Macroeconomic Conditional Pre-Provision Net Revenue Forecasting


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This presentation provides an introduction to Pre-Provision Net Revenue (PPNR), reviews regulatory expectations, and discusses emerging methodologies for calculating PPNR.

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Macroeconomic Conditional Pre-Provision Net Revenue Forecasting

  1. 1. Stress Testing Webinar Series: Macroeconomic Conditional Pre-Provision Net Revenue (PPNR) Forecasting January 28, 2014 Presented by: Moody‟s Analytics
  2. 2. Agenda 1. Introduction 2. Regulatory Expectations: PPNR 3. Emerging Quantitative Methodologies: New Ideas on Old Processes 4. Practical Implementation Issues: Innovation and the Road Ahead 5. Conclusion: Better Start than Good 6. Next Webinar: Stress Testing Methodologies: Enhancing Data and Loss Estimation for DFAST Banks March 18, 2014 | 12:00pm EST 2
  3. 3. 1 Introduction 3
  4. 4. Overall Progress: Integrated Financial Risk Forecasting Movie: “Flight of the Phoenix” » Post Financial Crisis, it was clear that the manner in which risk was analyzed resulted in wreckage. » From the wreckage, the DFA mandated regular stresstests, and the FRB designed and executed. » Many bank managers and supervisory authorities were essentially flying blind. » Very little discussion around alternative design, perhaps hampering innovation. » Data wasn‟t actionable and risk could not be aggregated and analyzed on demand. » Current tools are providing some lift; however, legacy processes were not designed for integrated financial and risk analytics. Current state remains brittle. » New ideas were needed. » Banking is changing. Banks need to be fast, agile, and able to run away from the competition. » Innovation is needed – new ideas that allow the bank to maintain performance in good times and in bad. » In order to design the proper “risk management platform” and analytics, new ideas are needed – ideas that can break outmoded barriers to effective risk management. 4
  5. 5. Stress-Testing and Capital Planning Industry Observations: » The stress-testing process requires an unprecedented amount of coordination and collaboration across numerous front, middle, and back office functions. » Communication, documentation, and well defined business processes are required, and assumptions made to conditional forecasts require justification. Commercial Lending Retail Lending Trading Credit Risk Capital Planning » Governance of the process can be as important as the result(s). Finance and Accounting Funding » Risk quantification is critical at all levels, with challenger approaches considered sound practice. » PPNR estimates are notoriously complex in that centralized estimates may miss necessary SME input from lines of business (e.g., how the business(es) would actually react under stress). Quantification processes are generally preferred, with overlays well justified. » Creating increased efficiency in the process is necessary, create cost savings, and improve operational resilience. Discretionary Portfolio Treasury Financial and Risk Forecast » » » » » » » Pro-forma balance sheet (under scenarios) PPNR Losses, charge-offs, and recoveries Valuations Operational risk(s) Accounting measures (e.g., DTA, Goodwill) Documentation and Validation 5
  6. 6. CCAR/DFAST: Process Complexity – PPNR Issues 1. Finance, Treasury, and Risk: Develop Forecast and Risk Estimates Stress-Testing and Capital Planning Committee Treasury/ALM and Finance Balances, Revenues , Expenses, Accounti ng Credit Risk PPNR, loss estimates, charge-off estimates, rating distribution(s), non-performing levels, new originations and new origination spreads, capital estimates, operational losses, and other measures. Scenario(s) and Economic Research Financial Forecast PPNR Scenario Analyzer Q1, Q2, Q3, …Q13 Loss Estimates, NPAs, Deli nquencies, Ratings, et c. Work Package Economics Group » Messaging » Document Management Collaboration » Assumption Management » Auditability » Transparency » On-Demand Region 2 Region 3 Region 4 3. PPNR, Losses, Charge-off, Recovery, etc Validation and Challenge Workflow 1 Commercial Lending Region 1 » Workflow Portfolio and Credit Research Other 2. Workflow 2 Workflow “n” » Model Management » Input/Output Management » Scenario Management » Data exchange » Regulatory Reporting » Dashboard Reporting » System Integration Results Data-mart FRY14A 4. » Process Governance, Automation, Assumptio n, Model, and Results Management are critical for an effective CCAR/DFAST program. » Assembling stress-test reports, and validating results from the bottomup, requires structured processes. » For PPNR, validating and agreeing estimates should also be “bottomup” and leverage LOB models and SME input. Line of Business: Challenge Models and Results 6
