Stress Testing Webinar Series: Macroeconomic Conditional Pre-Provision Net Revenue (PPNR) Forecasting
January 28, 2014
Presented by: Moody‟s Analytics
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
3
Introduction 1
4
Overall Progress: Integrated Financial Risk Forecasting
» Post Financial Crisis, it was
clear that the manner in
which risk was analyzed
resulted in wreckage.
» Many bank managers and
supervisory authorities were
essentially flying blind.
» Data wasn‟t actionable and
risk could not be aggregated
and analyzed on demand.
» New ideas were needed.
» From the wreckage, the DFA
mandated regular stress-
tests, and the FRB designed
and executed.
» Very little discussion around
alternative design, perhaps
hampering innovation.
» Current tools are providing
some lift; however, legacy
processes were not designed
for integrated financial and
risk analytics. Current state
remains brittle.
» 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.
Movie:
“Flight of the
Phoenix”
5
Stress-Testing and Capital Planning
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
Commercial
Lending
Retail
Lending
Discretionary
Portfolio
Finance and
Accounting
Treasury
Funding
Credit Risk
Trading
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.
» Governance of the process can be as important as the
result(s).
» 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.
6
CCAR/DFAST: Process Complexity – PPNR Issues
Treasury/ALM
and Finance
Balances,
Revenues, Expenses,
Accounting
Economics Group
Credit Risk
Other
Stress-Testing and Capital Planning
Committee
Financial Forecast
Q1, Q2, Q3, …Q13
Loss Estimates,
NPAs, Delinquencies,
Ratings, etc.
Portfolio and Credit
Research
Scenario(s) and
Economic Research
PPNR
Commercial Lending
Region 1 Region 2 Region 3 Region 4
Line of Business: Challenge Models and Results
Finance, Treasury, and Risk: Develop
Forecast and Risk Estimates
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 Analyzer
» Workflow
» Messaging
» Document Management
» On-Demand Collaboration
» Assumption Management
» Auditability
» Transparency
» Model Management
» Input/Output Management
» Scenario Management
» Data exchange
» Regulatory Reporting
» Dashboard Reporting
» System Integration
» Process Governance, Automation,
Assumption, Model, and Results
Management are critical for an
effective CCAR/DFAST program.
» Assembling stress-test reports, and
validating results from the bottom-
up, requires structured processes.
» For PPNR, validating and agreeing
estimates should also be “bottom-
up” and leverage LOB models and
SME input.
Workflow 1
Workflow 2
Workflow “n”
Results
Data-mart
FRY-
14A W
ork
Packag
e
Valid
ati
on
an
d C
hallen
ge
PPNR, Losses, Charge-off, Recovery, etc
1. 2.
3.
4.
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 Pull as of Sept-30
Fill-in “Missing” Data with Proxy Data (inc. Tags)
Populate Required Fields for FRY-
14M/Q
Document Workflow, Version, and Audit the Data
Receive Scenarios Expand and
“Regionalize” Scenarios
Ensure Market Data is Consistent with
the Scenario Tailor 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
Calculated Expected NIR and
NIE in Each Scenario
Determine Charge-Off and ALLL in Each Scenario
Assess and Apply Other Losses,
Including Ops Risk
Calculate Appropriate Pro-
Forma Regulatory Capital
Populate Required Regulatory
Reporting Forms
Reconcile Reports to FRY-9C and
Other Reporting
Assess and Validate Results
Apply Measures to Capital Plan
Data
Scenario
Design
Analytics
Reporting
8
PPNR = “Interest Income” – “Interest Expense” + “Non-Interest Income” – “Non-Interest Expense”
PPNR Requires a “N” Quarter Forecast: Full Balance Sheet
Net Interest Income + Non-interest Income - Non-interest Expense
= Pre-provision Net Revenue (PPNR) Note: PPNR includes Losses from Operational Risk Events, Mortgage Put-
back 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
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.
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
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.
ALCO
Report
FRY-
14A
Risk
Report
Deal
Analysis
» C&I
» CRE
» Other Asset Classes
Interest Rate and
PPmt Process
Credit Risk
Model
Calibration
Framework
Migration
Matrice(s)
Inte
rfa
ce
Joint interest rate and
credit dynamics
Results
Data-mart
» P&I Cash Flows
» Credit/non-credit
adjusted
» Prepayments
» Additional property
sets
» Pro-rata NIR and
NIE allocation
» RWA
» Regulatory and
Economic Capital
ETL
Process
Use of result
output for
sensitivity
analysis
Import cash
flows to
ALM/FP&A
Import cash
flows to
regulatory
reporting
Pricing and
Performance
measurement
» ETL out
process
» Data
consolidation
and reporting
Loan level. Ability to roll-up to any
hierarchy level. Supports all
reporting and business processes.
