Interest rate risk modeling day sun_gard_ambit banking

3/20/2011

SunGard Banking
IRR Modeling and Management

[Business or Company that Needs to Know More about
Balance Sheet Management] – ask me to come help
DATE
LOCATION

1

Standard Disclaimer and Ground Rules

 The views, expressions and ideas of this
presentation are those of the author and do not
necessarily reflect the views and opinions of
Ambit or SunGard.

 Thank you to the [COMPANY] SunGard and all
[COMPANY],
the participants and organizers of this
conference.

 Ground rules:
 Network: Make friends and stay in touch
 Engage: Ask many questions
 Apply: How do we make a difference tomorrow when
on-site? 2

Who is SunGard?

Financial Services

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3/20/2011

Ambit Risk Academy: Bio of Speaker

Thomas Day
SunGard Ambit – Risk & Performance
Director, Risk & Policy
(617) 780-4140 – thomas.day@sungard.com

Role at SunGard:
Tom Day is the Managing Director of Risk Solutions and Policy at SunGard Ambit, where he is a key source of thought
leadership for the company and its financial institution clients. Leveraging an extensive twenty-year career in banking, risk
management and bank supervision, Mr. Day assists clients in navigating through diverse market challenges, regulatory policy
changes and business opportunities. His overall technical knowledge across bank balance sheets, capital management and
enterprise-risk is comprehensive, and Mr. Day is well known for practical solutions to financial problems from a best-practice,
business, accounting and strategic perspective.

Background prior to SunGard:
Prior to re-joining SunGard, Mr. Day was Senior Risk and Policy Advisor within the US Treasury Department. In this role, Mr.
Day oversaw the TARP program for various chartered institutions and was a member of the interagency review committee that
recommended billions of dollars of capital investments into banks and financial institutions. He has also served in senior roles
at the Office of the Comptroller of the Currency, the Federal Reserve Board of Governors, AmSouth Bancorporation, SouthTrust
Bancorp and Barnett Bank. He previously served as the SVP and head of product management and development at SunGard
BancWare, one of the world’s leading providers of risk management software.

Mr. Day has a BS in Economics from Auburn University and is a Commissioned National Bank Examiner and a Commissioned
Federal Bank and Financial Holding Company Examiner. He is on the Board of the Professional Risk Managers’ International
Association (PRMIA), the Regional Director of the D.C. Chapter of PRMIA, a frequent speaker at various industry conferences
and events, a routinely published writer, and an avid fan of American football (particularly the SEC).
4

Ambit Risk & Performance:
ALM, Capital/Stress-Testing, Liquidity, Budgeting, Planning, and FTP

Ambit Risk Academy
Advice, Tools, and SMEs for a Changing Financial Landscape

CEBS The Dodd-Frank Act Basel 3

Too Big to Fail
European Systemic Risk Board

Balance Sheet Management: Never More Critical
SIFI and G-SIFI
Systemic Risk
Incurred v. Expected Losses
v
G-20 and FSB

Stress-Testing and Capital Planning
Financial Stability Oversight Council
International Harmonization
Capital Buffers Interest Rate and Liquidity Risk Management

Incentive Compensation and Performance Measurement
http://www.centerforcapitalmarkets.com/resources/dodd-frank-wall-street-reform-and-consumer-
Interconnectedness
protection-act-of-2010-regulatory-authority/
Cross-Border Resolution

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Impact of Financial Reform on Risk Management?

• Risk Management Impact?

• Full employment at:
p y
 Deloitte, KPMG, PwC, BAH,
Promontory
 Have regulatory experience,
will hire
• Major areas of need:
 Infrastructure issues
 ERM is “for keeps”
 Massive need for education
 Cultural challenges
 Regulatory challenges
7

Major Areas of Impact: Balance Sheet Management

Agenda: Interest Rate Risk Modeling and Measurement

Supervisory and Business Expectations

ALM5 Capabilities and Limitations

Using the Tool: Risk versus Compliance

IRR Architecture and Key Issues

Summary Review and Conclusions

Sungard Account Management

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Convergence:
Balance Sheet and Enterprise-Wide Risk Management

Gap Analyses

Duration Analysis
NII Simulation
Economic Value Analysis

Dynamic Simulation Techniques

Fair Value Accounting / Credit Adjustment
Integration of
Risk
Monte-Carlo-VaR Management
Value-at-Risk (VaR)
Stress Tests
Sensitivity Analysis