  7. 7. Stylized Workflow for DFAST/CCAR Exercise » While presented as a sequential workflow, this is not realistic or practical. The CCAR/DFAST workflow must be instantiated to work in an asynchronous fashion and robustly address numerous hand-offs, edits checks, task schedules, and interactions. The entire “chain of custody” must be transparent and auditable. Data Scenario Design Analytics Reporting 7
  8. 8. PPNR Requires a “N” Quarter Forecast: Full Balance Sheet PPNR = “Interest Income” – “Interest Expense” + “Non-Interest Income” – “Non-Interest Expense” Net Interest Income + Non-interest Income - Non-interest Expense = Pre-provision Net Revenue (PPNR) Note: PPNR includes Losses from Operational Risk Events, Mortgage Putback Losses, and OREO Costs PPNR + Other Revenue - Provisions - AFS/HTM Securities Losses Trading and Counterparty Losses - Other Losses (Gains) = Pre-tax Net Income Note: Provisions = Change in the Allowance for Loan and Lease + Net Charge-offs Pre-tax Net Income - Taxes + Extraordinary Items Net of Taxes = After-tax Net Income After-tax Net Income - Net Distributions to Common and Preferred Shareholders and Other Net Reductions to Shareholder's Equity = Change in Equity Capital Change in Equity Capital - Deductions from Regulatory Capital + Other Additions to Regulatory Capital = Change in Regulatory Capital 8
  9. 9. Pre-Provision Net Revenue (PPNR) » One of the most challenging components of the stress-testing exercise – an emerging area of practice with little available research. – Biggest areas of challenge: 1) joint modeling of credit , interest rate, and capital risk in a unified framework and/or calculation, 2) data, 3) NIR and NIE, and 4) conditional balance sheet dynamics » An area of note by the Federal Reserve as “lacking coherence” between credit loss estimates and the resulting impact on net interest income, and other areas of income and expense. » Banks are required to forecast quarterly by FRB defined business segment, as well as a BHC view. Revenues should tie to the FRY9C net of any valuation adjustment for the firm‟s own debt and operational expenses. – May require new dimensions within a firm‟s ALM Chart of Accounts – Various metrics required, such as average yields, average rates on interest bearing liabilities, WAM, deposit repricing betas and estimated WAL of non-maturity deposits – Significant historical PPNR data and metrics are also required to be submitted » The NII by business segment must be FTP adjusted, based on the firm‟s own internal FTP pricing methodologies. 9
  10. 10. End-State Goals: Areas for Consideration (2014) » Developing tangible, practical business uses for stress-testing investments. For example, the same process that creates stressed measures should be capable of: – – – – Sensitivity analysis around “expected” results, not just major systemic shocks Computation of many more scenarios than the extreme shocks required for the regulatory exercise Integrating analytical capabilities into useful tools for on-going deal and relationship analysis Creating side-by-side views of economic and regulatory returns on capital, at any required dimension » A single “run-time” compute that accommodates monthly credit loss and PPNR coherence, by scenario and by asset class. – Provides coherence among interest income, FTP interest expense, prepayment, credit loss, credit migration, economic and regulatory capital calculations » A single environment to manage data and work packages that are sequenced through Treasury, Finance, and Credit Risk. The environment should permit: – Management of multiple hierarchies across numerous lines of business and entities – Use and re-use of current and historical scenarios, market data, and instrument data – Serve as a single point of entry for management and use of multiple models, with input and output results versioned and persisted – Act as the main aggregation area for regulatory and management reporting » Enhancements to conditional volume and spread estimates » Enhancements to conditional estimates of NIR and NIE 10
