Credit Portfolio Risk Management with Heavy-Tailed Risk Factors

林怡伶(Yi-Ling Lin)

指導教授 ： 韓傳祥

國立清華大學/科技管理學院/計量財務金融系/碩士(2010年)

https://doi.org/10.6843/NTHU.2010.00351

摘要

It is basic and important to compute the portfolio VaR (PVaR) and the loss density function of a credit portfolio in risk management. To estimate these quantities, we focus on calculating the (joint) default probabilities. We model the market returns data by copula methods such as Gaussian copula and Student's t copula. Student's t copula provides a heavy-tailed distribution, so it is more commonly used in practice than normal distribution.

Basic Monte Carlo method doesn't work well for rare event simulation because its standard error is relatively large. We develop efficient importance sampling algorithms to accurately estimate the (joint) default probabilities. These algorithms are capable of increasing the possibilities of rare events and reducing the standard error of the estimators. We use a transformation method developed by Glasserman et al. (2002) to construct moment generating functions associated with proposed importance sampling algorithms. We compare the efficiency of our importance sampling with other (conditional) importance sampling algorithms.

For applications, we evaluate (1) the default leg premium of Basket Default Swap (2) the loss density function of a portfolio and (3) PVaR. The first application is useful for CDO pricing. The second and third applications are useful for risk management of a credit portfolio. In particular for the third application, we apply Gaussian copula and Student's t copula model to fit a company's stock price and its CDS price and estimate the PVaR. Some backtesting diagnostics are used to examine the performance of PVaR estimates.

關鍵字

信用風險管理 ； 重點抽樣法 ； 投資組合風險值 ； 厚尾

並列摘要

無資料

並列關鍵字

Credit Risk Management ； Importance sampling ； Portfolio VaR ； Heavy-Tailed

參考文獻

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