Black-Scholes, Basel I RiskMetrics Basel II
Greeks

1970s 1980s 1990s 2000s

The ALM Risk Domain

 Supervisors expect that IRRM will cover:
 SHORT-TERM RISK to EARNINGS:
 Risk to margin, net interest income (NII), and net income
 Includes base-runoff cash-flows and new business projections

 LONG-TERM RISK to VALUE:
 Often called “economic value of equity” (EVE); may be other
economic equity
approaches that are acceptable

 Bank supervisors (and others) often forget ALM should
also encompass:
 Liquidity
 FTP, profitability, performance and budget
 Certain aspects of credit risk
 Certain aspects of operational risk

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BS&R Expectations and Examination Requirements

 With regard to interest rate risk management, BS&R
should be as interested in the risk-management process
as the model (i.e., 80/20 rule)

 SunGard has considerable experience with the all
regulatory groups and many examiners are familiar with
ALM5

 Consistent with SR 95-51, SR 96-13, and the January 6,
2010 Advisory, the regulators should be looking at:
 Board and senior management oversight
 Policies, procedures and limits
 Not just ALM and IRR
 Model risk management, investment portfolio and liquidity
 Risk monitoring systems, controls and infrastructure
 Internal and external audit, reporting and disclosure
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Good Starting Point: Pragmatic Approach

“No matter how good the theory, the first question any good quant
should ask of a model: Under what conditions will it fail. The
second: When will it occur? You see, your organization needs to
be aware that your models are wrong. Some people use the word
mis-specified, but I don’t like that because there is no obvious
Platonic specification of the financial world.” - Emmanual Derman

 Models are analytical approximations, or abstractions, of reality that
simplify complex phenomena and relationships into the essential
i lif l h d l ti hi i t th ti l
elements that drive risk.
 Models are designed to solve or provide direction towards the solution of
a particular problem.
 What is model risk? The risk that is derived from using assorted
variables and mathematical specifications within a model class to
produce estimates, forecasts and solutions that do not conform to real
world outcomes.
 The impact of such risk is felt by an organization when decision-makers
make economic commitments based on the results of such output.

Summary of Key Guidance


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The Evolution of IRR Supervisory Guidance

The Federal Deposit Insurance Corporation Improvement Act of 1991
• Section 305
• Focus on “Capital Charge”
• OTS adopts a unique approach
• Internal Models Approach (all other Agencies)

Earnings versus Value Debate
• NII forecast
• EVE (beauty is in the eye of the beholder)

Interagency Policy Statement (1996)
• The Board should…
• Senior management should…
• Treasury management should…
• Reporting processes should…
• Rate risk systems should…
• Internal audit should…


The January 2010 Guidance on IRR

 Corporate Governance
 Regular, timely, broad range of participation
 Policies and Procedures
 No mention of credit spreads: value and earnings impacts
 Measurement and Monitoring of IRR
 Input data, cash-flow, aggregation, forecast horizon (
p , , gg g , (5-7 year issue),
y ),
dynamic and “static” simulations
 Stress-Testing
 Broader range of scenarios, call for deterministic scenarios
 Assumptions
 Sensitivity analysis, special attention around behavioral models
 Risk-mitigation
 Internal Controls and Validation








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Accrual Book Maturity Model: Maturity Map

Leadership Target

Accrual Book IRR: Maturity Map What Does Maturity Mean?
• End-state target environment
Proactive Quadrant Leadership
Quadrant • Governance excellence
• Consolidated and
comprehensive on-and off-
balance sheet coverage
ctice

• Numerous stress, sensitivity,
, y,
Industry Best Prac

2011 (goal)
( l)
and strategy scenarios:
earnings and value
Early-stages Reactive Quadrant • Decision-support/
computational speed
• Monthly builds/ model
2010 “weight”
2009 • FTP method ownership
• Integrated liquidity analysis
• Resource strength (human
Firm-Level Maturity
and technical)
• Validation and control


