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研究生:陳冠憲
論文名稱:由馬可夫轉換模型與CAPM預測台股報酬率及變異數
指導教授:林修葳林修葳引用關係
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:國際企業學研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
中文關鍵詞:馬可夫轉換模型高低波動隨機漫步
外文關鍵詞:Markov switchingSWARCHCAPM
相關次數:
  • 被引用被引用:13
  • 點閱點閱:964
  • 評分評分:
  • 下載下載:236
  • 收藏至我的研究室書目清單書目收藏:1
本文在作臺股報酬率與變異數的預測,在預測方法上採用CAPM模型,搭配馬可夫轉換模型(Markov switching)與SWARCH模型(Markov-switching ARCH)進行預測,希望在考量臺股與那斯達克指數(Nasdaq)、道瓊股價指數(Dow Jones)及世界股價指數的連動性之後,可以改進以前文獻只用馬可夫轉換模型與SWARCH模型對臺股的預測能力。
相較於以往的相關研究,本論文特點為;
1、 將影響臺股報酬率或變異數的隨機變數,分別加以分析預測,並且將各隨機變數本身波動現像也納入考量。
2、 藉由各種模型在臺股不同波動狀態下的預測效果,探討臺股與世界指數或那斯達克指數超額報酬在不同波動狀態的連動性。
3、 重視投資人因資訊的增加,可以對母體參數重新估計的動態過程,本文將投資人因時間的經過所擁有的資訊增加,可對模型的參數重新估計納入考量,以期增加模型的預測能力。
4、 本文著重於樣本外的預測,因為樣本外的預測結果對實務界的幫助也最大。
5、 本文除了作報酬率的預測也作變異數的預測,希望投資人在不同的風險偏好下,有一良好的決策準則。
本研究實證結果發現
1、 在報酬率的預測上,如果CAPM加入馬可夫轉換模型以及SWARCH模型可以改進單純使用馬可夫轉換模型及SWARCH模型對臺股的預測能力
2、 當臺股在高波動期間,與國外的連動性較高,因此搭配CAPM模型的預測效果更顯著
3、 在變異數的預測上,CAPM搭配SWARCH模型也可以改進單純使用SWARCH模型的預測能力,但是CAPM搭配馬可夫轉換模型卻反而增加預測誤差
4、 隨機漫步模型並非無法打敗,尤其在變異數的預測,隨機漫步模型的優勢更低。
This thesis focuses on the predictions of stock returns and variance in Taiwan. The predictions on returns and variance are relying on using CAPM with Markov model or CAPM with SWARCH model. We hope when the contagion is considered in the prediction model, the accuracy of predictions will be increased. This thesis has the following characteristics:
1. Our model includes the variables that have contagion with the Taiwan stock market and also considers the status of these variables.
2. To study the extent of contagion between the Taiwan stock market, the Nasdaq, the Dow Jones and the world index from the accuracy of prediction.
3. Since our model assumes that the investors will have more information with the time passing, the processes of investors revising the parameters based upon the new information are included in our model.
4. We focus on the prediction accuracy of out of the samples.
5. We will study the predictions on both stock returns and variance.
According to our research results, we have the following inclusions:
1. The prediction performance is better using CAPM with Markov model or CAPM with SWARCH model than using Markov model or SWARCH model.
2. During the high volatility periods in Taiwan stocks, the Taiwan stocks have stronger contagion with the foreign stock market.
3. The CAPM with SWARCH model will decrease the prediction error of the SWARCH model. The CAPM with MS model will not increase the prediction accuracy of the MS model.
4. The Random walk model has better prediction performance in stock returns than in stock variance.
第一章 緒論及文獻探討1
第一節 緒論1
第二節 文獻探討3
MS與SWARCH4
CAPM(資本資產訂價模式)6
第二章 樣本與研究設計7
第一節 樣本資料7
第二節 模型介紹10
(一)、MS(馬可夫轉換模型)10
(二).SWARCH模型10
(三)本文採用的模型11
(四)、關於狀態的劃分12
第三節研究方法設計與研究限制13
權數保有期的設計13
二、研究限制14
第三章 模型的實証研究16
第一節 迴歸係數的估計方法說明18
第二節 SWARCH與MS預測方法之說明19
二、 MS模型的分析及預測方式說明23
三、 臺股本身藉SWARCH與MS預測方式說明26
四、臺股藉那斯達克指數預測(CAPM ND )及藉道瓊、世界指數預測(CAPM DJ WD)26
第三節 變異數的分析與說明27
第四章 實證結果與說明29
第一節 波動狀態的劃分說明29
第二節 各模型對臺股報酬率的預測結果及探討30
(壹)、全部預測期間的實證結果30
(貳)、不同波動期間各模型的預測能力33
第三節 各模型對臺股報酬率變異的預測結果及探討35
(一)各模型變異數計算方法說明35
(二)整個預測期間模型的預測能力36
(三)高低波動期間各模型的預測能力38
第五章結論42
結論43
參考文獻46
附錄一 傳統模型的介紹49
一、ARIMA模型49
二、ARCH(p,q)模型49
錄附二 隨機漫步模型與預測能力指標介紹50
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