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研究生:廖千慧
研究生(外文):Chien-Huei Liao
論文名稱:雙重狀態貝它係數之國際資本資產訂價模式建構與檢測-國際主要股價指數報酬實證研究
論文名稱(外文):The Two-Beta-Regime International Capital Asset Pricing Model Created and Examined-An Empirical Study on Major Stock Market Index Returns
指導教授:黎明淵黎明淵引用關係
指導教授(外文):Ming-Yuan Li
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:經濟學系
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:69
中文關鍵詞:國際資本資產訂價模型股價指數報酬馬可夫轉換模型
外文關鍵詞:International Capital Asset Pricing ModelStock ReturnsMarkov-switching models
相關次數:
  • 被引用被引用:5
  • 點閱點閱:367
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
本文運用馬可夫轉換 (Markov-switching) 模型劃分國際股市相異高、低波動狀態,利用指示函數的方法,結合GARCH (generalized autoregressive conditional heteroscedasticity) 模型,建構雙重狀態貝他係數與變異數非固定之國際資本資產訂價模式 (international capital asset pricing model ,ICAPM)。採以美國道瓊指數、日本恆生指數、德國法蘭克褔指數、英國倫敦時報FTSE100指數、加拿大多倫多TSE300指數、澳洲雪梨綜合股價指數、台灣加權股價指數、香港恆生指數、新加坡海峽時報指數、韓國加權股價指數等十個國家的股價指數週報酬視為各單一證券報酬;至於市場報酬,以摩根史坦利公司所編製的世界指數之報酬率為替代變數,藉以檢測與分析所建構的模型在相異股市波動狀態下,系統風險貝他係數的非齊一性。
本實證研究結果發現:
1、國際股市報酬及世界指數報酬其變異數均具有雙重狀態現象。
2、傳統的ICAPM其單一的系統風險為各種市場波動狀下的平均值,實際的系統風險並非齊一,且具有統計顯著性。
3、在我們所建構雙重狀態β與變異數非固定之ICAPM的模型中,其最佳與次佳的MSE、MAE預測能力指標的表現、R2及最大概似估計值均落於我們所建構的9個模型中,優於傳統的ICAPM模型。
The paper adopts Markov-switching (hereafter, MS) models to create and examine Two-Beta-Regime International Capital Asset Pricing (hereafter, ICAPM) model. Specifically, we use MS model to identify the high or low volatility regimes of stock market index returns, and incorporate indicator function settings and GARCH models to control a time varying of stock index return volatility and a state varying of betas in the ICAPM model. Ten major stock markets weekly index returns discussing in this paper include Dow Jones, Nikkei, Frankfurt-Commerzbank Index, London-FTSE100, Toronto-TSE300, Sydney Stock Exchange, Taiwan Stock Exchange, Hang-Seng, Singapore-Straitrs and Seoul-Composite. Besides, we adopt Morgan Stanley World index to server as the market returns. To create and examine in the different volatility regimes of stock market if the beta is single.
According to our research results, we have the following inclusions:
1.There are two volatility regimes of stock market index returns of ten major stock market and Morgan Stanley world index.
2. The single beta of traditional ICAPM is the average of different volatility regimes of stock market. In fact, there are two betas and they are statistical significantly.
