英文标题：
《Methodological thoughts on expected loss estimates for IFRS 9
  impairment: hidden reserves, cyclical loss predictions and LGD backtesting》
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作者：
Wolfgang Reitgruber
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最新提交年份：
2015
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英文摘要：
  After the release of the final accounting standards for impairment in July 2014 by the IASB, banks will face the next significant methodological challenge after Basel 2. In this paper, first methodological thoughts are presented, and ways how to approach underlying questions are proposed. It starts with a detailed discussion of the structural conservatism in the final standard. The exposure value iACV(c) (idealized Amortized Cost Value), as originally introduced in the Exposure Draft 2009 (ED 2009), will be interpreted as economic value under amortized cost accounting and provides the valuation benchmark under IFRS 9. Consequently, iACV(c) can be used to quantify conservatism (ie potential hidden reserves) in the actual implementation of the final standard and to separate operational side-effects caused by the local implementation from actual credit risk impacts. The second part continues with a quantification of expected credit losses based on Impact of Risk(c) instead of traditional cost of risk measures. An objective framework is suggested which allows for improved testing of forward looking credit risk estimates during credit cycles. This framework will prove useful to mitigate overly pro-cyclical provisioning and to reduce earnings volatility. Finally, an LGD monitoring and backtesting approach, applicable under regulatory requirements and accounting standards as well, is proposed. On basis of the NPL Dashboard, part of the Impact of Risk(c) framework, specific key risk indicators are introduced that allow for a detailed assessment of collections performance versus LGD in in NPL portfolio (bucket 3).
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中文摘要：
在国际会计准则理事会于2014年7月发布减值最终会计准则后，银行将面临新巴塞尔协议之后的下一个重大方法挑战。本文首先介绍了方法论思想，并提出了如何处理潜在问题的方法。本文首先详细讨论了最终标准中的结构保守主义。风险敞口价值iACV（c）（理想化摊余成本价值）最初在2009年风险敞口草案（ED 2009）中引入，将被解释为摊余成本会计下的经济价值，并根据IFRS 9提供估值基准。因此，iACV（c）可用于量化最终标准实际实施过程中的稳健性（即潜在隐藏储量），并将当地实施引起的运营副作用与实际信用风险影响分开。第二部分继续对基于风险影响（c）而非传统风险成本度量的预期信用损失进行量化。提出了一个客观的框架，该框架允许在信贷周期内改进对前瞻性信贷风险估计的测试。这一框架将被证明有助于缓解过度顺周期供应，并减少收益波动。最后，提出了一种适用于监管要求和会计准则的LGD监控和回溯测试方法。在NPL仪表盘（风险影响（c）框架的一部分）的基础上，引入了具体的关键风险指标，以便详细评估不良贷款组合中的收款绩效与LGD（桶3）。
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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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