Publications-Theses

題名 台灣股市的波動外溢效果之研究
作者 吳旻容
Wu, Min Jung
貢獻者 饒秀華
Rau,Hsiu Hau
吳旻容
Wu, Min Jung
關鍵詞 多變量
GARCH模型
波動性
外溢效果
不對稱性
multivariate
GARCH model
volatility
spillover effect
asymmetry
日期 2007
上傳時間 18-Sep-2009 14:14:27 (UTC+8)
摘要 本研究使用相關係數隨時間變動的雙變量GARCH(1,1)模型(time-varying correlation bivariate GARCH(1,1) model),討論台灣股票市場中,大公司與小公司之間的報酬、衝擊(shock)、波動(volatility)是否互為影響為主軸。其次,為了了解不同估計方法、相關係數的設定和解釋變數對結果造成的影響,亦設立了3種模型,作為本研究的比較模型。
本研究發現大公司與小公司過去的報酬,存在雙向的報酬外溢效果。換句話說,大公司與小公司過去的報酬分別都對「本身報酬」有影響外,對「對方的報酬」也有影響。進一步發現到:大公司過去受到的衝擊和波動不僅對本身的條件變異數造成影響,也影響到小公司的條件變異數。但相反地,小公司過去受到的衝擊和波動,只對本身的條件變異數有影響,對大公司的條件變異數沒有影響,所以大、小公司間的衝擊外溢效果和波動外溢效果有不對稱的現象。
從不同模型之比較也發現,在討論大公司與小公司的報酬及波動時,應重視兩者彼此相互影響的關係,在估計時使用多變量的方法,以捕捉彼此相依的條件共變異數及條件變異數之動態過程。除此之外,也應考量兩者的相關係數隨時間變動的特性,及過去的波動對描述對方條件變異數的重要性。

關鍵字:多變量、GARCH模型、波動性、外溢效果、不對稱性
參考文獻 [1] 王元章(1990),交易量、股價波動性及波動性外溢─台灣股市之實證研究,中華財務學會1999年會暨財務金融學術論文研討會,pp.995~1016
[2] 王英明(2007),台股報酬波動與訊息到達之關係硏究,國立政治大學國際貿易硏究所,碩士論文
[3] 郭俊宏(2004),多變量條件變異數模型之比較分析,國立台灣大學經濟學硏究所,碩士論文
[4] Akaike(1973),Information theory and an extension of the maximum likelihood principle. In B.N. Petrov and F. Csaki(eds.),2nd International Symposium on Information Theory,pp.267~281
[5] Akgiray(1989),Conditional heteroskedasticity in time series of stock returns: Evidence and forecasts,Journal of Business 62,pp.55~80
[6] B.B.Mandelbrot (1963),The variation of certain speculative prices, The Journal of Business of the University of Chicago 36,pp.394~419
[7] Bollerslev(1986),Generalize autoregressive conditional heteroskedasticity.,Journal of Econometrics 31,pp.307~327
[8] Bollerslev, Engle, and Wooldridge(1988),A capital-asset pricing model with time-varying covariances,Journal of Political Economy 96,pp.116~131
[9] Bollerslev(1990),Modelling the coherence in short-run nominal exchange rates: Multivariate generalized ARCH approach,Review of Economics and Statistics 72,pp.498~505
[10] Chelley-Steeley and Steeley(1996),Volatility transmission in the UK equity market.,The European Journal of Finance 2,pp.145~160
[11] Chou, R. F. (1988),Volatility persistence and stock valuations: Some empirical evidence using GARCH,Journal of Applied Econometrics 3,pp.279~294
[12] Conrad, Gultekin, and Kaul(1991),Asymmetric predictable of conditional variances.,The Review of Financial Studies 4,pp.597~622
[13] Engle(1982),Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations.,Journal of Financial Economics 19,pp.987~1007
[14] Engle and Kroner(1995),Multivariate simultaneous generalized ARCH, Econometric Theory 11,pp.122~150
[15] Engle(2002),Dynamic conditional correlation: A simple class of multivariate GARCH models,Journal of Business and Economic Statistics 20,pp.339~350
[16] French, Schwert, and Stambaugh(1987),Expected stock returns and volatility,Journal of Financial Economics 19,pp.3~29
[17] Glosten, Jagamnnathan, and Runkle(1993),On the relation between the expected value and the volatility of the nominal excess return on stocks,Journal of Finance 48,pp.1779~1801
