Transcript FHLB Income Based IRR Measurement: Alternative

FHLB Income-Based IRR Measurement:
Alternative Approaches and Issues
- Potentially Useful Lessons from the Private Sector -
Page 1
Agenda
 Background
 Classification of IRR Measurement Techniques
 Opportunities and Challenges of:
 Stochastic income measures
 Earnings-at-Risk (EaR)
 Background: Citicorp’s Risk Measurement Challenge ~1987
 Background: Citicorp’s Solution
 Application to Income Output from an FHLB using QRM or
BancWare
 Applications




Page 2
Risk Limits
Decomposition of the IRR Measure: Improved Understanding and Management
Hedging both Income and Value
Regulatory
Background
 IRR measurement and management in private banks is largely
focused on reported income
 Mortgage banks more oriented toward value because accounting is closer
to market value accounting
 Bank America implemented an IRR measurement solution to hedge
earnings and value simultaneously in 1990s
 IRR measurement in many FHLBs has focused on controlling
value-based risk measures
 FHLBs and its regulators are starting to place more emphasis
on income-based risk measures and effects on retained
earnings
The private sector has
implemented methodologies
that are potentially useful to
FHLBs and their regulators
Page 3
Background
 Some methodological issues that arise in private banks when
measuring income-at-risk:
 Which definition of “income” to model
 How to simulate interest rates
 Whether to include new business assumptions and how to vary
new business assumptions in different rate scenarios
 Over what period to model income-based risk (a.k.a., the “time
horizon” problem)
 How to set risk limits for income-based IRR
All of these questions are relevant when
designing income-based risk measures
Page 4
Deterministic vs. Stochastic
Three Dimensions of IRR Measurement Methodologies
Time Horizon
Both value and income based
IRR measures can be
categorized using this three
dimensional framework
Page 5
Three Dimensions of IRR Measurement Methodologies
Income
Based
Deterministic
Stochastic
Existing Business Only
Existing + New
Business
Value
Based
Deterministic
Existing Business
Only
Stochastic
12 – 18
Months
Short Term
18 Months
to ~5 Yrs
360
Months
Medium Term
Long Term
-------Time Horizon -----Page 6
Income Based IRR Measurement Methodologies
Existing Business
Only
Deterministic
Income Based IRR
Existing + New
Business
Stochastic
12 – 18
Months
18 Months to
5 Yrs
360
Months
Income Based
Methodologies
Time Horizon
Existing & New
Business Treatment
Existing Only
Short Term:
12 - 18 Months
Existing + New
Existing Only
Medium Term:
18 Months - 5 Yrs
Existing + New
Page 7
Rate Generation Technique
Deterministic
Stochastic
Classification of Income Based IRR Measurement
Time Horizon
Existing & New
Business
Existing Only
Rate Generation Technique
Deterministic
Stochastic
Comment
Not Useful
Time horizon too short.
Stochastic rate distributions
limited by time frame
EaR
Not Useful
EaR: standard in the industry.
Stochastic solution: same as
above
Citicorp's
SMEAR
Potentially Very
Useful, but
Complicated &
Costly
Deterministic: easy to produce
in vendor ALM models.
Stochastic solution is not
available without proprietary
model
Not Useful
Short Term:
12 - 18 Months
Existing + New
Existing Only
Medium Term:
18 Months - 5 Yrs
Assumption
Existing + New Dependent, Not
Useful
Page 8
Assumption
Modeling new business
Dependent, Not sensitivities over long horizons
Useful
is very assumption intense
Classification of Useful Income Based IRR Measurement
Time Horizon
Existing & New
Business
Rate Generation Technique
Deterministic
Stochastic
Existing Only
Not Useful
Not Useful
Existing + New
EaR
Not Useful
Existing Only
Citicorp's SMEAR
Potentially Very
Useful, but
Complicated & Costly
Existing + New
Assumption
Dependent, Not
Useful
Assumption
Dependent, Not
Useful
Short Term:
12 - 18 Months
Medium Term:
18 Months - 5 Yrs
Opinion: There are only three approaches to measuring income
at risk that offer risk managers much value added and one of
them is very complicated and beyond the capabilities of vendor
based ALM systems.
Page 9
Opportunities and Challenges of Stochastic Income Measures
Opportunities

