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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳文方 | |
dc.contributor.author | Hsi-Man Lu | en |
dc.contributor.author | 盧錫滿 | zh_TW |
dc.date.accessioned | 2021-06-08T06:57:34Z | - |
dc.date.copyright | 2009-07-17 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-07-15 | |
dc.identifier.citation | Basak, S. and Shapiro, A. (2001). 'Value at Risk Based Risk Management: Optimal Policies and Asset Prices.' The Review of Financial Studies 14(2): 371-405.
Basel Committee on Banking Supervision (1996). Supervisory Framework for the Use of Backtesting in Conjunction with the Internal Models Approach to Market Risk Capital Requirements, http://www.bis.org/publ/bcbs22.pdf. Bodjanova, S. (1999). 'Exploratory Analysis of Empirical Frequency Distributions Based on Partition Entropy.' Information Sciences 121(1-2): 135-147. Bollerslev, T. (1986). 'Generalized Autoregressive Conditional Heteroskedasticity.' Journal of Econometrics 31(3): 307-327. Bollerslev, T., Chou, R.Y. and Kroner, K.F. (1992). 'ARCH Modeling in Finance : A Review of the Theory and Empirical Evidence.' Journal of Econometrics 52(1-2): 5-59. Bystrom, H. N. E. (2004). 'Orthogonal GARCH and Covariance Matrix Forecasting: the Nordic Stock Markets during the Asian Financial Crisis 1997-1998.' European Journal of Finance 10(1): 44-67. Cathy W.S., Chen, M. K. P., S. and R. H., G. (2005). Assessing and Testing for Threshold Nonlinearity in Stock Returns, Aust. N. Z. J. Stat. 47(4): 473-488. Diebold, F.X. (1988). Empirical Modeling of Exchange Rate Dynamics, Springer Verlag, Berlin. Engel, J. and Gizycki, M. (1999). 'Conservatism, Accuracy and Efficiency: Comparing Value at Risk Models.' Working Paper 2, Australian Prudential Regulation Authority. Glosten, L. R., Jagannathan, R., et al. (1993). 'On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.' Journal of Finance 48(5): 1779-1801. Jackson, P., Maude, D.J. and Perraudin, W. (1997). 'Bank Capital and Value at Risk.' Journal of Derivatives 4(3):73-89. Jorion, P. (1996). 'Risk : Measuring the Risk in Value at Risk.'Financial Analysts Journal 52(6): 47-56. Kuan, C.M., Yeh, J.H., et al. (2009). 'Assessing Value at Risk with CARE, the Conditional Autoregressive Expectile Models.' Journal of Econometrics 150(2): 261-270. Liu, L. M. (2006). Time Series Analysis and Forecasting, Second Edition,Chicago: Scientific Computing Associates. Ljung, G. M. and Box, G. E. P. (1979). 'The Likelihood Function of Stationary Autoregressive-Moving Average Models.' Biometrika 66(2): 265-270. McLeod, A. I and Li, W.K. (1983). 'Diagnostic Checking ARMA Time Series Models Using Squared-Residual Autocorrelations.' Journal of Time Series Analysis V2: 301-305. McMillan, D. G. and Speight, A. E. H. (2007). 'Value at Risk in Emerging Equity Markets: Comparative Evidence for Symmetric, Asymmetric, and Long Memory GARCH Models.' International Review of Finance 7(1/2): 1-19. Phillips, P. C. B. (1987). 'Time Series Regression with a Unit Root.' Econometrica 55(2): 277-301. Schwert, G. W. (1989). 'Tests for Unit Roots: A Monte Carlo Investigation.' Journal of Business & Economic Statistics 7(2): 147-159. Zakoian, J. M. (1994). 'Threshold Heteroskedastic Models.' Journal of Economic Dynamics and Control 18(5): 931-955. 徐靖淵,2008,「考量波動性風險下之投資組合配置-以台灣股票市場為例」,台灣大學工業工程學研究所碩士論文。 陳啟斌、連文仁、李昆遠和裴蕾,2004,「股票投資組合風險衡量模型精確度之評估」,立德學報,頁次31-48。 楊奕農,2005,「時間序列分析-經濟與財務上之應用」,雙葉書廊出版社。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/25957 | - |