3.The best and the second models fall on the two-Beta-Regime International Capital Asset Pricing model that we create 9 models, including the prediction error of MSE, MAE and R2 as well as the value of maximum likelihood estimation, it is better than traditional ICAPM model
目錄
中文摘要………………………………………………………………………i
英文摘要……………………………………………………………………i i
第一章 緒論
第一節 研動機與目的……………………………………………………1
第二節 本文架構…………………………………………………………3
第二章 文獻探討
第一節 理論文獻………………………………………………………4 第二節 實證論文獻…………………………………………………………5
第三章 模型設定
第一節 傳統ICAPM模型-線性單重狀態與變異數固定模型………8
第二節 變異數非固定之ICAPM模型…………………………………10
第三節 雙重狀態β與變異數非固定之ICAPM模型…………………11
第四章 實證結果與分析
第一節 資料基本統計特性與說明……………………
一、 研究對象及研究期間………………………………………17
二、 資料基本統計特性…………………………………………18
第二節 各國與世界股市指數報酬之相關係數………………………21
第三節 檢定股價是否有轉換現象……………………………………22
第四節 各國股市波動狀態劃分 ……………………………………25
第五節 模型的統計檢定
一、傳統ICAPM模型的估計結果…………………………………30
二、異質變異之ICAPM模型的統計檢定結果……………………30
三、雙重狀態β與異質變異之ICAPM模型的統計檢定結果……30
四、比較所有模型間的系統風險及最大概約估計式及R2
(一) 比較所有模型間的最大概約估計式…………………33
(二) 比較所有模型間的R2…………………………………33
(三) 比較所有模型間的系統風險…………………………34
第六節 模型預測能力比較…………………………………………36
第五章 結論………………………………………………………………….37
參考文獻………………………………………………………………………65
表 次
表1 各國與世界股市指數報酬之基本統計量……………………………38
表2 各國與世界股市指數報酬之相關係數矩陣…………………………39
表3 線性單重狀態模型……………………………………………………40
表4 變異數具狀態轉換……………………………………………………41
表5 各國主要股市波動性狀態劃分期間…………………………………42
表6 國際股市處於高波動狀態- 政經突發事件一覽表…………………26
表7 台灣股市處於高波動狀態期間- 政經突發事件一覽表……………28
表8 傳統ICAPM模型- 線性單重狀態與變異數固定模型的估計結果…44
表9 異質變異之ICAPM模型統計檢定結果……………………………….45
表 10-1 模型1國內股市處於高波動………………………………………46
表 10-2 模型2國際股市處於高波動………………………………………47
表 10-3 模型3國際及國內股市處於高波動狀態…………………………48
表 10-4 模型4國內股市處於高波動狀態且國際股市處於小幅波動……49
表 10-5 模型5國內股市處於低波動狀態且國際股市處於高波動狀態…50
表 10-6 模型6國際及國內股市均處於低波動狀態………………………51
表 10-7 模型7國際股市及各國國內股市均處於高波動狀態且國際股市處於下跌…………………………………………………………………………52
表 10-8 模型8國際股市及國內股市均處於高波動狀態且國內股市處於下跌………………………………………………………………………………53
表10-9 模型9-90年代以後國際股市及國內股市均處於高波動狀態……54
表11 比較所有模型間的最大概似函數值…………………………………55
表12 比較所有模型間的R2…………………………………………………55
表13 比較所有模型間的系統風險…………………………………………56
表14 模型預測能力比較-以MSE做為預測能力的指標……………………57
表15 模型預測能力比較-以MAE為預測能力的指標………………………57
表16 不同模型設定下,決策的改變………………………………………58
圖 次
圖1 紐約道瓊工業平均指數週報酬率值……………………………………………59
圖2 紐約道瓊工業平均指數週報酬波動態平滑機率值…………………59
圖3 東京日經道瓊平均指數週報酬率值…………………………………59
圖4 東京日經道瓊平均指數週報酬波動態平滑機率值…………………59
圖5 德國法克福商銀指數週報酬率值……………………………………60
圖6 德國法克福商銀指數週報酬波動態平滑機率值……………………60
圖7 倫敦時報FTSE100指數週報酬率值……………………………………60
圖8 倫敦時報FTSE100指數週報酬波動態平滑機率值……………………60
圖9 加拿大多倫多TSE300指數週報酬率值………………………………61
圖10 加拿大多倫多TSE300指數週報酬波動態平滑機率值………………61
圖11 澳洲雪梨綜合股價指數週報酬率值…………………………………61
圖12 澳洲雪梨綜合股價指數週報酬波動態平滑機率值…………………61
圖13 台灣加權股價指數週報酬率值………………………………………62
圖14 台灣加權股價指數週報酬波動態平滑機率值………………………62
圖15 香港恆生指數週報酬率值……………………………………………62
圖16 香港恆生指數週報酬波動態平滑機率值……………………………62
圖17 新加坡海峽時報指數週報酬率值……………………………………63
圖18 新加坡海峽時報指數波動態平滑機率值……………………………63
圖19 韓國加權股價指數週報酬率值………………………………………63
圖20 韓國加權股價指數週報酬波動態平滑機率值………………………63
圖21 摩根史坦利公司的世界指數週報酬率值……………………………64
圖22 摩根史坦利世界指數週報酬波動態平滑機率值……………………64
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