臺灣博碩士論文加值系統

策略資產配置是影響長期投資績效的重要因素，很多研究都曾將不同理論模型運用在策略資產配置上，如：多變量模型(Campbell et al.(2003))、動能交易策略(Bange and Thomas(2004))、(Brouwer and Philippe(2009))等，而本研究採用共整合分析法在策略資產配置上。共整合自1987年被提出後，很多研究採用共整合分析法去尋找非定態時間序列的長期關係，而Lucas(1997)認為共整合會影響戰術和策略上的財務決策，故本研究欲探究共整合分析法在策略資產配置上的風險分散效果。
本研究以12檔避險基金指數及代表傳統資產的標準普爾500指數(S&P 500)、那斯達克指數(NASDAQ)與J.P.摩根債券指數(J.P. Morgan bond)為研究對象，採用共整合分析法，探究經共整合測試後的策略資產配置之效率前緣。實證結果顯示在平均數-變異數及平均數-條件風險值的報酬-風險架構下，以不具共整合的資產為選取標的之策略資產配置能有較高報酬，且風險較低。

The strategic asset allocation is an important factor that influenced long-term investment performance; many researchers have already made use of different theories in the strategic asset allocation, such as multivariate model (Campbell et.al. (2003)), momentum trading strategy (Bange and Thomas (2004)), behavioral investing (Brouwer and Philippe (2009)), while this study applied cointegration analysis in the strategic asset allocation. Cointegration had been defined and developed since 1987, a lot of studies used cointegration to look for the long-turn relationship among the nonstationary time series. Lucas(1997) believed that cointegration affects both tactical and strategic financial decision making. This study wants to investigate cointegration analysis in strategic asset allocation effect on diversification potential.
This study applies cointegration analysis to a sample containing 12 hedge
fund indices and indices representative of traditional assets, such as S&P500、NASDAQ and J.P. Morgan bond to explore the efficient frontier of strategic asset allocation.Empirical results show that under mean-risk framework, such as mean-variance model and mean-conditional value at risk model, the strategic asset allocation based on the portfolio of non-cointegrated assets has higher return and lower risk.

目錄
摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第三節 研究架構 4
第二章 文獻探討 5
第一節 策略資產配置 5
第二節 共整合在財務上之應用 7
第三節 風險值、條件風險值和兩者在資產配置上應用 7
第三章 研究方法 12
第一節 研究流程 12
第二節 單根檢定與共整合 12
第三節 M-V與M-CVaR模型之效率前緣 21
第四章 實證資料與結果分析 25
第一節 資料來源 25
第二節 敘述統計與單根檢定 28
第三節 共整合測試與向量誤差修正模型(VECM) 30
第四節 有無落入共整合之投資組合效率前緣比較 35
第五章 結論與建議 39
參考文獻 41
一、中文部分 41
二、英文部分 41
附錄一：M-V模型之各群組有無落入共整合效率前緣比較表 45
附錄二：M-CVaR模型之各群組有無落入共整合效率前緣比較表 46
附錄三：各群組M-V與M-CVaR模型有無落入共整合效率前緣比較表 47
附錄四：M-V模型之各群組有無落入共整合投資組合權重比較表 48


表目錄
表4.1 樣本資料敘述統計 29
表4.2 ADF及PP單根檢定 30
表4.3 向量誤差修正模型延遲期數AIC、SC值 31
表4.4 共整合個數檢定結果 33
表4.5 混合市場中立群組變數係數檢定 34
表4.6 作多/作空股權、方向性交易和特殊策略群組變數係數檢定 34
表4.7 風險最小投資組合 36
表4.8 市場投資組合比較表 37



圖目錄
圖1.1 研究架構 5
圖2.1 條件風險值示意圖 10
圖3.1 研究流程 13

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