Risk Management lessons learned from financial crisis

Risk Management lessons learned from
financial crisis.

Evan Picoult
June 16, 2011

Managing Director
Risk Architecture
Citi

Adjunct Professor
Decisions, Risk and Operations Department
Columbia Business School

These slides represent my own views.
I am not speaking for either Citigroup or Columbia University.

1

Contents Page
The housing bubble, some underlying causes and some
2
consequences of its bursting.

Some failures in risk management at financial institutions

Securitization and errors of ratings and risk assessment. 12

Market Risk measurement errors 23

Groupthink and management structural errors 30

Some solutions

Integrated Risk Management 34

Enhanced measurement of market risk in trading book 37

2

Mortgage debt grew dramatically until the housing bubble burst.

Next page

⇐ 34 years: 1975 – 2009 ⇒

• Observe the enormous growth in the annual increase in household mortgage debt in the
ten year period 1996 to 2006.

• The annual change in commercial mortgage debt also increased, primarily in the period
2004 to 2007.

3 3

. . . which was accompanied by a material increase in mortgage securitization

• Annual Issuance of US
debt securities
o Look at growth of
issuance of mortgage
related securities after
1996 through 2007 and
large growth of issuance
of US Treasury debt
after 2008.

• Outstanding US debt
securities
o Look at growth of
issuance of mortgage
related securities after
from 1996 to 2007 and
the large growth of
outstanding US Treasury
debt after 2008.

4 4

. . . primarily with agency (i.e. GSE) default insurance.

• Drill down to see some
change in relative
composition of Agency vs.
Non-Agency mortgage
related securities issued
each year.

• “Agency” means conforming
to standards of GSE’s (e.g.
Fannie Mae, Freddy Mac) and
with default insurance from
these entities.

5 5

This led to and was fed by a dramatic increase in housing prices, until the
housing bubble burst

S&P / Case-Shiller Home Price Indices ®
:
• 1987-current
• 10 City Composite index

Data downloaded from:
www.standardandpoors.com
• Click on Indices
• Click on S&P/Case-Shiller ®

• Annual percentage change of index=

(Index(t) - Index(t - 12) )
=
Index(t - 12)

Light Green Rectangle =
U.S. Economic Recession

6

The nation-wide increase in housing prices from 1995-2005 was
unprecedented in the context of 115 years of prior history

• Copied from
Barron’s June 2005
article on Prof.
Shiller of Yale:

“Despite what Alan
Greenspan says, there's
a huge housing bubble,
argues Yale economist
Robert Shiller, that
gradually could push
real prices down 50%
after it bursts. Why he's
worth listening to.”

7

Contributing underlying causes of financial crisis

• Macroeconomic policies were an underlying cause of the housing
bubble
– Low level of interest rates
– Federal housing policy

• Securitization facilitated and was a cause of the dramatic increase in
residential mortgages:
– It removed loans from originators balance sheets, freeing up capital to support
more loans.
– Investment banks that earned a fee from securitization wanted more raw material
to securitize; increasing demand to buy mortgages from originators.
– Some unscrupulous originators abandoned underwriting standards and did not
care about fraudulent assertions of borrowers.
– The process increased systemic risk by adding complexity and decreasing the
transparency of the assets the market invested in.

• This process was a positive feedback loop, facilitated by easy credit,
creating a dangerous housing bubble.

8

Context: US Housing Prices (from S&P/Case-Shiller Indices ®) and Fed Funds Rate

• Fed Funds Rate: 1987 - 2010

Federal Reserve – “Greenspan Put”
• Material easing of monetary policy
after each economic downturn laid
foundation for next bubble.

• Annual percentage change of
S&P/Case-Shiller Index: 1987-2010

9

National Housing Policy
National Housing Policy

• Republican and Democratic administrations pushed for “affordable housing”.

• “Fannie Mae” and “Freddie Mac”
– Nominally private firms with implicit government guarantee.
– Could borrow very inexpensively, buy mortgages from banks and then
securitize them and sell the securities with a guarantee against default. This
lowered cost and increased demand for homes, thereby increasing home
prices.
– In recent years began to make investments in “sub-prime” mortgages.
– Had very little capital relative to riskiness of assets.
– Highly politicized.

