IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i10p1427-d1389720.html
   My bibliography  Save this article

An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

Author

Listed:
  • Joseph L. Breeden

    (Deep Future Analytics LLC, 1600 Lena St., Suite E3, Santa Fe, NM 87505, USA)

Abstract

The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. Using a panel data structure, credit risk scores can be integrated with an APC framework using either logistic regression or machine learning. Such APC scores for default, payoff, and other key rates fit naturally into forward-looking cash flow estimates. Given an economic scenario, every applicant at the time of origination can be assigned profit and profit volatility estimates so that underwriting can truly be account-level. This process optimizes the most fallible part of underwriting, which is setting cutoff scores and assigning loan pricing and terms. This article provides a summary of applications of APC models across portfolio management roles, with a description of how to create the models to be directly integrated. As a consequence, cash flow calculations are available for each account, and cutoff scores can be set directly from portfolio financial targets.

Suggested Citation

  • Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1427-:d:1389720
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/10/1427/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/10/1427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R M Oliver & E Wells, 2001. "Efficient frontier cutoff policies in credit portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1025-1033, September.
    2. Michael Bailey & Rachel Cao & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2018. "Social Connectedness: Measurement, Determinants, and Effects," Journal of Economic Perspectives, American Economic Association, vol. 32(3), pages 259-280, Summer.
    3. Breeden, Joseph L., 2007. "Modeling data with multiple time dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4761-4785, May.
    4. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    5. Gregory S. Crawford & Nicola Pavanini & Fabiano Schivardi, 2018. "Asymmetric Information and Imperfect Competition in Lending Markets," American Economic Review, American Economic Association, vol. 108(7), pages 1659-1701, July.
    6. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    7. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    8. Tony Bellotti & Jonathan Crook, 2014. "Retail credit stress testing using a discrete hazard model with macroeconomic factors," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 340-350, March.
    9. Joseph L. Breeden & Jonathan Crook, 2022. "Multihorizon discrete time survival models," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(1), pages 56-69, January.
    10. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
    11. Erik Heitfield & Tarun Sabarwal, 2004. "What Drives Default and Prepayment on Subprime Auto Loans?," The Journal of Real Estate Finance and Economics, Springer, vol. 29(4), pages 457-477, December.
    12. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2012. "Survival Analysis in LGD Modeling," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2012(1), pages 6-27.
    13. Daniele De Leonardis & Roberto Rocci, 2008. "Assessing the default risk by means of a discrete‐time survival analysis approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(4), pages 291-306, July.
    14. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    15. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Dynamic survival models with varying coefficients for credit risks," European Journal of Operational Research, Elsevier, vol. 275(1), pages 319-333.
    16. Dean Karlan & Jonathan Zinman, 2019. "Long-Run Price Elasticities of Demand for Credit: Evidence from a Countrywide Field Experiment in Mexico," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(4), pages 1704-1746.
    17. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    18. Zhiyong Li & Ke Li & Xiao Yao & Qing Wen, 2019. "Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Lending," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(1), pages 118-132, January.
    19. So Young Sohn & Hyejin Jeon, 2010. "Competing Risk Model for Technology Credit Fund for Small and Medium‐Sized Enterprises," Journal of Small Business Management, Taylor & Francis Journals, vol. 48(3), pages 378-394, July.
    20. Robert Marquez, 2002. "Competition, Adverse Selection, and Information Dispersion in the Banking Industry," The Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 901-926.
    21. Zoltán Novotny-Farkas, 2016. "The Interaction of the IFRS 9 Expected Loss Approach with Supervisory Rules and Implications for Financial Stability," Accounting in Europe, Taylor & Francis Journals, vol. 13(2), pages 197-227, May.
