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研究生:陳怡寧
研究生(外文):Yining Chen
論文名稱:以不對稱GARCH模型探討台灣股市報酬率與波動性
論文名稱(外文):Applying Asymmetric GARCH Models to the Returns and the Volatility in Taiwan Stock Market
指導教授:崔可欣崔可欣引用關係
指導教授(外文):Dr.Keshin Tswei
學位類別:碩士
校院名稱:國立中正大學
系所名稱:企業管理研究所
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:2004
畢業學年度:92
語文別:中文
論文頁數:58
中文關鍵詞:不對稱非線性平滑轉換一般自我迴歸殘差質模型門檻式一般自我迴歸殘差質模型指數型一般自我迴歸殘差質模型槓桿效果波動性不對稱回復
外文關鍵詞:ANST-GARCH modelTGARCH modelEGARCH modelleverage effectvolatilityasymmetricreversal
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股票報酬之波動估計對於資本資產定價、衍生性金融商品定價或是動態避險策略上的應用具有決定性之影響,故長久以來,股價報酬波動估計不僅成為許多國內外研究學者所關注的議題,更是眾多投資者所關注的焦點,亦在財務領域上扮演重要的角色。因此,如何建立正確的波動估計模型是相當重要的。故本研究利用學者Nam、Chong與Augustine(2002)所提出具有非線性平滑轉換特質的不對稱ANST-GARCH模型並假設股票報酬以回復(reversal)「幅度」與「速度」顯示不對稱性。並比較其他不對稱GARCH模型,如TGARCH模型與EGARCH模型,進一步探討台灣股市日報酬率與波動行為。研究期間選自1987年1月6日至2002年12月31日止,共4451筆交易日。實證結果發現:
一、台灣股市之負向報酬較正向報酬迅速且呈現大幅度的回復(reversal)。
二、波動與報酬的序列相關之間的反向關係與不對稱性是無關聯的,即使波動效果持續,不對稱仍然是顯著的且不具決定性的。
三、TGARCH模型較EGARCH模型與ANST-GARCH-AR(3)模型其他兩種模型更能捕捉波動不對稱行為。
四、ANST-GARCH模型一捕捉台灣股市報酬率 之波動不對稱性較其他模型更具配適性。
The volatility of the stock returns is determinate for the pricing of the equity and the derivatives, and the application of the strategy of the dynamic hedging.Long since, the volatility estimation of the returns becomes popular issue at home and abroad, and it also plays a very important role in the field of finance. Thus, it’s a very important thing that how to establish the fit model in the volatility estimation. This paper uses the asymmetric nonlinear smooth-transition(ANST)GARCH(M) models which suggested by Nam、Chong and Augustine(2002). And they hypothesize that stock returns
exhibit an asymmetric reverting pattern in terms of both reverting tendency (or speed) and magnitude.
We analyze and compare further with the returns and volatility of the asymmetric models: ANST-GARCH、TGARCH and EGARCH model in Taiwan Stock Market. The findings of this paper from for daily returns of Taiwan Stock market over the period of 1987:01
—2002:12 are: (a) negative returns on average reverted more quickly, with a greater reverting magnitude, to positive returns than positive returns revert to negative returns; (b) the results are not associated with the negative relationship between volatility and serial correlation in stock returns; (c) TGARCH model captures the asymmetric behavior well than EGARCH model and ANST-GARCH-AR(3) model; (d) the first type of the ANST-GARCH model more fit and captures the asymmetry well than the others in Taiwan Stock market.
目 錄
頁次
中文摘要…………………………………………………………Ⅰ
英文摘要…………………………………………………………Ⅱ
目錄………………………………………………………………Ⅲ
圖表目錄…………………………………………………………Ⅴ
第壹章 緒論
第一節 研究背景與動機…………………………………………1
第二節 研究目的…………………………………………………2
第三節 研究架構…………………………………………………3
第貳章 國內外文獻回顧
第一節 股票報酬之行為…………………………………………5
第二節 不對稱GARCH模型………………………………………7
第參章 理論基礎模型
第一節 ARCH模型…………………………………………………12
第二節 GARCH模型………………………………………………13
第三節 EGARCH模型………………………………………………15
第四節 TGARCH模型………………………………………………17
第五節 ANST-GARCH模型…………………………………………18
第肆章 研究方法
第一節 資料來源…………………………………………………25
第二節 單根檢定…………………………………………………25
第三節 常態性檢定………………………………………………27
第伍章 模型實證結果分析
第一節 資料處理與分析…………………………………………29
第二節 模型實證分析……………………………………………35
第陸章 結論與建議
第一節 結論………………………………………………………46
第二節 未來研究方向與建議……………………………………47
中英文參考文獻
一、中文文獻
二、英文文獻
圖 目 錄
圖 1-1.研究架構流程圖………………………………………4
圖 5-1.台灣股市加權股價指數走勢圖………………………29
圖 5-2.台灣股市股價指數報酬率變化圖……………………32
表 目 錄
表 5-1.台灣股市加權股價指數之ADF單根檢定表…………30
表 5-2.台灣股市股價指數報酬率之ADF單根檢定表………31
表 5-3.台灣股市股價指數報酬率之統計檢定量結果………33
表 5-4.均數方程式OLS估計結果……………………………33
表 5-5.LM統計量估計值………………………………………34
表 5-6.GARCH(1,1)模型估計結果……………………………35
表 5-7.EGARCH模型估計結果…………………………………36
表 5-8.TGARCH模型估計結果…………………………………38
表 5-9.ANSTGARCH-AR(3)模型估計結果……………………39
表 5-10.ANSTGARCH之模型一與模型二估計結果……………42
表 5-11.ANSTGARCH之模型三與模型四估計結果……………43
一、中文文獻
1.王甡,民國八十四年,「報酬衝擊對條件波動所造成之不對稱效果-台灣股票市場之實證分析」,證券市場發展季刊,第七卷第一期。
2.林楚雄,民國八十六年,「不對稱GARCH模型之建立:我國股票市場之實證研究」,國立中山大學企業管理所博士論文。
3.林楚雄,劉維琪,吳欽杉,民國八十八年,「不對稱GARCH模型的研究」,管理學報,第十六卷第三期。
4.鄭天德,民國九十一年,「ARMA-TGARCH模型之建立」,國立交通大學經營管理研究所碩士論文。
5.陳英生,民國八十八年,「台灣股市日內報酬波動之研究」,國立成功大學國際企業管理所碩士論文。
6.蔡美華,民國八十七年,「台股指數期貨與現貨報酬波動性關係之研究」,私立東吳大學企業管理所碩士論文。
7.蔡麗茹,葉銀華,民國八十八年,「不對稱GARCH族模型預測能力之比較研究」,輔仁管理評論,第七卷第一期。
8.鍾惠民,吳壽山,周賓凰,范懷文,民國九十一年,財金計量,一版,雙葉書廊有限公司。
二、英文文獻
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3.Black, F., 1976, “Studies of Stock, Price Volatility Changes”, Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, 177-181.
