Moody‟s Analytics Workshop

Moody‟s Analytics Workshop:Balancing risk and reward: using market-based signals to

strengthen your risk management and investment

processes

October 2012

How to Use Market Signals in Your Risk Management Processes

October 2012David Munves, CFA, David Hamilton, PhD, Capital Markets Research Group

How to Use Market Signals in Your Risk Management Processes October 2012

What We‟ll Cover in this Session

1. Market Implied Ratings (MIR) and Expected Default Frequency

(EDF) Metrics Explained

2. Credit Market Signals: A Deep Dive

» Credit Spreads, Credit Default Rates, and Ratings

» Equity Prices as Signals of Credit Risk

» Spreads and Ratings for Confidence-sensitive Entities

» Credit Model Outputs vs. Levels of Credit Exposure

3. Effective Monitoring and Early Warning with EDF Measures

3


Market Implied Ratings (MIR) and Public Firm Expected Default Frequency (EDF) Metrics Explained1


5-year median CDS spreads

CDS-implied ratings are simply CDS spreads mapped to the Moody‟s rating scale. Establishing median spreads is a key step.

Users‟ Tip: MIR is a

“rich/cheap” model

5


We also have bond-implied ratings, which are based on spread term structures for each rating category

Sample bond market median credit spreads (there are 21 in total)

Cre

dit S

pre

ad

(bp

)

Years to Maturity

6


Moody‟s Analytics‟ public firm EDF model

» Asset value = value of equity + value of liabilities

» The two key drivers of the EDF model are:

– Financial risk: measured by a firm’s market leverage = value of liabilities /

asset value

– Business risk: measured by the volatility of a firm’s assets

» Distance to default (DD):

– Financial risk and business risk combine to form DD

– Represents how far away a firm’s asset value is from the value of liabilities

that would trigger a default, scaled by the firms asset volatility

» An EDF measure = 1% means that out of a portfolio of 100 firms, we would

expect one default by the end of the year

)ln()ln( XADD

7


A side-by-side comparison of a strong and a weak company illustrates the interplay between an EDF and its drivers

EDFs and key inputs for Johnson & Johnson and RadioShack (as of October 2012)

8


J&J is a very low risk company, combining a large gap between its MVA and Default Point and minimal asset volatility

Time

$400bn

$0

$200bn

$180bn

$100bn

DP = $38bn

MVA = $247bn

Oct 2012 Oct 2013

$280bn

$300bn

$350bn

Key drivers of J&J’s EDF

No. of

Std. Dev. % Probability

"Normal

Dist" PD

1 68%2 96%

3 99.7%

4 99.993666%

5 99.9999426697%

6 99.9999998027% <0.01%6 99.9999998027% <0.01%

$220bn

$70bn

$260bn

9


RadioShack is a very high risk company, combining a small gap between its MVA and Default Point and excessive asset volatility

Time$0

$1,250mn

$800mn

$400mn

DP = $1,179mn

MVA = $1,459mn

Oct 2012 Oct 2013

$2,250mn

$2,600mn

Key drivers of RSH’s EDF

No. of

Std. Dev. % Probability"Normal Dist"

PD

1 68%

$2,000mn

$2,400mn2 96%1.3 80.63% 9.7%

10


A big problem is that defaults are NOT normally distributed. So a firm‟s DD understates its real default risk. EDFs address this via our Empirical Mapping.

» EDFs are derived from an empirical

mapping of DDs to historical default

rates

» Public firm EDFs were calibrated

using US corporates from 1980 to

2007, including over 8,000 defaults.

This is being extended to take into

account the more recent

experience.

DD = 4 maps to a 0.003% PD in

the simple BSM model, but to a

0.4% EDFTM metric

Note: the EDF-DD curve in the graph is a stylized representation

of the actual DD to EDF mapping function

Users‟ Tip: The DD to

EDF empirical mapping is

based on the world‟s

largest default database.

11


Credit Market Signals: A Deep Dive1. Credit spreads, default rates, and ratings2. Equity prices as signals of credit risk3. Spreads and ratings for confidence-sensitive

entities4. Credit model outputs vs. levels of credit exposure

2


Credit spreads track default rates reasonably well in high yield…Average US HY spread and 1yr HY default rate

13


…But this is often not the case in investment grade

Average US Baa spread and the 5-year Baa default rate

14


Agency ratings have successfully rank ordered default risk over many years, and for long horizons

5-year default rates by rating category: 1926-2011

15


Rank ordering risk is extremely useful, but users shouldn‟t equate ratings and PDs

Single B 1-year default rate :1922-2011

Users‟ Tip: Don‟t assume

that a portfolio of „B‟

rated exposures will

default at a 4% rate!

16


EDFs (Expected Default Frequencies) are PDs, so they lead realized defaults by 1 Year. The level is (usually) correct as well.

