英文标题：
《Conduct Risk - distribution models with very thin Tails》
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作者：
Peter Mitic
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最新提交年份：
2017
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英文摘要：
Regulatory requirements dictate that financial institutions must calculate risk capital (funds that must be retained to cover future losses) at least annually. Procedures for doing this have been well-established for many years, but recent developments in the treatment of conduct risk (the risk of loss due to the relationship between a financial institution and its customers) have cast doubt on \'standard\' procedures. Regulations require that operational risk losses should be aggregated by originating event. The effect is that a large number of small and medium-sized losses are aggregated into a small number of very large losses, such that a risk capital calculation produces a hugely inflated result. To solve this problem, a novel distribution based on a one-parameter probability density with an exponential of a fourth power is proposed, where the parameter is to be estimated. Symbolic computation is used to derive the necessary analytical expressions with which to formulate the problem, and is followed by numeric calculations in R. Goodness-of-fit and parameter estimation are both determined by using a novel method developed specifically for use with probability distribution functions. The results compare favourably with an existing model that used a LogGamma Mixture density, for which it was necessary to limit the frequency and severity of the losses. No such limits were needed using the proposed exponential density.
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中文摘要：
监管要求规定，金融机构必须至少每年计算一次风险资本（必须保留以弥补未来损失的资金）。这样做的程序已经建立了多年，但最近在处理行为风险（金融机构与其客户之间的关系导致的损失风险）方面的发展对“标准”程序产生了怀疑。法规要求运营风险损失应按始发事件汇总。其结果是，大量中小规模的损失被合并为少量非常大的损失，因此风险资本计算产生了一个大大夸大的结果。为了解决这一问题，提出了一种基于指数为四次方的单参数概率密度分布的新方法，其中需要估计参数。符号计算用于推导出必要的解析表达式来描述问题，然后在R中进行数值计算。拟合优度和参数估计都是通过使用专门为概率分布函数开发的新方法来确定的。与使用对数伽马混合密度的现有模型相比，结果更为有利，因此有必要限制损失的频率和严重程度。使用建议的指数密度，不需要这样的限制。
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分类信息：

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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