  11. 11. Calculation Engine: Unified Credit and Interest Rate » For some hard to model asset classes, creating a unified calculation capability can be managed by calling a separate library that directly incorporates primary and challenger credit models. Inputs to the credit model may be: 1) PD/LGD/EAD (monthly/loan level), 2) parameter estimates (e.g., bank internal credit risk models), and/or 3) native (library) credit model. Consistent input data Import cash flows to ALM/FP&A Joint interest rate and credit dynamics Scenario Data Market Data Contractual Terms Interface Interest Rate and PPmt Process Credit Risk Model Calibration Framework Migration Matrice(s) Internal Models » FRY-14Q/M (subset/monthly) » Monthly stress-scenarios » Monthly calibrated market data Loan level. Ability to roll-up to any hierarchy level. Supports all reporting and business processes. » C&I » CRE » Other Asset Classes Results Data-mart ETL Process » P&I Cash Flows » ETL out » Credit/non-credit process adjusted » Data » Prepayments consolidation » Additional property and reporting sets » Pro-rata NIR and NIE allocation » RWA » Regulatory and Economic Capital ALCO Report Import cash flows to regulatory reporting FRY14A Use of result output for sensitivity analysis Risk Report Pricing and Performance measurement Deal Analysis 11
  12. 12. Example: Integrated Dashboard Report – Current State 1 2 3 4 5 6 12
  13. 13. 2 Regulatory Expectations: PPNR 13
  14. 14. Regulatory Methodological Expectations » PPNR must be estimated over the same range of scenarios used for loss estimation – Implies that market data used for calculations are consistent with the economic conditions » Banks must consider scenario impact on current position business as well as how origination strategy may change in different scenarios. Banks are expected to model the balance sheet using contractual terms and capture behavioral characteristics. – Deposit growth, new business pricing, balances, line usage, changing fees, expenses, etc – Quantitative techniques help support more subjective estimates – Baseline estimates should be consistent with internal plans and ALM assumptions, and proper adjustments to optimistic baseline plans must be considered in the scenarios » Pro-forma RWA calculations should consider how management actions may impact capital ratios – Can require the modeling of the credit quality of new origination, and losses that may be attributed to those balances » Balance sheet and income statement projections should present a “coherent story” » Better practice involves a robust interaction between FP&A, credit risk/business lines, and central treasury. Challenge processes are normally used. » Clear mapping between internal projections and the FRY14 categories 14
  15. 15. Where to Start: Creating Tactical Value » Demand and supply functions by asset class – What available lending will prevail in the various macroeconomic scenarios? – What is the assumed credit quality of these balances and how are losses estimated? – How are they allocated to various business lines? – What are the earnings (interest and non-interest) on these balances? That is, how does credit spread change across scenarios, product type and assumed credit quality? » Deposit growth and pricing » Full incorporation of Basel I and III estimates, inclusive of changing credit and mix » Integrating credit loss estimates into a coherent calculation of net interest income – Top-down adjustment(s) to asset balances based on aggregate loss estimates – Transition matrices (quarterly) by asset class, by scenario, scaled to target projected nonperforming asset levels indicated by quantitative and qualitative assessment – Direct integration of loss model into loan-level cash flow compute (i.e., treating default as a proper behavioral option) » Scenario conditioned non-interest revenue and expense modeling 15
  16. 16. 3 Emerging Quantitative Methodologies: New Ideas on Old Processes 16
  17. 17. PPNR consists of numerous components from income and expenses from various areas of a bank PPNR = Interest Income – Interest Expense + Non-Interest Income – Non-Interest Expense Interest Income* » Loans Non-Interest Income » Credit Related Fees – Existing Book – Less all run-off – Plus new loans – By Product / Line of Business – Origination vs. Servicing (esp. for resi mortgage) – Credit Card » Securities » Non-Credit Related – Existing Book – Less all run-off – Plus new securities – – – – Investment Banking Investment Management / Trust Deposit Service Fees Trading Interest Expense* » Deposits – – – – Non-Interest Expense » Employee Compensation Interest vs. non-interest bearing By Line of Business / Product Client vs. wholesale funded Term structures » Bonds – Existing – Funding gap for additional bond issuances – Salary – Benefits – Bonuses » » » » Processing / Software Occupancy (Plant, Property & Equipment) Credit / Collections Residential Mortgage Repurchases * Note: Interest Income less Interest Expense = Net Interest Income (NII) 17