• “Community Reinvestment Act”
– Required banks to lend to borrowers in poorer communities.

10

The bursting of the housing bubble led to large losses in the market value of
subprime CDOs, which led to the failure of Bear and Lehman . . .

– Some events of crisis of 2007-2008
– 4Q 2007:
» Merrill Lynch, Citi, UBS and other banks report enormous trading losses caused by retained holdings of
subprime CDO tranches.
» Off-balance assets in SIVs are brought onto the balance sheet because of investor fears over SIV
solvency
– 1H 2008
» Bear Stearns is unable to fund its liabilities because of concerns about its solvency and is sold to JPM at a
steep discount to its recent market price (see House of Cards and, Street Fighters).
– September, 2008
» Sept 7: - GSEs (Fanny and Freddie) are explicitly nationalized.
» Sept 14: - Lehman collapses. Merrill Lynch sold to BofA
» Sept 15: - Lehman files for bankruptcy
» Sept 16: - Moody’s and S&P downgrade AIG. As consequence AIG is contractually obligated to post
around $18 billion in margin for the CDS swaps it had previously sold.
Read
Too Big To Fail - Reserve Primary Fund “breaks the buck” because of the amount of Lehman paper it owns.
This causes a run (i.e. large net withdrawals) on money market mutual funds, which
causes collapse in the commercial paper market. (a key source of liquidity to many firms)
» Sept 17: - Fed lends AIG $85 billion to avoid insolvency and default.
» etc., etc.

Which caused a drying up of trading and funding liquidity
– Which caused credit spreads to materially widen, particularly after Lehman defaulted

11

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Some solutions



12

Securitization and errors
Securitization enormously facilitated and contributed to the growth of housing bubble.
•Securitization
– In low yield environment, investors looked for higher returns. There was a
demand for securitization tranches. This led to demand by banks with
securitization desks for more residential mortgages to securitize.
– The demand for mortgages to securitize enabled some lenders to lower their
lending standards, because they would not be stuck holding the defaulted
loans.
– Many investors in securitization assumed housing prices would continue to
increase (or could not fall by much) and that there would be little risk of loss.
They took ratings at face value and did not dig deeper into changing
underwriting standards or think about the causes or consequences of an
unprecedented housing bubble.
– Material errors by:
• Rating agencies
• Banks which had large securitization desks and kept the “Super-Senior” CDO tranches
because they actually believed they were essentially risk free.
• Investors who bought these tranches.

– Not all firms or investors made these errors.
• See The Big Short, by Michael Lewis. Also John Paulson’s fund.
• Some banks, JPM and GS, had excellent risk management/senior management processes
to avoid catastrophic losses. 13

Securitization
• Simple example
– This is a complex topic, with many subtleties.
– Here is a simple picture of some of the essential cash flows of the cash securitization of bank assets.

Assets of SPE may be:
• Bank continues to service loans after • Originator and Administrator of
• Residential mortgages
selling them to SPE. It passes interest SPE receive fees.
• Commercial mortgages
and principal payments to SPE.
• The Originator and • Credit card receivables
• Bank earns a servicing fee for this. Administrator might be initial • Corporate loans
bank, another commercial bank • Bonds
• Mortgage Servicing Rights (MSR) is an
or an investment bank. • Tranches of other securitization
asset on bank’s balance sheet.
• etc.

Initial Bank Special Purpose Entity
(SPE)
Buy securities that
Assets Liabilities Assets Liabilities are issued by the
SPE SPE funds SPE Investor
Asset 1 purchases itself by
Debt assets issuing
Asset 2
securities Investor
...
Asset n Equity Receive cash flows ...
of principal and
Interest on Investor
securities issued
• Bank sells assets
by SPE
to SPE, from
which it receives
payment.

14

Securitization: Tranching
Definition:
– Tranching is a form of securitization in which the liabilities that fund the assets of the SPE are
assigned a hierarchical ranking to claims on the generated cash flows. Credit defaults of the
underlying assets are first absorbed by the lowest rated tranche, then by the next lowest rated
tranche, etc.
– Tranche is French for “slice”.

