Retail Risk Modeling Framework in the Current Environment. BRAD BRADLEY, SunTrust JUAN M. LICARI, Moody s Analytics
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1 Retail Risk Modeling Framework in the Current Environment BRAD BRADLEY, SunTrust JUAN M. LICARI, Moody s Analytics OCTOBER 2015
2 Retail Risk Modeling Framework in the Current Environment Brad Bradley, SunTrust Juan M. Licari, Moody s Analytics» Using models and analysis to optimize expense: auto collections case study» Cutting-edge retail credit methodologies: leveraging Bayesian techniques for simulation and model robustness purposes» Effects of alternative methodologies on credit portfolio losses Retail Risk Modeling Framework in the Current Environment, October
3 1 Cutting-edge Retail Credit Methodologies Retail Risk Modeling Framework in the Current Environment, October 2015
4 Frequentist vs. Bayesian Estimation Methods Auto-Loan Vintage Example Vintage-level default rates as a (nonlinear) function of (i) Lifecycle (seasoning of the accounts) set at Frequentist values (ii) Vintage quality (rank-ordering of the cohorts)s (iii) Sensitivity to macroeconomic drivers Part of the Bayesian process (from prior to posteriors) (ii) Vintage quality (fixed-effects) (iii) Macro drivers Investment growth (q/q) Unemployment rate Home price growth (q/q) Retail Risk Modeling Framework in the Current Environment, October
5 Frequentist vs. Bayesian Estimation Methods (cont.) Auto-Loan Vintage Example (cont.) Posterior Bayesian Distributions for Economic Parameters Investment growth (q/q) Unemployment rate Home price growth (q/q) Retail Risk Modeling Framework in the Current Environment, October
6 Frequentist vs. Bayesian Simulations Auto-Loan Vintage Example (cont.) Starting from Simulations for Macroeconomic Drivers Unemployment Rate, %: Forecasts per Quarter Home-Price Growth, % Q/Q Home-Price Growth, % Q/Q: Forecasts per Quarter +Q1 +Q2 +Q3 +Q4 +Q5 +Q6 +Q7 +Q8 +Q9 +Q1 +Q2 +Q3 +Q4 +Q5 +Q6 +Q7 +Q8 +Q9 Retail Risk Modeling Framework in the Current Environment, October
7 Frequentist vs. Bayesian Simulations (cont.) Auto-Loan Vintage Example (cont.) PD Simulations at +Q5, All Vintages Fixed betas, no residual/error simulations, 100% scenario-driven Frequentist, all scenarios Bayesian, all scenarios Frequentist, top 1/3 Bayesian, top 1/3 Frequentist, mid 1/3 Bayesian, mid 1/3 Frequentist, worst 1/3 Bayesian, worst 1/3 Retail Risk Modeling Framework in the Current Environment, October
8 Frequentist vs. Bayesian Simulations (cont.) Auto-Loan Vintage Example (cont.) PD Simulations at +Q5, All Vintages Simulated betas, with residual/error simulations Freq, all Bayes, all Freq, top 1/3 Bayes, top 1/3 Freq, mid 1/3 Bayes, mid 1/3 Freq, worst 1/3 Bayes, worst 1/3 Retail Risk Modeling Framework in the Current Environment, October
9 Frequentist vs. Bayesian Simulations (cont.) Auto-Loan Vintage Example (cont.) PD Simulations at +Q5, Selected Vintages: Old, Mid-age, Young and Future Cohorts Simulated betas, with residual/error simulations Retail Risk Modeling Framework in the Current Environment, October
10 Frequentist vs. Bayesian Simulations (cont.) Auto-Loan Vintage Example (cont.) Simulated Portfolio Losses Frequentist - Cumulative loss at +Q9 Fixed betas, No residual/error simulations Bayesian - Cumulative loss at +Q9 Fixed betas, No residual/error simulations Retail Risk Modeling Framework in the Current Environment, October
11 Frequentist vs. Bayesian Simulations (cont.) Auto-Loan Vintage Example (cont.) Simulated Portfolio Losses (cont.) Frequentist - Cumulative loss at +Q9 Simulated betas, with residual/error simulations Bayesian - Cumulative loss at +Q9 Simulated betas, with residual/error simulations Retail Risk Modeling Framework in the Current Environment, October
12 Frequentist vs. Bayesian Simulations (cont.) Credit Card Vintage Example PD Simulations at +Q5, All Vintages Fixed betas, no residual/error simulations, 100% scenario-driven Frequentist, all scenarios Bayesian, all scenarios Frequentist, top 1/3 Bayesian, top 1/3 Frequentist, mid 1/3 Bayesian, mid 1/3 Frequentist, worst 1/3 Bayesian, worst 1/3 Retail Risk Modeling Framework in the Current Environment, October
