An Innovative and Efficient Importance Sampling Technique for Credit Risk Modeling
In credit risk modeling, Gaussian and Student's t-variates arise primarily from the copula method to retain certain correlation structures among defaultable assets. Dr. Chuan-Hsiang (Sean) Han will propose efficient importance sampling algorithms to estimate lower tail probabilities of these two variates in any finite dimension. Variances of importance sampling estimators are shown asymptotically optimal by means of the large deviation theory and a truncation argument. Numerical comparisons with commercial codes, such as mvncdf.m and mvtcdf.m demonstrate robustness and efficiency of our proposed algorithms. Moreover, the flexibility of these algorithms can be seen from two applications in risk management. They include estimations of default probabilities of the nth-to-default, i.e., the nth order statistic, of a large credit portfolio, and Value at Risk/Conditional Value-at-Risk (VaR/CVaR) estimation of a large portfolio.
La Trobe University, Bundoora Campus, Martin Building
Meeting Room 141, Melbourne, Victoria 3086, Australia
Dr. Chuan-Hsiang (Sean) Han, Associate Professor, Department of Quantitative Finance, National Tsing-Hua University, Taiwan
12:00PM - 1:00PM
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For more information, please contact GARP Director, La Trobe University Chapter:
Wei-Han Liu, Senior Lecturer, Department of Finance, Faculty of Business, Economics, and Law, La Trobe University - W.Liu@latrobe.edu.au