Enterprise-wide Stress Testing


Published on

In this webinar-on-demand, hosted by American Banker and presented by Moody's Analytics, Thomas Day discussed enterprise-wide CCAR DFAST stress testing, including: best practices for expected loss (EL) and pre-provision net revenue (PPNR) forecasting, integrating stress testing into your existing business architecture, and transforming stress testing from a regulatory exercise to a strategic management tool.

Published in: Economy & Finance, Business
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Enterprise-wide Stress Testing

  1. 1. Welcome to Today’s Web Seminar! September 17, 2013 12:00 PM ET Sponsored By: Hosted By:
  2. 2. MODERATOR: Michael Sisk is a New York-based journalist who has covered business and the financial markets for 15 years, including stints as the investor editor at Red Herring, editor-at-large at American Banker, and contributing editor at Bank Technology News. His articles have appeared in numerous publications, including American Banker, Barron's, Crain's New York Business, Inc., Institutional Investor, strategy + business and Worth. Michael has co-written and edited three books; the most recent was Merge Ahead: Mastering the Five Enduring Trends of Artful M&A (McGraw-Hill 2009).
  3. 3. PRESENTER: Thomas Day Senior Director, Risk Solutions Moody's Analytics Thomas works to solve difficult stress-testing, capital planning, and risk management problems across complex portfolios and product sets for financial organizations worldwide. Day’s primary areas of focus include CCAR/DFA stress testing, pre-provision net revenue (PPNR) calculations, systems, and methodologies, advanced liquidity risk quantification and reporting, capital planning, performance and balance sheet management. As a former Board member and Vice-Chairman of the membership driven Professional Risk Managers’ International Association (PRMIA), Day is a recognized industry expert with over twenty-two years of increasingly senior roles with multifaceted experience in financial risk management, corporate governance, business development and leadership.
  4. 4. Stress Testing Webinar Series: Enterprise-wide Stress Testing September 17, 2013 Presented by: Thomas Day, Senior Director - Moody’s Analytics
  5. 5. Agenda 1. About stress testing 2. Best practices for expected loss (EL) and pre-provision net revenue (PPNR) forecasting 3. Integrating stress testing into existing business architecture 4. Techniques to make it worthwhile 5. Next webinar: Macroeconomic Conditional Loss Forecasting – October 29, 2013 6. Question and answers 5
  6. 6. 1 About Stress Testing 6
  7. 7. Starting Point Assumptions » Loss estimation (i.e., asset models) is the first most important element of the stress test: – Estimates of losses, revenues and expenses must all be “synchronized” with the same economic and market conditions. Estimates must be driven by a variable selection process that is consistent with the FRB scenarios, but may be more or less broad and these variables may be different from one asset-model to another, as well as for PPNR. » Integration of loss estimates into PPNR modeling has been weak; however, this integration is required in order to get a proper quarterly ALLL and “net income” number. » Stress-testing requires unprecedented coordination between heretofore “siloed” risk and financial planning processes. » Data, data-management, and risk and finance integration are key elements of success or failure. 7
  8. 8. Stress-Testing and Capital Planning » August 19, 2013, the FRS issued a report entitled, “Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice.” » While the requirements for smaller banks, those between $10 and $50 billion, are less onerous (see FR Vol 78, No 150, 8/5/2013) for the initial submission (i.e., March 2014), the underlying principles are important for all firms. » One key lesson learned is that firms: “…failed to adequately identify the potential exposures and risks stemming from their firm-wide activities” and that one of the key weaknesses was the inability of firms to simulate risks exposures, across the enterprise, in a comprehensive and integrated fashion.” » If one looks at the specification of the stress-test and Capital Plan Rule with an objective eye, it seems plain that a primary goal of the exercise is to spur a significant improvement in the internal infrastructure, planning, risk, and forecasting capabilities of financial organizations. » Conclusion: A significant amount of work on data, analytics, and integrated risk, finance, and management reporting is required in order to create a repeatable, sustainable, and transparent stress-testing and capital planning process. What does that work-flow entail? 8
