Random thinning with credit quality vulnerability factor for better risk management of credit portfolio in a top-down framework
Random thinning with credit quality vulnerability factor for better risk management of credit portfolio in a top-down framework
复制标题
使用信用质量脆弱性因子进行随机细化，以便在自上而下的框架中更好地管理信用组合的风险
DOI：
10.1007/s13160-016-0216-x
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发表时间：
2016
影响因子：
0.9
通讯作者：
Masaaki Sugihara
中科院分区：
文献类型：
--
作者：
Suguru Yamanaka;Hidetoshi Nakagawa;Masaaki Sugihara
在自顶向下的投资组合信用风险建模方法中，我们使用所谓的随机稀疏模型来评估子投资组合的信用风险，该模型将投资组合风险分解为子投资组合的贡献。在本文中，我们提供了一个随机细化模型，该模型将子组合规模和因子称为“信用质量脆弱性因子”，以考虑信用质量脆弱性的子组合。利用随机稀疏模型，我们估计了工业部门的信用质量脆弱性。对几个信用组合风险评估的算例表明，我们的随机稀疏模型是有用的，以检测如何构成工业部门的比例影响组合的信用风险。
In the top-down approach of portfolio credit risk modeling, we assess credit risks of sub-portfolios with the so-called random thinning model, which dissects the portfolio risk into sub-portfolio contributions. In this paper, we provide a random thinning model incorporating the sub-portfolio size and the factor called “credit quality vulnerability factor”, in order to take into account credit quality vulnerability of sub-portfolios. With our random thinning model, we estimate credit quality vulnerability of industrial sectors. Numerical examples on assessing the risks of several credit portfolios show that our random thinning model is useful to detect how the proportions of constituent industrial sectors affect portfolio credit risks.