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研究生: 王聖文
Wang, Sheng-Wen
論文名稱: 聯合系統與獨特風險下之信用違約交換評價
Joint pricing of CDS spreads with Idiosyncratic and systematic risks
指導教授: 翁久幸
Weng, Ruby Chiu-Hsing
廖四郎
Liao, Szu-Lang
學位類別: 碩士
Master
系所名稱: 商學院 - 統計學系
Department of Statistics
論文出版年: 2009
畢業學年度: 97
語文別: 英文
論文頁數: 38
中文關鍵詞: 信用違約交換系統風險獨特性風險狀態空間模型Variance Gamma過程
外文關鍵詞: credit default swaps, systematic risk, idiosyncratic risk, state-space model, Variance Gamma process
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  • 本研究透過聯合系統與獨特風險綜合評估違約的強度,假設市場上經濟變數或資訊影響系統之違約強度,然若直接考慮所有經濟變數到模型中將可能會有共線性或維度過高之疑慮,因此透過狀態空間模型來設定狀態變數以及經濟變數之關係並將萃取三大狀態變數分別用以描述市場實質活動面、通貨膨脹以及信用環境。另外,將透過結構式模型來計算獨特性風險大小,當個別潛在的變數低於一定數值將導致個別的違約事件發生。而因布朗運動可能無法描述或校準市場上違約之鋒態以及偏態,將進一步考慮Variance Gamma過程用以更準確描述真實違約狀況。最後透過結合以上兩個風險綜合評估下,考慮一個聯合違約模型來評價信用違約交換之信用價差。


    Systematic and idiosyncratic risks are supposed to jointly trigger the default events. This paper identifies three fundamental risks to capture the systematic movement: real activity, inflation, and credit environment. Since most macroeconomic variables fluctuate together, the state-space model is imposed to extract the three variables from macroeconomic data series. In the idiosyncratic part, the structural model is applied. That is, idiosyncratic default
    is triggered by the crossing of a barrier. For improvement of the underlying lognormal distribution, we assume the process for the potential variable of the firm follows a Variance Gamma process, sufficient dimensions of which can fit the skewed and leptokurtic distributions. Under the specific setting of combinations of the two risks (the so-called joint default model), we price credit default swaps.

    謝辭­ i
    摘要 ii
    Abstract iii
    Contents iv
    List of Figures vi
    List of Tables vii
    1 Introduction 1
    2 State-Space Models and the Gibbs Sampler 4
    2.1 State-Space Models and the Kalman Filter . . . . . . . . . . . . . . . . . . 4
    2.2 Parameter estimation and the Gibbs Sampler . . . . . . . . . . . . . . . . . 7
    2.2.1 Generating State Vectors . . . . . . . . . . . . . . . . . . . . . . . 9
    2.2.2 Generating Hyperparameters . . . . . . . . . . . . . . . . . . . . . 10
    2.3 Data Description and Estimation Results . . . . . . . . . . . . . . . . . . . 11
    3 Lévy Process 14
    3.1 Introduction to Lévy Process . . . . . . . . . . . . . . . . . . . . . . . . . 14
    3.2 Variance Gamma Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
    3.3 The Monte Carlo Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 18
    4 Identification of the Systematic and Idiosyncratic Risks 20
    4.1 Identification of the Systematic Risk . . . . . . . . . . . . . . . . . . . . . 20
    4.2 Identification of the Idiosyncratic Risk . . . . . . . . . . . . . . . . . . . . 25
    5 The Joint Default Model 28
    5.1 The Joint Default Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
    5.2 CDS Spreads Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
    6 Conclusion 34
    References 36

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