Michael Jacobs

Correspondence: Michael Jacobs, michael.jacobsjr@pnc.com

PNC Financial Services Group, USA

pdf (1960.19 Kb) | doi:

Abstract

The CECL revised accounting standard for credit loss provisioning is intended to represent a for-ward-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. .

Keywords:

  Accounting Rule Change, Current Expected Credit Loss, Allowance for Loan and Lease Losses, Credit Provisions, Credit Risk, Financial Crisis, Model Risk.


References

Basel Committee on Banking Supervision (2006) ‘International Convergence of Capital Measurement and Capital Standards: A Revised Framework’, The Bank for International Settlements, Basel, Switzerland.

Basel Committee on Banking Supervision (2009) ‘Principles for Sound Stress Testing Practices and Supervision - Consultative Paper No. 155’, The Bank for International Settlements, Basel, Switzerland.

Batchelor, R. A., & Dua, P. (1990). Product differentiation in the economic forecasting industry. International Journal of Forecasting 6(3), 311-316.

Berrospide, J., & Edge. R. (2010). The effects of bank capital on lending: What do we know, what does it mean. International Journal of Central banking 6(4), 5-55.

Bernanke, B. S., & Lown C. S. (1991). The credit crunch. Brookings Papers on Economic Activity 2, 205-247.Board of Governors of the Federal Reserve System. (2011) ‘Supervisory Guidance on Model Risk Management’, Supervisory Letter 11-7, Washington, D.C., April 4th.

Box, G., & Jenkins, G. (1970). Times series analysis: forecasting and control. San Francisco, C.A.: Holden-Day. Brockwell P.J., & Davis R.A. (1991). Time series: theory and methods. New York, N.Y.: Springer-Verlag.

Carlson, M., H. Shan, & Warusawitharana, M. (2013). Capital ratios and bank lending: A matched bank approach.  Journal of Financial Intermediation 22, 663-687.

Chae, S. Sarama, R. Vocjtech, C., &.Wang, J. (2017). The impact of the current expected credit loss standard (CECL) on the timing and comparability of reserves, SSRN Working Paper (October).

Commandeur, J. J. F., & Koopman, S.J. (2007). Introduction to state space time series analysis. New York, N.Y.: Oxford University Press.

Cornett, M., McNutt, J., Strahan, P., & Tehranian, H. (2011). Liquidity risk management and credit supply in the financial crisis.  Journal of Financial Economics 101, 297-312.

Financial Accounting Standards Board (2012) “Accounting Standards Update No. 2012-260, Financial Instruments—Credit Losses (Subtopic 825-15): Measurement of Credit Losses on Financial Instruments,” December.

Financial Accounting Standards Board (2016) “Accounting Standards Update No. 2016-13, Financial Instruments—Credit Losses (Topic 326): Measurement of Credit Losses on Financial Instruments,” June.

Francis, W.B., & Osborne, M. (2009). Bank regulation, capital and credit supply: measuring the impact prudential standards, Occasional Paper 36, Financial Services Authority.

Glasserman, P., & Xu, X. (2013). Robust risk measurement and model risk. Quantitative Finance, 14(1), 29-58.

Hanan, E.J. (1971). The identification problem for equation systems with moving average errors. Econometrica, 39, 751-766.

Hanan, E.J. (1988). The statistical theory of linear systems. New York, N.Y.: John Wiley.

Hansen, L.P., & Sargent, T.J. (2007). Robustness. Princeton, N.J.: Princeton University Press.

Hirtle, B. A., Kovner, A., Vickery, J. &.Bhanot, M. (2015). Assessing financial stability: the capital and loss assessment under stress scenarios (CLASS) model. Federal Reserve Bank of New York Staff Report No. 663 (July).

International Accounting Standards Board (2014) “International Reporting for Financial Statement Number 9,” July.

Jacobs, Jr., M. (2015). The quantification and aggregation of model risk: perspectives on potential approaches. The Journal of Financial Engineering and Risk Management, 2(2), 124–154.

Jacobs, Jr., M., Klein L. and Merchant, A., 2015a (September), "Emerging Trends in Model Risk Management", Accenture Consulting.

Jacobs, Jr., M., Karagozoglu, A.K , & Sensenbrenner, F.J. (2015b). Stress testing and model validation: application of the Bayesian approach to a credit risk portfolio. The Journal of Risk Model Validation, 9(3), 41–70.

Jacobs, Jr., M.. (2017). A mixture of distributions model for the term structure of interest rates with an application to risk management. American Research Journal of Business and Management, 3(1), 1-17.

Jacobs, Jr., M., & Sensenbrenner, F.J. (2018a). A comparison of methodologies in the stress testing of credit risk – alternative scenario and dependency constructs. Quantitative Finance and Economics, 2(2), 294

Jacobs, Jr., M. (2018b). The validation of machine learning models for the stress testing of credit risk. The Journal of Risk Management in Financial Institutions 11(3), 1-26.–324.

Kishan, R., and Opiela, T (2000). Bank size, bank capital, and the bank lending channel.  Journal of Money, Credit, and Banking 32, 121-141.

R Development Core Team, 2019: “R: A Language and Environment for Statistical Computing.” R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.

Sims, C.A. (1980). Macroeconomics and reality. Econometrica,48, 1-48.

Skoglund, J., 2018 (April), "Quantification of Model Risk in Stress Testing and Scenario Analysis", SAS Institute.

Stock, J.H., & Watson, M.W. (2001). Vector autoregressions. Journal of Economic Perspectives 15(4), 101-115.

International Markets


Live World Indices are powered by Investing.com

Sponsors and Partners

Stock quotes

Leading Stock Quotes powered by Investing.com