摘要翻译：
通常，操作风险损失报告超过一个阈值。拟合高于恒定阈值的数据是一个众所周知和研究的问题。然而，在实践中，在拟合之前，损失是根据业务和其他因素进行缩放的，因此阈值在缩放的数据样本中是不同的。当银行改变其报告政策时，报告级别也可能改变。我们提出了最大似然法和贝叶斯马尔可夫链蒙特卡罗法来拟合频率和严重损失分布。给出了考虑参数不确定性的年损失分布估计。
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英文标题：
《Modeling operational risk data reported above a time-varying threshold》
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作者：
Pavel V. Shevchenko and Grigory Temnov
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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：
  Typically, operational risk losses are reported above a threshold. Fitting data reported above a constant threshold is a well known and studied problem. However, in practice, the losses are scaled for business and other factors before the fitting and thus the threshold is varying across the scaled data sample. A reporting level may also change when a bank changes its reporting policy. We present both the maximum likelihood and Bayesian Markov chain Monte Carlo approaches to fitting the frequency and severity loss distributions using data in the case of a time varying threshold. Estimation of the annual loss distribution accounting for parameter uncertainty is also presented.
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PDF链接：
https://arxiv.org/pdf/0904.4075