Convexity and correlation effects in expected credit loss calculations for IFRS9/CECL and stress testing
Abstract
This paper demonstrates that the convexity of PD functions as well as the correlation among probability of default (PD), loss given default (LGD) and exposure at default (EAD) outcomes impart skewness to the credit-loss, probability-distribution function (PDF) and thereby increase the expected values of credit losses (ECLs) by as much as 20 per cent or more according to estimates presented later. With regard to convexity, the magnitude of the effect on ECLs depends on the amount of convexity in PD functions as well as the extent of the random dispersion in the credit-risk factors that affect PDs. With regard to correlation, the magnitude of the effect depends on the amount of correlated variation in PD, LGD and EAD outcomes. In accounting for these effects, one may apply credit-cycle indices in modifying the existing PD, LGD and EAD models so that they produce point-in-time (PIT) estimates that move together over time in the way implied by common, credit-cycle effects. Having done that, an institution can account for convexity and correlation effects in producing the unbiased estimates of ECLs needed in determining losses under stress scenarios or impairments under IFRS9/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 global head of research at Aguais and Associates (AAA), an associate company of Deloitte. 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 banks’ 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. (2017, February 1). Convexity and correlation effects in expected credit loss calculations for IFRS9/CECL and stress testing. In the Journal of Risk Management in Financial Institutions, Volume 10, Issue 1. https://doi.org/10.69554/VWXW7019.
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