現貨與期貨之波動性,並將估計結果當作估計多變量條件相關係數模型的基礎。
因為能源現貨與期貨市場間相關性結構可能存在不對稱性。因此適合引用混合Copula函數(M-Copula),來協助刻畫兩市場間動態相關結構。具體而言,M-Copula函數為三種不同Archimedean copulas (Gumbel copula, Clayton copula和Frank copula)的線性組合。因此,本研究擬藉由結合M-Copula函數與GJR-GARCH模型建構更完整的雙變量模型。最後,為了研究推論的頑強性(robustness),本研究將依非對稱多變量模型與傳統對稱之條件OLS模型進行能源期貨避險績效之比較與分析,亦進一步藉由Wilcoxon等級符號檢定,推論出最適合用來當做能源期貨最適動態避險策略的方法。
根據本研究實證結果顯示,若進行短期避險則以A-CARR模型估計期貨之非對稱波動性所建構的避險策略可獲得較佳之績效;中長期則以GJR-GARCH模型為佳。其次,在考量動態基差效果之下,條件最小平方法之避險績效將普遍優於非對稱條件相關係數模型。然而,若進一步考量現貨與期貨相關性之非對稱結構,則藉由M-Copula函數描繪現貨與期貨相關性所建構之避險模型,係為較富彈性之能源期貨避險方法,可有效地降低能源價格波動之風險。
This study proposes employing various asymmetric multivariate models to formulate optimal dynamic hedging strategy. To construct optimal dynamical hedging ratio, two types of the asymmetric models, namely the conditional correlation model and the Copula-GARCH model, are utilized in this study. The former explores static and dynamic correlation models, respectively, applying asymmetric constant conditional correlation (A-CCC) model with structural breaks and asymmetric generalized dynamic conditional correlation (AG-DCC) model to capture the asymmetric correlation process between futures and their corresponding underlyings. To simultaneously consider possible asymmetric volatility effects between futures and their corresponding underlyings, the GJR-GARCH model, asymmetric conditional autoregressive range model (A-CARR), and multiplicative error model for asymmetric realized volatility model (AMEM-RV) are further applied to estimate the volatility of futures and their corresponding underlyings. The latter Copula-GARCH model employs dynamic mixed copula (M-Copula) function to investigate the time-varying bivariate dependent structure since the dependent structure of spot and futures prices possibly exhibit asymmetric structure among spot and futures markets.
To achieve the robustness of inference, we compare the hedging performance of the proposed asymmetric multivarite models with those of the symmetric conditional OLS models for energy spot and futures markets. The empirical results of this study indicate that using the A-CARR model to estimate volatility of futures in the short-term could obtain better hedging performance, while using the GJR-GARCH model in the medium and long-term. Under consideration of the dynamic basis effect, the hedging performance of conditional OLS model will be generally superior to that of the asymmetric conditional correlation models. However, if further consider the asymmetric structure of spot and futures correlation, the hedging strategy constructed with M-Copula function is relatively effective and flexible for energy futures, and also can reduce the risk with enormous fluctuation of energy prices.
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究架構 5
第貳章 文獻探討 6
第一節 資產波動性之理論與文獻 6
第二節 資產相關性之理論與文獻 11
第三節 期貨避險理論與文獻 17
第參章 研究方法 21
第一節 對稱型傳統避險法 22
第二節 非對稱條件相關係數模型避險法 26
第三節 非對稱混合Copula函數與條件異質變異模型避險法 36
第四節 避險績效衡量 44
第肆章 實證分析 47
第一節 資料來源與期間 47
第二節 資料基本統計分析 51
第三節 避險績效分析 62
第伍章 結論與建議 99
參考文獻 101
中文部分
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