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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/31216
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/31216


    Title: 極值理論與整合風險衡量
    Authors: 黃御綸
    Contributors: 廖四郎
    龐元愷

    黃御綸
    Keywords: 風險值
    極值理論
    value at risk
    extreme value theory
    copula
    AR(1)-GARCH(1,1)
    Date: 2003
    Issue Date: 2009-09-14 09:33:01 (UTC+8)
    Abstract: 自從90年代以來,許多機構因為金融商品的操縱不當或是金融風暴的衝擊數度造成全球金融市場的動盪,使得風險管理的重要性與日俱增,而量化風險模型的準確性也益受重視,基於財務資料的相關性質如異質變異、厚尾現象等,本文主要結合AR(1)-GARCH(1,1)模型、極值理論、copula函數三種模型應用在風險值的估算,且將報酬分配的假設區分為三類,一是無母數模型的歷史模擬法,二是基於常態分配假設下考量隨機波動度的有母數模型,三是利用歷史資料配適尾端分配的極值理論法來對聯電、鴻海、國泰金、中鋼四檔個股和台幣兌美元、日圓兌美元、英鎊兌美元三種外匯資料作一日風險值、十日風險值、組合風險值的測試。
    實證結果發現,在一日風險值方面,95%信賴水準下以動態風險值方法表現相對較好,99%信賴水準下動態極值理論法和動態歷史模擬法皆有不錯的估計效果;就十日風險值而言,因為未來十日資產的報酬可能受到特定事件影響,所以估計上較為困難,整體看來在99%信賴水準下以條件GPD+蒙地卡羅模擬的表現相對較理想;以組合風險值來說, copula、Clayton copula+GPD marginals模擬股票或外匯組合的聯合分配不論在95%或99%信賴水準下對其風險值的估計都獲得最好的結果;雖然台灣個股股價受到上下漲跌幅7%的限制,台幣兌美元的匯率也受到央行的干涉,但以極值理論來描述資產尾端的分配情形相較於假設其他兩種分配仍有較好的估計效果。
    Reference: 一、中文部分
    1.周裕峰 (2001),「結合波動性時間序列模式與極端值理論之涉險值評估模式」,未出版碩士論文,銘傳大學金融研究所。
    2.王永慶 (2001),「參數型與半參數型極端涉險值模型之估計及其於壓力測試上之應用」,未出版碩士論文,銘傳大學金融研究所。
    3.王君文 (2001),「極值理論風險值評估模式之探討」,未出版碩士論文,中正大學財務金融研究所。
    4.周業熙 (2002),「GARCH-type 模型在VaR 之應用」,未出版碩士論文,東吳大學經濟研究所。
    5.楊佩珍 (2002),「運用極值理論評估風險值-以台灣股匯市為例」,未出版碩士論文,中央大學財務金融研究所。
    6.徐嘉彬 (2002),「極值理論動態風險值模型研究」,未出版碩士論文,中正大學財務金融研究所。
    二、英文部分
    1.Ane, T. and Kharoubi, C.(2003),“Dependence Structure and Risk Measure.”,Journal of Business,vol.76, 3,pp. 411-438.
    2.Bystrom, H.(2001),“Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory.”,Working Paper,Dep. of Econometrics,Lund University.
    3.Bouyé, E.(2002),“Multivariate Extremes at Work for Portfolio Risk Measurement.”,FERC Working Paper.
    4.Breymann, W., Dias, A., and Embrechts, P.(2003),“Dependence Structures for Multivariate High-Frequency Data in Finance.”, Quantitative Finance, 3,pp. 1-14.
    5.Danielsson, J., and Casper G. de Vries.(1997a),“Tail index and quantile estimation with very high frequency data.”,Journal of Empirical Finance, 4,pp. 241-257.
    6.Danielsson, J., and Casper G. de Vries.(2000),“Value-at-Risk and Extreme Returns.”,London School of Economics,FMGDiscussion Paper no.273.
    7.Di Clemente, A. and Romano, C.(2003),“Measuring portfolio value at risk by a copula-EVT based approach.”,Working Paper,University of Rome,“La Sapienza”.
    8.Embrechts, P., C. Klüppelberg, and T. Mikosch(1997),“Modelling extremal events for insurance and finance.”,Springer,Berlin.
    9.Embrechts, P., A. J. McNeil and D. Straumann(1999),“Correlation and Dependence in Risk Management: Properties and Pitfalls.”, To appear in Risk Management: Value at Risk andBeyond, ed. By M. Dempster and H. K. Moffatt, Cambridge University Press.
    10.Embrechts, P., F. Lindskog and A. J. McNeil(2001),“Modelling dependence with copulas and applications to risk management.”,ETH Zurich,preprint.
    11.Joe, H.(1997),“Multivariate Models and Dependence Concepts.”, Chapman & Hall, London.
    12.Jorion, P.(1997),“Value-at-Risk:The new benchmark for controlling market risk.”,Chicago:Irwin. Publishing.
    13.Junker, M.,May, A.and Szimayer, A.(2002),“Measurement of Aggregate Risk with Copulas.”,Preprint CAESAR.
    14.Longin, F. M.(1999)“From value at risk to stress testing: the extreme value approach.”,Journal of Banking and Finance”,24,pp. 1097-1130.
    15.McNeil, A.J.(1999),“Extreme value theory for risk managers.”, Internal Modelling and CAD II published by RISK Book.
    16.McNeil, A,J, and R. Frey(2000),“Estimation of tail-related risk measures forheteroscedastic financial time series: an extreme value approach.”,Journal of Empirical Finance,7, pp. 271-300.
    17.Nelsen, R.(1998),“An Introduction to Copulas.”,Springer, New York.
    18.Romano, C.(2002a),“Applying Copula Function to Risk Management.”, Working Paper,University of Rome,“La Sapienza”.
    19.Tsay, R.S.(2002),“ Analysis of Financial Time Series.”,John Wiley & Sons, New York.
    20.Wang, S. S.(1999),“Aggregation of Correlated Risk Portfolios: Models & Algorithms.”,CAS Committee on Theory of Risk,Working Paper.
    Description: 碩士
    國立政治大學
    金融研究所
    91352021
    92
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0913520211
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

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