  18. 18. Two Approaches to estimate Interest Income and Balance: Direct and Granular Segmentation Direct Approach » Moody‟s models Total Balance for each segment directly. For revolvers, the balance model estimates the total commitment, but together with Moody‟s Usage model obtains estimates for the outstanding drawn amount. » Simplifying assumptions are required for the interest earned on the balance Granular Approach » Moody‟s models Usage, New Origination, and Runoff (prepayment, maturity and amortization, provisioning, etc.). Together, these models produce an estimate for balance » Moody‟s models Interest Rate Charged for New Origination. Together with the rates paid by the surviving loans from previous period, this model can be used to calculate an estimate for total interest earned » Direct and Granular approaches to modeling balance both allow for consistency across the balance sheet and income statement if applied to both PPNR and Loss models. 18
  19. 19. Example: Direct Balance Model for Term Loans Balance (2003 Q3) = 100 Term Loans 140 130 120 110 100 90 80 Quarter Actual Fitted Base Adverse Severe 19
  20. 20. Modeling Runoff for the Granular Approach » The granular approach involves modeling the different components responsible for balance development over time. For example, Term Loans: » Runoff includes balance depletion due to Prepayment, Maturity, Amortization, Provisioning, … » Modeling Runoff – Derive Runoff using the Balance and New Originations models: – Explicit runoff modeling allows for differentiation across Runoff components: » Maturity and Amortization: Model the relationship between maturity/amortization of new origination and the macro environment at origination using Moody‟s CRD data (LAS dataset) and the institution‟s own data – Can be combined with segmentation by Tenor for the Interest Charged model to refine the interest earned projections » Provisioning: Leverage Loss stress testing models » Prepayment: Leverage Moody‟s Analytics lattice model (details in next slide) 20
  21. 21. Using the Moody‟s Lattice* to Model Prepayments » Prepayments for floating loans are mostly driven by improvements to borrower‟s credit quality » The Moody‟s Lattice model captures borrowers‟ credit migration dynamics, and can produce prepayment rates (even at the individual loan level) given the current credit state and contractual interest charged » The Lattice model also accounts for prepayment penalty/cost, which can be calibrated to empirical prepayment data Valuation Lattice 115 Credit State 212 Prepay 39 46 53 6 0 (Default)0 1 2 3 4 5 Time (Year) *Moody’s Lattice is available in RiskFrontierTM 21
  22. 22. Modeling C&I Balance, New Origination, Interest Charged, Usage and EAD with the Credit Research Database (CRD) » World‟s Largest Historical Time Series of Private Firm Middle Market Data for C&I Loans – Consortium of 49 Banks Operating Globally including 19 from the US – Defaulted and Non-defaulted Private Firm Financial Statement Data – Obligor & Loan Level Accounting Data » Allows for segmentation based on risk factors that can mimic the institution‟s portfolio – Borrower PD – Industry: for example, Financial vs. Non-Financial – Firm Size – Geographical location – Loan Tenor – New vs. Old borrower 22
  23. 23. Segmentation by Credit Quality for Balance and New Origination Term Loan Balance Term Loan New Orig. over Balance (NoB) 0.14 140 0.12 120 0.1 100 80 NoB Balance (2003 Q3 =100) 160 60 40 0.08 0.06 0.04 20 0.02 2003 Q3 2004 Q1 2004 Q3 2005 Q1 2005 Q3 2006 Q1 2006 Q3 2007 Q1 2007 Q3 2008 Q1 2008 Q3 2009 Q1 2009 Q3 2010 Q1 2010 Q3 2011 Q1 2011 Q3 2012 Q1 0 Low_PD Quarter High_PD All_PD 0 All_PD Quarter High_PD Low_PD » In general, high and low credit quality segments exhibit different time dynamics, suggesting it‟s beneficial to model them separately. » Balance of the High PD segment increased significantly in 2006-2008. » During the crisis, both PD segments exhibit a sharp decline in balance (slightly steeper for low PD firms). » However, post-crisis lending to low PD (high quality) firms has recovered much faster than to high PD (low quality) firms. 23