1-year HY EDF vs. the 1-year HY default rate

17


EDFs have also been able to Identify eventual defaulters some time prior to the eventEDFs for all European corporates and for 2008-2010 defaulters

Users‟ Tip: Focus on changes in

an entity‟s EDF vs. its sector

18


Credit Market Signals: A Deep Dive1. Credit spreads, default rates, and ratings2. Equity prices as signals of credit risk3. Spreads and ratings for confidence-sensitive


2


Cumulative Accuracy Plots (CAPs) are data visualization tools that summarize how well a rating system rank orders credit risk in a portfolio

At the 10th percentile of the portfolio, the blue risk measure has

captured 80% of defaults compared to just 60% under the green

risk measure

20


The Accuracy Ratio is a statistic that summarizes the information in the CAP curve

Actual Outcome

Low Risk High Risk

Predicted by

Credit Measure

Low RiskCorrect

PredictionType II Error

High Risk Type I ErrorCorrect

Prediction

The accuracy ratio is a type of correlation statistic, ranging between zero and

one, that shows how well a risk measure makes correct predictions.

An accuracy ratio of 100%, for example, indicates that a risk measure perfectly

rank ordered firms that defaulted from those that did not, while an AR of 0%

indicates that the risk measure had no predictive power.

Accuracy Ratio

21


EDFs provide superior default detection compared to equity returns, and by a considerable measureAverage CAP curves and Accuracy Ratios for EDFs and 6-month equity returns

22


Accuracy Ratios for EDFs are consistently high across time as wellCAP Curves and Accuracy Ratios for EDFs and 6-month equity returns

23


Credit Market Signals: A Deep Dive1. Credit spreads, default risk, and ratings2. Equity prices as signal of credit risk3. The challenges of using spreads and ratings of confidence-sensitive entities4. Credit model outputs vs. levels of credit exposure

2


Similarly rated corporates and FIs used to trade at bout the same level…

Average bond spreads for European Investment Grade Corps and FIs, 2000-June 2007

25


…This is no longer the case. The “dislocation” has now gone on for five years. What‟s this saying about ratings and risk levels?

Average bond spreads for European Investment Grade Corps and FIs, July 2007 - current

26


The key point is that Moody‟s ratings and market signals for banks function differently, mainly due to banks‟ confidence-sensitive natures

Average CDS-Implied Rating and average Moody’s rating for European banks

Users‟ Tip: Credit spreads incorporate

“tail risk”, while ratings are based on

central assumptions

27


The degree of “signal divergence” varies at the entity level, with US banks generally trading closer to their Moody‟s ratings

Moody’s Ratings, CDS-Implied Ratings, and Spreads of Selected Banks

28


AUT

BEL

DNK

FIN

FRA

DEU

IRL

ITA

NLD

NOR

PRT

ESP

SWE

CHE

TUR

GBRUSA

Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1Baa2Baa3 Ba1 Ba2 Ba3 B1 B3 Caa1Caa2Caa3 Ca C

Aaa

Aa1

Aa2

Aa3

A1

A2

A3

Baa1

Baa2

Baa3

Ba1

Ba2

Ba3

B1

B3

Caa1

Caa2

Caa3

Ca

C

Ave

rage

Ban

ing

Syst

em C

DS-

IR

Sovereign CDS-IR

Countries rated Aaa by Moody'sare shown in bold

Market signals for sovereigns vs. banks: the weaker the county, the stronger the link to the banking system (according to the CDS market)

European national banking system avg. CDS-IR vs. sovereign CDS-IR

Users Tip: The fates of the

perceived weaker countries are

bound to those of its banks: The

weaker the country, the weaker the

banks, and the more likely a rescue

will drag down the country

29


Most highly rated European sovereigns trade below their Moody‟s ratings, largely due to tail risk at the bank and sovereign levels. Eurozone membership is also a factor.

CDS-implied ratings and MIS ratings for European sovereigns

PRT

ISL

FIN

DEU

USA

AUT

BEL

DNK

ITA

SWE

GBR

IRL

FRA

NLD

NOR

ESP

CHE

TUR

Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1Baa2Baa3 Ba1 Ba2 Ba3 B1 B3 Caa1Caa2Caa3 Ca C

Aaa

Aa1

Aa2

Aa3

A1

A2

A3

Baa1

Baa2

Baa3

Ba1

Ba2

Ba3

B1

B3

Caa1

Caa2

Caa3

Ca

C

CDS-

Impl

ied

Rati

ng

Moody's Rating

30


Credit Market Signals: A Deep Dive1. Credit spreads and default signals2. Equity prices and credit risk3. Spreads and ratings for confidence-sensitive


2


It‟s Important to understand what the model is telling you: markets trade banks cheaply to their implied ratings, with the EDF-based signal the lowest. This reflects the model‟s design.