Strategic Balance Sheet Management
ALM Process
• Import position and market data
• Calculate risk measures (income and value)
• Generate ALM report package (min frequency
monthly)
1
Risk Committees
• Active balance sheet risk
management
Analyze Reports
• Enhance risk/return profile • EVE sensitivities
• Modify plans – tactical and (contractual cash flows)
• Standard EVE
strategic
t t i • MVPE
• DV01/KRD
• NII Simulation
4 2
• Re-pricing Gap analysis

3
What-if Simulation
• Analyze risk exposure – forecast and stress
• Measure impact of hedging strategy
• Measure sensitivity to key assumptions
• Understand behavioral dynamics

Governance: Critical Success Factor

Organizational Design is critical to effective process management
• Effective governance drives the entire balance sheet management process
• ALCO is the action-vehicle
• Treasury must have a clear mandate with well articulated policies and procedures
• Line of business buy-in and cooperation is essential
• Modeling is designed to support the governance process; anything else is
ancillary/secondary
/
• Reporting and feedback align various components
• Enforce accountability
• Create action items, e.g. hedging
• Value-added, e.g. pricing optimization, elimination of expensive options, funding optimization

All players must understand roles and responsibilities
• Education/Qualification of resources
• Creation of risk management culture

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Governance Framework

• Governance
o Comprehensive oversight
o ALCO is action vehicle
• Organizational Structure and Responsibilities
o ERM sponsorship
o Treasury ownership
o LOB cooperation
• Data and Assumptions
o Ownership
o Quality Assurance/Control
• Risk Models
o Design
o Efficiency/Usability vs. Granularity
o Consistent management/analysis across all line models
• Reporting
o Comprehensive presentation of risk limits and exposures
o Actionable information

Effective Business-Level ALM: Communication

Risks and Opportunities
ompetitive Positioning

Plans and Forecasts

3rd Parties
On-going Commu

Subsidiaries On-going Communication
Retail
cation

ALM/
Strategic Focus and Co

On-going Communic

Forecasting
F ti
unication

C&I Auto Finance
A t Fi
Unit

On-going Communication
Investment Banking CRE

Leasing/Factoring

ALM is the “huddle” that brings all business activities
together into an overall picture of balance sheet strength
and weakness. Ultimately, ALM is an enterprise-risk
function

Range of Practice at Large Banks

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Range of Practice at Large Banks (continued)

Range of Practice at Large Banks (continued)

Some Interest in Stochastic NII

• Produce a range of NII outcomes
based on sampling around the
tails rather than the mean.

• Interesting practice for stress-
testing; however, terribly difficult to
interpret and communicate the
usefulness given business
contingencies and other factors.

• Ambit encourages a wide-range of
approaches with the goal of
establishing a range of views as to
the impact of rate and spread
movements on the firm’s overall
structural position risk.


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How ALM5 is Used within Financial Organizations

 ALM5 is a dynamic balance sheet income simulation and valuation model
 Can fully meet all business and regulatory requirements
 Can scale to meet any size and complexity requirements
 Embeds best-of-breed analytics, access to third-party prepayment and structure models, and
valuation technologies

 Used to ensure consistency between risk, budgeting/planning, FTP, and
liquidity risk management groups. Increasingly used for macro credit
analysis.
l i

 The model is used to create multiple market scenarios to ascertain
sensitivity to all sources of IRR
 Repricing, Basis, Yield Curve, and Options Risk
 Is used to understand sensitivities to business plan, spread, prepayment, funding and customer
behavioral risks

 Drives better business decisions through precision of cash flow
calculations

Common Use Case: Defined More Clearly
Net Interest Income from this
$ Valuation from this point is the point is typical “risk” measure
typical “risk” measure from from S0 to/against S1, S2, Sn
Scenario0 (S0) against S1, S2, Sn
”X”%
Forecast error. Ties to
Forecast Growth budget and planning.
CP$
27%
New Business Roll Static balance sheet
(a.k.a., flat balance
sheet)
Baserunoff
73%
What is “VaR” at this future
point? Forward looking risk
measures. T12 or any Tn
t0 t3 t6 t9 t12
ALM5 can measure all of these risk components. The manner in which cash flows are
modeled (from both an income and valuation view) requires a wide range of
assumptions related to the market environment, data, calculations, and risk measures
(i.e., output).

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Pragmatism in ALM: Necessary, but not Sufficient

 ALM5 is a forward-looking, state-of-the-art risk management system
used to identify and understand risk → A/L management is not an
accounting exercise.