  24. 24. Segmentation by Credit Quality for Interest Charged » In general, High PD borrowers are charged higher spreads than Low PD borrowers. – This is especially pronounced during the financial crisis, where the Market Price of Risk was highest. Average Term Loan Spread by Credit Quality 0.06 0.05 0.03 0.02 0.01 0 2001 Q1 2001 Q3 2002 Q1 2002 Q3 2003 Q1 2003 Q3 2004 Q1 2004 Q3 2005 Q1 2005 Q3 2006 Q1 2006 Q3 2007 Q1 2007 Q3 2008 Q1 2008 Q3 2009 Q1 2009 Q3 2010 Q1 2010 Q3 2011 Q1 2011 Q3 2012 Q1 2012 Q3 Spread 0.04 Quarter All_PD Low_PD High_PD 24
  25. 25. Segmentation by Financial vs. Non-Financial » Prior to the crisis, both segments exhibit a similar growth pattern, with financial firms growing faster right before the crisis. 160 140 120 100 80 60 40 20 0 2012 Q1 2011 Q3 2011 Q1 2010 Q3 2010 Q1 2009 Q3 2009 Q1 2008 Q3 2008 Q1 2007 Q3 2007 Q1 2006 Q3 2006 Q1 2005 Q3 2005 Q1 2004 Q3 2004 Q1 » The financial crisis seems to have affected new origination to Financials more severely than Non-Financials: 2003 Q3 Balance (2003 Q3 =100) Term Loan Balance Quarter Non Financial Financial All – Even though financials tend to be larger and safer, lending to them has remained constant since 2010, while lending to non-financial firms has recovered. Term Loan New Orig. over Balance (NoB) 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 For Financials All Quarter Financials Non Financials 2012 Q2 2011 Q3 2010 Q4 2010 Q1 2009 Q2 2008 Q3 2007 Q4 2007 Q1 2006 Q2 2005 Q3 2004 Q4 2004 Q1 2003 Q2 2002 Q3 2001 Q4 % Large (>80MM) % High PD 2001 Q1 NoB – During the crisis, lending to financials starts shrinking a few quarters earlier than lending to non-financials, and at a faster pace. Nonfinancials exhibit a drop only after the Lehman Brothers collapse. For Non-Financials % Large (>80MM) % High PD Term Loan 39% 26% Term Loan 5% 46% 25
  26. 26. Segmentation by Size for Balance and New Origination » In general, both segments exhibit different time dynamics, suggesting it is beneficial to model them separately. 160 140 120 100 80 60 40 20 0 » Prior to the crisis, both segments experienced steady growth, with large firms experiencing a higher increase than small firms right before the crisis. » During the crisis, lending to both segments decreased. 2003 Q3 2004 Q1 2004 Q3 2005 Q1 2005 Q3 2006 Q1 2006 Q3 2007 Q1 2007 Q3 2008 Q1 2008 Q3 2009 Q1 2009 Q3 2010 Q1 2010 Q3 2011 Q1 2011 Q3 2012 Q1 Balance (2003 Q3=100) Term Loan Balance Quarter Large Small » Large firms show fast recovery after the crisis » On the contrary, lending to small firms has remained low since the crisis - resulting in continued decrease in balance, and increasing the gap between the two segments. For Large (>80MM) Term Loan All_size 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 % Financial % High PD All_size Quarter Large Small 2012 Q2 2011 Q3 2010 Q4 2010 Q1 2009 Q2 2008 Q3 2007 Q4 2007 Q1 2006 Q2 2005 Q3 2004 Q4 2004 Q1 2003 Q2 2002 Q3 2001 Q4 For Small 2001 Q1 NoB Term Loan New Orig. over Balance (NoB) % Financial % High PD 64% 21% Term Loan 15% 48% Note: for financial firms, large firms have total assets > 80MM. For nonfinancial firms, large firms have total sales > 80MM. 26
  27. 27. Data and Modeling Approach for CRE Interest Income » For CRE Interest Income, the same modeling approaches described for C&I Interest Income (i.e., Direct and Granular) can be used, except for – CRE lines of credit are uncommon, so there is no need for a Usage model – Different segmentation is recommended for CRE loans (by property type) » Data for CRE modeling – Balance Model: Call Reports and FR Y-9C data on CRE loan growth rates segmented by property type for large commercial banks and BHCs – New Origination Model: Mortgage Banker Association„s New Origination Index – Interest Charged Model: Moody‟s CMM provides credit quality measures (PD, LGD), that can be translated to loan-level spreads » Loan-level granularity on interest income forecasts » Loan-level spread models for new origination, based on credit quality (LTV, DSCR etc) and terms » Integration with runoff estimates (or assumptions) » Consistent integration with stress testing losses: same PD, LGD, Balance and New Origination models used in loss and income calculations 27