Hypothetical Example (with some crude assumptions)
– Assets of SPE = $1,000mm:
• 1,000 corporate bonds, par amount = $1mm each.
• Average rating of B+ (with an associated annual probability of default of 5.6% )

– Liabilities of SPE = $1,000mm:

Tranche Amount Pct. Of Subordination (%) Rating Annualized Prob
$mm Deal of Default
“Equity” Tranche 72 7.2% None CCC 22.0%
Tranche 2 13 1.3% 7.2% BB 1.87%
Tranche 1 915 91.5% 8.5% AAA 0.01%

• How is this possible?
• How does a portfolio of B+ bonds get transformed into more highly rated securities?
• What assumptions underlie the ratings of this structure?

15

Tranching and waterflow analogy
1. The individual who owns the home continues to deal with the bank
Assets Liabilities
Special Purpose Entity that he borrowed from, sending the bank monthly principal and
Mortgage_1 AAA Tranche interest payments on the mortgage.
Mortgage_2 BBB Tranche
2. The bank(s) that originate the mortgages but sold them to the SPE,
Mortgage_3 Equity Tranche
continue to service the mortgage for a fee.
....
Mortgage_1,000 3. The originating bank(s) sends the cash flows from the mortgages to
the bank that administers the SPE.

$$ Cash Hypothetical attachment and
inflows detachment points
from
Tranche
mortgages Rating
This illustration switches the
underlying assets from
corporate bonds to
0%
mortgages – but the
fundamental points are the Equity Tranche
7.1%
same. BBB Tranche
8.5%

Pool of monthly cash flows
from underlying assets of
SPE (i.e. from the pool of AAA Tranche
mortgages)

100%

16

Cumulative loss distribution of portfolio and thickness of tranches
• Basis for tranching and thickness of each tranche:
The following simple example assumes bond defaults are independent, which is unrealistic. We also will assume
recovery in the event of default is zero (which is also unrealistic).

• This is simply part of the • This is simply part
binomial distribution, with of the cumulative
p = 5.6% binomial
distribution, with p
• The full distribution would = 5.6%
extend the X axis to 1,000.

Equity BB AAA
Tranche Tr. Tranche

Truncate graph at 300 Truncate graph at 100

• The key lesson is that given the assumed cumulative loss distribution, the manager who structures
the tranches can determine the width of each tranche required for a specific credit rating for the
tranche. • The expected magnitude of
each of these quantities is
• The cumulative loss distribution rest on assumptions about: conditioned by the expected
• Probability of default of underlying assets. state of the economy.

• Correlation of default of underlying assets. • Especially for real estate, which
historically is cyclical, one
• Distribution of loss given default (LGD) of underlying assets. needs to look over a very long
time to estimate the range of
future scenarios.
17

Importance of assumption about correlation of default.
Expected Loss vs. Risk
• Consider two portfolios, each with 1,000 bonds, such that each bond has a default
probability of 5.6%.

• Assume two different correlations of default:
• The bonds in Portfolio 1 have a correlation of default of 0%
• The bonds in Portfolio 2 have a correlation of default of 100%

Correlation = 0% Correlation = 100%
Expected number of defaults 56 56
Likelihood of 1,000 defaults 0% 5.6%

• Under the assumption of correlation = 0%, we were able to assign a AAA rating to 91.5% of the securities
– because the likelihood of a loss on more than 85 securities was less than 1bp.

• Under correlation =0% the likelihood of more than 999 securities defaulting (i.e. likelihood of 1,000
defaults), is zero.

