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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/31186


    Title: 巴塞爾內部法下銀行可能的資本節制-以GARCH type 模型為例
    Authors: 陳婉真
    Contributors: 李桐豪
    陳婉真
    Keywords: 風險值
    回顧測試
    風險管理
    GARCH
    Date: 2006
    Issue Date: 2009-09-14 09:29:47 (UTC+8)
    Abstract: 因應國際接軌,台灣在風險管理上將採用國際清算銀行所公佈的規範,其中,在市場風險方面,巴塞爾銀行監理委員會(BaselCommittee on Banking Supevision, BCBS)允許銀行能夠使用風險值(Value at Risk, VaR)模型來衡量市場風險,同時明文規定可透過事後的回顧測試作模型適合度的檢查,若模型有不正確的疑慮,監理機構可給予較高的乘數因子,以要求銀行提高資本保留。本研究嘗試以四種模型,包含EWMA、AR(1)-GARCH(1,1)、AR(1)-EGARCH(1,0)、AR(1)-TAR-GARCH(1,1)搭配兩種分配(常態分配、拔靴法)計算風險值並進行回顧測試。透過實證結果,發現一般金融資產在假設常態分配下使用GARCH-type估算風險值時,有低估風險的現象,伴隨著的是,進行回顧測試時會有較高的例外數(exception)。本文建議以拔靴法搭配GARCH-type模型進行風險值的估算,更能表達金融資產厚尾(fat tail)高峰的特性,此為本文貢獻之一。同時,實證結果亦指出透過不同模型估算出的風險值,對相同的投資組合能產生不同的資本提列,並且,更進一步發現,對於某些低估風險的模型,即使已經透過增加乘數的方式,其提撥的資本仍舊較低。對銀行來說,能藉著選取模型來節約成本追求利潤極大;相反的,對金融監理機構而言,則表示在目前的規範下,並無法有效的促使銀行提列足夠的資本保留。
    Reference: 1.周業熙(民91), 「GARCH-type模型在VaR之應用」,東吳大學經濟所未出版碩士論文。
    2.沈中華(民94), 金融市場-全球的觀點,初版,台北:新陸。
    3.周大慶、沈大白、張大成、敬永康、柯瓊鳳(民91), 風險管理新標竿-風險值之理論與應用,初版,台北:智勝。
    4.劉美纓(民92),「銀行風險值模型之回顧測試與壓力測試-保守性、準確度及效率性」, 2003商情資料分析庫分析與建置研究之成果發表會,東吳大學。
    5.翟慧雯(民92),「銀行資本適足性之模擬研究-市場風險探討」,國立中山大學財務管理學系研究所碩士在職專班未出版碩士論文。
    6.Da Veiga B., F. Chan, and M. McAleer (2005), “It Pays to Violate: Model Choice and CriticalValue Assumption for Forecasting Value-at-Risk Thresholds,” MODSIM 2005 International Congress on Modelling & Simulation, Australia, Modelling and Simulation Society of Australia and New Zealand .
    7.Basel committee on banking supervision, 1996, Supervisory framework for the use of backtesting in conjunction with the internal models approach to market risk.
    8.Boes, D. C., A. M. Mood, and F. A. Graybill(1976), Introduction to the Theory of Statistics, McGraw-Hill.
    9.Enders, W. (1995), Applied Econometric Time Series, John Wiley & Sons.
    10.Glosten, L. R., R. Jagannathan and D. E. Runkle (1993), “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, 48.
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    12.Nelson, D.B. (1991), “Conditional heteroskedasticity in asset returns: a new approach,” Econometrica, 59(2), pp.347-370.
    13.Pascual, L., E. Ruiz, and J. Romo (2006), “Bootstrap prediction for returns and volatilities in GARCH models,” Computational Statistics & Data Analysis, 50, pp.2293-2312
    14.Tsay, R. S. (1989), “Testing and modeling threshold autoregressive process,” Journal of the American Statistical Association, 84, pp.231-240
    15.Tsay, R. S. (2000), Analysis of financial time series, John Wiley & Sons.
    Description: 碩士
    國立政治大學
    金融研究所
    94352021
    95
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094352021
    Data Type: thesis
    Appears in Collections:[Department of Money and Banking] Theses

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