 Future balances and behavioral assumptions are forecast with
uncertainty, but must foot to plan.

 There is a tradeoff between granularity and efficiency.

 Too much granularity (i.e. not enough aggregation) will hinder
efficiency and slow-down effective decision-making.

 The trade-off between accuracy and risk-management pragmatism
is an art, but best practices exist as well as supervisory/regulatory
expectations. Need to reinforce each other.

Treasury and Risk Management Organization

 Implementing improvements in data workflows, ownership of
assumptions, and utilization of industry leading practice risk
measures will support an organization’s ALCO Process, linking
risk groups and decisions in a practical and efficient manner.

 This will also successfully address regulatory requirements for
adequacy of IRR management.

 ALM5 has all necessary capabilities to support an enterprise-
wide ALCO and balance-sheet management process.

 It is critical that staff – bank and supervisory – understand that
the processes that surround the balance sheet management
process are as (often more) important than the model itself.
 Too many get trapped on the vendor model rather than internal
resourcing, staffing, policies and procedures

What can ALM5 do?
(and a side-note about ALM6)

 All income and all valuation scenarios
 Spot and future
 Static and new business
 Daily versus monthly
 Integrated liquidity risk management
 Deposit shock scenarios
 Integration of Contingency Funding Plan
 Haircuts to pledged and non-pledged assets
 Designed to be actively used
 Not a compliance tool, but a decision-making tool
 Rapid compute times are a priority. Scales linearly with number of
CPUs added to analytical server farm
 Be adjusted to accommodate unique business problems and
needs; designed to be an “open” system

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Risk Tensions

 Senior Management and ALM goals often can conflict
with each other
Sr. Mgt: Give me
more earnings.
NOW!

ALM: No problem.
ALM: No problem.
Here is less
Here is some more
earnings.
risk.

Sr. Mgt: Wait a
minute. Give me
less risk!

The Role of ALCO: Process is Important

 To manage the structure of the bank’s balance sheet and the
level of banking book market risk(s)

 Ensure effective measurement and reporting systems

 Include heads of all major LOBs
 May also include marketing and investor relations
y g

 Balance LOB optimism on business plans with practical
financial realities (i.e., a forum for debate across LOBs)

 Ensure Treasury’s “discretionary” actions are in alignment with
core banking plans and activities

 When using correspondent services, all of the above are even
more critical for the examiner to review

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Key Risks Traditional Covered by IRRM

Maturity and Repricing Mismatch
The risk that “gaps” between maturity or repricing
dates will affect earnings and value.

INTEREST-RATE RISK
Basis Risk Yield-curve Risk
Earnings-Based Measures
E i B dM
The risk that the correlations The risk to changes in
between rates upon which my the slope of the yield-
Economic-Based Measures
assets and liabilities are based curve
will move in a fashion which
is detrimental to an
organization’s earnings and
value

Options Risk Volatility?
The risk that embedded
and explicit options
present to an organization

Dimensions of Risk

 Static Risk
 How do changes to risk factors impact my inherent risk position?
 EXAMPLE:
 Economic value of equity, as traditionally applied, is a static risk measure
 Gap reporting, as traditionally applied, is a static risk measure

 Dynamic Risk
 How do changes in risk factors – over time – impact my risk position –
over time?
 Note: risk factors are moving AND my risk position is moving
 This is equivalent to a holding period measure of risk where position
risk is likely changing relative the movement of the risk factors
 EXAMPLE:
 Net interest income simulation is a dynamic measure of risk wherein the risk
factors AND the position risk is changing over the holding period

Typical Model Data Flow

Loan Investmnt General Deposit Other
Systems Systems Ledger System Systems

Balance sheet data and Specific rate
Earnings
aggregation, repricing
gg g , p g forecasts, future
, Interest Rate Risk
information and other growth assumptions Model Simulations
inputs and assumptions and other key inputs

Beginning Month 1 Month 2 Ending
Balance Balance Balance Etc. Balance
Sheet Sheet Sheet Sheet Value
Simulations

Performance Performance Etc. Periodic
for Month 1 for Month 2 Performance

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Interest Rate Risk Management Concepts

