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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/33921
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/33921


    Title: 信用損失分配之尾端機率估計-大樣本投資組合與區型塊投資組合
    Authors: 吳秉昭
    Contributors: 劉惠美
    吳秉昭
    Keywords: 信用風險
    同質近似法
    尾端機率
    Date: 2004
    Issue Date: 2009-09-17 18:48:55 (UTC+8)
    Abstract: 本文延伸Glasserman(2004)的同質近似法,假設系統性風險因子為常態分配,非系統性風險因子為標準化t分配下,來推導近似損失分配。我們藉由四種特性不同的投資組合,討論真實分配與近似分配分別在混合分配與常態分配的近似效果。結果顯示,當投資組合中的所有債務人都受到相同的系統性風險衝擊時,利用同質近似法近似真實損失分配的效果最好。
    本文改變損失起始值與系統性風險因子,發現近似效果的變化與投資組合特性以及近似分配假設為何有關。本文接著加入雜訊,發現加入雜訊後的近似效果會比無雜訊下的近似效果好,然而因為債務人人數減少,近似效果會變差。
    金融機構可能由於資訊不足,引入過多(少)系統性風險因子數,本文發現在大損失處,以常態分配假設的近似分配效果比混合分配來的好,至於要以多因子或是少因子模型來近似端賴投資組合的特性。金融機構或因為資訊不對稱下加入(忽略)雜訊時,以有雜訊的常態分配來近似大損失處的真實損失效果最好。
    在資訊不足與資訊不對稱下,金融機構可能面臨高(低)估損失機率:投資組合的損失產生若是因為共同或重複的系統性風險因子越多,引入的因子數目變少(多),會高(低)估尾端的損失機率;相反的,若投資組合的損失來自不同系統性風險因子的衝擊,引入的因子數目變少(多),會低(高)估了尾端的損失機率;無(有)雜訊的近似模型明顯高(低)估尾端的損失機率。
    金融機構若高估損失機率,可能會因為沒有生意而倒閉;低估了損失機率,卻可能面臨贏家的詛咒。本文於是進而討論是否存在一個最佳的分配選擇,可以良好近似不同分配下的真實損失。結果顯示,同質近似法的近似效果會因為分配假設不同而有差異,若金融機構只專注大損失的情況,以常態分配假設來近似真實損失的效果最好,債務人數目的增加將擴大此效果。但是,若金融機構欲將損失程度分段監控,須採正確的假設分配才能良好近似真實損失。
    Reference: 1.Anderson, R. and S. Sundaresan (1996). Design and Valuatof Debt Contracts. Review of Financial Studies 9, 37-68.
    2.Black, F. and J. Cox (1976). Valuing Corporate Securities: Some Effects of Bond Indenture Provisions. Journal of Finance 31, 351-367.
    3.Duffie, D. J. and K. J. Singleton (1998). Modeling Term Structures of Defaultable Bonds. Review of Financial Studies 12, 687-720.
    4.Geske, R. (1977). The Valuation of Corporate Liabilities as Compound Options. Journal of Financial and Quantitative Analysis 12, 541-552.
    5.Glasserman, P. (2004) Tail Approximations for Portfolio Credit Risk. The Journal of Derivatives 12, 24-42.
    6.Hull, J. and A. White (1995). The Impact of Default Risk on the Prices of Options and Other Derivative Securities. Journal of Banking and Finance 19, 299-322.
    7.Hull, J. and A. White (1998). Value at Risk When Daily Changes in Market Variables Are Not Normally Distribution. Journal of Derivative 5, 9-19.
    8.Hull, J. and A. White. (2004). Valuation of a CDO and an nth to Default CDS without Monte Carlo Simulation. Journal of Fixed income 14, 72-81.
    9.Jarrow, R. and S. Turnbull (1995). Pricing Derivatives on Financial Securities subject to Credit Risk. Journal of Finance 50, 53-85.
    10.Lee,C.W. C.K. Kuo, and J. L. Urrutia. (2004). A Poisson Model with Common Shocks for CDO Valuation. Journal of Fixed Income 14, 72-81.
    11.Li, D. (2000). On Default Correlation: A Copula Function Approach, Journal of Fixed Income 9, 43-54.
    12.Madan, D.B. and H. Unal (2000). A Two-Factor Hazard Rate Model for Pricing Risky debt and the Term Structure of Credit Spreads. Journal of Financial and Quantitative Analysis 35, 43-65.
    13.Nocedal, J. and M. Wright (1999). Numerical Optimization. New York: Springer-Verlag.
    14.Shonbucher, P. (2001). Factor models: Portfolio Credit Risk When Defaults Are Correlated. Journal of Risk Finance 3, 45-56.
    Description: 國立政治大學
    統計研究所
    92354021
    93
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0923540211
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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