本論文建立在資產報酬二階動差的動態結構下研究三個議題，第一議題是與資產報酬波動有關，第二議題是關於資本資產定價模型，第三議題是關於報酬序列相關的研究。
有關第一個議題的研究，就台灣加權股價指數收盤價及其成分股的實證部分，本文發現(一)市場投資組合超額報酬變異數( )與平均公司層次變異數( )、以及平均相關係數( )均為恆定數列；(二)平均公司層次變異數( )有時間趨勢，隨著時間經過而遞增，而平均相關係數( )則是隨時間而遞減。由上述的結果，顯示公司特有風險愈來愈重要，可能會影響資本資產的定價。對投資組合的意涵為投資經理人若要建構消極的投資組合，以追蹤特定指數時，不必持有該特定指數的全部成份股，只要持有部分成份股即可。
就台灣加權股價指數與台灣50、電子類股、金融類股報酬的相關性的實證部分，本文發現(一)顯示台灣50、金融指數與台灣加權股價指數的相關係數均為恆定序列，而電子指數與台灣加權股價指數的相關係數具有非隨機趨勢，且該趨勢隨時間而增加；(二)三指數與台灣加權股價指數的相關係數皆呈現多空頭不對稱的現象，即在空頭市場時，三指數與台灣加權股價指數的相關係數較高，與Erb, Harvey & Viskanta(1994)、Longin & Solnik(1995)的實證結果相同；(三)不受限模型(模型1)較能解釋三指數與台灣加權股價指數相關係數的動態關係。
有關第二個議題的研究，本文的實證發現(一)無論是以 、 或 為解釋變數，加權指數及絕大部分產業類股指數的估計參數均為負值，顯示以 、 或 單獨為解釋變數並無法預測下一期台灣加權股價指數及十九類類股指數，故台灣股價市場並非是一個整合的市場，亦非完全區隔的市場，而介於二者之間，顯示類股報酬與大盤報酬相關，但亦與本身所屬產業特性有關；(二)無論是全樣本、擴充樣本或滾動樣本，均顯示 係數（共偏態）可解釋產業報酬橫斷面的差異，故以產業報酬而言，投資人對具有負偏態的資產，會要求較高的風險溢酬來補償；(三) 為消費-財富比率( )的良好代理變數，可為投資人未來市場報酬預期；(四)發現19大類產業指數中，符合3M-CAPM的類股有2類，符合CCAPM的類股有7類，符合3M-CAPM+CCAPM的類股有5類，而3M-CAPM、CCAPM及3M-CAPM+CCAPM三模型皆無法解釋的類股有3類，故在定價各類指數宜採權變(contingence)的定價模型；(五)比較 3M-CAPM模型之 係數與CCAPM模型之 係數的橫斷面相關係數的時間序列與台灣加權股價指數43個月移動平均超額報酬，發現除了少部分時段，橫斷面相關係數與大盤超額報酬皆呈正向關係。
有關第三個議題的研究，本文實證發現如下：(一)正向回饋交易的確影響台灣指數期貨短期報酬動態。在高波動期間，由於正向回饋交易者對價格有較大的影響力，因此，期貨錯價的程度可能較大，但波動大亦表示理性投資人的套利風險增加，此時，風險規避的理性投資人不一定會進場套利，因而此價格偏離在短期內可能無法消除。(二)當市場處於空頭時，投資人易因財富效果所導致的停損交易及因資訊效果而追逐趨勢的行為，終將全面引發市場一連串的同方向交易，而造成期貨價格大幅且快速的變動，形成所謂的價格瀑布。(三)開放期貨經理業務後，代客操作的精明專業投資人非但不會利用其私有資訊改正錯價，反而會推波助瀾，搶搭正向回饋交易者的順風車以賺取雜訊交易所產生的超額利潤，而使得期貨價格更加不穩定。

The work presented in this dissertation can be grouped around three major themes.
The first theme relates to risk, the second theme relates to asset pricing, whereas the
third theme relates to serial correlation of asset returns. The three chapters of this
dissertation investigate these themes
Chapter Two analyses the behavior over time of market risk, aggregate idiosyncratic
risk and correlations in portfolio of Taiwan listing stocks and studied pattern of
aggregate correlation between the 3 most important Taiwan stock index and Taiwan
value-weighted index. We find (1) Idiosyncratic risk is trended upwards; (2) The
conditional stock returns correlation process is asymmetric. The implication of our
finding is (1) It takes more stocks to achieve a given level of diversification; (2)
Diversification strategies perform poorly in bear markets.
Chapter Three investigates the role of the asset co-skewness and conditioning
information in asset pricing. First, I estimate long-run predictive regressions of asset
returns to test whether aggregate idiosyncratic risk is a price factor of industrial
returns. Then I use data on Taiwan 19 industry portfolios to fit various assets pricing
models. I find (1) the cross-sectional ctional correlation between 2 i
β (the gamma coefficient
from the 3M-CAPM equation) and 3 i
ϕ (the interaction coefficient from the CCAPM
equation) is positive and fairly large. (2) The firm-level volatility is a good proxy for
cay as conditioning information variable. (3) The gamma coefficient can pick up the
extent of beta co-vary with the market wide excess-return over the business cycle.
(4)among 19 industrial returns, the 2 industrial returns can be explained by
3M-CAPM; the 7 industrial returns can be explained by CCAPM; the 5 industrial
returns can be explained by 3M-CAPM+CCAPM, Others can’t be explained by either
of three models.
Chapter Four examines the impact of positive feedback trading behavior of the
investors on the short-term dynamics of return for four Taiwan index futures contracts
by utilizing the framework of the model developed by Sentana & Wadhwani(1992).
Use of the Asymmetric Nonlinear Smooth Transition GARCH Model demonstrates that positive feedback trading of investors is the main determinant of short-term
dynamics of return for Taiwan index futures contracts. Moreover, it shows that
positive trading is more intense during market declines than it is during market
advances due to extensive use of spot-loss trading for investors. Finally it is shown
that the sophisticated professional investors intend to take positive feedback trades
wave so that they lead to increase positive feedback trading in Taiwan index futures
since the government opened the enterprises for managed futures.