1. Repricing Gap

2. Net Income Simulation and Earnings at Risk (EaR)

3. Economic Value of Equity (EVE)

4. Scenario/Sensitivity Analysis

5. Data Integrity and Assumptions

6. Limits and Controls

Repricing Gap Analysis

 Repricing Gap provides a simple, yet limited, view of re-pricing
mismatches along the yield curve.
 Best Practices
 Availability of re-pricing details on all balance sheet instruments
 Significant granularity of re-pricing cash flow, e.g. 0-3, 3-6, 6-9, 9-12
months, 1, 2, 3, 4, 5, 6-10, 11-15, 16-20, 21-30 years
 Multiple shock scenarios in order to observe mismatch volatility
 Critical Assumptions
 Distribution of non-maturity deposit balances should be consistent with
FTP process; risk management and incentive comp should be aligned
 Limitations
 Analysis is limited to current balance sheet; ignores future business
 Inadequate for capturing impact of optionality, e.g. caps, floors,
swaptions, convexity, etc.
 Levered vs. Unlevered


Example: Repricing Gap Report


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1. Repricing Gap






Net Interest Income (NII) Simulation

 NII simulation provides an estimate of near-term earnings
sensitivity to changes in market interest rates.
 Intuitive and easily understood by ALCO and Exec. Management
 Best Practices
 Dynamic balance sheet derived from LOB forecasts of future business
 LOB ownership of key model assumptions
 Sensitivity analysis to key model assumptions, e.g. deposit re-pricing,
y y y p , g p p g,
prepayment speeds, credit performance
 Large number of rate scenarios including shocks, ramps, twists
 Analysis of below-the-margin line items in order to achieve a NI or EPS
sensitivity measure
 Regular model validation and back-testing of near-term projections
 Critical Assumptions
 Static versus dynamic balance sheet
 Flat versus forward curve
 Static (constant speeds) versus dynamic prepayment functions (ADCo)

Net Interest Income (NII) Simulation (cont’d)

 Limitations
 Short term in nature; common practice is 1-2 year horizon
 Misses impact of options that are beyond model horizon, e.g. mortgage
prepay/extension risk, long-term FHLB optionality, TRuP options
 Longer term, e.g. 5-7 years, horizons are recommended by regulators,
but become very assumption driven
 Ignores changes in value of MTM instruments (and potential
earnings impact of impairment charges), e.g. trading book, MTM
hedging portfolio

 Deterministic vs Probability-based Yield Curve evolution
 Traditional methods rely upon single path rate scenarios
 More current methods utilize stochastic yield curve models to derive
a range of earnings estimates
 Provides more granular analysis of cash flow variability


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Example: Net Interest Income Report


Example: Net Interest Income Report



1. Repricing Gap






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Economic Value of Equity (EVE) Measures

 EVE analysis provides an estimate of the sensitivity of the
“economic value” of balance sheet equity to changes in market
interest rates
 Captures all cash flows associated with current balance sheet
 Better captures exposure to optionality risk
 Inconsistent views across regulatory authorities
 FDIC prefers LT income simulation over EVE
 OCC somewhat ambivalent about EVE; have been known to give a
“pass” to some LFIs on EVE in lieu of long-term income simulation
 FRB more interested in EVE
 12 CFR § 3.10 provides that national banks can be assessed higher
minimum capital ratios based on significant exposures to declines in
the economic value of its capital.
 Proposed new accounting rules may make EVE considerably more
important as it will translate directly into reported earnings volatility


Economic Value of Equity (EVE) Measures (cont’d)

 Best Practices
 Stochastic interest rate model
 Dynamic prepayment functionality
 Two-factor yield curve models
 Incorporates credit-spread volatility
 Unique credit spreads for different asset classes
 Limitations
 Does not translate/correlate directly to an intuitive earnings-based
measure of risk
 Does not translate/correlate directly to market cap sensitivity
 Does not indicate optimal hedging strategies
 Ignores the impact of future business
 All of the above lead to difficulty in limit setting
 Requires significant computing power
 Not all balance sheet instruments can be valued accurately with a single
type model

Other Valuation Based Measures

 DV01
 Dollar value impact of 1 bp move in an interest rate
 Common risk measure in fixed-income portfolio management
 Not as common in balance sheet management
 Key Rate Duration
 Measures the effect of a change in the yield curve that is localized at
a particular maturity point
 Necessary for development of detailed hedging strategies
 Mitigates risk to changes in curve shape
 OAS
 In contrast to the simple “yield curve spread" measurement of bond
premium over a pre-determined cash-flow model, the OAS describes
the market premium over a model including two types of volatility:
variable interest rates and variable prepayment rates.