  28. 28. Projecting Deposit Interest Expense » Banks typically have tactic models that forecast the runoff of existing deposits and link new deposit volumes to interest rates offered and non-interest expenses » Statistical models that project total deposit balances under various macroeconomic scenarios, can be used in combination of a bank‟s tactic models to project interest expenses onto existing stock and on new volumes » Possible modeling approaches include: – Historical deposit data of an individual bank – often reflect idiosyncratic events of the bank and may not sufficiently capture how future macro-economic factors would impact deposit volumes. – Call report data from peer institutions can be used to develop deposit balance models for broad deposit categories: interest checking, non-interest checking, MMDA, other savings, time deposits » Deposit balances often exhibit seasonality; season dummy variables are often useful and significant in regression analyses » Regression coefficients are estimated based on historical data Parameters estimated from historical data are used to produce deposit balance projections under various future scenarios: – 28
  29. 29. Sample Deposit Balance Modeling Results 29
  30. 30. Modeling Non-Interest Income and Expense Data Type Entity Type Primary Source Description Line Items for FR Y-14A FDIC insured subsidiaries Call Reports, SDI data » Data from 2001 onward. BHCs FR Y-9C » Mergers and acquisitions can be accounted for by consolidating historical statements of merged institutions. » Macro factors are based on CCAR scenarios Bankspecific Macro variables FR Y-14Q All » FR Y-14Qs allow us to adjust model outputs to the level of granularity needed to populate FR Y-14A. Federal Reserve CCAR 2013 Scenarios 30
  31. 31. Example: Modeling Overhead Expense Using Peer Groups Overhead Expense over Assets (BHCs Data) historical 1-quarter projection baseline adverse severe 1.00% 0.90% OE / Asset 0.80% 0.70% 0.60% 0.50% 0.40% 0.30% Year, Quarter 31
  32. 32. 4 Practical Implementation Issues: Innovation and the Road Ahead 32
  33. 33. Leveraging Balance Sheet Management Systems for CCAR » CCAR is a daunting task for any financial institution. For the first time banks are required to assemble enterprise projections for earnings and capital that model the joint dynamics of market and credit risk under multiple macro-economic scenarios. » Existing Balance Sheet Management (BSM) systems can serve as a source for CCAR stress testing outputs CCAR INPUTS » Macro-economic data » Market data i.e. rates/prices » Detailed rate/maturity data » Detailed repricing data » New volume assumptions including rates and spreads » Prepayment assumptions » PD/LGD Assumptions » Charge offs PROCESSING CCAR Outputs Net Interest Income: Cash Flow Engine Chart of Accounts Behavior Models FTP Market Data Mgr. Risk Data Credit Default/Loss Pro-forma Bal. Sheet Formula Builder Value at Risk Scenarios » Forecast balances » Interest income Credit Exposure: » PD/LGD/EAD » Expected loss Analytics: » Credit Migration » Fair value » Charge offs/impairment Reporting Enterprise Data Warehouse 33
  34. 34. Modeling the Pro-forma Balance Sheet » The Balance Sheet Strategy (BSS) is a practical way to model the joint dynamics of the enterprise level balance sheet. » New volumes may be modeled in great detail i.e. by term, credit rating, etc. within a single account yielding a better and more accurate loss, income, and capital forecast » Contractual product features may be controlled by forecast period permitting more dexterity in terms of responding to macro-economic forces with contractual and option-like features. 34
  35. 35. Modeling Demand Functions » Formula builders can automate the impact of macro-economic variables on new volumes » A powerful language based syntax can allow the user to express logic and mathematical equations » Formulas allow cross product references for balances, market data, and economic variables. Allows user to specify scenario based demand functions. » Many formula builders are interpreted. The MA platform formula builder is compiled adding speed and flexibility. Lagged Market Data Macro-economic indices Dynamic Credit Metrics Segment Balances 35
