Point-in-time loss-given default rates and exposures at default models for IFRS 9/CECL and stress testing
Abstract
In 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.
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Author's Biography
Gaurav Chawla is team leader — models and methodologies — at Aguais and Associates (AAA), an associate company of Deloitte. He leads the application of AAA-built techniques at various clients’ sites. Gaurav has over 13 years of experience building risk models across large banks and academic institutions. In the past, Gaurav led the model development team at GE Capital that was responsible for developing CCAR and IFRS9 focused credit risk models. Before that, Gaurav worked at RBS on the development of methodologies, credit risk models (Basel II AIRB PD, LGD, EAD) and loss and stress testing models. He holds an eclectic mix of degrees in Engineering, Mathematics, Business and Law.
Lawrence R. Forest Jr. is head of research at AAA. He leads all of the firm’s credit risk analytics research, development and design. Lawrence has over 25 years of experience developing and designing advanced credit analytics solutions for large banking institutions. He has spent the last 12 years leading the econometric design and development of advanced Basel II PD, LGD and EAD credit models and Dual Ratings at Barclays Capital and RBS. Most recently he has been reviewing US bank’s credit models for PWC.
Scott D. Aguais is managing director of Aguais and Associates (AAA), an associate company of Deloitte. He leads the firm’s efforts in marketing, strategic partner development and project delivery. Scott has 25 years of experience developing and delivering advanced credit analytics solutions to large banking institutions. He spent 10 years delivering credit models and analytics through consulting at DRI/McGraw-Hill, AMS and KPMG. He then moved on to Algorithmics and has spent the last 12 years developing advanced credit models and supporting the successful Basel II Waivers at Barclays Capital and RBS.
Citation
Chawla, Gaurav, Forest Jr., Lawrence R. and Aguais, Scott D. (2016, June 1). Point-in-time loss-given default rates and exposures at default models for IFRS 9/CECL and stress testing. In the Journal of Risk Management in Financial Institutions, Volume 9, Issue 3. https://doi.org/10.69554/IBHL6152.
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