• However, if the correlation were 100%, the likelihood of more than 999 securities defaulting is now 5.6%.
There is no diversification benefit. All tranches should be assigned a rating of B+

18

Subprime CDOs: Two levels of securitization and tranching
• The assets of Subprime CDOs are BBB or A tranches of ABSs.
Original subprime First level of Second level of
mortgages securitization securitization

Asset Backed
“CDO” is both the name of the
Security 1
special purpose entity that buys
Sub-prime mortgage
Assets Liabilities debt securities as well as the
name of the securities it issues
Sub-prime mortgage Sub-prime Senior
Mortgages
CDO
... Mezzanine BBB tranche
Subprime CDO securities

Sub-prime mortgage Equity
Assets Liabilities

Asset Backed Super AAA +

Security 2 Senior

Sub-prime mortgage Assets Liabilities AAA AAA+
BBB
... Sub-prime Senior
tranches AA AA
Mortgages
Sub-prime mortgage Mezzanine BBB tranche of Asset
Backed A A
Equity
Securities
. ...
.
. Equity
Asset Backed
Security N Equity
Sub-prime mortgage
Assets Liabilities
... Sub-prime Senior
Mortgages Valuation of riskiness of CDO
Sub-prime mortgage Mezzanine BBB tranche
tranches critically depends on
Equity assumptions about riskiness and
correlation of underlying assets

19

Subprime CDO Waterflow analogy
Underlying Pools of cash
pools of flows for each Pools of
mortgages Asset Backed cash flows
SPE
for each
CDPO SPE Cash flows
… to
tranches
ABS
ABS 1 BBB tranche of CDO
SPE
#1 Equity tranche

BBB tranche
CDO
SPE
… ABS
ABS 2 BBB tranche
SPE
AAA tranche
#2

• In 2007-2008, Citi and Merrill Lynch created a huge
volume of CDOs on mortgages, including sub-prime
… ABS ABS 3 BBB tranche mortgages.
SPE • For sub-prime mortgages, they found buyers for the
#3 riskiest tranches (which paid a high return) but not for
the least risky tranche, which paid a very low return.

... • They retained these “super-senior” subprime tranches
in inventory, in every increasing volumes until late 2007
when things blew up.

… ABS
ABS N BBB tranche
• Merrill had purchased Credit Default Swaps (CDOs) on
SPE the tranches they retained from AIG, until AIG decided
#N not to sell any more.

20

Securitization model errors
• Errors at rating agencies
– Sub-Prime CDO model assumed constituent assets of CDO (the first level
securitization BBB tranches below) had relatively week correlation; the same
correlation as corporate bonds.
– In fact, most of the diversification of idiosyncratic risk is obtained by the first level
of securitization. Consequently, these first level securities have a high degree of
systemic risk.

– The analysis of the
risk of these tranches
required measuring
their value under
systemic stress
conditions.

21

Securitization: Tranching and portfolio diversification

• Correlation and assumptions about portfolio diversification
– The key to understanding the economic value of tranching are the concepts of
correlation and portfolio diversification.
– A mis-estimation of portfolio diversification is one of the principal errors that
rating agencies and banks made with regard to subprime CDOs.

• Error of ignoring context and mechanically estimating the future from
the past
– Another error was to look blindly at only the recent history of housing prices and
default rates to estimate the future change in prices, default rates and
correlations, without asking if the context had changed: e.g. if underwriting
standards had changed, if there was an unprecendented increase in housing
prices.
– It was an error to have examined only the historic DECREASE in housing prices
nationwide since the 1930s without also examining if the recent INCREASE in
housing prices had been unprecedented, as per Shiller’s analysis.
– Before one mechanically looks at history to estimate what may happen in the
future one needs to ask if anything has fundamentally changed, in order to know
whether the past may be a guide to the future.
22

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Some solutions



23

Errors in market risk measurement
• Trading book losses at many large banks greatly exceeded the bank’s
measurement of internal economic capital for trading risk and the
regulatory measure of capital for trading.
– The losses were particularly large for sub-prime CDO tranches
– More generally, losses of credit sensitive portfolios exceeded risk model
estimations because of the dramatic increase in credit spreads and the drying up
of trading liquidity.

• Many banks estimated their economic risk by annualizing a daily VAR.
– This ignored the material changes in correlation and volatility that could occur
during an economic crisis:
• Annualized VAR is based on assumption of the distribution of daily P/L being i.i.d..
However during a crisis:
– Changes in systemic factors dominate the changes in market rates, materially increasing
correlations within asset classes.
– Serial correlation of daily changes in market rates materially increased – e.g. credit spreads
widening almost daily.

– . . . which were driven by deleveraging and the subsequent drying up of trading
and funding liquidity.

