IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for Non-Rated Companies

2019 ◽  
Author(s):  
David Delgado-Vaquero ◽  
José Morales-Díaz ◽  
Constancio Zamora-Ramírez
Keyword(s):  
Probability Of Default ◽  
Expected Loss ◽  
Ifrs 9

How to estimate expected credit losses – ECL – for provisioning under IFRS 9

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mariya Gubareva
Keyword(s):  
Risk Premium ◽  
Default Risk ◽  
Financial Regulation ◽  
Credit Default Swap ◽  
High Yield ◽  
Expected Loss ◽  
Content Type ◽  
Term Structures ◽  
Ifrs 9 ◽  
Investment Grade

PurposeThis paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.Design/methodology/approachThis paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.FindingsThis study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.Research limitations/implicationsMany issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.Practical implicationsThe proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.Originality/valueNo previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.

scholarly journals Gevolgen van invoering IFRS 9: Europese banken onder de loep

2017 ◽  
Vol 91 (11/12) ◽  
pp. 421-437 ◽  
Author(s):  
Job Huttenhuis ◽  
Ralph ter Hoeven
Keyword(s):  
Expected Loss ◽  
Loss Model ◽  
Hedge Accounting ◽  
Ifrs 9

Banken dienen volgens IAS 8 zowel in hun jaarrekening als halfjaarberichten inzicht te geven in de impact van IFRS 9. Op basis van een analyse van jaarrekeningen over 2016 van 50 Europese banken komen we tot de conclusie dat in beperkte mate kwantitatieve informatie over de impact van IFRS 9 op de classificatie van financiële activa, de hoogte van voorzieningen alsmede het bankkapitaal is opgenomen. De verstrekte informatie door banken laat zien dat IFRS 9 naar verwachting leidt tot een toename van de voorzieningen, hetgeen in lijn is met de verwachting bij overgang naar een expected loss-model. Banken hebben in alle gevallen een IFRS 9-toelichting in hun jaarrekening opgenomen en zijn hierin vaak concreet over de toepassing van hedge accounting. Geen enkele bank in onze populatie heeft de keuze gemaakt IFRS 9 voor 1 januari 2018 volledig toe te passen. In de onderzochte halfjaarberichten over de eerste helft van 2017 is door meer banken concrete informatie over de impact van IFRS 9 opgenomen dan in de jaarrekeningen over 2016. De geschatte negatieve impact van IFRS 9 op voorzieningen en bankkapitaal is afgenomen, waarschijnlijk als gevolg van verbeterde economische omstandigheden per 30 juni 2017. In de halfjaarberichten 2017 wordt de impact van IFRS 9 op het bankkapitaal vaker gekwantificeerd dan de impact op de voorzieningen. Een verklaringsgrond hiervoor kan worden gevonden in het grote belang dat aan het bankkapitaal wordt toegekend alsook in de locatie van de toelichting (bestuursverslag).

scholarly journals Aggregation of Incidence and Intensity Risk Variables to Achieve Reconciliation

Risks ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 107
Author(s):  
Clive Hunt ◽  
Ross Taplin
Keyword(s):  
Intensity Ratio ◽  
Important Task ◽  
Utilization Rate ◽  
Probability Of Default ◽  
Aggregate Level ◽  
Expected Loss ◽  
Total Risk ◽  
Loss Given Default ◽  
The Individual ◽  
Ratio Variables

The aggregation of individual risks into total risk using a weighting variable multiplied by two ratio variables representing incidence and intensity is an important task for risk professionals. For example, expected loss (EL) of a loan is the product of exposure at default (EAD), probability of default (PD), and loss given default (LGD) of the loan. Simple weighted (by EAD) means of PD and LGD are intuitive summaries however they do not satisfy a reconciliation property whereby their product with the total EAD equals the sum of the individual expected losses. This makes their interpretation problematic, especially when trying to ascertain whether changes in EAD, PD, or LGD are responsible for a change in EL. We propose means for PD and LGD that have the property of reconciling at the aggregate level. Properties of the new means are explored, including how changes in EL can be attributed to changes in EAD, PD, and LGD. Other applications such as insurance where the incidence ratio is utilization rate (UR) and the intensity ratio is an average benefit (AB) are discussed and the generalization to products of more than two ratio variables provided.

Calculating lifetime expected loss for IFRS 9: which formula is measuring what?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bernd Engelmann
Keyword(s):  
Design Methodology ◽  
Cash Flows ◽  
Management Systems ◽  
Expected Loss ◽  
Loan Loss ◽  
Fixed Rate ◽  
Content Type ◽  
Mathematical Proofs ◽  
Ifrs 9 ◽  
Practical Implications

PurposeThe purpose of this article is to derive formulas for lifetime expected credit loss of loans that are required for the calculation of loan loss reserves under IFRS 9. This is done both for fixed-rate and floating rate loans under different assumptions on LGD modeling, prepayment, and discount rates.Design/methodology/approachThis study provides exact formulas for lifetime expected credit loss derived analytically together with the mathematical proofs of each expression.FindingsThis articles shows that the formula most commonly applied in the literature for calculating lifetime expected credit loss is inconsistent with measuring expected loss based on expected discounted cash flows. Formulas based on discounted cash flows always lead to more conservative numbers.Practical implicationsFor banks reporting under IFRS 9, the implication of this research is a better understanding of the different approaches used for computing lifetime expected loss, how they are connected, and what assumptions are underlying each approach. This may lead to corrections in existing frameworks to make applications of risk management systems more consistent.Originality/valueWhile there is a lot of literature explaining IFRS 9 and evaluating its impact, none of the existing research has systematically analyzed the calculation of lifetime expected credit loss for this purpose and how the formula changes under different modeling assumptions. This gap is filled by this study.

Part II Commercial Banking, 10 The Internal Ratings-Based Approach

2018 ◽  
Author(s):  
Gleeson Simon
Keyword(s):  
Internal Models ◽  
Commercial Banking ◽  
Probability Of Default ◽  
Expected Loss ◽  
Loss Given Default ◽  
Total Capital ◽  
Regulatory Standard ◽  
Model Probability ◽  
Irb Approach

This chapter discusses the internal ratings-based approach (IRB). The IRB permits a bank to use its internal models to derive risk weights for particular exposures. There are two available bases for the IRB: foundation (F-IRB), which permits the bank to model Probability of Default (PD), but relies on regulatory standard figures to determine Loss Given Default (LGD) and Exposure at Default (EAD); and advanced (A-IRB), in which all three of these are modelled. The A-IRB IRB approach models PD, LGD, EAD, and M. Both IRB approaches model both expected loss (EL) and unexpected loss (UL), and IRB banks are expected to recognise the EL derived from their models in their capital calculations. Consequently, a bank using an IRB approach will generally have a different total capital level from that which it would have if it were an SA bank.

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