13 Frequentist vs. Bayesian Simulations (cont.) Credit Card Vintage Example (cont.) PD Simulations at +Q5, Selected Vintages: Old, Mid-age, Young and Future Cohorts Simulated betas, with residual/error simulations Retail Risk Modeling Framework in the Current Environment, October
14 Frequentist vs. Bayesian Simulations (cont.) Credit Card Vintage Example (cont.) Simulated Portfolio Losses Frequentist - Cumulative loss at +Q9 Fixed betas, No residual/error simulations Bayesian - Cumulative loss at +Q9 Fixed betas, No residual/error simulations Retail Risk Modeling Framework in the Current Environment, October
15 Frequentist vs. Bayesian Simulations (cont.) Credit Card Vintage Example (cont.) Simulated Portfolio Losses (cont.) Frequentist - Cumulative loss at +Q9 Simulated betas, with residual/error simulations Bayesian - Cumulative loss at +Q9 Simulated betas, with residual/error simulations Retail Risk Modeling Framework in the Current Environment, October
16 Concluding Remarks» Room for R&D on Bayesian estimation and simulation methods» Advantages in model robustness and parameter stability» Simulated risk parameters can be linked to dynamic credit portfolio analysis» The methodology can be extended to granular, loan-level models or top-down (aggregated) portfolio frameworks» Cohort heterogeneity can be incorporated to account for idiosyncratic effects within any given vintage (without having to estimate the models at a loan-level) Retail Risk Modeling Framework in the Current Environment, October
17 Juan M. Licari, Ph.D. Senior Director +44 (0) tel +44 (0) mobile Moody's Analytics UK Ltd. moodys.com
18 2015 Moody s Analytics, Inc. and/or its licensors and affiliates (collectively, MOODY S ). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY COPYRIGHT LAW AND NONE OF SUCH INFORMATION MAY BE COPIED OR OTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED, DISSEMINATED, REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY SUCH PURPOSE, IN WHOLE OR IN PART, IN ANY FORM OR MANNER OR BY ANY MEANS WHATSOEVER, BY ANY PERSON WITHOUT MOODY S PRIOR WRITTEN CONSENT. All information contained herein is obtained by MOODY S from sources believed by it to be accurate and reliable. Because of the possibility of human or mechanical error as well as other factors, however, all information contained herein is provided AS IS without warranty of any kind. Under no circumstances shall MOODY S have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting from, or relating to, any error (negligent or otherwise) or other circumstance or contingency within or outside the control of MOODY S or any of its directors, officers, employees or agents in connection with the procurement, collection, compilation, analysis, interpretation, communication, publication or delivery of any such information, or (b) any direct, indirect, special, consequential, compensatory or incidental damages whatsoever (including without limitation, lost profits), even if MOODY S is advised in advance of the possibility of such damages, resulting from the use of or inability to use, any such information. The ratings, financial reporting analysis, projections, and other observations, if any, constituting part of the information contained herein are, and must be construed solely as, statements of opinion and not statements of fact or recommendations to purchase, sell or hold any securities. NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY, TIMELINESS, COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OF ANY SUCH RATING OR OTHER OPINION OR INFORMATION IS GIVEN OR MADE BY MOODY S IN ANY FORM OR MANNER WHATSOEVER. Each rating or other opinion must be weighed solely as one factor in any investment decision made by or on behalf of any user of the information contained herein, and each such user must accordingly make its own study and evaluation of each security and of each issuer and guarantor of, and each provider of credit support for, each security that it may consider purchasing, holding, or selling. Retail Risk Modeling Framework in the Current Environment, October
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