When used with the right software it’s the only
methodology available to optimize hedges when hedging
from both a value and income perspectives using
stochastic methodologies

For portfolios where value and income accounting are
aligned then the potential issues are minimized
o When mortgage bankers use value based stochastic risk
measurement tools they are also approximately hedging
income
Page 10
Opportunities and Challenges of Stochastic Income
Measures
Challenges

Difficult to compute:
o New business equations are difficult to specify and results are
sensitive to these assumptions
o Excluding new business helps, but in private sector defining
new business for core deposits is assumption intense
o Most balance sheets requires two yield curves to simulate
unless basis risk is ignored or work-arounds applied
o Resource intensive to get a credible measure

Not easy to produce a validated measure in QRM.
o BW’s stochastic model is inferior
Page 11

Not available in trading models with superior stochastic
engines that focus on value based risk measurement

Not aware of any commercial bank that is using stochastic
income for hedge design.
Opportunities and Challenges of EAR
Opportunities
Page 12

Scenarios can be predefined shocks of almost any form or
“what if” scenarios

ALM models are built for this type of analysis

Very useful for short term analyses

Easy to understand and communicate results

Risk attributes can be computed
Opportunities and Challenges of EaR
Challenges

Can be misused
o ALM models allow targeting balancing procedures, which can
mask risk
o Limited time horizon for analysis allows risks to be pushed
“beyond the radar”
o Hedging transactions often beyond the time horizon
Page 13

Not useful for assessing long term and strategic risks

Risk limits are not applicable when new business
sensitivities are included

Many users do not know how to decompose risk into
characteristics and can generate “non-actionable” results
Citicorp’s Risk Measurement Challenge ~ 1987
Background:
Page 14

7 retail banks, 3 thrifts, a mortgage bank, and large
credit cards businesses with decentralized management
structure

Corporate management was concerned that smaller
thrifts could take a risk position and bankrupt the
corporation

Perceived need to develop common, understandable,
and actionable risk management metrics and language
across multiple management units

Requirement to understand risk of the combined units

Risk measures were needed to limit risk in a way that
could not be “gamed” by new business assumptions

No vendor solutions were available
Citicorp’s Solution
Page 15

“SMEAR”: Spot Measure of Earnings-at-Risk

Designed by Gary Lachmund, former President of
National Asset Liability Management Association (NALMA)
and then head of ALM at Citibank

Originally developed proprietary model in-house;
Can (now) be easily generated in vendor ALM models

Complementary to analyses of risk that do include new
business sensitivities

Addresses several of the issues relevant to measuring
income-based IRR in the FHLBs by the regulators

Was utilized to limit risk of short- and long-term earnings
sensitivity
Application to FHLBs and FHFB

Provides method a solution to “time horizon problem”

Easily produced in BancWare and QRM

Measures are complementary to income-based risk
measures that include new business

Potential to creates common methodology across 12
regulated banks so risk measures can be consolidated and
compared
o Regulators can measure position of system
o Regulators can rank order positions of individual FHLBs
Page 16
SMEAR Procedures

Start with current balance sheet

Shock interest rates instantaneously, by multiple
increments
 May use flat rates or forwards, but forwards are preferred
 Key: all rates shocked same amount*

Run-off balances based on contractual maturity- or
model-based prepayment in each scenario

As balances run-off replace with overnight funding or
placements (a.k.a. the balancing item) at scenariodependent rate

For repricing assets and liabilities reprice according to
contractual rules

Allow no new business
* i.e parallel shocks; This assumption eliminates repricing effects from analysis
Page 17
SMEAR Procedures

Treat equity as an indefinite term maturity item

Compute “Pretax Rate Sensitive Earnings” (PRSE) in each
scenario and as many time periods as relevant
 This allows for fee income, direct expenses, and gains-on-sale
 Generates a matrix of solutions for each time period and shock
 Calculate differences in each time period relative to the base case
Page 18