  9. 9. Stylized Workflow (Steps) for DFAST/CCAR Exercise » Beyond meeting the “use-test”, the biggest challenge of the DFAST/CCAR exercise is the ability to integrate, automate, and validate the entirety of the business process. Data Data Pull as of Sept-30 Fill-in “Missing” Data with Proxy Data (inc. Tags) Populate Required Fields for FRY14M/Q Document Workflow, Version, and Audit the Data Scenario Design Tailor Scenarios Ensure Market Data is Consistent with the Scenario Expand and “Regionalize” Scenarios Receive Scenarios Calculate Conditional ELs Across All Assets Determine Business Strategy in Each Scenario Create Proper Assumption Input for Integrated PPNR Calculate Expected NII/NIM and Balance Sheet for Each Scenario Calculate Appropriate ProForma Regulatory Capital Assess and Apply Other Losses, Including Ops Risk Determine ChargeOff and ALLL in Each Scenario Calculated Expected NIR and NIE in Each Scenario Populate Required Regulatory Reporting Forms Reconcile Reports to FRY-9C and Other Reporting Assess and Validate Results Apply Measures to Capital Plan Analytics Reporting 9
  10. 10. Case Study: CCAR Integration Framework Regulatory Reporting Market Context Scenario Context Pro-Forma Income Statement Pro-Forma Balance Sheet COA ALM System Current position Basecase Runoff New Business Basecase Runoff 1. Volume 2. Price 3. Maturity Behavioral Assumptions Recon • Prepayment model(s) and tables • Valuation method(s) • Amortization type(s) • Other factors PPNR Calculator FP&A Step (client defined) New Business Plan Scenarios Credit RVM NIR/NIE Only volume, rate, and maturity Results tables – Runoff (Basecase) Results tables • Basecase scenario • Alternative scenarios – New bus. (for reference and (Basecase) recon only) Basecase Plan – 9Q forecast Scenario Planning FP&A Step (client defined) Moody’s Analytics RiskFoundationTM Datamart New Business Plan FP&A • Rolling 9-quarter • Credit dimension • Non-interest income and expense • RWA allocation Moody’s Analytics RiskFoundationTM Datamart 10
  11. 11. 2 Best Practices for EL and PPNR Forecasting 11
  12. 12. Requirements of an Effective Process » Expected loss (EL) estimates must be integrated into the “forecast”. Questions that arise: – Should we utilize a “top-down” or a “bottom-up” approach? Does it matter by asset-class? – Regardless of method, how do loss estimates ingrate into existing processes? – Who “owns” the loss calculations? » Pre-provision net revenue (PPNR) requires the integration of credit “and” business planning into the pro-forma forecast. Question that arise: – How do we estimate “conditional” new business volumes under stress? What is the correct “volume” estimate? What is the “correct” credit conditioned “price” rate? – How do we estimate the “credit quality” and “EL” of new business production under stress? How does this relate to capital planning and pro-forma RWA calculations? – How do we hit the right NPA levels and how do we create the right “drag” on earnings from increased NPA as well as increased charge-off and reserves? 12
  13. 13. Expected Loss: Question #1 – Top Down or Bottom-up? » Consider the following: “Companies may choose loss estimation processes from a range of available methods, techniques, and levels of granularity.” – Primary v. Challenger models  Need both! They should be “integrated.” – Wholesale (i.e., idiosyncratic and heterogeneous) v. Retail (i.e., homogeneous) » Challenges are addressed by: – Using your own data, but supplementing the data where needed (with documented explanation) – Focus on how the models will be used for business purposes, not simply the stressed metric » FRB Principle 2 for Designing and Implementing a Stress Testing Framework Expects Banks to Use Multiple Approaches to Stress Testing: An effective stress testing framework employs multiple conceptually sound stress testing activities and approaches All measures of risk, including stress tests, have an element of uncertainty due to assumptions, limitations, and other factors associated with using past performance measures and forward-looking estimates. Banking organizations should, therefore, use multiple stress testing activities and approaches …, and ensure that each is conceptually sound. Stress tests usually vary in design and complexity, including the number of factors employed and the degree of stress applied. A banking organization should ensure that the complexity of any given test does not undermine its integrity, usefulness, or clarity. In some cases, relatively simple tests can be very useful and informative. Furthermore, almost all stress tests, including well-developed quantitative tests supported by high-quality data, employ a certain amount of expert or business judgment, and the role and impact of such judgment should be clearly documented. Interagency Guidance on Stress Testing for Banking Organizations with Total Consolidated Assets of More Than $10Bn SR Letter 12-7, May 14, 2012 13
