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9 - Validation of Retail Credit Risk Models

Published online by Cambridge University Press:  02 March 2023

David Lynch
Affiliation:
Federal Reserve Board of Governors
Iftekhar Hasan
Affiliation:
Fordham University Graduate Schools of Business
Akhtar Siddique
Affiliation:
Office of the Comptroller of the Currency
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Summary

Retail credit risk is an important risk for many banks. This chapter describes various retail credit risk models in great detail and reviews the ways they may be validated. Validation principles are described for models used for risk management, stress testing and other applications. The classes of models include both static scoring models and multi-period loss forecasting models. Within the latter class, roll rate model, vintage-based model, and various other models are described. Account/loan level models are also described, including the Cox Proportional Hazard rate model and multinomial logit model. In each case, the authors discuss the academic underpinnings, the industry usage, and choices that are commonly made under various circumstances. The role of data in determining these choices is also discussed.

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Publisher: Cambridge University Press
Print publication year: 2023

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References

Ai, Chunrong and Norton, Edward C. (2003). Interaction terms in logit and probit models. Economic Letters, 80, 123129.Google Scholar
Athey, Susan. (2017). Beyond prediction: Using big data for policy problems. Science, 355, 483485.Google Scholar
Athey, Susan. (2018). The Impact of Machine Learning on Economics. The economics of artificial intelligence: An agenda, 507547. University of Chicago Press.Google Scholar
Anderson, J. R., Cain, J. R., and Gelber, R. D. (1983). Analysis of survival by tumor response. Journal of Clinical Oncology, November, 1(11), 710–719.Google Scholar
Basel Committee on Banking Supervision, 2005, Studies on the Validation of Internal Rating Systems, Working Paper No. 14, May.Google Scholar
Basel Committee on Banking Supervision, 2004, International Convergence of Capital Measurement and Capital Standards, A Revised Framework, June.Google Scholar
Begg, Colin and Gray, Robert. (1984). Calculation of polychotomous logistic regression parameters using individualized regressions. 71(1), 1118.Google Scholar
Belloni, Alexander, Chernozhukov, Victor and Hansen, Christian. (2014). High dimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives, 28(2), 123.Google Scholar
Board of Governors of the Federal Reserve System. (2015), Federal Reserve Guidance on Supervisory Assessment of Capital Planning and Positions for LISCC Firms and Large and Complex Firms (SR-15–18), December.Google Scholar
Board of Governors of the Federal Reserve System, (2015). Federal Reserve Guidance on Supervisory Assessment of Capital Planning and Positions for Large and Noncomplex Firms (SR-15–19), December.Google Scholar
Board of Governors of the Federal Reserve System, (2013). Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice, August.Google Scholar
Board of Governors of the Federal Reserve System and Office of the Comptroller of the Currency, 2011, Supervisory Guidance on Model Risk Management, April 4.Google Scholar
Breeden, Joseph L. (2010). Reinventing Retail Lending Analytics. Risk Books.Google Scholar
Breiman, Leo. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199215.Google Scholar
Breiman, Leo, Friedman, Jerome, Stone, Charles J. and Olshen, R. A. (1984). Classification and Regression Trees. Taylor & Francis.Google Scholar
Buchak, Greg, Gregor, Matvos, Tomasz, Piskorski, and Amit, Seru. (2018). Fintech, regulatory arbitrage, and the rise of shadow banks. Journal of Financial Economics, 130(3), 453483.Google Scholar
Cameron, Colin A. and Trivedi, Pravin K. (2005). Microeconometrics: Methods and Applications. Cambridge University Press.Google Scholar
Castle, Jennifer, Doornik, Jurgen and Hendry, David. (2011a). Evaluating automatic model selection. Journal of Time Series Econometrics, 3(1), 19411928.Google Scholar
Castle, Jennifer, Qin, X. and Reed, Robert. (2009). How to Pick the Best Regression Equation: A Review and Comparison of Model Selection Algorithms, WP. 13/2009, Dept. of Economics, University of Canterbury.Google Scholar
Castle, Jennifer, Qin, X. and Reed, Robert. (2011b). Using Model Selection Algorithms to Obtain Reliable Coefficient Estimates, WP. 03/2011, Dept of Economics, University of Canterbury.Google Scholar
Committee on the Global Financial System, BIS, and the Financial Stability Board, 2017, FinTech Credit, Market Structure, Business Models and Financial Stability Implications, May 2017.Google Scholar
Cox, David. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187220.Google Scholar
Deng, Yongheng, Quigley, John and Van Order, Robert. (2000). Mortgage terminations, heterogeneity, and the exercise of mortgage options. Econometrica, 68(2), 275307.Google Scholar