Calculate differences in each time period relative to the
base case

Graph the calculated differences
Definition of Income Applicable to an FHLB Risk Measure
Income measure = the net revenues in each time period associated
with the book of existing business (i.e. “the risks you already own”) or
“NII associated with Existing Book of Business” (NII-EBS)
 Gains-on-sale are not currently a component of income sensitivity
 Since this measure explicitly excludes net revenues associated with
new business, it does not fall into one of the standard income
definitions
 FHLBs have derivatives that do not qualify for hedge accounting. The
NII-EBS incorporates these obligations by calculating net cash flow
differences as their contribution to the income-based risk measure
 In order to accommodate a GAAP earnings measure, market value
sensitivity of derivative instruments not qualified for hedge
accounting treatment can be added back into the analysis separately
Using a blend of market-value accounting
and accrual accounting in an income-based
risk measure can lead to non-economic risk
management decisions
Page 19
Notes on Long Term Earnings at Risk Measure
1) Focus is on “the risks you own” (certainty) vs. risks you
only incur over time in an uncertain future
2) Ignorance of long term earnings effects can lead to risk
positions that increase longer-term exposures that are off
the radar screen
3) Market value sensitivity analyses is not a substitute for
longer term earnings exposures
Page 20
SMEAR Calculation Steps
Step 1: Calculate NII-EBS in each period for the base (“expected” or
forward curve) scenario and for each “rate shock” (or
“stress”) scenario
Step 2: Subtract NII-EBS shock scenario values from those of the
FWD case
Step 3: Plot the value changes for each stress scenario
Step 4: Connect the dots
Page 21
SMEAR Example: Income-Based Simulation Results
Scenario
D200
D100
FWD
U100
U200
U300
U400
U500
Year 1
119
139
150
117
133
159
192
223
NII-EBS ($M)
Year 2
-9
67
135
114
121
134
146
160
Year 3
-47
37
120
125
159
200
241
280
Step 1: Calculate NII-EBS in each scenario and period
Page 22
Transforming Income Simulations to Risk Measures
Scenario
D200
D100
FWD
U100
U200
U300
U400
U500
Year 1
-31
-11
0
-33
-17
9
42
73
D NII-EBS ($M)
Year 2
Year 3
-144
-167
-68
-83
0
0
-21
5
-14
39
-1
80
11
121
25
160
Step 2: Calculate DNII-EBS relative to FWD case
Page 23
Transforming Income Simulations to Risk Measures
Income Based IRR
200
D NII-EBS ($M)
150
100
50
`
(50)
Yr 1
Rate Shock
Yr 2
Step 3: Plot the relative values
Page 24
Yr 3
U500
U400
U300
U200
U100
D100
D200
(150)
FWD
(100)
Transforming Income Simulations to Risk Measures
Total Income-Based IRR
200
D NII-EBS ($M)
150
100
50
(50)
`
(100)
Rate Shock
Yr 1
Yr 2
Step 4: Connect the dots
Page 25
Yr 3
U500
U400
U300
U200
FWD
D100
D200
(200)
U100
(150)
Summary
So far:
 We’ve transformed tables to graphs.
 We’ve extended the time horizon for income-based risk analyses.
 Time horizon can be extended as far into the future as needed for controlling
longer term earnings sensitivity associated with the existing balance sheet.
 Number of shocks can be added so that a broader range of rate shocks is applied
as the time horizon is extended
Page 26
Further Applications
Further Application can Extend the Benefits of the SMEAR
Risk Measurement Technique:
 Application I:
Risk limits in the SMEAR framework
 Application II: Decomposition of risk
 Application III: Ability to assess value-based hedging on
income-based IRR
 Application IV: Regulatory
Page 27
SMEAR RISK LIMIT FRAMEWORK: Application I
Risk Limits - Year 1
75
D NII-EBS ($M)
50
25
(25)
`
U500
U400
U300
U200
U100
D100
D200
(75)
FWD
(50)
Rate Shock


Width of rate shock band can be linked to observed market
volatilities
Size of rate shock may vary with the direction of shock if view is
that rates are approximately log-normally distributed.
 Note that the width of the limit is no longer tied to the exact rate
shock used in the calculations.