[18] Hamao,Masulis,and Ng(1990),Correlation in price changes and volatility across international stock markets,The Review of Financial Studies,pp.281-307
[19] King and Wadhwani (1990),Transmission of Volatility between Stock
Markets.,Review of Financial Studies 3,pp.5~33
[20] Lo and Mackinlay(1990),When are contrarian profits due to stock market over-reaction?,Review of Financial Studies 3,pp.175~205
[21] Nelson(1991),Conditional heteroskedasticity in asset returns: A new approach, Econometrica 59,pp.347~370
[22] Pyun, Lee, and Nam(2000),Volatility and information flows in emerging equity market: A case of the Korean stock exchange.,International Review of Financial Analysis 9,pp.405~420
[23] Reyes(2001),Asymmetric volatility spillover in the Tokyo stock exchange.”,Journal of Economics and Finance 25,pp.206~213
[24] Ross(1989),Information and volatility: The no-arbitrate martingale approach to timing and resolution irrelevancy.,Journal of Finance 44,pp.1~17
[25] Schwert and Seguin(1990),Heteroskedasticity in stock returns,Journal of Finance 45,pp.1129~1156
[26] Tse and Tsui(2002),A multivariate GARCH model with time-varying correlations,Journal of Business & Economic Statistics 10,pp.351~362
描述 碩士
國立政治大學
國際經營與貿易研究所
95351021
96
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0095351021
資料類型 thesis
dc.contributor.advisor 饒秀華zh_TW
dc.contributor.advisor Rau,Hsiu Hauen_US
dc.contributor.author (Authors) 吳旻容zh_TW
dc.contributor.author (Authors) Wu, Min Jungen_US
dc.creator (作者) 吳旻容zh_TW
dc.creator (作者) Wu, Min Jungen_US
dc.date (日期) 2007en_US
dc.date.accessioned 18-Sep-2009 14:14:27 (UTC+8)-
dc.date.available 18-Sep-2009 14:14:27 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 14:14:27 (UTC+8)-
dc.identifier (Other Identifiers) G0095351021en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/35133-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營與貿易研究所zh_TW
dc.description (描述) 95351021zh_TW
dc.description (描述) 96zh_TW
dc.description.abstract (摘要) 本研究使用相關係數隨時間變動的雙變量GARCH(1,1)模型(time-varying correlation bivariate GARCH(1,1) model),討論台灣股票市場中,大公司與小公司之間的報酬、衝擊(shock)、波動(volatility)是否互為影響為主軸。其次,為了了解不同估計方法、相關係數的設定和解釋變數對結果造成的影響,亦設立了3種模型,作為本研究的比較模型。
本研究發現大公司與小公司過去的報酬,存在雙向的報酬外溢效果。換句話說,大公司與小公司過去的報酬分別都對「本身報酬」有影響外,對「對方的報酬」也有影響。進一步發現到:大公司過去受到的衝擊和波動不僅對本身的條件變異數造成影響,也影響到小公司的條件變異數。但相反地,小公司過去受到的衝擊和波動,只對本身的條件變異數有影響,對大公司的條件變異數沒有影響,所以大、小公司間的衝擊外溢效果和波動外溢效果有不對稱的現象。
從不同模型之比較也發現,在討論大公司與小公司的報酬及波動時,應重視兩者彼此相互影響的關係,在估計時使用多變量的方法,以捕捉彼此相依的條件共變異數及條件變異數之動態過程。除此之外,也應考量兩者的相關係數隨時間變動的特性,及過去的波動對描述對方條件變異數的重要性。

關鍵字:多變量、GARCH模型、波動性、外溢效果、不對稱性
zh_TW
dc.description.tableofcontents 目錄
1.緒論…………………………………………………………………………….6
2.文獻探討……………………………………………………………………….7
3.研究方法……………………………………………………………………….14
3.1單變量GARCH(1,1)模型
─[模型1]:單變量GARCH(1,1)模型…………………………………….15
3.2多變量GARCH(1,1)模型…………………………………………………..19