CONFIDENTIAL – FOR INTERNAL USE ONLY Sungard Account Management

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Example Reports: EVE and NII Scenario Dashboard


Example Reports: EVE



1. Repricing Gap






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Scenario/Sensitivity Analysis

 In addition to establishing and monitoring standard risk limits for
the impact of changes in market interest rates, sensitivity analysis
to key model assumptions must be performed periodically, e.g.
interest rate sensitivity and balance decay functions on non-
maturity deposits
 Sensitivity analysis tests a model’s parameters without relating those
changes to an underlying event or real world outcome

 Additional business scenarios, e.g. rapidly growing balance sheet,
de-levering balance sheet, economic recession, corporate credit
event, need to be periodically modeled
 Scenario analysis uses the model to predict a possible future
outcome given an event or series of events

Other Matters

 The use of IRR models for balance sheet management, e.g.
corporate forecasting, strategic analysis, M&A support, adds
considerably to a firm’s credibility with regulatory authorities.
 If a model is used only for IRR management, the question arises as
to its credibility/veracity.
 On January 6, 2010, the financial regulators issued an advisory to
remind institutions of supervisory expectations regarding sound
practices for managing interest rate risk (IRR).
 “In the current environment of historically low short-term interest
rates, it is important for institutions to have robust processes for
measuring and, where necessary, mitigating their exposure to
potential increases in interest rates.”
 Regulators fear banks going out the curve (and funding short) in the
quest for yield. Post credit-crisis, regulators do not want banks
immediately increasing risk along another dimension.



1. Repricing Gap






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Data Issue Overview: The High-Altitude View

 There has been a significant increase in the use of technology
to increase efficiencies over the last two decades (you never
“arrive”, but are always in the process of “arriving”)
 Banks have gotten bigger, risk-taking got more “scientific” (models)
 Funding liquidity seemed endless (pre-crisis)
 Products became more complex
 Competition, globally, became more intense

 Economies-of-scale seemed to “rule” and
“diseconomies” of scale were “inconceivable” in banking
 Our experience indicates:
 Multiple source systems
 Legacy data problems
 Inability to pull current position balance sheets together
 Inability to evaluate, plan and budget over such a large book
 Governance by volume and performance, not by risk

Key Assumptions: Income Simulation

1. Starting Balance Sheet
a) Data preparation and maintenance is often > 60% of the challenge
b) Some institutions are unable to compile complete starting position in a
timely basis (possible red flag)
2. Selection of rate scenarios and driver rates
a) +100, +200, +300, non-parallel, ramps, forward curve, etc
b) How many driver rates? How to correlate? Manage?
3. New business (e.g., roll-rates)
a) How will new business “roll” onto books? When? With what
characteristics? How to source these assumptions? Maintain?
4. Repricing attributes
a) Important for models not at instrument level
5. Core deposit behavior – earnings and valuation
6. Prepayment assumptions

Key Assumptions: EVE

 Discount rates utilized

 Spreads used, if any
 Maybe 1) OAS, 2) credit, and/or 3) other

 Core deposit average lives and valuation method(s)
 Are the same base runoff cash flows used for valuation and for
income simulation?

 How are loans of similar type aggregated and evaluated?

 How are embedded options valued?
 How do methods differ across the chart-of-account(s)?

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Data Integrity and Assumptions

 Documentary support for derived assumptions used in the model:
 Core deposit assumptions – rate betas and lags (for NII) and decay
rates/durations (for EVE)
 Deposit behaviors for risk management should be similar/identical as
those for FTP
 LOB sponsorship/ownership of pro forma inputs/estimates
 Synchronization of pro forma prepayment speeds with historical
experience

 Data limitations do exist and large, complex institutions will have
to strike a balance between a quality process and overload.
 Sensitivity analysis should be used to test exposures resulting from poor data
quality, data aggregation as well as imperfect model assumptions, e.g. core
deposit rate betas.


