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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/54549
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/54549


    Title: 在序列相關因子模型下探討動態模型化投資組合信用風險
    Dynamic modeling portfolio credit risk under serially dependent factor model
    Authors: 游智惇
    Yu, Chih Tun
    Contributors: 劉惠美
    游智惇
    Yu, Chih Tun
    Keywords: 序列相關因子模型
    投資組合信用風險
    貝氏分析
    蒙地卡羅最大概似法
    蒙地卡 羅期望最大法
    Serially dependent factor model
    Portfolio credit risk
    Bayesian inference
    Monte Carlo Expectation Maximization
    Monte Carlo maximum likelihood
    Date: 2011
    Issue Date: 2012-10-30 11:20:43 (UTC+8)
    Abstract: 獨立因子模型廣泛的應用在信用風險領域,此模型可用來估計經濟資本與投資組合的損失率分配。然而獨立因子模型假設因子獨立地服從同分配,因而可能會得到估計不精確的違約機率與資產相關係數。因此我們在本論文中提出序列相關因子模型來改進獨立因子模型的缺失,同時可以捕捉違約率的動態行為與授信戶間相關性。我們也分別從古典與貝氏的角度下估計序列相關因子模型。首先,我們在序列相關因子模型下利用貝氏的方法應用馬可夫鍊蒙地卡羅技巧估計違約機率與資產相關係數,使用標準普爾違約資料進行外樣本資料預測,能夠證明序列相關因子模型是比獨立因子模型合理。第二,蒙地卡羅期望最大法與蒙地卡羅最大概似法這兩種估計方法也使用在本篇論文。從模擬結果發現,若違約資料具有較大的序列相關與資產相關特性,蒙地卡羅最大概似法能夠配適的比蒙地卡羅期望最大法好。
    The independent factor model has been widely used in the credit risk field, and has been applied in estimating the economic capital allocations and loss rate distribution on a credit portfolio. However, this model assumes independent and identically distributed common factor which may produce inaccurate estimates of default probabilities and asset correlation. In this thesis, we address a serially dependent factor model (SDFM) to improve this phenomenon. This model can capture both dynamic behavior of default risk and dependence among individual obligors. We also address the estimation of the SDFM from both frequentist and Bayesian point of view. Firstly, we consider the Bayesian approach by applying Markov chain Monte Carlo (MCMC) techniques in estimating default probability and asset correlation under SDFM. The out-of-sample forecasting for S&P default data provide strong evidence to support that the SDFM is more reliable than the independent factor model. Secondly, we use two frequentist estimation methods to estimate the default probability and asset correlation under SDFM. One is Monte Carlo Expectation Maximization (MCEM) estimation method along with a Gibbs sampler and an acceptance method and the other is Monte Carlo maximum likelihood (MCML) estimation method with importance sampling techniques.
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    Description: 博士
    國立政治大學
    統計研究所
    95354501
    100
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0095354501
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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