We propose a model for conduct risk losses, in which conduct risk losses are characterized by having a small number of extremely large losses (perhaps only one) with more numerous smaller losses. It is assumed that the largest loss is actually a provision from which payments to customers are made periodically as required. We use the pseudo-marginal (PM) Markov chain Monte Carlo method to decompose the largest loss into smaller partitions in order to estimate 99.9% value-at-risk. The partitioning is done in a way that makes no assumption about the size of the partitions. The advantages and problems of using this method are discussed. The PM procedures were run on several representative data sets. The results indicate that, in cases where using approaches such as calculating a Monte Carlo-derived loss distribution yields a result that is not consistent with the risk profile expressed by the data, using the PM method yields results that have the required consistency.

中文翻译：

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使用伪边际马尔可夫链蒙特卡罗估计行为风险损失的风险价值

我们提出了一个行为风险损失模型，其中行为风险损失的特点是有少量的非常大的损失（可能只有一个）和更多的小损失。假设最大的损失实际上是按要求定期向客户付款的准备金。我们使用伪边际 (PM) 马尔可夫链蒙特卡罗方法将最大损失分解为较小的分区，以估计 99.9% 的风险价值。分区是以不假设分区大小的方式完成的。讨论了使用这种方法的优点和问题。PM 程序在几个有代表性的数据集上运行。结果表明，