  14. 14. Expected Loss: Question #2 – How to Integrate? While some banks partially integrated loss projections into net interest income projections, some “…BHCs were unable to demonstrate coherence between NII projections and loss projections, generally because one or both modeling approaches did not fully capture the behavioral characteristics of the loan portfolio.” » Consider the following: Source: FRB’s August 19.2013 paper entitled, “Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice” – We need to forecast the balance sheet and income statement, but ALM systems are often insufficient – How do we “initialize” the ALM computation so we avoid MRAs? » Solution: – If an ALM system is being used, it must be properly “seeded” with consistent inputs from the credit, finance, and risk groups, including defining the proper input factors for market conditions. – Develop transition matrices by asset class – by quarter (bottom up). – Matrices should be derived from the bank’s champion models that are used for loss estimation and reporting. – Define and agree on “conditional” new business credit spreads and volume estimates. – Business strategy must include credit, regulatory capital, and proper reporting data tags. – Connection between credit quality and prepayment is critical, but often missed. – Must consider legal entity, cost centers, and other non-traditional dimensions! 14
  15. 15. Expected Loss: Question #3 – Who Owns the Loss Calcs? » Should conditional expected loss calculations be owned by a central function, or more integrated with front-office risk origination systems and processes? “Loan pricing should be consistent with both scenario conditions and competitive and strategic factors, including projected changes to the size of the portfolio. Origination assumptions should be the same for projecting loan balances, related loan fees, origination costs, and loan losses.” Source: FRB’s August 19.2013 paper entitled, “Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice” » In order to pass the “use-test”, the loss estimates must be consistent with the manner in which risk is originated and priced, actual and projected. Therefore, loss models used to estimate risk and determine deal structure should be consistent with loss models for CCAR/DFAST. » Conclusion: Credit loss models must be integrated with front-office systems, and the line-of-business managers should have a stake in validating/approving the conditional loss estimates from models that are deployed. » Line managers should determine stress-loss measures that may be important in deal pricing, deal structuring, and “return-on” measures. Should support RAPM. 15
  16. 16. PPNR: Question #1 – Estimating New Business Volume? Review: » Most new business volume forecasts are: – Not conditioned for credit and associated EL contribution(s). – Non-conditional on the macro-economic scenario. – Defined by SME input only. – Usually tied to the budget and planning process and are, thus, aspirational. Thesis: » FP&A need more accurate methods to estimate conditional new business volumes. Solution: » Quantitatively estimated approaches for new business volumes and credit spreads that are “agreed” between the planners, the LOBs, and the model output. » Sensitivity analysis around the range of estimates to determine the impact on capital. 16
  17. 17. PPNR: Question #2 – New Business Under Stress? Review: » The manner in which a firm’s “origination strategy” will change is heavily influenced by the expected economic conditions. » For stress-testing, many banks assume business mix either 1) stays the same or 2) changes in ways not necessarily properly tied to scenario design and evolution. » What level of NB estimation is needed? Thesis: » Since we must conduct pro-forma RWA calculations, each asset class must possess a new business credit distribution over time, and will generate EL. For example, new C&I must show rating grade origination by industry, by geography, by quarter in order to produce an accurate RWA calc. » These assumptions must “seed” any PPNR calculation; new business EL estimates must be reviewed and confirmed with credit, risk and finance staff (and ALLL impacts). Solution: » This level of new business planning is not a normal element of existing finance/FP&A processes. Thus, a certain level or BPM re-engineering is normally needed, as well as the technology to support this re-engineering. 17
  18. 18. PPNR: Question #3 – NPA and ALLL Influence on NI Calculation? Review: » As loans “transition” to non-accrual, they create a “drag” on net interest income. » The FRB has identified the integration of PPNR and credit as a key weakness. » The current sound practice is to use “conditional” transition matrices, by asset class. » Charge-off forecasting should be driven by a similar process, and calibrated to existing charge-off history/experience. Thesis: » As credit transition from performing to non-performing rating grades under various scenarios, the impact on earnings should be direct and transparent. Solution: » Integrating conditional loss models with the PPNR calculation engine is required. For forecasting NPA levels, a key linkage (input assumption) are conditional transition matrices. » Charge-offs are relatively easy once the ALLL modeling method is chosen and linked to the EL estimation methodology, and calibrated to loss history. 18
  19. 19. 3 Integrating Stress Testing Into Existing Business Architecture 19