Data Integrity and Assumptions

 "Best in Class" ALM process
 Frequency: Complete monthly reporting of all risk limits; constant analysis of
model assumptions, data quality and accuracy of output
 Data aggregation: As little as possible w/o sacrificing compute efficiency
 For static scenario models, data granularity can be maximized while data
aggregation (pooling) can be minimized.
 For stochastic models, data aggregation needs to be maximized in order to get
compute results in a timely manner. Granularity, and hence accuracy, will be
p y y, y,
sacrificed to some extent. Period (quarterly or annual) analysis using more
granular inputs can be used to ballpark errors associated with efficiency in regular
process.
 Regular LOB involvement in validation of model assumptions and
results
 Modeling effort that demonstrably drives corporate balance sheet
management process



1. Repricing Gap






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Policies, Procedures and Limits

 Policy comprehensiveness is critical
 Is it Board approved annually?
 Overall policy objectives and IRRM philosophy are well articulated
 Outlines major drivers of IRR and liquidity risk for the entity(ies)
 Details responsibilities and authorities
 Board
 ALCO(s)( )
 Management processes and procedures
 Establishes risk appetite (how?) and tolerance
 NII and EVE and exposure of each to major IRR sources (m,y,o,b)
 Re-pricing GAP misses many risk factors and is not largely used
 Exceptions to policy (process for escalation and remediation)
 Relationship to capital, dividend, off-balance sheet, derivatives, investment
and accounting policies
 Has it been “benchmarked” against peer?


Limit Structures

 Limits need to be developed based on a risk appetite
analysis, or similar “threshold” exercise

 Most large banks place limits to several baseline scenarios
for dynamic income simulation NII (over 12 and 24 month
horizons) and static Economic-Value of Equity (
) q y (EVE) )
analyses
 Not uncommon to have ramps, stair-steps or shock-based NII limits
 EVE is by nature a shock-based (instantaneous) measure

 January 2010 Interagency “advisory” made some suggested
changes to the 1996 statement, but is only an advisory


Difference between limits and robust “analysis”

 Measuring risk against policy limits is a 1st-order expectation;
numerous additional scenarios need to be performed
 Net interest income
 Exposure to yield curve, basis and options (volatility) risk
 Exposure to spread risks

 Economic value of equity
 Exposure to credit spread (should be a MVPE baseline, not EVE)
 Not uncommon for a separate model to be run for EVE analysis
 Quant, R&D and market-data sourcing (prices and spreads) is a
critical element (can have organizational design impacts)
 Migration over time to a full-blown OAS method, but most banks are
not at this stage yet


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3/20/2011

Policies, Procedures and Limits

 Policy comprehensiveness is critical
 Is it Board approved annually?
 Overall policy objectives and IRRM philosophy are well articulated
 Outlines major drivers of IRR and liquidity risk for the entity(ies)
 Details responsibilities and authorities
 Board
 ALCO(s)( )
 Management processes and procedures
 Establishes risk appetite (how?) and tolerance
 NII and EVE and exposure of each to major IRR sources (m,y,o,b)
 Re-pricing GAP misses many risk factors and is not largely used
 Exceptions to policy (process for escalation and remediation)
 Relationship to capital, dividend, off-balance sheet, derivatives, investment
and accounting policies
 Has it been “benchmarked” against peer?


Limit Structures

 Limits need to be developed based on a risk appetite
analysis, or similar “threshold” exercise

 Most large banks place limits to several baseline scenarios
for dynamic income simulation NII (over 12 and 24 month
horizons) and static Economic-Value of Equity (
) q y (EVE) )
analyses
 Not uncommon to have ramps, stair-steps or shock-based NII limits
 EVE is by nature a shock-based measure

 January 2010 Interagency “advisory” made some suggested
changes to the 1996 statement, but is only an advisory


Internal Controls and Model Validation

 There is an expectation that Internal or Corporate Audit
Staff play a role in the IRR model review process
 In a decentralized model, various check-points and procedures will
need to be established and flow-through to the annual corporate
audit report(ing)
 The IRR element plays a significant role in the CAMELS rating
process of the banks, and the RFI/C rating for the BHC/FHC.

 Model Validation
 Various policy statements: OCC 2000-16 and FHFA recent policies
are valuable reference points
 Model validations needs to be conducted by quant personnel,
independent from modeling crew. The validation needs to be
coordinated with audit. Needs to be rigorous. Elements of the Jan
2010 statement need to be incorporated.

