Estimating Tail Probability of Credit Loss Distribution with Closed Skew Normal
| Autor: | Tsao, Li-Yu, 曹立諭 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 The credit risk of the portfolio is often estimated using the Normal Copula model, but the parameters that the model can adjust are limited. This paper uses the Closed Normal Copula model to derive. The CSN distribution has the nature of normal distribution, and also has the adjustment distribution skewness. The degree of parameters make it more suitable for interpreting the degree of dependency between portfolios. When measuring the rare events of a portfolio, the probability value is not easy to simulate, but it contains a large loss in the event of a high-value Asset Default. Using Monte Carlo to simulate its credit risk, the simulation takes longer than usual and varies greatly. We use the importance sampling method proposed by Glasserman and Li (Management Science, 51(11), 1643-1656, 2005) and Chiang et al. (Journal of Derivatives, 15(2), 8-19, 2007). Referred to as GIS method and MIS method, it is deduced and extended. The simulation is carried out under the portfolio of Closed Skew Normal Copula model, and the simulation efficiency of the two methods is measured by the reduction effect of variance. The numerical results show that in the single factor model, the MIS method takes less time than the GIS method, and the Variance Reduction effect is significant. In the multi-factor model, the GIS method can be applied to a wide range, through the two-stage importance sampling method. The Variance Reduction effect is good, and the simulation time is shortened compared with the Monte Carlo method. Both methods have their applicable models and also have good estimation accuracy and simulation stability. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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