  36. 36. The Impact of changes in Credit State on Cash Flows » The Fed is very explicit about incorporating the impact of market volatility and credit forces on cash flows: - “The methods BHCs use to project their net interest income should be able to capture dynamic conditions for both current and projected balance sheet positions. Such conditions include but are not limited to prepayment rates, new business spreads, re-pricing rates due to changes in yield curves, behavior of embedded optionality that Capture FAS 91 adjustments related to prepayment changes as caps or floors, call options, and/or changes in loan performance (that is, transition to nonperforming or default status) consistent with loss estimates.”; Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice; Board of Governors of the Federal Reserve System; August 2013; Page 33 Credit State State specific prepayment assumptions 36
  37. 37. Other Assets/Liabilities and Non Interest Income/Expense » The accounting identity „Assets = Liabilities + Equity‟ must be true; if not, the proforma balance sheet and NI projection fall into question. » Good BSM systems have the ability to natively incorporate „systems accounts‟ but many banks do not use them fully. Examples include: - Accrued interest receivable/payable Accrued principle receivable AFS/HTM gains and losses/impairment Charge Offs and Provision - Balancer accounts Retained earnings Taxes/Deferred tax liability Dividends » For stand alone applications like pre-trade analytics, IRR quantification, FTP, or capital management, the bare minimum was good enough. However, in CCAR, the regulatory community is saying that BSM needs to more prospective and holistic. Therefore, all of the macro-economic, risk factor, and accounting interrelationships matter. » BHCs should clearly establish and incorporate into their scenario analysis the relationships among and between revenue, expense, and on- and off-balance sheet items under stressful conditions. Most BHCs used asset-liability management (ALM) software as a part of their enterprise-wide scenario-analysis toolkit, which helps integrate these items. BHCs that do not use ALM software must have a process that integrates balance sheet projections with revenue, loss, and new business projections. BHCs with more tightly integrated procedures were better able to ensure appropriate relationships among the scenario conditions, losses, expenses, revenue, and balances. Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice; Board of Governors of the Federal Reserve System; August 2013; Page 37 37
  38. 38. Non Interest Income and Expense » Modeling Non Interest Income and expense items can be tough because the GL may not match the granular account structure of BSM systems. » However, many BSM systems have income/expense accounts that can be specified either as interest earning/interest costing or non-interest earning/non-interest costing. » BSM systems typically have features that permit the user to allocate income/ expense items from aggregates to detailed income expense items. Therefore, if desired, very rich and detailed fee schedules can be created. » In addition, the Formula builder can be used to create models that generate fee and expense schedules based on balances or other financial results i.e. deposit servicing fees. 38
  39. 39. ALL, Provision, and Charge Offs » Allowance for Loan and Lease Losses (ALLL) is an asset contra account where provisions are capitalized on the balance sheet until the corresponding assets default and are charged off. » Provision can be specified as a function of both an ALLL target and forecast charge-offs. Some BSM systems have the ability to target the provision and perform re-provisioning from pre-tax net income based on native loss calculations and custom frequencies. Therefore, a high degree of automation and consistency among results is possible at many institutions. » The timing of charge offs may vary based on the asset class. Therefore, a BSM system that is used to produce charge offs must have the ability to forecast loan status and have rules that determine when assets should be charged off and when ALLL needs to be re-provisioned. » Some of the advantages of modeling the whole balance including mark to market gains and losses and provision in a single BSM engine are efficiency, consistency across multiple risk management functions, and the capability to capture balance sheet interrelationships including the compounding of equity. 39