第一章、緒論
第一節　研究動機…………………………………………………………… 1
第二節　研究目的…………………………………………………………… 6
第三節　研究架構…………………………………………………………… 7
第二章、資產報酬二階動差的動態結構：對投資組合管理的研究
第一節　文獻探討…………………………………………………………… 9
第二節　模型建構…………………………………………………………… 13
第三節　研究方法…………………………………………………………… 17
第四節　資料、統計量彙總與趨勢分析…………………………………… 22
第五節　結論………………………………………………………………… 41
第三章、資產報酬二階動差的動態結構：對資本資產定價的研究
第一節　文獻探討…………………………………………………………… 42
第二節　理論模型建構……………………………………………………… 45
第三節　實證模型與研究方法……………………………………………… 58
第四節　資料、統計量彙總與實證結果…………………………………… 64
第五節　結論………………………………………………………………… 85
第四章、資產報酬二階動差的動態結構：對資產報酬序列相關的研究
第一節　文獻探討…………………………………………………………… 86
第二節　理論模型…………………………………………………………… 89
第三節　研究方法與資料初步分析………………………………………… 90
第四節　實證分析…………………………………………………………… 93
第五節　結論………………………………………………………………… 100
第五章、總結……………………………………………………………………… 101
參考文獻 ………………………………………………………………………… 104



表目錄

表1、 、 與 的單根檢定結果表……………………………………..
29
表2、 、 與 的靜態模型及動態模型估計…………………………
31
表3、DCC-MVGARCH模型之參數估計……………………………………….. 34
表4、臺灣股價指數及十九類股指數報酬序列基本統計量……………………. 65
表5、 對加權股價指數及十九類類股指數報酬預測能力之實證結果…….
68
表6、 對加權股價指數及十九類類股指數報酬預測能力之實證結果……
69
表7、 對加權股價指數及十九類類股指數報酬預測能力之實證結果…..
70
表8、 與 對加權股價指數及十九類股報酬預測能力之實證結果….
71
表9、 與 對加權股價指數及十九類股報酬預測能力之實證結果…..
72
表10、3M-CAPM模型－第一階段迴歸之實證結果…………………………… 75
表11、3M-CAPM模型－二階段迴歸之實證結果………………………………. 76
表12、CCAPM模型－第一階段迴歸之實證結果………………………………. 78
表13、CCAPM模型－第二階段迴歸之實證結果………………………………. 79
表14、3M-CAPM模型＋CCAPM模型－第一階段迴歸之實證結果………….. 81
表15、3M-CAPM模型＋CCAPM模型－第二階段迴歸之實證結果…………. 82
表16、各類股3M-CAPM模型之 係數與CCAPM模型之 係數相關係數…
83
表17、大台指期、電子期、金融期與小台指期報酬率之敘述統計表………… 92
表18、正向回饋交易策略對期指短期報酬動態之實證結果…………………… 95
表19、停損交易的執行是否誘發空頭市場正向回饋交易頻率增加之實證結果 97
表20、開放期貨經理業務是否增加正向回饋交易之實證結果……………….. 99




圖目錄

圖1、研究流程與架構……………………………………………………………. 8
圖2、台灣加權指數成份股之 、 與 時間序列圖…………….
24
圖3、台灣加權指數成份股之 佔 比率之時間序列圖………………
25
圖4、台灣加權指數成份股之平均相關係數之時間序列圖…………………….. 27
圖5、台灣50與台灣加權股價指數之相關係數時間序列圖………………… 35
圖6、電子類股指數與台灣加權股價指數之相關係數時間序列圖………….. 36
圖7、金融類股指數與台灣加權股價指數之相關係數時間序列圖………….. 37
圖8、台灣50與台灣加權股價指數之 、 與 時間序列圖…..
38
圖9、電子類指數與台灣加權指數之 、 與 時間序列圖……
39
圖10、金融類指數與台灣加權指數之 、 與 時間序列圖……
40
圖11、3M-CAPM模型之 與CCAPM模型之 相關係數的時間序列圖……
84
圖12、台灣加權股價指數43個月移動平均超額報酬…………………………. 84

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