• Most risk models and many risk managers ignored the potential for
and the consequence of trading liquidity risk drying up and for a large
systemic changes in volatilities and correlations of market rates. .
24

Consequences of crisis

• Positive feedback from deleveraging led to widening of credit spreads
and the drying up of trading liquidity
– Leveraged firms had to meet margin calls on assets that were funded through
short-term securities financing.
– To meet margin calls leveraged firms had to sell assets
– This depressed asset prices, leading to more margin calls and more asset sales.
– This became a positive feedback loop, leading to ever decreasing prices, wider
spreads, reduced demand and a drying up of trading liquidity.

• Because of the uncertainty on the value of assets, and the riskiness of
banks, funding costs also materially increased as funding liquidity
dried up.

25

The Two Fundamental Risks to Financial Institutions
• Financial Institutions face two fundamental, inter-related risks:
– Insolvency:
• The condition in which the firm’s assets < firm’s liabilities.

– Illiquidity:
• There are two related forms of Liquidity Risk; one is focused on the ability and
cost of borrowing money, the other focused on the price at which assets can be
sold. These are the two ways a firm can raise cash to pay its short term liabilities.

Funding Liquidity Risk: Trading Liquidity Risk
• Firm specific/idiosyncratic: • The inability to liquidate assets,
except at a steep discount to their
• The inability to borrow money
long term value. i.e. market spreads
except at a spread much higher
of debt securities are materially
than the market, for a specified
higher than even stressed estimate of
rating.
expected loss, due to an increase in
• Systemic: the following components of market
spreads:
• A material, systemic increase in
market spreads above a • Credit risk premium
benchmark. • Trading liquidity risk premium
• Funding liquidity risk premium

26

The interaction of funding and solvency risk led to deleveraging and . . .

Interaction of funding, liquidity and solvency risk
– If the market thinks the financial institution has a material likelihood of becoming insolvent, or
defaulting on its liabilities, it will cease funding that FI. This is particularly important for FIs that
do not have a large, stable funding base (e.g. a lot of equity, insured deposits and long term
debt securities). This can become a vicious circle:

– The more the market stops funding, the higher the cost of funding (or the higher the loss on the
fire sale of assets) and the greater the probability of insolvency or default.

Increased market
concern with
solvency of FI

Increased funding cost and/or Decreased funding liquidity
losses on fire sale of assets, available for the Financial
generating losses. Institution

Increase of
idiosyncratic
liquidity risk for FI.

27

. . . the most material widening of credit spreads since the 1930s

AAA and BBB Rated Corporate
Bond Spreads to Treasury

January 1925 - February 2009

Annual change in
BBB Rated Corporate Bond
Spreads to Treasury


28

Spreads increased for bonds and in the LIBOR market.
Ted Spread is a
TED Spread measure of the
= Three-month LIBOR – Three-month Treasury rate market’s
evaluation of
interbank lending
All data from Fed Reserve website: https://research.stlouisfed.org/ risk.

From January
2002 to January
2007, the TED
spread was
approximately in
the range from 20
bp to 60 bp.

In October 10,
2008,
TED spread was
465 bp!
This is NOT a
normal
distribution!

29

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Some solutions



30

Context that was overlooked

• Questions that should have been asked by senior management, risk
managers, traders and modelers about the housing bubble:
– Given unprecedented nationwide increase in housing prices during period
1995-2005, what relevance was the magnitude of the historical decrease in
housing prices?
• Available evidence: Case-Shiller index and Case-Shiller estimation of 110 years of
housing prices.

– Had underwriting standards changed?
• Available evidence:
– Securitization documentation (Read The Big Short );

– Anecdotal evidence.

– What would be the consequence of a decrease in housing prices on sub-
prime mortgages PDs, LGDs and correlations?
• Available evidence:
– Reasonable estimations (The Big Short; John Paulson’ trades.

– Quote from The Last Man Standing

31

The evidence of an unprecedented increase in housing prices was there.
The deterioration of underwriting standards was also available for discovery

• Copied from
Barron’s June 2005
article on Prof.
Shiller of Yale:

“Despite what Alan
Greenspan says, there's
a huge housing bubble,
argues Yale economist
Robert Shiller, that
gradually could push
real prices down 50%
after it bursts. Why he's
worth listening to.”