23

3/20/2011








Model Architecture: Account Level Detail

1. Ideally, you want to limit the number of accounts you
model at the group or pool level and maximize the
number of accounts you model at the transaction
level.

2. Some accounts you may simply have to model at the
group or pool level using weighted average attributes


Model Architecture

• These constitute the “end-nodes” of
your chart-of-account (COA) structure
• Varies from vendor to vendor
• Such structure can allow for dynamic
dimensions

e.g., COA “end-nodes”
Business Banking:London:CRE:Fixed:
Business Banking:London:CRE:Libor3M:
Business Banking:Hong Kong:C&I:Libor3M
Business Banking:New York:Mtg:Treasury12M:

24

3/20/2011

Model Architecture

Model Architecture (continued)

Key Issues: Inputs

 Identify model vendor and version number of data transformation engine; note
specific modules or add-ons
 Identify and assess ETL (extract, transform, and load) process, e.g. BDI, Access,
Excel, SQL, etc.
 Determine data sources, data owners, availability and control environment
 Determine reconciliation process for product balances
 Determine extent of data transformation and pooling logic
 Select sample of records for testing (
p g (will follow through computational engine)
g p g )
 Review and test pooling logic
 Review data filling routines
 Review behavioral overrides in data
 Review data record (from source system) granularity (accuracy vs. efficiency)
 Review pool record (feed to computational engine) granularity (accuracy vs.
efficiency)
 Review USD and FX market spot rate and curve inputs (and consistency across
models)
 Review USD and FX internal spot rate and curve inputs (and consistency across
models)

25

3/20/2011

Key Issues: Assumptions and Compute Engine

 Identify and review key behavioral assumptions, e.g. prepayments and deposit
decay rates; determine source, ownership, review and revision procedures
 Review and test model rate generation processes, e.g. stochastic and forward curves
 Review and document chart of accounts and key behavioral settings
 Select sample of accounts for testing [sampling methodology TBD]
 Verify cash flows for sample accounts, including principal and II/IE
 Review new business chart of accounts, key behavioral settings
 Determine assumption sources and validation methodologies for new business
 Determine market value methodologies, e.g. static or stochastic
 Review setup and calibration of stochastic rate model used for EAR or EVE
calculations
 Review EVE discounting (flat or with spread?). Basis for assumptions. Support.
 Review key rate and DV01 methodologies

Outputs: Reporting

 Review ALCO policy to identify risk limits
 Review existing interest rate risk reports
 Review GAP report construction, including balance sheet
line-item inclusion/exclusion criteria
 Review NIM forecast
 Review analysis and measurement of mismatch basis yield
mismatch, basis,
curve, and options risk
 Review funding/hedging recommendations
 Review economic forecast and recommending balance
sheet strategies
 Determine if IRR reporting is appropriate given size and
complexity of organization’s balance sheet


Governance: Internal Routine and Control
 Review model operating procedures
 Review back-testing of NIM and synchronization with budget or FP&A forecast
 Review analytical reports/studies used to verify model results
 Review stress testing
 Review “what-if” scenarios
 Review business line analysis of model results
 Review disclosure of key behavioral assumptions, e.g. prepayments and deposit
decay rates
 Review model documentation for disclosures around model use and limitations
 Review model documentation for discussions of model theory
 Review change control procedures
 Review recent audit findings for model/modeling process
 Review model validation policy
 Review previous model validation findings
 Determine testing and approval process prior to model deployment


26

3/20/2011








Summary

 Governance as much as the model
 Policies and Limits
 Committees and active senior management and board oversight
 Routine validations and accountability mechanisms in place

 Break down silos. Use the model to drive business, not
compliance
 Robust stress-testing and sensitivity analysis
g y y
 Non-parallel curve shifts critical in today’s environment
 Duration and convexities are critical in today’s environment

 Validation, Assumption management and Version control
 Resources – does the bank have sufficient internal
resources?
 Income and value measures
 Create a network of experts you can lean on when needed

Q&A?

Any questions or comment?


27

3/20/2011

SunGard Banking
IRR Modeling and Management

[NAME]

THANK YOU !
thomas.day@sungard.com
@dcprmia
http://linkd.in/DC_PRMIA_LI (DC PRMIA LinkedIn Community)
82

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