Page 28
Risk limit may vary by time period or direction of shock due to
expected offsets in new business activity
SMEAR RISK LIMIT FRAMEWORK: Application I
Risk Limits - Year 3
Risk Limits - Year 2
150
50
D NII-EBS ($M)
D NII-EBS ($M)
100
-
(50)
X
`
(100)
50
(50)
X
(100)
`
Rate Shock
Limit violation marked in “X” occurs when line intersects the
bottom of the SMEAR “limit box”

Size of shock utilized in limit increases with time, as does size of
limit

Income limits in future periods typically become less restrictive because
opportunities exist to mitigate the risk
Citicorp limits were invoked out to Year 10, requiring a broader
range of rate shocks than shown
U500
U400
U300
U200
FWD
D100
D200
U500
U400
U300
U100
Rate Shock


Page 29
U200
(200)
U100
D100
D200
(150)
FWD
(150)
Challenge: “Rates Don’t Move in Parallel Shocks”

Citicorp limits were invoked out to Year 10, requiring a broader
range of rate shocks than shown

The purpose of risk measurement and a risk limit system is to
guide risk management actions

“Actionable understanding” is critical. Graphical framework
translates to a visual picture of risk components and points the
way to managing risk

The actual number used to limit risk is a proxy and shouldn’t be
equated with “what if” analyses