  20. 20. Initial Steps » Start a stress-testing and capital planning “office.” » Emphasis on project planning and program management early in the process. » Ensure that someone in the “office” has responsibility for determining current and “future state” architecture. » Understand that the evolution of the future state will require integration across numerous “legacy siloed” risk and finance systems. Need Board and Senior Management champions. » Understand that the process is multi-step, not single step. » Be proactive - don’t simply wait for an MRA and regulatory pressure (the writing is on the wall.) Many “…financial companies simply failed to adequately indentify the potential exposures and risks stemming from their firm-wide activities..." due in part to "...a failure of information technology and MIS, the often fractured nature of which made it difficult for some companies to identify and aggregate exposures across the firm." Source: FRB’s August 19.2013 paper entitled, “Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice” 20
  21. 21. Credit Loss Models For All Asset Classes All Methodologies: Top-Down, Hybrid, and Bottom-up Probability of Default | Loss Given Default | Exposure at Default Retail Banking Commercial Real Estate » Residential Mortgages, 1st and 2nd Liens » Commercial Mortgages » Auto Loans & Leases » Income Producing » Credit Cards » Construction » Equipment Leasing » Fixed & Floating Rate CCAR: CCAR: Baseline Adverse Treasury & Asset Management Charge Offs Commercial & Industrial » Non-Agency & Agency RMBS » Public Companies » ABS (credit cards, autos, student loans, etc) » Private Companies » CMBS & CLOs` CCAR: Scenario 4: Scenario 3: Scenario 2: Scenario 1: Severely Adverse Depression Scenario Deeper Second Recession Mild Second Recession Quicker Recovery Baseline and Custom 21
  22. 22. Three Phases to Developing a Comprehensive DFAST/CCAR Platform » Three-tier (and “N” tier) architecture is fundamental to good systems design. » Modular, comprehensive platforms creates a “future proof” design that embraces internal and 3rd party technologies. Reporting Layer: 3 The DFAST/CCAR reports are complex, and must be reconciled to FRY-9C, FFIEC 031/041, Basel FFIEC 101, and other internal management reports. Automating this process must leverage work performed from the Analytic Layer and the Data Layer. 1 Analytic Layer: For DFAST/CCAR purposes, best practice is to begin with the analytical layer and supporting models while working towards automation of data and reporting. Data Layer: 2 For DFAST/CCAR purposes, and to target required data reporting, many banks must launch a technology project. The goal is to target a single data platform to support risk, finance, credit, and regulatory reporting and capital planning needs. 22
  23. 23. End-to-End Software Solution MANAGEMENT AND REGULATORY REPORTING BALANCE SHEET & INCOME STATEMENT DATA / DATAMART RiskFoundationTM Scenario AnalyzerTM Modular, Flexible and Comprehensive – Allowing for Straight Through Risk Processing » Management Reporting / Key Performance Indicators Our solution design accommodates comprehensive regulatory reporting, internal risk and LOB reporting, plus dimension / hierarchy management: ALLL PPNR RWA Instantiation of the organization’s Risk Appetite Framework(s) – Regulatory Reporting Executive and board-level reporting – Existing and expected liquidity risk reporting Drill-through and scenario dependent PPNR, balance sheet, new business volume – NCOs – – Risk & Performance Management Comprehensive wholesale and retail credit portfolio reporting (Wholesale & Retail) Budgeting & Planning Systems Outputs Datamart Spreading System Core Systems (e.g. GL, Loan Accounting) » By linking results from point solutions to the reporting layer (RiskFoundation), Moody’s can empower the bank by providing key linkage between input data and output results. » Risk Management and ALM Systems Outputs Moody’s is able to work within existing analytical layer to coordinate, enhance and improve risk transparency RiskFoundation Datamart as an integrated risk and finance data layer, is the foundation for stress testing » Credit Models » RiskFoundation can be fully integrated with various data sources, including enterprise data warehouses and core banking systems » This platform layer is used for Dodd-Frank-mandated reporting (e.g. CCAR stress testing), Basel II and III 23
  24. 24. 4 Techniques to Make it Worthwhile 24
  25. 25. A few recent examples: Lower Cost of Credit Delivery » As capital and liquidity costs increase, the need to run a bank more efficiently becomes paramount. One way to achieve this is to automate and integrate disconnected, bulky, and older technologies into a common framework that allows for “straight-through-risk-processing” across the accrual book. Capital Arbitrage » The regulatory capital required for a bank may be far more than the economic capital required of an entity that the bank can lend to. Lending to this “new entity” may attract lower regulatory capital than “direct” lending to an obligor. Understanding these nuances requires integrated calculations. Return on Capital After Stress (ROCAS) » Not all businesses or relationships are created equal. Some industries and businesses are