3.2.1[模型2]:雙變量GARCH(1,1)模型─相關係數為固定常數………19
3.2.2[主要模型]:雙變量GARCH(1,1)模型─相關係數隨時間變動…...23
3.2.3[模型3]:雙變量GARCH(1,1)模型─相關係數隨時間變動………24
(沒有考慮波動外溢效果)
4.觀察資料說明………………………………………………………………….26
4.1使用資料……………………………………………………………………26
4.2敘述性統計量及資料特性…………………………………………………26
5.實證結果……………………………………………………………………….30
5.1變數篩選……………………………………………………………………30
5.2模型檢查……………………………………………………………………32
5.3外溢效果……………………………………………………………………33
6.結論…………………………………………………………………………….42
參考文獻…………………………………………………………………………43


表目錄
表1:本研究與過去相關研究的比較…………………………………………12
表2:本研究使用的模型………………………………………………………15
表3:使用資料之敘述性統計量………………………………………………27
表4:使用資料之單根檢定……………………………………………………30
表5:使用資料在AR(p)模型,p=0~10時的AIC值…………………………..31
表6:使用資料在AR(4)模型時的係數估計………………………………….32
表7:[模型1]、[模型2]、[主要模型]、[模型3]的模型檢查……………….33
表8:[模型1]的估計結果……………………………………………………...34
表9:[模型2]的估計結果……………………………………………………...36
表10:[主要模型]的估計結果…………………………………………………38
表11:[模型3]的估計結果…………………………………………………….40
表12:本研究使用模型的實證結果…………………………………………..41


圖目錄
圖1:大公司與小公司的日報酬率取自然對數之後的樣本相關係數………...22
圖2:TWN50日報酬率取自然對數的ACF和PACF………………………….28
圖3:TM100日報酬率取自然對數的ACF和PACF…………………………..29
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0095351021en_US
dc.subject (關鍵詞) 多變量zh_TW
dc.subject (關鍵詞) GARCH模型zh_TW
dc.subject (關鍵詞) 波動性zh_TW
dc.subject (關鍵詞) 外溢效果zh_TW
dc.subject (關鍵詞) 不對稱性zh_TW
dc.subject (關鍵詞) multivariateen_US
dc.subject (關鍵詞) GARCH modelen_US
dc.subject (關鍵詞) volatilityen_US
dc.subject (關鍵詞) spillover effecten_US
dc.subject (關鍵詞) asymmetryen_US
dc.title (題名) 台灣股市的波動外溢效果之研究zh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] 王元章(1990),交易量、股價波動性及波動性外溢─台灣股市之實證研究,中華財務學會1999年會暨財務金融學術論文研討會,pp.995~1016zh_TW
dc.relation.reference (參考文獻) [2] 王英明(2007),台股報酬波動與訊息到達之關係硏究,國立政治大學國際貿易硏究所,碩士論文zh_TW
dc.relation.reference (參考文獻) [3] 郭俊宏(2004),多變量條件變異數模型之比較分析,國立台灣大學經濟學硏究所,碩士論文zh_TW
dc.relation.reference (參考文獻) [4] Akaike(1973),Information theory and an extension of the maximum likelihood principle. In B.N. Petrov and F. Csaki(eds.),2nd International Symposium on Information Theory,pp.267~281zh_TW
dc.relation.reference (參考文獻) [5] Akgiray(1989),Conditional heteroskedasticity in time series of stock returns: Evidence and forecasts,Journal of Business 62,pp.55~80zh_TW
dc.relation.reference (參考文獻) [6] B.B.Mandelbrot (1963),The variation of certain speculative prices, The Journal of Business of the University of Chicago 36,pp.394~419zh_TW
dc.relation.reference (參考文獻) [7] Bollerslev(1986),Generalize autoregressive conditional heteroskedasticity.,Journal of Econometrics 31,pp.307~327zh_TW
dc.relation.reference (參考文獻) [8] Bollerslev, Engle, and Wooldridge(1988),A capital-asset pricing model with time-varying covariances,Journal of Political Economy 96,pp.116~131zh_TW
dc.relation.reference (參考文獻) [9] Bollerslev(1990),Modelling the coherence in short-run nominal exchange rates: Multivariate generalized ARCH approach,Review of Economics and Statistics 72,pp.498~505zh_TW