Setting of size of actual risk limit in each case (defined by direction
of shock and time) is critical component of system.
 The limits should take account of evolution of new business but not
the evolution of new interest rate risk positions
Page 30
Decomposing income-based Risk Measure: Application II
Why Decompose income-based IRR?
Decomposition of income-based IRR is a:
1) Risk communication tool, because portfolio composition effects
are difficult for the non-technical audience to comprehend (e.g.,
some members of ALCOs)
2) Risk measurement validation tool, because specific risk
measures can be ascribed to individual product characteristics and
errors can frequently (but not always) be seen
3) Risk education tool, because it reduces the complexity associated
with understanding complex risk characteristics and, therefore,
builds broader understanding of the complexity risk management
among treasury and non-treasury professional staff
Page 31
Decomposing income-based Risk Measure: Approach
 With instantaneous parallel rate shocks income-based risk can be
decomposed into:
 Repricing Risk: caused by mismatches in the repricing characteristics of
assets and liabilities already on the balance sheet; and
 Option Risk: caused by the options embedded in the structures of financial
instruments (e.g., prepayment, calls, and puts)
 Basis Risk can be added to option and repricing risk by shocking
the CO curve by a different amount than the LIBOR curve and
adding the results to those generated with parallel shocks
Page 32
Decomposing income-based Risk Measure: Approach
 Yield Curve Risk is directly calculated from product-level
decompositions of option risk.
 Whereas, basis risk can be added to the other risk calculations in the
SMEAR framework, total calculated yield curve risk is partially
duplicative and cannot be added
 Repricing risk component of yield curve risk has already been
calculated by shocking interest rates
 Missing component is options related effects which can be discerned
at the product level
 If desired, income limits can be applied to options risks directly
Page 33
Decomposing the Income-Based IRR Measure: Example
Total Income Based IRR
200
D NII-EBS ($M)
150
Total IRR
100
50
=
(50)
`
(100)
U500
U400
U300
U200
FWD
D100
D200
(200)
U100
(150)
Rate Shock
Yr 1
Yr 2
Yr 3
Repricing Risk
Options Risk
Total Option Risk incl Swaptions
200
150
150
100
100
50
-
+
(50)
`
(100)
(150)
D NII-EBS ($M)
50
(50)
`
(100)
Rate Shock
Yr 1
Page 34
Yr 2
Rate Shock
Yr 3
Yr 1
Total IRR = Repricing Risk + Options Risk
Yr 2
Yr 3
U500
U400
U300
U200
U100
FWD
(200)
D100
U500
U400
U300
U200
FWD
D100
D200
(200)
U100
(150)
D200
D NII - EBS ($M)
Total Repricing Risk incl Non Cancelable Swaps
200
Decomposing income-based IRR: Repricing Risk
Repricing Risk
 Repricing risk is the sum of the “implicit” repricing exposures on each
product type. However, it can be calculated at any level of
aggregation, including the entire balance sheet.
 Aggregate measure of repricing risk includes equity.
 Repricing risk is best viewed at the balance sheet level. Focusing on
offsets at the product level can introduce undesired noise at the
balance sheet level.
 When rates are shocked by equal amounts, repricing risk is “linear” in
the risk graphs
 Since fix-pay (or fix-receive) swap risk profiles are also linear, the
mitigating transactions that reduce pricing risk can be easily identified
and calculated.
Swaps can be designed to be almost “perfect”
hedges of measured repricing or “Gap” risk
Page 35
Decomposing income-based IRR: Option Risk
Option Risk
 Option risk is the sum of the options exposures associated with each
product.
 It can be calculated at the aggregate level by subtracting repricing risk
from total risk. However, graphical representations of options risk can
be complicated when more than one type of option is present.
 Options risks are best hedged with options, although options exposures
are frequently partially hedged with swaps
 Measurement of options related risks are highly model sensitive
because the exact conditions determining when the option is exercised
are often based on specific modeling assumptions.
Whereas repricing risk is best analyzed at the
balance sheet level, options risk is better
understood at the product level.
Page 36
Decomposing income-based IRR: Option Risk
Identified Embedded Options in the Illustrative FHLB Balance Sheet
Product
Option
Agencies
CMO
MBS
MPP
Cancelable Advances
Cancelable COs
Cancelable Swap (Adv)
Cancelable Swap (CO)
Swaptions
Caps
Call
Prepay
Prepay
Prepay
Call
Call
Put
Put
Call
Cap
Bank's
Position
Short
Short
Short
Short
Long
Long
Short
Short
Long
Long
Size ($B)
12.0
6.2
0.2
9.5
5.4
15.7
1.0
6.3
1.0
0.5
Total Option Risk equals the sum
of options risks embedded in all
products and derivative instruments
Page 37
Decomposing income-based IRR: Option Risk
Classification Scheme for Graphs to Follow
Option Classification
Callable Agency
Mortgage Prepayment
Cancelable Advances Cancelable COs
Cancelable Swaps
Swaptions & Caps
Page 38
Decomposing Options Risk : Prepayment and Call Risk
Callable Agencies ex Repricing Risk
Mortgage Prepayment Risk
50
50
(150)
Yr 2
Yr 3
Yr 1
Yr 2
Yr 3
Callable Agencies have no extension risk, unless
they are expected to be called in the Forward Rate
shock.
Mortgages have extension risk as prepayment
