Journal of Risk & Control

An Analysis of the Impact of Modeling Assumptions in the Current Expected Credit Loss (CECL) Framework on the Provisioning for Credit Loss

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• Abstract

  The CECL revised accounting standard for credit loss provisioning is intended to represent a forward-looking and proactive methodology that is conditioned on expectations of the economic cycle.  In this study we analyze the impact of several modeling assumptions - such as the methodology for projecting expected paths of macroeconomic variables, incorporation of bank-specific variables or the choice of macroeconomic variables – upon characteristics of loan loss provisions, such as the degree of pro-cyclicality.  We investigate a modeling framework that we believe to be very close to those being contemplated by institutions, which projects various financial statement line items, for an aggregated “average” bank using FDIC Call Report data.  We assess the accuracy of 14 alternative CECL modeling approaches.  A key finding is that assuming that we are at the end of an economic expansion, there is evidence that provisions under CECL will generally be no less procyclical compared to the current incurred loss standard.  While all the loss prediction specifications perform similarly and well by industry standards in-sample, out of sample all models perform poorly in terms of model fit, and also exhibit extreme underprediction.  Among all scenario generation models, we find the regime switching scenario generation model to perform best across most model performance metrics, which is consistent with the industry prevalent approaches of giving some weight to scenarios that are somewhat adverse.  Across scenarios that the more lightly parametricized models tended to perform better according to preferred metrics, and also to produce a lower range of results across metrics.  An implication of this analysis is a risk CECL will give rise to challenges in comparability of results temporally and across institutions, as estimates vary substantially according to model specification and framework for scenario generation.  We also quantify the level of model risk in this hypothetical exercise using the principle of relative entropy, and find that credit models featuring more elaborate modeling choices in terms of number of variables, such as more highly parametricized models, tend to introduce more measured model risk; however, the more highly parametricized MS-VAR model, that can accommodate non-normality in credit loss, produces lower measured model risk.  The implication is that banks may wish to err on the side of more parsimonious approaches, that can still capture non-Gaussian behavior, in order to manage the increase model risk that the introduction of the CECL standard gives rise to.  We conclude that investors and regulators are advised to develop an understanding of what factors drive these sensitivities of the CECL estimate to modeling assumptions, in order that these results can be used in prudential supervision and to inform investment decisions.  .