Credit risk optimization with Conditional Value-at-Risk criterion
Credit risk optimization with Conditional Value-at-Risk criterion
复制标题
DOI：
10.1007/pl00011399
复制
发表时间：
2001
影响因子：
2.7
通讯作者：
F. Andersson;Helmut E. Mausser;D. Rosen;S. Uryasev
中科院分区：
文献类型：
--
作者：
F. Andersson;Helmut E. Mausser;D. Rosen;S. Uryasev
本文研究了一种新的信用风险优化方法。该模型基于条件风险价值（CVaR）风险度量，即预期损失超过风险价值。CVaR也被称为平均超额、平均短缺或尾部VaR。该模型可以同时调整金融工具投资组合中的所有头寸，以使受交易和回报约束的CVaR最小化。通过蒙特卡罗模拟生成了信用风险分布，并利用线性规划有效地求解了优化问题。该算法非常高效；它可以在合理的计算机时间内处理数百种仪器和数千种场景。新兴市场债券投资组合证明了这种方法。
This paper examines a new approach for credit risk optimization. The model is based on the Conditional Value-at-Risk (CVaR) risk measure, the expected loss exceeding Value-at-Risk. CVaR is also known as Mean Excess, Mean Shortfall, or Tail VaR. This model can simultaneously adjust all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return constraints. The credit risk distribution is generated by Monte Carlo simulations and the optimization problem is solved effectively by linear programming. The algorithm is very efficient; it can handle hundreds of instruments and thousands of scenarios in reasonable computer time. The approach is demonstrated with a portfolio of emerging market bonds.