Operational Risk Measurement Beyond the Loss Distribution Approach: An Exposure-Based Methodology

Michael Einemann, Joerg Fritscher, M. Kalkbrener
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引用次数: 1

Abstract

The loss distribution approach (LDA) has evolved as the industry standard for operational risk models despite a number of known weaknesses. In particular, LDA’s traditional focus on historical loss data often neglects expert knowledge that is available for operational risk types of a more predictable nature. In this paper, we present an alternative quantification technique, so-called exposure-based operational risk (EBOR) models, which aim to replace historical severity curves by measures of current exposures and use event frequencies based on actual exposures instead of historical loss counts. We introduce a general mathematical framework for exposure-based modeling that is applicable to a large number of operational risk types. As an example, an EBOR model for litigation risk is presented. Further, we discuss the integration of EBOR and LDA models into hybrid frameworks facilitating the migration of operational risk subtypes from a classical to an exposure-based treatment. The implementation of EBOR models is a challenging task since new types of data and a higher degree of expert involvement are required. In return, EBOR models provide a transparent quantitative framework for combining forward-looking expert assessments, point-in-time data (eg, current portfolios) and historical loss experience. Individual loss events can be modeled in a granular way, which facilitates the reflection of loss-generating mechanisms and provides more reliable signals to risk management.
超越损失分配法的操作风险度量:一种基于暴露的方法
尽管存在许多已知的弱点,损失分配方法(LDA)已经发展成为操作风险模型的行业标准。特别是,LDA的传统重点是历史损失数据,往往忽略了可用于更具可预测性的操作风险类型的专家知识。在本文中,我们提出了一种可替代的量化技术,即所谓的基于暴露的操作风险(EBOR)模型,该模型旨在通过测量当前暴露来取代历史严重性曲线,并使用基于实际暴露的事件频率而不是历史损失计数。我们为基于暴露的建模引入了一个通用的数学框架,该框架适用于大量操作风险类型。以诉讼风险为例,提出了EBOR模型。此外,我们讨论了将EBOR和LDA模型集成到混合框架中,以促进操作风险亚型从经典处理向基于暴露的处理的迁移。实现EBOR模型是一项具有挑战性的任务,因为需要新的数据类型和更高程度的专家参与。作为回报,EBOR模型提供了一个透明的定量框架,将前瞻性专家评估、时间点数据(如当前投资组合)和历史损失经验结合起来。可以对单个损失事件进行粒度化建模,便于反映损失产生机制,为风险管理提供更可靠的信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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