Efron, Bradley and Hastie, Trevor. (2016). Computer Age Statistical Inference, Algorithms, Evidence, and Data Science. Cambridge University Press.Google Scholar
Elliott, G. and Timmermann, A. (2016). Economic Forecasting. Princeton University Press.Google Scholar
Financial Stability Board (2017). Artificial Intelligence and Machine Learning in Financial Services: Market Developments and Financial Stability Implications, November 2017.Google Scholar
Glennon, Dennis, Kiefer, Nicholas M., Larson, C. Erik and Choi, Hwan-Sik. (2007). Development and Validation of Credit-Scoring Models, Working Papers 07-12, Cornell University, Center for Analytic Economics.Google Scholar
Han, Aaron and Hausman, Jerry. (1990). Flexible parametric estimation of duration and competing risk models. Journal of Econometrics, 5(1), 128.Google Scholar
Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome. (2009). The Elements of Statistical Learning, Data Mining, Inference, and Prediction, 2nd edition. Springer.Google Scholar
Institute of International Finance (2018). Machine Learning in Credit Risk: Detailed Survey Report, March.Google Scholar
International Monetary Fund (2017). Household Debt and Financial Stability, Chapter 2 of Global Financial Stability Report October 2017: Is Growth at Risk?Google Scholar
International Monetary Fund, (2012). Dealing With Household Debt, Chapter 3 of World Economic Outlook.Google Scholar
Jorda, O., Schularick, M. and Taylor, A. (2016). The great mortgaging: Housing finance, crises and business cycles. Economic Policy, 31(85), 107–52.Google Scholar
Lewis, E. M. (1992). An Introduction to Credit Scoring. Athena Press.Google Scholar
Li, Phillip, Qi, Min, Zhang, Xiaofei and Zhao, Xinlei. (2016). Further investigation of parametric loss given default modeling. Journal of Credit Risk, 12(4), 1747.Google Scholar
Liang, Kung-Yee and Zeger, , Scott L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), (April), 1322.Google Scholar
McFadden, Daniel. (1981). Econometric models of probability choice, in Structural Analysis of Discrete Data with Economic Applications. ed. Manski, C. F. and McFadden, D.. Cambridge, MA: MIT Press, 198272.Google Scholar
Mian, Atif, and Sufi, Amir. (2014). House of Debt. Chicago: University of Chicago Press.Google Scholar
Mian, A., Sufi, A. and Verner, , E. (2017). Household debt and business cycles worldwide. Quarterly Journal of Economics, 132(4), 17551817.Google Scholar
Mincer, J. A. and Zarnowitz, , V. (1969). The evaluation of economic forecasts. In Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, 146. NBER.Google Scholar
Mullainathan, Sendhil and Spiess, , Jann. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87106.Google Scholar
Ng, Serena. (2013). Variable selection predictive regressions. Handbook of Forecasting, Vol2B, 753786.Google Scholar
Papke, Leslie E. and Wooldridge, , Jeffrey M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan. Journal of Applied Econometrics, 11, 619632.Google Scholar
Philippon, Thomas. (2015 ). Has the US finance industry become less efficient? American Economic Review, 105(4), 1408–38.Google Scholar
Qi, Min, and Zhao, , Xinlei. (2011). Comparison of modeling methods for loss given default. Journal of Banking & Finance, 35(11), 28422855.Google Scholar
Rajan, Uday, Seru, , Amit and Vig, , Vikrant. (2015). The failure of models that predict failure: Distance, incentives, and defaults. Journal of Financial Economics, 115(2015), 237260.Google Scholar
Scott, Steven and Varian, , Hal. (2015). Bayesian variable selection for nowcasting economic time series. In Goldfarb, Avi, Greenstein, Shane, and Tucker, Catherine, editors, Economic Analysis of the Digital Economy: University of Chicago Press.Google Scholar
Shumway, Tyler. (1999). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, January 2001, 101–124.Google Scholar
Sirignano, Justin A., Sadhwani, , Apaar and Giesecke, , Kay. (2016). Deep Learning for Mortgage Risk arXiv preprint arXiv:1607.02470.Google Scholar
Taddy, Matt. (2019). The technological elements of artificial intelligence. In Agarwal, Ajay K, Gans, Joshua, and Goldfarb, Avi, editors, The Economics of Artificial Intelligence: An Agenda. University of Chicago Press.Google Scholar
Thomas, Lyn C. (2009). Consumer Credit Models: Pricing, Profit, and Portfolios. Oxford University Press.Google Scholar
Thomas, Lyn C. (2000). A survey of credit and behavioral scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16, 149172.Google Scholar
van Houwelingen, Hans C. (2007). Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics, 70–85, 2007.Google Scholar
Varian, Hal. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 328.Google Scholar
Wooldridge, Jeffrey. (2010). Econometrics of Cross Section and Panel Data, 2nd edition. MIT Press.Google Scholar
Zabai, Anna. (2017). Household debt: Recent developments and challenges. BIS Quarterly Review, December, 39–54.Google Scholar

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