speeds slow relative to those modeled in the Forward
Rate shock.
Note: Graphs are not drawn on same scale.
Page 39
U500
U400
Rate Shock
Rate Shock
Yr 1
U300
U500
U400
D100
U300
(250)
U200
(100)
U100
(200)
FWD
(75)
`
U200
`
U100
(50)
(100)
FWD
(25)
(50)
D100
-
D200
D NII-EBS ($M)
-
D200
D NII-EBS ($M)
25
Decomposing Options Risk: Cancelable Advances & COs
Cancelable Advances ex Repricing
Risk
200
150
150
D NII-EBS ($M)
100
50
-
100
50
-
Rate Shock
Yr 1
Yr 2
Rate Shock
Yr 3
Yr 1
Yr 2
Yr 3
Cancellation features in CO portfolios raise the
average coupon in lower rate levels. In turn, this
raises income relative to the forward scenario. In the
illustrative balance sheet the CO portfolio was far
larger than the Advances portfolio.
Page 40
U500
U400
U300
U200
FWD
D100
(50)
D200
U500
U400
U300
`
U200
FWD
D100
D200
U100
`
(50)
U100
D NII-EBS ($M)
Cancelable and Putable COs ex Repricing
Risk
200
Decomposing Options Risk: Derivatives with Options
Cancelable Swaps
D NII-EBS ($M)
(50)
(100)
25
-
(25)
Yr 2
Yr 3
Yr 1
Yr 2
Yr 3
Swaptions include options to purchase fixed
receive as well as fixed pay swaps. There is
greater prevalence of cancelable swaps than
swaptions and caps observed on FHLB balance
sheets.
Note: Graphs are not drawn on same scale.
Page 41
U500
U400
U300
Rate Shock
Rate Shock
Yr 1
U200
FWD
D100
D200
U500
U400
U300
FWD
D100
D200
U200
(50)
(200)
U100
`
`
U100
D NII-EBS ($M)
-
(150)
Swaptions and Caps
50
50
Decomposing income-based IRR: Option Risk
Callable Agencies ex Repricing Risk
Mortgage Prepayment Risk
50
50
Rate Shock
150
Yr 1
100
Yr 1
200
100
50
-
U500
U500
Yr 1
Yr 2
Yr 3
Swaptions and Caps
50
D NII-EBS ($M)
(50)
(100)
25
-
(25)
FWD
D100
D200
U500
U400
U300
FWD
D100
D200
U200
(50)
(200)
Rate Shock
Yr 1
Yr 2
U300
`
`
U200
(150)
U100
D NII-EBS ($M)
U400
Yr 3
-
Page 42
U300
Rate Shock
Yr 2
Cancelable Swaps
50
U400
Rate Shock
Yr 1
U200
FWD
D200
D100
(50)
U500
U400
U300
`
U200
D100
FWD
(50)
U100
Yr 3
U500
50
-
D200
Yr 2
U400
100
`
Yr 1
Yr 3
150
150
Rate Shock
Yr 2
Cancelable and Putable COs ex Repricing
Risk
U100
U500
U400
U300
U200
FWD
D100
D200
(200)
U100
(150)
Yr 3
D NII-EBS ($M)
`
D NII-EBS ($M)
=
(50)
(100)
Yr 2
Cancelable Advances ex Repricing
Risk
200
-
Rate Shock
U100
50
U300
`
D200
U500
U400
U200
D100
U300
(250)
U100
(100)
FWD
(200)
200
D NII-EBS ($M)
(150)
U200
`
(75)
U100
(50)
(100)
FWD
(25)
(50)
D100
-
D200
Total Option Risk incl Swaptions
-
D NII-EBS ($M)
D NII-EBS ($M)
25
Rate Shock
Yr 3
Yr 1
Yr 2
Yr 3
Value vs. income-based IRR Hedging: Application III
Background
 Bank of America built a stochastic interest rate model that
calculated both income and economic value simultaneously. The
model incorporated consistent simulation of two yield curves
(Treasury and LIBOR)
 An optimizer was constructed to find hedges that minimized both value-based
and income-based risk measures
 A trade-off was calculated
 Given senior management input on preferences for minimizing variances of
value and income over time, an optimal hedge solution was calculated
 Several FHLBs are designing hedges focused exclusively on valuebased IRR measures and have asked:
 How will value-based hedges impact income-based IRR measures?
 What methodology can be employed to measure the impact of value-based
hedges on income-based IRR measures?
Page 43
Value vs. income-based IRR Hedging: Application III
Considerations and an Approach using SMEAR
 The income-based risk measure that most coincides conceptually to
the value-based risk measure includes long-term earnings and
excludes new business
 Bank America findings from hedging from both perspectives:
 The size of hedge adjustments varied by product
 Adjustments could be thought of as duration neutral adjustments to the
cash flow timing
 Significant improvement to reducing earnings variances that did not
sacrifice value based risk measure could be determined by trial and
error
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Value vs. income-based IRR Hedging: Application III
Simple SMEAR Test on Current FHLB Positions
 Calculate the SMEAR risk in two subsequent time periods
 Use risk measures in each period to evaluate the effects of value
based risk measures on income at risk
 Subtract the risk measures
 This is called “Delta SMEAR”
 Use Delta SMEAR to evaluate the stability of the value based hedge
in term of income based risk
 Iterate the process and adjust the hedges accordingly
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Regulatory Extensions and Applications: Application IV
Regulators Need an Income-Based IRR Methodology that Can Be:
 Applied consistently across 12 independently managed FHLBs
 Used to assess risk at each bank as well as all banks
 Used to assess relative risk of 12 banks
 Used to limit risk at individual banks
 Regulatory limits can be set relative to individual FHLB’s “real” capital
 Total risk of 12 FHLBs can be limited and limits can be allocated
 Produced with minimum additional effort, utilizing QRM or
BancWare models
 Used in conjunction with FHLBs other risk measures
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Contact Information
ALCO Partners, LLC
15 Fairway Drive, Novato CA 94949
Mike Arnold, Principal
(415) 382-1263
[email protected]
Bruce Campbell, Principal (949) 715-0944 [email protected]
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