dc.description.abstract | 投資人若瞭解並事前掌握股票報酬率波動性,較能作出正確的投資決策。為瞭解股票報酬率的波動性,本論文首先採用單變量GARCH (Generalized Autoregressive Conditional Heteroskedasticity)或TGARCH (Threshold GARCH)模型來計算各支股票報酬率的變異數;而後將單變量GARCH模型擴充至多變量Constant Correlation GARCH 或Orthogonal GARCH模型,以建構一隨時間變化的共變異數矩陣;最後再應用「熵權重法」求得投資組合中各支股票的權重,並計算出不同信賴水準下投資組合的風險值。本論文於模型建構、推導時發現,使用Orthogonal GARCH模型時,因考慮到所有主成份的影響,較能精確地衡量出每日的風險值;而使用Constant Correlation GARCH模型時,因假設各股間的相關係數不變,可能會造成風險值低估的現象。在數值計算方面,本論文於MSCI中挑選數支股票進行投資組合之風險值估算,結果發現利用Orthogonal GARCH模型來衡量投資組合的風險值較應用Constant Correlation GARCH模型來得精確、有效。 | zh_TW |
dc.description.abstract | Investors can make appropriate decisions if they can grasp and control volatility of the stock return rate in advance. The objective of this research is to find an appropriate VaR (Value at Risk) model that can estimate the time-varying volatility of a portfolio. When developing the VaR model, the portfolio return covariance matrix is a key factor. This matrix contains two parts. The first part consists of a univariate GARCH model and an asymmetric GARCH model called TGARCH. And the second part consists of a simplified multivariate GARCH model which, in turn, can be a Constant Correlation GARCH model or an Orthogonal GARCH model. The former is then incorporated into the latter part to generate four different time-varying covariance matrices. The other factor affecting a VaR model are weights of the stocks invested. Entropy weighting method is used to obtain weights of stocks in the portfolio. Finally, covariance matrices and weights are used to compute VaR under different confidence levels. It is found that, considering all principal components, a VaR model can provide more accurate estimation if the Orthogonal GARCH model is employed. On the other hand, the assumption that stock correlation matrix is constant under Constant Correlation GARCH model may underestimate VaRs. The above proposed VaR model is used to analyze a few selected common stocks from Taiwan equity market. The forecasting results of VaR models are compared with the actual return of portfolio to examine the appropriateness of the proposed model. It is found that the VaR model under Orthogonal GARCH model gives us more accurate result than that under Constant Correlation GARCH model. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T06:57:34Z (GMT). No. of bitstreams: 1 ntu-98-R95546009-1.pdf: 1386248 bytes, checksum: 9dc7a0d2e32f77a0c99bc583712eb946 (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | 目錄
中文摘要 i Abstract ii 目錄 iii 圖目錄 v 表目錄 vi 第1章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 3 1.4 研究流程 4 1.5 章節概要 6 第2章 文獻探討 7 2.1 風險值定義 7 2.2 風險值的估計方法 9 2.3 風險值模型檢定 11 2.4 波動性的估計方法 11 2.5 投資組合資產權重 14 第3章 投資組合波動性風險的問題模型 17 3.1 資料穩定性檢定 19 3.2 單變量GARCH模型 21 3.3 多變量GARCH模型 28 3.4 投資組合權重之決定-熵權重法(Entropy Weighting) 31 3.5 投資組合風險值之計算 34 3.6 風險值模型之驗證 36 3.7 投資組合的波動性風險模型 41 第4章 實証資料與結果分析 45 4.1 資料來源與處理 45 4.1.1 資料選取 45 4.1.2 資料統計特性 47 4.1.3 數列單根檢定 47 4.2 單變量GARCH模型估計 48 4.3 多變量GARCH模型估計 54 4.3.1 Constant Correlation GARCH模型 54 4.3.2 Orthogonal GARCH模型 54 4.4 投資組合權重分配 59 4.5 風險值結果分析 61 4.5.1 風險值模型系統架構 61 4.5.2 風險值模型驗證 63 4.5.3 風險值模型比較 67 第5章 結論與未來研究建議 68 5.1 結論 68 5.2 未來研究建議 69 參考文獻 70 | |
dc.language.iso | zh-TW | |
dc.title | 以GARCH模型衡量投資組合的波動性風險-
台灣股票市場為例 | zh_TW |
dc.title | Using GARCH Models in Estimating the Volatility
Risk of Portfolio with Taiwan Equity Market | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 吳政鴻,洪一薰 | |
dc.subject.keyword | 波動性,GARCH模型風險值,投資組合,正交,回溯測試, | zh_TW |
dc.subject.keyword | Volatility,GARCH Model,VaR,Portfolio,Orthogonal,Back testing, | en |
dc.relation.page | 72 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2009-07-16 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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