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摘要翻译：
为满足新巴塞尔协议对先进计量方法的监管要求，银行的内部模型必须包括使用内部数据、相关外部数据、情景分析以及反映业务环境和内部控制系统的因素。操作风险的量化不能仅基于历史数据，而应包括情景分析。历史的内部操作风险损失数据对未来行为的预测能力有限，而且银行没有足够的内部数据来充分估计低频高影响事件。历史外部数据因体量不同等因素难以使用。此外，内部和外部数据具有生存偏见，因为通常情况下，没有所有倒闭公司的数据。情景分析的思想是通过专家意见，考虑银行环境因素，参考其他银行已经发生（或可能发生）的事件，估计风险事件的频率和严重程度。情景分析具有前瞻性，能够反映银行环境的变化。重要的是，不仅要量化操作风险资本，而且要激励业务单位改进其风险管理政策，这可以通过情景分析来实现。情景分析本身具有很强的主观性，但与损失数据相结合，是估计操作风险损失的有力工具。贝叶斯推理是一种非常适合结合专家意见和历史数据的统计技术。本文给出了操作风险量化的贝叶斯推理方法的实例。
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英文标题：
《The Structural Modelling of Operational Risk via Bayesian inference:
Combining Loss Data with Expert Opinions》
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作者：
P. V. Shevchenko and M. V. W\"uthrich
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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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英文摘要：
To meet the Basel II regulatory requirements for the Advanced Measurement Approaches, the bank's internal model must include the use of internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. Quantification of operational risk cannot be based only on historical data but should involve scenario analysis. Historical internal operational risk loss data have limited ability to predict future behaviour moreover, banks do not have enough internal data to estimate low frequency high impact events adequately. Historical external data are difficult to use due to different volumes and other factors. In addition, internal and external data have a survival bias, since typically one does not have data of all collapsed companies. The idea of scenario analysis is to estimate frequency and severity of risk events via expert opinions taking into account bank environment factors with reference to events that have occurred (or may have occurred) in other banks. Scenario analysis is forward looking and can reflect changes in the banking environment. It is important to not only quantify the operational risk capital but also provide incentives to business units to improve their risk management policies, which can be accomplished through scenario analysis. By itself, scenario analysis is very subjective but combined with loss data it is a powerful tool to estimate operational risk losses. Bayesian inference is a statistical technique well suited for combining expert opinions and historical data. In this paper, we present examples of the Bayesian inference methods for operational risk quantification.
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PDF链接：
https://arxiv.org/pdf/0904.1067
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