臺灣博碩士論文加值系統

本文採用雙變量GARCH模型估計台灣加權股價指數正負基差的非對稱性，就是考慮正基差與負基差對現貨與期貨報酬的變異數與共變異數影響。為了考慮負面消息對報酬率變異數與共變異數的影響，本文在雙變量GARCH模型中也加入了GJR效果與延展了DCC方法，使用ADCC方法來估計共變異數。此外，本篇再將基差效果加入ADCC中，計算出最佳的避險模型來提供投資人使用。本文比較了 (1) 傳統線性迴歸法(OLS)、(2) 指數加權移動平均法（EWMA）、(3) 雙變量GARCH模型、(4) 雙變量GARCH-DCC模型、(5) 基差不對稱雙變量GARCH-DCC模型、(6) 基差不對稱雙變量GARCH-GJR-DCC模型、(7) 雙變量GARCH-ADCC模型、(8) 基差不對稱雙變量GARCH-ADCC模型、(9) 基差不對稱雙變量GARCH-GJR-ADCC模型。研究發現現貨與期貨的基差效果是不對稱的，負基差比正基差對變異數影響更甚，但正基差對共變異數的影響卻比負基差來的大。在將GJR效果納入考量後，發現負面消息對報酬率的波動是具有影響的。估計結果顯示基差不對稱雙變量GARCH-GJR-ADCC模型為避險績效最佳的模型，這也提供了投資人在台灣加權股價指數基差變動時最佳的避險策略。

This paper use Bivariate GARCH model to estimate the asymmetric basis effect in Taiwan stock index futures. That is, we also considering the positive and negative basis effect on time-varying variance-covariance of spot and future return. In addition, the bad news effect on time-varying variance-covariance of spot and futures, we add GJR effect in the Bivariate GARCH model and extend the DCC model to ADCC model to estimate the covariance. Besides, we add basis effect in ADCC model to evaluate and choose the best model for investors. We compare (1) the OLS model, (2) EWMA model, (3) BGARCH model, (4) BGARCH-DCC model, (5) asymmetric BGARCH-DCC model, (6) asymmetric BGARCH-GJR-DCC model, (7) BGARCH
-ADCC model, (8) asymmetric BGARCH-ADCC model, and (9) asymmetric BGARCH-GJR-ADCC model. The empirical results find that spot and future have asymmetric basis effect, and the negative basis has greater impact than the positive basis. After consider GJR effect, we found the bad news have effect on the volatility of return. It is found that asymmetric BGARCH-GJR-ADCC model is the best hedging model. These provide the best hedging policy in Taiwan stock futures market.

目 錄
第一章 緒 論……………………………………………………… 1
第一節 研究動機…………………………………………………… 1
第二節 研究目的…………………………………………………… 2
第三節 研究流程…………………………………………………… 3
第四節 研究架構…………………………………………………… 4
第二章 理論基礎與文獻回顧……………………………………… 5
第一節 股價指數期貨契約………………………………………… 5
第二節 台灣股價指數期貨契約…………………………………… 7
第三節 避險理論回顧……………………………………………… 10
第四節 國外文獻探討……………………………………………… 13
第五節 國內文獻探討……………………………………………… 16
第三章 研究方法…………………………………………………… 21
第一節 單根檢定…………………………………………………… 21
第二節 指數加權移動平均法……………………………………… 24
第三節 不對稱雙變量GARCH-GJR模型 …………………………… 27
第四節 不對稱動態相關係數模型………………………………… 35
第五節 最小變異數避險比率……………………………………… 41
第四章 實證分析…………………………………………………… 42
第一節 資料來源…………………………………………………… 42
第二節 雙變量常態分配及平賭過程定檢………………………… 43
第三節 單根定檢結果……………………………………………… 45
第四節 ARCH效果的檢定…………………………………………… 48
第六節 台股指數期貨的避險績效………………………………… 49
第五章 結論………………………………………………………… 56
中文參考文獻………………………………………………………… 57
英文參考文獻………………………………………………………… 58
表 次
【表2-1】 台股指數現貨為標的之股價期貨契約………………… 9
【表4-1】 台股指數現貨與期貨報酬的統計資料………………… 44
【表4-2】 指數現貨與期貨價格時間序列資料之ADF單根檢定 … 46
【表4-3】 指數現貨與期貨價格時間序列資料之PP單根檢定…… 46
【表4-4】 台股加權指數殘差項ARCH效果檢定…………………… 48
【表4-5】 基差不對稱BGARCH模型下平均數方程式的基差參數值 52
【表4-6】 現貨報酬變異數方程式之參數值……………………… 52
【表4-7】 期貨報酬變異數方程式之參數值……………………… 53
【表4-8】 現貨與期貨殘差之共變異數方程式參數值…………… 53
【表4-9】 各模型之避險投資組合變異數及避險績效…………… 55
圖 次
【圖1-1】各模型研究流程圖……………………………………… 3
【圖4-1】台股指數現貨與期貨價格……………………………… 47
【圖4-2】台股指數現貨與期貨報酬率…………………………… 47
【圖4-3】台股指數期貨走勢圖與基差分布圖…………………… 54
【圖4-4】台股指數現貨與期貨條件共變異數走勢圖與基差分布圖 54

中文部分
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3. 李命志、吳佩珊、鄭婉秀 (2004)，「基差訊息運用對避險績效之影響」，朝陽商管評論，第3卷，第1期，頁101-120。
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