Scenario Data
Market Data
Contractual
Terms
» FRY-14Q/M
(subset/monthly)
» Monthly stress-scenarios
» Monthly calibrated market
data
Consistent input data
Internal Models
12
Example: Integrated Dashboard Report – Current State
1
2
3
4
5
6
13
Regulatory Expectations: PPNR 2
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
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 non-
performing 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
16
Emerging Quantitative Methodologies: New Ideas on Old Processes 3
17
PPNR = Interest Income – Interest Expense + Non-Interest Income – Non-Interest Expense
Interest Income*
» Loans – Existing Book
– Less all run-off
– Plus new loans
» Securities – Existing Book
– Less all run-off
– Plus new securities
Interest Expense*
» Deposits – Interest vs. non-interest bearing
– By Line of Business / Product
– Client vs. wholesale funded
– Term structures
» Bonds – Existing
– Funding gap for additional bond issuances
Non-Interest Income
» Credit Related Fees – By Product / Line of Business
– Origination vs. Servicing (esp. for resi mortgage)
– Credit Card
» Non-Credit Related – Investment Banking
– Investment Management / Trust
– Deposit Service Fees
– Trading
Non-Interest Expense
» Employee Compensation – Salary
– Benefits
– Bonuses
» Processing / Software
» Occupancy (Plant, Property & Equipment)
» Credit / Collections
» Residential Mortgage Repurchases
* Note: Interest Income less Interest Expense = Net Interest Income (NII)
PPNR consists of numerous components from income and expenses from various areas of a bank
18
Two Approaches to estimate Interest Income and Balance: Direct and Granular
Direct Approach Granular 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
» 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
Seg
men
tati
on
» 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.
19
Example: Direct Balance Model for Term Loans
80
90
100
110
120
130
140
Bal
ance
(2
00
3 Q
3)
= 1
00
Quarter
Term Loans
Actual Fitted Base Adverse Severe
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)
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
0
3
6
9
12
15
0 1 2 3 4 5
Time (Year)
Cre
dit
Sta
te
Prepay
6 (Default)
1
3
4
5
2
*Moody’s Lattice is available in RiskFrontierTM
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
23
Segmentation by Credit Quality for Balance and New Origination
» 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.
0
20
40
60
80
100
120
140
160
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
Bal
ance
(2
00
3 Q
3 =
10
0)
Quarter
Term Loan Balance
Low_PD High_PD All_PD
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
No
B
Quarter
Term Loan New Orig. over Balance (NoB)
All_PD High_PD Low_PD
24
Segmentation by Credit Quality for Interest Charged
0
0.01
0.02
0.03
0.04
0.05
0.06 2
00
1 Q
1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
Spre
ad
Quarter
Average Term Loan Spread by Credit Quality
All_PD Low_PD High_PD
» 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.
25
Segmentation by Financial vs. Non-Financial
0
20
40
60
80
100
120
140
160
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
Bal
ance
(2
00
3 Q
3 =
10
0)
Quarter
Term Loan Balance
All Financial Non Financial
» Prior to the crisis, both segments exhibit a
similar growth pattern, with financial firms
growing faster right before the crisis.
» The financial crisis seems to have affected
new origination to Financials more severely
than Non-Financials:
– During the crisis, lending to financials starts
shrinking a few quarters earlier than lending to
non-financials, and at a faster pace. Non-
financials exhibit a drop only after the Lehman
Brothers collapse.
– 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.
0 0.02 0.04 0.06 0.08
0.1 0.12 0.14
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
No
B
Quarter
Term Loan New Orig. over Balance (NoB)
All Financials Non Financials
For Financials Term Loan
% Large (>80MM) 39%
% High PD 26%
For Non-Financials Term Loan
% Large (>80MM) 5%
% High PD 46%
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.
» 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.
» 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.
Note: for financial firms, large firms have total assets > 80MM. For non-
financial firms, large firms have total sales > 80MM.
0 20 40 60 80
100 120 140 160
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
Bal
ance
(2
00
3 Q
3=
10
0)
Quarter
Term Loan Balance
Small Large All_size
0 0.02 0.04 0.06 0.08
0.1 0.12 0.14 0.16
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
No
B
Quarter
Term Loan New Orig. over Balance (NoB)
All_size Large Small
For Large (>80MM) Term Loan
% Financial 64%
% High PD 21%
For Small Term Loan
% Financial 15%
% High PD 48%
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
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 macro-
economic 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:
–
29
Sample Deposit Balance Modeling Results
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.
» Mergers and acquisitions can be accounted
for by consolidating historical statements of
merged institutions.