32

Groupthink as a cause of widespread, but not universal, failure in management
• Management failure and groupthink
– Definition of groupthink:
• A pattern of thought characterized by self-deception, forced manufacture of consent, and
conformity to group values and ethics. from Marrian-Webster online dictionary.

– Underlying causes (from Wikipedia)
• High group cohesiveness
• Structural failures
– Lack of impartial leadership
– Lack of norms requiring methodological procedures (should come from independent risk management
and independent business).
• Situational

– Symptoms
• Overestimation of the group and its power and wisdom
– “We are making a lot of money so we must be the best underwriters/traders on the street.”
– “Best risk managers on the street”
• Closed mindedness
– Rationalization of warnings that might challenge group assumptions.
– Stereotyping those that oppose group as weak, evil, etc.
• Pressure to uniformity
– Self-censorship of ideas that deviate from group consensus
– Direct pressure to conform placed on those who question the group, couched in terms of disloyalty (e.g.
via bonus, promotion, demotion)
33

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Some solutions



34

What is the purpose of Risk Management: Three Views

• Should it be only a compliance function?
– Compliance with external regulations
– Compliance with internal policies

• Should it be only a constraint on the risk that businesses take?
– Risk management as the process of setting limits, and monitoring the risk
that is taken against limits, to prevent blow ups.

• Should it be a critical tool for making rational business decisions?
– Measuring, limiting and monitoring risk.
– A component of performance measurement – retrospective and prospective.
– A component of pricing and of customer/market selection
– A component of the efficient allocation of assets, capital and budget.

• This is related to the philosophical question of why one needs principles
to guide one’s actions and choices.

35

Lessons from history
 Solution: Thorough integration of risk policies and practices into business decision
making:

• Strong, independent risk management, to provide an objective analysis of risks
and rewards.

• Comprehensive and complete capture and measurement of all risk information.

• Comprehensive risk limits and definition of risk appetite for solvency risk.

• Comprehensive risk measurement, limits and stress scenarios on liquidity risk.

• Robust Economic Capital measurements, which take potential stressed economic
conditions and illiquid markets into account.

• Use of return on risk, or economic value added calculations in:
• Performance evaluation and compensation
• Allocation of capital and other scarce resources
• Risk based pricing
• Customer / product selection

• Thorough integration of risk into business decision making, to optimize return on
a limited resource. 36

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Some solutions



37

Example: Enhancing Economic Capital Framework at Citi
• Principles retained from the Citi’s previous (pre-2008) EC framework
– Measure EC over 1-year horizon at 99.97%CL
– EC defined based on unexpected loss (not expected loss)
– Comprehensive, covering all risk types;
– Constant level of risk over the year

• New principles
– Overall principle: going-concern perspective, not a liquidation
– Distinction between Price Risk and Value Risk
• Price Risk used where risk is changing market prices
• Value Risk used where exposure is held to maturity and funded to maturity
– Full inclusion of tail risks
• Fat tails (non-normal price behavior) for individual market factors
• High correlations during stress periods
• Lack of liquidity during stress periods
– Take liquidity horizon into account for trading portfolio
– Avoid pro-cyclicality
• Changes in risk capital primarily driven by changes in position
38

Risk and Valuation
• Accounting Reminder
EC for Interest Rate Risk In EC for Credit Risk in
Banking Book (ALM Risk) Loan Portfolio

Accounting Net Income ∆ Book Capital
Category

Accrual or HTM Net accrued income in period Net Income
− Net credit write-offs in period
− ∆ Loan Loss Reserve
Mark-to-market ∆ Mark-to-market in period Net Income
+ Net realized cash flows.