dc.relation.reference (參考文獻) [10] Chelley-Steeley and Steeley(1996),Volatility transmission in the UK equity market.,The European Journal of Finance 2,pp.145~160zh_TW
dc.relation.reference (參考文獻) [11] Chou, R. F. (1988),Volatility persistence and stock valuations: Some empirical evidence using GARCH,Journal of Applied Econometrics 3,pp.279~294zh_TW
dc.relation.reference (參考文獻) [12] Conrad, Gultekin, and Kaul(1991),Asymmetric predictable of conditional variances.,The Review of Financial Studies 4,pp.597~622zh_TW
dc.relation.reference (參考文獻) [13] Engle(1982),Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations.,Journal of Financial Economics 19,pp.987~1007zh_TW
dc.relation.reference (參考文獻) [14] Engle and Kroner(1995),Multivariate simultaneous generalized ARCH, Econometric Theory 11,pp.122~150zh_TW
dc.relation.reference (參考文獻) [15] Engle(2002),Dynamic conditional correlation: A simple class of multivariate GARCH models,Journal of Business and Economic Statistics 20,pp.339~350zh_TW
dc.relation.reference (參考文獻) [16] French, Schwert, and Stambaugh(1987),Expected stock returns and volatility,Journal of Financial Economics 19,pp.3~29zh_TW
dc.relation.reference (參考文獻) [17] Glosten, Jagamnnathan, and Runkle(1993),On the relation between the expected value and the volatility of the nominal excess return on stocks,Journal of Finance 48,pp.1779~1801zh_TW
dc.relation.reference (參考文獻) [18] Hamao,Masulis,and Ng(1990),Correlation in price changes and volatility across international stock markets,The Review of Financial Studies,pp.281-307zh_TW
dc.relation.reference (參考文獻) [19] King and Wadhwani (1990),Transmission of Volatility between Stockzh_TW
dc.relation.reference (參考文獻) Markets.,Review of Financial Studies 3,pp.5~33zh_TW
dc.relation.reference (參考文獻) [20] Lo and Mackinlay(1990),When are contrarian profits due to stock market over-reaction?,Review of Financial Studies 3,pp.175~205zh_TW
dc.relation.reference (參考文獻) [21] Nelson(1991),Conditional heteroskedasticity in asset returns: A new approach, Econometrica 59,pp.347~370zh_TW
dc.relation.reference (參考文獻) [22] Pyun, Lee, and Nam(2000),Volatility and information flows in emerging equity market: A case of the Korean stock exchange.,International Review of Financial Analysis 9,pp.405~420zh_TW
dc.relation.reference (參考文獻) [23] Reyes(2001),Asymmetric volatility spillover in the Tokyo stock exchange.”,Journal of Economics and Finance 25,pp.206~213zh_TW
dc.relation.reference (參考文獻) [24] Ross(1989),Information and volatility: The no-arbitrate martingale approach to timing and resolution irrelevancy.,Journal of Finance 44,pp.1~17zh_TW
dc.relation.reference (參考文獻) [25] Schwert and Seguin(1990),Heteroskedasticity in stock returns,Journal of Finance 45,pp.1129~1156zh_TW
dc.relation.reference (參考文獻) [26] Tse and Tsui(2002),A multivariate GARCH model with time-varying correlations,Journal of Business & Economic Statistics 10,pp.351~362zh_TW
dc.relation.reference (參考文獻) zh_TW