摘要翻译：
在本文中，我们通过允许风险剖面随时间随机演化并且是相依的，来建立操作风险之间的相依性模型。这允许一种灵活的相关结构，在这种结构中，不同风险类别的频率之间、不同风险类别的严重程度之间以及风险类别内部的依赖关系可以被建模。该模型采用贝叶斯推理方法进行估计，允许在估计过程中结合内部数据、外部数据和专家意见。我们使用一种专门的马尔可夫链蒙特卡罗模拟方法，称为切片抽样，从产生的后验分布中获得样本，并估计模型参数。
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英文标题：
《Dynamic operational risk: modeling dependence and combining different
  sources of information》
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作者：
Gareth W. Peters, Pavel V. Shevchenko and Mario V. W\"uthrich
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最新提交年份：
2009
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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：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
  In this paper, we model dependence between operational risks by allowing risk profiles to evolve stochastically in time and to be dependent. This allows for a flexible correlation structure where the dependence between frequencies of different risk categories and between severities of different risk categories as well as within risk categories can be modeled. The model is estimated using Bayesian inference methodology, allowing for combination of internal data, external data and expert opinion in the estimation procedure. We use a specialized Markov chain Monte Carlo simulation methodology known as Slice sampling to obtain samples from the resulting posterior distribution and estimate the model parameters.
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PDF链接：
https://arxiv.org/pdf/0904.4074