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The Impact of the Current Expected Credit Loss Standard (CECL) on the Timing and Comparability of Reserves
The new forward-looking credit loss provisioning standard, CECL, is intended to promote proactive provisioning as loan loss reserves can be conditioned on expectations of the economic cycle. We study the degree to which one modeling decision?expectations about the path of future house prices ? affects the size and timing of provisions for first-lien residential mortgage portfolios. While we find that provisions are generally less pro-cyclical compared to the current incurred loss standard, CECL may complicate the comparability of provisions across banks and time. Market participants will need ...
Can We Take the “Stress” Out of Stress Testing? Applications of Generalized Structural Equation Modeling to Consumer Finance
Financial firms, and banks in particular, rely heavily on complex suites of interrelated statistical models in their risk management and business reporting infrastructures. Statistical model infrastructures are often developed using a piecemeal approach to model building, in which different components are developed and validated separately. This type of modeling framework has significant limitations at each stage of the model management life cycle, from development and documentation to validation, production, and redevelopment. We propose an empirical framework, spurred by recent developments ...
Benefits and Challenges of the “CECL” Approach
This note provides an overview of the Current Expected Credit Loss ("CECL") accounting approach for credit losses. It also discusses the potential benefits and challenges of the CECL approach to financial institutions and users of their financial statements.
CECL Implementation and Model Risk in Uncertain Times: An Application to Consumer Finance
I examine the challenges of economic forecasting and model misspecification errors confronted by financial institutions implementing the novel current expected credit loss (CECL) allowance methodology and its impact on model risk and bias in CECL projections. We document the increased sensitivity to model and macroeconomic forecasting error of the CECL framework with respect to the incurred loss framework that it replaces. An empirical application illustrates how to leverage simple machine learning (ML) strategies and statistical principles in the design of a nimble and flexible CECL modeling ...