It is emerging operational risks that are habitually cited as keeping executives awake at night. These risks are dynamic and inspire the fear of the unknown, and it is precisely these characteristics that make their quantification so challenging. While there are no perfect solutions, this paper sets out techniques for:

• identifying systematically emerging threats, their timescales, and interrelation- ships (eg, feedback loops and domino effects);

• quantifying operational risks through structured scenario analysis processes that analyze the drivers of impacts and likelihoods; and

• validating the outputs of scenario analysis through backtesting against internal and external data sources.

The paper then applies these techniques to three categories of emerging operational risks, providing:

• an explanation of the mechanism of how economic shocks lead to operational risk losses through a real options model;

• cyber and IT risk taxonomies, aligned to Basel II, based on analysis of industry events; and

• case studies illustrating how some emerging risks come in waves, peaking and then declining, leading to their potential overestimation, while others are yet to result in losses, leading to their potential underestimation (the techniques set out early in the paper are modified to mitigate these different challenges).


中文翻译：

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将现有情景技术应用于新出现的操作风险的量化

人们习惯性地将新出现的运营风险称为让高管夜不能寐。这些风险是动态的，激发了对未知事物的恐惧，正是这些特征使它们的量化变得如此具有挑战性。虽然没有完美的解决方案，但本文提出了以下技术：

• 系统地识别新出现的威胁、它们的时间尺度和相互关系（例如，反馈循环和多米诺骨牌效应）；

• 通过结构化情景分析流程量化运营风险，分析影响和可能性的驱动因素；

• 通过针对内部和外部数据源的回测验证情景分析的输出。

然后，本文将这些技术应用于三类新出现的操作风险，提供：

• 通过实物期权模型解释经济冲击如何导致操作风险损失的机制；

• 基于对行业事件的分析，与巴塞尔协议 II 一致的网络和 IT 风险分类法；•

案例研究说明一些新兴风险如何一波一波地出现，达到顶峰然后下降，从而导致其潜在的高估，而其他风险尚未导致损失，导致其潜在的低估（本文前面提出的技术已修改为缓解这些不同的挑战）。