more or less correlated with business cycles, and existing portfolio dynamics. Thus, the need to include more advanced analytics, and to emphasize the continued importance of economic capital, the need to consider correlations, portfolio shape, and granularity remain important considerations. Enhance data model to include “latitude, longitude and elevation” of collateral » The recent FRB guidance placed an emphasis on tailoring scenarios to the firm’s business model, mix of assets and liabilities, geographic footprint, portfolio characteristics, and revenue drivers. Tailoring included linking to things like natural disaster, particular counterparty default(s), and regional events/issues. This speaks to some of the “vision” that some banks have vis-à-vis the data model and the desire to expand it for more customized, idiosyncratic scenario analysis 25
  26. 26. Poor DFAST/CCAR Data, Analytics or Processes Can Cause a Failed Stress Test – With Severe Consequences » Poor DFAST/CCAR data, analytics, and processes may lead to: – An inability to pay dividends – Prohibition from buying back stock (treasury stock repurchase programs) – Leverage limitations – Capital and liquidity surcharges – Prohibition on growth – organic and M&A – Fines – Informal and formal actions (e.g., WA, MOU, C&D, Capital Directive) » Failure can originate from poor processes, weak governance, or analytical, infrastructure and reporting shortcomings. » Most common causes of failure (to date) are related to data and infrastructure weaknesses. “I was being asked to attest to this. It is worse than SOX 404. I hired [CRO] to have him sign it. I’m not signing this thing.” 26
  27. 27. 5 Next Webinar 27
  28. 28. Moody’s Analytics Stress Testing Webinar Series Macroeconomic Conditional Loss Forecasting October 29, 2013 at 12:00pm EDT Join Thomas Day and other Moody’s Analytics experts for a webinar covering: » The primary challenges confronting banks when forecasting macroeconomic conditional losses. » Best practices for forecasting macroeconomic conditional losses. » Tools and techniques used by Moody’s Analytics to address the challenges and/or close any gaps between best practices and current challenges. Register at: http://www.cvent.com/d/h4qj0l/4W 28
  29. 29. 6 Questions? 29
  30. 30. 3 0 moodysanalytics.com Thomas Day Senior Director Direct: 404.617.8718 Thomas.Day@moodys.com 7 World Trade Center at 250 Greenwich Street New York, NY 10007 www.moodys.com
  31. 31. Find out more about our award-winning solutions www.moodysanalytics.com 31
  32. 32. 7 World Trade Center 250 Greenwich Street New York, NY 10007 (212) 553-1653 121 North Walnut Street Suite 500 West Chester PA 19380 (610) 235-5299 405 Howard Street Suite 300 San Francisco, CA 94105 (415) 874-6000 www.moodysanalytics.com @MoodysAnalytics Stay current with the latest risk management and assessment news, insights, events, and more. @dismalscientist View global economic data, analysis and commentary by Mark Zandi and the Moody's Analytics’ economics team. @CSIGlobalEd Read the latest financial services education information @MA_CapitalMkts Keep up to date on credit and equity market signals reflecting investment risk and opportunities for issuers and sectors. Moody's Analytics Follow our company page to view risk management content, such as white papers, articles, webinars, and other insightful content and news. The Economic Outlook This group features insightful discussions and knowledge sharing among business, economics, and policy professionals regarding the economic outlook. Risk Practitioner Community This group brings together risk management practitioners from around the world to discuss best practices, share ideas and insights, and gain networking opportunities. 32
  33. 33. 33 moodys.com Thomas Day Senior Director Direct: 404.617.8718 Thomas.Day@moodys.com 7 World Trade Center at 250 Greenwich Street New York, NY 10007 www.moodys.com
  34. 34. © 2013 Moody’s Analytics, Inc. and/or its licensors and affiliates (collectively, “MOODY’S”). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY LAW, INCLUDING BUT NOT LIMITED TO, 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. Any publication into Australia of this document is pursuant to the Australian Financial Services License of Moody’s Analytics Australia Pty Ltd ABN 94 105 136 972 AFSL 383569. This document is intended to be provided only to “wholesale clients” within the meaning of section 761G of the Corporations Act 2001. By continuing to access this document from within Australia, you represent to MOODY’S that you are, or are accessing the document as a representative of, a “wholesale client” and that neither you nor the entity you represent will directly or indirectly disseminate this document or its contents to “retail clients” within the meaning of section 761G of the Corporations Act 2001. 34
  35. 35. Q&A Session Questions???
  36. 36. For More Information Contact: Thomas Day Senior Director, Risk Solutions Moody's Analytics 7 World Trade Center at 250 Greenwich Street New York, NY 10007 Thomas.Day@moodys.com Direct: 404.617.8718