  40. 40. Calculation Engine: Unified Credit and Interest Rate » For some hard to model asset classes, creating a unified calculation capability can be managed by calling a separate library that directly incorporates primary and challenger credit models. Inputs to the credit model may be: 1) PD/LGD/EAD (monthly/loan level), 2) parameter estimates (e.g., bank internal credit risk models), and/or 3) native (library) credit model. Consistent input data Import cash flows to ALM/FP&A Joint interest rate and credit dynamics Scenario Data Market Data Contractual Terms Interface Interest Rate and PPmt Process Credit Risk Model Calibration Framework Migration Matrice(s) Internal Models » FRY-14Q/M (subset/monthly) » Monthly stress-scenarios » Monthly calibrated market data Loan level. Ability to roll-up to any hierarchy level. Supports all reporting and business processes. » C&I » CRE » Other Asset Classes Results Data-mart ETL Process » P&I Cash Flows » ETL out » Credit/non-credit process adjusted » Data » Prepayments consolidation » Additional property and reporting sets » Pro-rata NIR and NIE allocation » RWA » Regulatory and Economic Capital ALCO Report Import cash flows to regulatory reporting FRY14A Use of result output for sensitivity analysis Risk Report Pricing and Performance measurement Deal Analysis 40
  41. 41. Consistency Across Basel III and Treasury Risk Management Functions Cash Flows & Behavior Models 41
  42. 42. 5 Conclusions: Better Start than Good 42
  43. 43. Integrated Financial and Risk Forecasting Three-tier (and “N” tier) architecture is fundamental to good systems design. A proper platform is modular and Comprehensive, and creates a “future proof” design that embraces internal and 3rd party technologies. Reporting Layer: 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. 3. 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: For DFAST/CCAR purposes, and to target required data reporting, many banks must launch a DataFoundation data project. The goal is to target a single data platform to support risk, finance, credit, and regulatory reporting and capital planning needs. 2. 43
  44. 44. Fully Integrated Architectural Design Modular, Flexible and Comprehensive – Allowing for Straight Through Risk Processing REPORTING - Our solution design accommodates comprehensive regulatory reporting, internal risk and LOB reporting, plus dimension / hierarchy management: Management Reporting / Dashboard NCOs Risk & Performance Management ALLL PPNR ANALYTIC LAYER SCENARIO ANALYZER TM RiskAuthority Budgeting & Planning System Output DATA LAYER Risk and Finance Datamart (Inputs and Results) Spreading System RiskAnalyst / RiskOrigins Executive and board-level reporting Instantiation of the organization‟s Risk Appetite Framework(s) Existing and expected liquidity risk reporting Drill-through and scenario dependent PPNR, balance sheet and new business volume - Comprehensive wholesale and retail credit portfolio reporting RWA Risk Management and ALM System Data Credit Models (Wholesale & Retail) - Regulatory Reporting Core Systems (e.g. GL, Loan Accounting) - Moody‟s is able to work with existing Treasury, FP&A and Risk systems to coordinate, enhance and improve stressed cashflow calculation and transparency - By linking results from point solutions to the reporting layer, Moody‟s can empower the bank by providing key linkage between input data and output results. - RiskFoundation Datamart as an integrated risk and finance data layer is the foundation for stress testing - RiskFoundation can be integrated with various data sources, including enterprise data warehouses and core banking systems - Part of Potential Moody‟s Solution - Bank‟s Internal / Third Party Systems 44
  45. 45. 6 Next Webinar 45
  46. 46. Moody‟s Analytics Stress Testing Webinar Series Stress-Testing Methodologies: Enhancing Data and Loss Estimation for DFAST Banks March 18, 2014 at 12:00pm EST Topics to be covered include: » Regulatory expectations surrounding data and loss estimation for DFAST banks » Common themes and issues: Rating systems, origination and scoring systems, and use of models » Conditional measures using macroeconomic conditioned correlation models Register at: 46
  47. 47. 7 Questions 47
  48. 48. 4 8 Thomas Day Senior Director Direct: 404.617.8718 7 World Trade Center at 250 Greenwich Street New York, NY 10007 Amnon Levy, PhD Managing Director Direct: 415.874.6279 405 Howard Street Suite 300 San Francisco, CA 94105 Robert Wyle, CFA Senior Director Direct: 415.874.6603 405 Howard Street Suite 300 San Francisco, CA 94105
  49. 49. Find out more about our award-winning solutions 49
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