OCI (Other • ∆ AFS credit risk temp impaired • ∆ AFS credit risk temp impaired
Comprehensive o No effect on net income o ∆ Mark-to-market
Income)
• ∆ USD value of non-USD Book • ∆ USD value of non-USD Book
Equity due to change in Equity due to change in
exchange rate. exchange rate.
o None o ∆ Mark-to-market

39

Economic Capital For Credit Risk of Loan Portfolios (Value Risk)
• EC Definition for Value Risk
EC = Cumulative Unexpected Loss (UL) due to default over life of portfolio

= UL due to default loss during first year +
UL due to default from end of first year to maturity

= UL due to default loss during first year +
UL of change in Loan Loss Reserve (LLR) at end of year, when LLR
measures remaining lifetime loan loss reserve.

• Drivers of EC for Value Risk
– Unexpected default loss during first year depends on:
• Expected Default Losses: Driven by base case macroeconomic scenario.
• Stress Default Losses: Driven by stressed macroeconomic scenarios (systemic
risk) and statistical factors (idiosyncratic risk).

– Unexpected change in LLR at end of year for remaining lifetime depends on:
• Expected LLR: Driven by base case macroeconomic scenario.
• Stressed change in LLR: Driven by stressed macroeconomic scenarios and
statistical actors (i.e. idiosyncratic risk).

– Macroeconomic variables required to specify scenarios may be different for
wholesale vs. retail.
40

Economic Capital For Credit Risk of Loan Portfolios (Value Risk)
• Citi review after crisis: Did not have to make fundamental changes in
method for calculating EC for loan portfolio.
– Needed to make tweaks to wholesale and retail.

41

Enhanced Approach for Economic Capital for Market Risk
• Previous EC for trading was based on an annualized VAR at the 99.97%CL
– Equaled more than twice the equivalent 1995 Basel standard
– Scaled VAR worked well for liquid markets. Failed most dramatically for
illiquid, credit-sensitive portfolios, including securitization.

• New Developed process to integrate three components:
– Systemic stress scenarios, with assigned likelihoods
• Two unconditional historic stress scenarios, based on analyzing 90-years of history.
• Three hypothetical conditional stress scenarios
– Business specific stress tests, with assigned likelihoods, to capture material,
complex risks not captured by systematic stress tests or VAR
– Annualized VAR

• Default risk is also included with no double counting
– Systematic default risk is included as adjustments to stressed spread shock
• Downgrade risk is explicitly covered by the stressed spread shocks
– Idiosyncratic default risk is included in the credit portfolio simulation model

42

Formulating Systematic Stress Scenarios
• Step 1: Identify credit spreads as primary risk factor
– Credit risk is the dominant risk factor for all banks across the entire portfolio’
• Trading, CVA, credit risk on AFS portfolios, leveraged finance, loan portfolios, etc.

• Examine and analyze spread changes 1925-2009

AAA and BBB
Spreads to Treasury


Annual change in BBB
Spreads to Treasury


43

Formulating Systematic Stress Scenarios (Cont.)
• Step 2: Analyze historical time series of other market factors
– Identify 10 largest annual changes in credit spreads. Starting assumption is
that liquidity has vanished and portfolios are subjected to market changes
over a one year horizon.
– For each of the ten largest changes in credit spreads, measure and examine
changes in other classes of market factors. This enabled us to identify the
type of correlations that exist during severe systemic stress events.

• Results
– For each large systemic increase in credit spreads, equity prices have a high
correlation of falling. This was particularly true for the largest spread
widening (2008, 1932).
– However, interest rates, commodity and energy prices exhibited two
orthogonal types of correlation, which we have characterized as:
• Deflationary Systemic Stress Shock (e.g. 2008 full year)
– Materially widening of credit spreads and fall of equity markets
– Fall in level of yield curves, commodity prices and energy prices

• Inflationary Systemic Stress Shock (e.g. 1974 full year)
– Materially widening of credit spreads and fall of equity markets
– Rise in level of yield curves, commodity prices and energy prices

44

Conclusion
• Need to take full context into account in evaluating risk, and not rely on
simplistic or mechanical analysis.

• Need for full integration of independent risk management into business
decisions, rather than viewing risk management as simply a compliance
or limit setting function.

– Critical need for full independence

• Need for taking potential stressed markets and stressed liquidity
conditions into account when formulating stress tests and other
processes to measure risk.

45

Risk Management lessons learned from financial crisis

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