    22. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    23. Farinelli, Simone & Ferreira, Manuel & Rossello, Damiano & Thoeny, Markus & Tibiletti, Luisa, 2008. "Beyond Sharpe ratio: Optimal asset allocation using different performance ratios," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2057-2063, October.
    24. Joseph L. Breeden, 2023. "Impacts of Drought on Loan Repayment," JRFM, MDPI, vol. 16(2), pages 1-14, February.
    25. repec:bla:jfinan:v:44:y:1989:i:2:p:375-92 is not listed on IDEAS
    26. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    27. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    28. Anthony A. DeFusco & Andrew Paciorek, 2017. "The Interest Rate Elasticity of Mortgage Demand: Evidence from Bunching at the Conforming Loan Limit," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 210-240, February.
    29. Dean Karlan & Jonathan Zinman, 2005. "Elasticities of Demand for Consumer Credit," Working Papers 926, Economic Growth Center, Yale University.
    30. Schmid, Volker J. & Held, Leonhard, 2007. "Bayesian Age-Period-Cohort Modeling and Prediction - BAMP," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i08).
    31. Breeden, Joseph L., 2016. "Incorporating lifecycle and environment in loan-level forecasts and stress tests," European Journal of Operational Research, Elsevier, vol. 255(2), pages 649-658.
    32. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    33. McCoy, Shawn J. & Walsh, Randall P., 2018. "Wildfire risk, salience & housing demand," Journal of Environmental Economics and Management, Elsevier, vol. 91(C), pages 203-228.
    34. repec:eme:mfppss:eb013696 is not listed on IDEAS
    35. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    36. Stanton, Richard, 1995. "Rational Prepayment and the Valuation Mortgage-Backed Securities," The Review of Financial Studies, Society for Financial Studies, vol. 8(3), pages 677-708.
    37. Al-Kaisi, Mahdi & Elmore, Roger & Guzman, Jose & Hanna, Mark & Hart, Chad E. & Helmers, Matthew J. & Hodgson, Erin & Lenssen, Andrew & Mallarino, Antonio & Robertson, Alison & Sawyer, John, 2013. "Drought Impact on Crop Production and the Soil Environment: 2012 Experiences from Iowa," Staff General Research Papers Archive 35963, Iowa State University, Department of Economics.
    38. Pan Kang & Stavros A. Zenios, 1992. "Complete Prepayment Models for Mortgage-Backed Securities," Management Science, INFORMS, vol. 38(11), pages 1665-1685, November.
    39. Cheng Hsiao, 2007. "Rejoinder on: Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 56-57, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    2. Arno Botha & Tanja Verster & Roelinde Bester, 2024. "The TruEnd-procedure: Treating trailing zero-valued balances in credit data," Papers 2404.17008, arXiv.org, revised Nov 2024.
    3. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    4. Oliver Blümke, 2022. "Multiperiod default probability forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 677-696, July.
    5. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    6. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    7. Justin Sirignano & Kay Giesecke, 2019. "Risk Analysis for Large Pools of Loans," Management Science, INFORMS, vol. 65(1), pages 107-121, January.
    8. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
    9. Alexandre, Michel & Antônio Silva Brito, Giovani & Cotrim Martins, Theo, 2017. "Default contagion among credit modalities: evidence from Brazilian data," MPRA Paper 76859, University Library of Munich, Germany.
    10. Jiří Witzany & Anastasiia Kozina, 2022. "Recovery process optimization using survival regression," Operational Research, Springer, vol. 22(5), pages 5269-5296, November.
    11. Calabrese, Raffaella & Crook, Jonathan, 2020. "Spatial contagion in mortgage defaults: A spatial dynamic survival model with time and space varying coefficients," European Journal of Operational Research, Elsevier, vol. 287(2), pages 749-761.
    12. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    13. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020, January-A.
    14. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    15. Tim Ölkers & Oliver Mußhoff, 2024. "Exploring the role of interest rates, macroeconomic environment, agricultural cycle, and gender on loan demand in the agricultural sector: Evidence from Mali," Agribusiness, John Wiley & Sons, Ltd., vol. 40(2), pages 484-512, April.
    16. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Dynamic survival models with varying coefficients for credit risks," European Journal of Operational Research, Elsevier, vol. 275(1), pages 319-333.
    17. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank.
    18. Hibbeln, Martin & Gürtler, Marc, 2011. "Pitfalls in modeling loss given default of bank loans," Working Papers IF35V1, Technische Universität Braunschweig, Institute of Finance.
    19. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    20. Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1427-:d:1389720. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.