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Wolfgang Reitgruber

Publications and source records attributed to Wolfgang Reitgruber.

2 recordsLinked to original sources

Methodological thoughts on expected loss estimates for IFRS 9 impairment: hidden reserves, cyclical loss predictions and LGD backtesting

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).

q-fin.RM↗

The Calculus of Expected Loss: Backtesting Parameter-Based Expected Loss in a Basel II Framework

The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss (EL) in the sense of a fair, unbiased estimate of risk expenses however is barely covered. So far there are no established backtesting procedures for EL, quantifying its impact with regards to pricing or risk adjusted profitability measures. In this paper, a practically oriented, top-down approach to assess the quality of EL by backtesting with a properly defined risk measure is introduced. In a first step, the concept of risk expenses (Cost of Risk) has to be extended beyond the classical provisioning view, towards a more adequate capital consumption approach (Impact of Risk, IoR). On this basis, the difference between parameter-based EL and actually reported Impact of Risk is decomposed into its key components. The proposed method will deepen the understanding of practical properties of EL, reconciles the EL with a clearly defined and observable risk measure and provides a link between upcoming IFRS 9 accounting standards for loan loss provisioning with IRBA regulatory capital requirements. The method is robust irrespective whether parameters are simple, expert based values or highly predictive and perfectly calibrated IRBA compliant methods, as long as parameters and default identification procedures are stable.

q-fin.RM↗