4.Bollerslev, T., 1986, “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics 31, 307-327.
5.Braun, P. A., D. B. Nelson, and A. M. Sunier, 1995, “Good News, Bad News, Volatility, and Beta”, Journal of Finance 50, 1575-1603.
6.Campbell, J.Y., Grossman, S.J., Wang, J., 1993, “Trading volume and serial correlation in stock returns”, Quarterly Journal of Economics 108, 905-939.
7.Campbell, J., and L. Hentschell, 1992, “No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns”, Journal of Financial Economics 31, 137-167.
8.Chris Brooks, 2002, Introductory Econometrics for Finance. ch5, ch7, ch8.
9.Christie, A., 1982, “The Stochastic Behavior of Common Stock Variances: Value, Leverage, and Interest Rate Effects”, Journal of Financial Economics 10, 407-432.
10.DeBondt, W.F.M., Thaler, R.H., 1985, “Does the stock market overreact?”, Journal of Finance 40, 793-805.
11.Dickey, D and W.A. Fuller, 1979, “Distribution of the Estimates for Autoregressive Time Series with a Unit Root”, Journal of the American Statistical Association 74, 427-431.
12.Dickey, D. A. and W. A. Fuller, 1981, “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root”, Econometrica 49, 1057-1072.
13.Engle, R. F., and V. Ng, 1993, “Measuring and Testing the Impact of News on Volatility”, Journal of Finance 45, 1749-1777.
14.Engle, R.F., 1982, “Autoregressive Condition Heteroskedasticity with Estimates of Variance of United Kingdom Inflation”, Econometrica 50, 987-1007.
15.Fornari, F., and A. Mele, 1997, “Sign and Volatility-Switching ARCH Models: Theory and and Applications to International Stock Markets,” Journal of Applied Econometrics 12, 49-65.
16.Gonzalez-Rivera, G., 1998, “Smooth transition GARCH models”, Studies in Nonlinear Dynamics and Econometrics 3, 61—78.
17.Granger, C. W. J. and P. Newbold, 1974, “Spurious Regressions in Econometrics”, Journal of Econometrics 2, 111-120.
18.Hentschel, L., 1995, “All in the Family Nesting Symmetric and Asymmetric GARCH Models”, Journal of Financial Economics 39, 71-104.
19.Koutmos, G. and J. Knif, 2002, “Time variation and asymmetry in systematic risk”, Journal of Multinational Financial Management 12, 261-271
20.Koutmos, G., 1998, “Asymmetries in the Conditional Mean and the Conditional Variance: Evidence From Nine Stock Markets”, Journal of Economics and Business 50, 277-290.
21.LeBaron, B., 1992, “Some relations between volatility and serial correlations in stock market returns”, Journal of Business 65, 199-219.
22.Lo, A.W., and MacKinlay, C. A., 1990, “An econometric analysis of nonsynchronous trading”, Journal of Econometrics 45, 181-211.
23.Lundbergh, S. and Teräsvirta, T., 1998, “Modelling Economic High-Frequency Time Series with STAR-STGARCH Models,” Working paper.
24.Nam, K., C.S. Pyun and A.C. Arize, 2002, “Asymmetric mean-reversion and contrarian profits: ANST-GARCH approach”, Journal of Empirical Finance 9, 563-588.
25.Nam, K., C.S. Pyum and S.W. Kim, 2003, “Is asymmetric mean-reverting pattern in stock returns systematic? Evidence from Pacific-basin markets in the short-horizon”, Journal of International Financial Markets, Institutions and Money 25, 807-824.
26.Nelson, D.B., 1991, “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica 59, 347-370.
27.Rabemananjara, R. and J. M. Zakolin, 1993, “Threshold ARCH Models and Asymmetries in Volatility”, Journal of Applied Econometrics 8, 31-49.
28.Said, S. and D. Dickey, 1984, “Testing for Unit Roots in Autoregressive-Moving Average Models with Unknown Order”, Biometrica 71, 599-607.
29.Schwert, G. W., 1990, “Stock Volatility and the Crash of 87”, Review of Financial Studies 3, 77-102.
30.Sentana, E., Wadhwani, S., 1992, “Feedback traders and stock return autocorrelations: evidence from a century of daily data”, Economic Journal 102, 415-425.
31.Zakoian, J.M., 1990, “Threshold Heteroskedastic Models”, manuscript, CREST, INSEE, Paris.
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