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    政大機構典藏 > 商學院 > 企業管理學系 > 學位論文 >  Item 140.119/83174
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/83174


    Title: 投資組合之風險評價:新模擬方法的應用
    Authors: 江義玄
    Chiang, I-Hsuan
    Contributors: 管中閔
    于卓民

    Kuan, Chung-Ming
    Joseph Yu, Chwo-Ming

    江義玄
    Chiang, I-Hsuan
    Keywords: 涉險值
    拔靴帶法
    移動區塊拔靴帶法
    定態拔靴帶法
    風險管理
    市場風險
    Value-at-Risk
    bootstrap
    moving block bootstrap
    stationary bootstrap
    risk management
    market risk
    Date: 2000
    Issue Date: 2016-03-31 13:27:11 (UTC+8)
    Abstract: 本文首次提出應用新的模擬方法:定態(stationary) bootstrap來估計涉險值(Value-at-Risk, 以下簡稱VaR)。VaR是衡量投資組合市場風險(market risk)的工具,由於1990年代以來國際間對市場風險管理的重視,且VaR較傳統風險衡量指標容易瞭解,又考慮整個投資組合資產間相關性降低風險的效果,加上VaR可作為企業內控、主管機關監督、以及投資人評估企業營運狀況等指標,故已廣為實務界及學界所接受。目前幾種主要衡量VaR的方法,包括變異數—共變數法、歷史模擬法、蒙地卡羅模擬法、classical bootstrap法以及壓力測試法等,各有其假設限制及優缺點。其中,classical bootstrap在衡量VaR時,使用的假設比較少,似乎非常適合衡量VaR。但是classical bootstrap會割裂了資料自我相關的性質,較適用於獨立且相同分配的樣本。我們在本文中介紹修正classical bootstrap的方法:移動區塊(moving block) bootstrap以及定態bootstrap,並利用統計模擬的方式證明定態bootstrap適合用於時間序列資料,對於捕捉真實分配的能力很強。接著我們選取11檔上市公司股票建構投資組合,並利用classical bootstrap以及定態bootstrap來衡量1999年共266個交易日的VaR。實證結果支持定態bootstrap能夠正確地衡量VaR,且其結果與classical bootstrap有明顯的不同。定態bootstrap法是個比較合理的衡量VaR方法,因此,我們建議風險管理者可採用定態bootstrap 衡量VaR。
    Reference: [1] 吳壽山,王甡與許孟方 (1999),《證券商市場風險管理之研究》,台北:證券暨期貨市場發展基金會。
    [2] 呂自勇 (1997),《金融資產投資組合風險值衡量--以台灣股市債市投資組合為例》,中央大學財務管理研究所碩士論文。
    [3] 宋文仁 (1998),《投資組合之關連度分析與使用 Value-at-Risk 模型衡量其市場風險》,中原大學企業管理研究所碩士論文。
    [4] 唐正儀 (1998),《銀行風險性資產之市場風險值估測--以自有模型法分析》,暨南國際大學經濟學研究所碩士論文。
    [5] 翁俊煜 (1997),《VAR 在建立市場風險管理系統上的實行步驟與作用》,輔仁大學金融研究所碩士論文。
    [6] 黃卉芊 (1998),《臺灣股匯市投資組合風險值之計算與評估》,中央大學財務管理研究所碩士論文。
    [7] 謝劍平 (1998),《現代投資學:分析與管理》,台北:智勝文化。
    [8] Beder, T. S. (1995), VAR: Seductive but Dangerous, Financial Analysts Journal, Sep-Oct, 12-24.
    [9] Best, P. (1998), Implementing Value at Risk, New York: John Wiley & Sons.
    [10] Dowd, K. (1998), Beyond Value at Risk: The New Science of Risk Management, New York: John Wiley & Sons.
    [11] Efron, B. and R. J. Tibshirani (1993), An Introduction to the Bootstrap, New York: Chapman & Hall.
    [12] Efron, B. (1979), Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, Vol. 7, 1-26.
    [13] Elliot, R. J. and P. E. Kopp (1999), Mathematics for Financial Markets, New York: Springer.
    [14] Enders, W. (1995), Applied Econometric Time Series, New York: John Wiley & Sons.
    [15] Hendricks, D. (1996), Evaluation of Value-at-Risk Models Using Historical Data, FRBNY Economic Policy Review, Apr., 39-69.
    [16] J. P. Morgan and Reuters (1996), RiskMetrics - Technical Document, 4th ed., http://www.riskmetrics.com/index.cgi.
    [17] Jorion, P. (1997), Value at Risk: The New Benchmark for Controlling Market Risk, Chicago, IL: Irwin.
    [18] Jorion, P. and S. J. Khoury (1996), Financial Risk Management: Domestic and International Dimensions, Cambridge, MA: Blackwell Publishers.
    [19] Kunsch, H. R. (1989), The Jackknife and the Bootstrap for the General Stationary Observations, The Annals of Statistics, Vol. 17, 1217-1241.
    [20] Kupiec, P. H. (1995), Techniques for Verifying the Accuracy of Risk Measurement Models, The Journal of Derivatives, Winter, 73-84.
    [21] Liu, R. Y. and K. Singh (1992), Moving Blocks and Bootstrap Capture Weak Dependence, Exploring the Limits of Bootstrap, New York: John Wiley & Sons.
    [22] Politis, D. N. and J. P. Romano (1992), A General Resampling Scheme for Triangular Arrays of α-mixing Random Variables with Application to the Problem of Spectral Density Estimation, The Annals of Statistics, Vol. 20, 1985-2007.
    [23] Politis, D. N. and J. P. Romano (1994), The Stationary Bootstrap, Journal of American Statistical Association, Dec., Vol. 89, 1303-1313.
    [24] Sharpe, W. F., G. J. Alexander and J. V. Bailey (1999), Investments, 6th ed., Englewood Cliffs, NJ: Prentice Hall.
    [25] Smithson C. W. (1998), Managing Financial Risk: A Guide to Derivative Products, Financial Engineering, and Value Maximization, 3rd ed., New York: McGraw-Hill.
    [26] Vazquez-Abad F. J. and Y. Champoux (1998), SimSpiders: Generation of Random Variables, http://www.ee.mu.oz.au/staff/fva/SimSpiders/GenerRV/Methods.html.
    Description: 碩士
    國立政治大學
    企業管理學系
    87355063
    Source URI: http://thesis.lib.nccu.edu.tw/record/#A2002002170
    Data Type: thesis
    Appears in Collections:[企業管理學系] 學位論文

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