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IFRS9 Expected Credit Loss Estimation: Advanced Models for Estimating Portfolio Loss and Weighting Scenario Losses

Bill Huajian Yang, Biao Wu, Kaijie Cui, Zunwei Du and Glenn Fei

MPRA Paper from University Library of Munich, Germany

Abstract: Estimation of portfolio expected credit loss is required for IFRS9 regulatory purposes. It starts with the estimation of scenario loss at loan level, and then aggregated and summed up by scenario probability weights to obtain portfolio expected loss. This estimated loss can vary significantly, depending on the levels of loss severity generated by the IFSR9 models, and the probability weights chosen. There is a need for a quantitative approach for determining the weights for scenario losses. In this paper, we propose a model to estimate the expected portfolio losses brought by recession risk, and a quantitative approach for determining the scenario weights. The model and approach are validated by an empirical example, where we stress portfolio expected loss by recession risk, and calculate the scenario weights accordingly.

Keywords: Scenario weight; stressed expected credit loss; loss severity; recession probability; Vasicek distribution; probit mixed model (search for similar items in EconPapers)
JEL-codes: C02 C1 C10 C13 C18 C22 C32 C46 C51 C52 C53 G1 G18 G31 G32 G38 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
Date: 2019-04-18
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