摘要翻译：
我们建立了一个结构模型来研究包含多个或多个信用合约的投资组合的信用风险。该模型基于风险因素（即公司资产）的跳跃-扩散过程。我们还包括公司之间的相关性。我们讨论了这类模型与统计物理和复杂系统理论中的其他问题有许多共同之处。我们分析研究了我们的模型的简化版本。此外，我们对整个模型进行了广泛的数值模拟。可观察到的是信贷组合的损失分布，它的矩和由此导出的其他量。我们汇编了关于这些可观测项的参数依赖性的详细信息。在建立和分析我们的模型的过程中，我们还对一个物理受众的信用风险模型进行了回顾。
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英文标题：
《Credit risk - A structural model with jumps and correlations》
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作者：
Rudi Sch\"afer, Markus Sj\"olin, Andreas Sundin, Michal Wolanski and
  Thomas Guhr
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
  We set up a structural model to study credit risk for a portfolio containing several or many credit contracts. The model is based on a jump--diffusion process for the risk factors, i.e. for the company assets. We also include correlations between the companies. We discuss that models of this type have much in common with other problems in statistical physics and in the theory of complex systems. We study a simplified version of our model analytically. Furthermore, we perform extensive numerical simulations for the full model. The observables are the loss distribution of the credit portfolio, its moments and other quantities derived thereof. We compile detailed information about the parameter dependence of these observables. In the course of setting up and analyzing our model, we also give a review of credit risk modeling for a physics audience.
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PDF链接：
https://arxiv.org/pdf/707.3478