» Macro factors are based on CCAR scenarios
BHCs FR Y-9C
Bank-
specific
FR Y-14Q » FR Y-14Qs allow us to adjust model outputs
to the level of granularity needed to populate
FR Y-14A.
Macro variables All Federal Reserve
CCAR 2013
Scenarios
31
Example: Modeling Overhead Expense Using Peer Groups
0.30%
0.40%
0.50%
0.60%
0.70%
0.80%
0.90%
1.00%
OE
/ A
sset
Year, Quarter
Overhead Expense over Assets (BHCs Data) historical 1-quarter projection baseline adverse severe
32
Practical Implementation Issues: Innovation and the Road Ahead 4
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
» 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
Enterprise Data Warehouse
Net Interest Income: » Forecast balances
» Interest income
Credit Exposure:
CCAR INPUTS CCAR Outputs PROCESSING
Cash Flow Engine
Risk Data Scenarios Reporting
Chart of Accounts Credit Default/Loss
Market Data Mgr.
Behavior Models
FTP
Pro-forma Bal. Sheet
Formula Builder
Value at Risk
» PD/LGD/EAD
» Expected loss
Analytics:
» Credit Migration
» Fair value
» Charge offs/impairment
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.
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.
Macro-economic indices
Lagged Market Data
Dynamic Credit Metrics
Segment Balances
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
37
Other Assets/Liabilities and Non Interest Income/Expense
» The accounting identity „Assets = Liabilities + Equity‟ must be true; if not, the pro-
forma 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
» 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
- Balancer accounts
- Retained earnings
- Taxes/Deferred tax liability
- Dividends
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.
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.
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.
ALCO
Report
FRY-
14A
Risk
Report
Deal
Analysis
» C&I
» CRE
» Other Asset Classes
Interest Rate and
PPmt Process
Credit Risk
Model
Calibration
Framework
Migration
Matrice(s)
Inte
rfa
ce
Joint interest rate and
credit dynamics
Results
Data-mart
» P&I Cash Flows
» Credit/non-credit
adjusted
» Prepayments
» Additional property
sets
» Pro-rata NIR and
NIE allocation
» RWA
» Regulatory and
Economic Capital
ETL
Process
Use of result
output for
sensitivity
analysis
Import cash
flows to
ALM/FP&A
Import cash
flows to
regulatory
reporting
Pricing and
Performance
measurement
» ETL out
process
» Data
consolidation
and reporting
Loan level. Ability to roll-up to any
hierarchy level. Supports all
reporting and business processes.
Scenario Data
Market Data
Contractual
Terms
» FRY-14Q/M
(subset/monthly)
» Monthly stress-scenarios
» Monthly calibrated market
data
Consistent input data
Internal Models
41
Consistency Across Basel III and Treasury Risk Management Functions
Cash Flows & Behavior Models
42
Conclusions: Better Start than Good 5
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.
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.
1.
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.
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.
44
Fully Integrated Architectural Design Modular, Flexible and Comprehensive – Allowing for Straight Through Risk Processing
Spreading System
RiskAnalyst / RiskOrigins
Core Systems
(e.g. GL, Loan Accounting)
Risk and Finance Datamart
(Inputs and Results)
DA
TA
LA
YE
R
- 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
- Our solution design accommodates comprehensive regulatory
reporting, internal risk and LOB reporting, plus dimension /
hierarchy management:
- 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
Management
Reporting /
Dashboard
Risk & Performance
Management
RE
PO
RT
ING
Regulatory
Reporting
- Part of Potential Moody‟s Solution
- Bank‟s Internal / Third Party Systems
- 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. Risk Management
and ALM System
Data
Credit Models
(Wholesale & Retail)
Budgeting &
Planning System
Output
AN
ALY
TIC
LA
YE
R
NCOs ALLL PPNR
SCENARIO ANALYZER TM
RWA
RiskAuthority
45
Next Webinar 6
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: http://www.cvent.com/d/z4qplc/4W
47
Questions 7
4
8 moodysanalytics.com
Thomas Day
Senior Director
Direct: 404.617.8718
7 World Trade Center at
250 Greenwich Street
New York, NY 10007
www.moodysanalytics.com
Amnon Levy, PhD
Managing Director
Direct: 415.874.6279
405 Howard Street
Suite 300
San Francisco, CA 94105
www.moodysanalytics.com
Robert Wyle, CFA
Senior Director
Direct: 415.874.6603
405 Howard Street
Suite 300
San Francisco, CA 94105
www.moodysanalytics.com
49
Find out more about our award-winning solutions
www.moodysanalytics.com
50
@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.
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
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.
51
© 2014 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.