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022 _a17528887
040 _aMSU
_bEnglish
_cMSU
_erda
050 0 0 _aHD61.J687 JOU
100 1 _aChawla, Gaurav
_eauthor
245 1 0 _aPoint-in-time loss-given default rates and exposures at default models for IFRS 9/CECL and stress testing/
_ccreated by Gaurav Chawla, Lawrence R. Forest and Scott D. Aguais
264 1 _aLondon :
_bHenry Stewart Publication,
_c2016.
336 _2rdacontent
_atext
_btxt
337 _2rdamedia
_aunmediated
_bn
338 _2rdacarrier
_avolume
_bnc
440 _aJournal of risk management in financial institutions
_vVolume 9, number 3
520 3 _aIn contrast with Basel II rules, which call for the use of through-the-cycle (TTC) probabilities of default (PDs) and downturn (DT) loss-given default rates (LGDs) and exposures at default (EADs), the regulatory stress tests and the new IFRS 9 and proposed Current Expected Credit Loss (CECL) accounting standards require institutions to use point-in-time (PIT) projections of PDs, LGDs and EADs. By accounting for the current state of the credit cycle, PIT measures track closely the variations in default and loss rates over time. In past publications the authors have described the derivation of industry-region credit-cycle indices (CCIs) and the use of those indices in converting legacy wholesale credit PD models, which typically understate cyclical variations, into fully PIT ones. This paper extends that framework to cover estimation of PIT LGDs and EADs for wholesale exposures. The authors offer options for the formulation of such models and discuss their experience in building PIT LGD and EAD models, and show that, by accounting for the probabilistic evolution over time in industry-region CCIs, one can derive joint, PD, LGD and EAD scenarios for use in the regulatory stress tests or in estimating the term structures of expected credit losses (ECLs) as needed for IFRS 9/CECL.
650 _aIFRS 9/CECL
_vExpected credit loss (ECL)
_xExposure at default (EAD)
700 1 _aForest, Lawrence R.
_eco author
700 1 _aAguais, Scott D.
_eco author
942 _2lcc
_cJA
999 _c167634
_d167634