Average implied ratings and Moody’s ratings for global banks

32


Moody’s rating and implied ratings for Fannie Mae

Jun06 Sep06 Dec06 Mar07 Jun07 Sep07 Dec07 Mar08 Jun08 Sep08 Dec08 Mar09 Jun09 Sep09 Dec09

Moody'sSeniorUnsecuredorEquivalentRating Bond-ImpliedRating

CreditDefaultSwap-ImpliedRating Moody'sKMVEquity-ImpliedRating

Aaa

Aa3

Baa1

Ba2

B3

Ca

A principal reason for this is that EDFs capture risk across a firm‟s capital structure

Users‟ Tip: know your

model and exposure type,

especially for FIs

33


This factor has become more important as risk differentials between classes of creditors have grown

Average Moody’s bank ratings on senior debt and preferred stock

Users‟ Tip: Bank EDFs are also

increased by 1) holdco/opco

considerations; 2) a strict

definition of capital; and 3) the

special nature of deposits

34


Effective Monitoring and Early Warning with EDF Measures3


Can we detect potential LEH outcomes early enough?

Lehman Brothers Holdings EDF Levels, 2000-2008

EDF = 1 bp

EDF = 5 bp

36


Triggers tied to EDF levels alone are insufficient

Box plots of EDF measures for US firms, January 2008 vs. January 2009

0

10

20

30

40

ED

F %

Jan 2008 Jan 2009

Review

Trigger Level

37


Effective monitoring using EDF measures

» Monitoring and early warning are problems of classification:

which firms in a portfolio should be considered relatively more

risky, and therefore merit deeper investigation?

» Classification is primarily about rank ordering

» Errors of classification are costly

– False positives are costly because they waste scarce resources

– False negatives are costly because they result in direct losses

38


Challenges to setting up an effective early warning process

» System must be set up to detect outcome(s) of

interest/importance

– Default

– Rating change

– Spread movement

» Filters are likely to be very sensitive to time period, peer group,

time horizon, etc.

» Triggers should also be a function of the economic importance

of the exposure

» What is the tolerance for different classification errors?

» Process can only filter results, cannot dictate actions

39


Developing an early warning toolkit

Public EDFs can be utilized in three broad ways to more effectively

monitor risk and signal early warning:

» EDF level

– Despite the aforementioned caveats, EDF levels remain powerful rank order

measures

– EDF levels vs. peer group EDF levels

– EDF-Implied Ratings

» EDF change

– EDFs exhibit momentum and mean-reversion

– These changes can signal incremental risk beyond EDF level

» EDF relative change

– Firms underperforming their industry peers are often more likely to default

40


EDF levels efficiently rank order default risk

2001-2007 2008-2010

EDF AR: 83.8%

ZScore AR: 66.8%

Defaults: 695

Firms: 6,437

EDF AR: 81.9%

ZScore AR: 66.4%

Defaults: 176

Firms: 4,336

EDF credit measures have exhibited a high degree of predictive accuracy relative to

other risk measures, such as Z scores

41


LEH EDF-implied ratings hinted at risk before default

Lehman Brothers Holdings EDF Measure and EDF-Implied Rating

42


Negative EDF momentum signals higher default risk

One-Year default rates conditioned on EDF momentum

43


EDF momentum signals future rating changes

One-year rating upgrade and downgrade rates conditioned on EDF momentum

Rating Downgrade Rates Rating Upgrade Rates

44


EDFs exhibit mean reverting behavior over the cycle

Caterpillar Corp.’s EDF and Through-the-Cycle EDF measures

45


Default rates are sensitive to EDF change versus sector

One-Year default rates conditioned on EDF decile and EDF change vs. sector change

1 2 3 4 5 6 7 8 9 10 ALL

1 0.05% 0.03% 0.02% 0.00% 0.00% 0.01% 0.03% 0.00% 0.00% 0.00% 0.02%

2 0.10% 0.05% 0.06% 0.06% 0.00% 0.00% 0.02% 0.07% 0.11% 0.27% 0.05%

3 0.10% 0.06% 0.01% 0.03% 0.01% 0.03% 0.07% 0.06% 0.03% 0.18% 0.05%

4 0.28% 0.12% 0.17% 0.15% 0.09% 0.10% 0.08% 0.09% 0.17% 0.30% 0.15%

5 0.32% 0.23% 0.24% 0.32% 0.22% 0.24% 0.21% 0.27% 0.22% 0.46% 0.27%

6 0.62% 0.44% 0.45% 0.34% 0.44% 0.56% 0.44% 0.72% 0.51% 0.97% 0.55%

7 0.71% 0.56% 0.66% 0.80% 0.64% 0.72% 0.73% 1.06% 1.18% 1.63% 0.89%

8 1.01% 1.01% 1.19% 1.25% 1.27% 1.44% 1.58% 1.65% 2.05% 3.10% 1.68%

9 3.14% 2.22% 4.83% 5.16% 5.25% 4.34% 4.87% 5.75% 6.37% 8.39% 5.60%

10 6.43% 4.68% 5.76% 7.70% 7.70% 6.96% 7.67% 9.31% 9.99% 13.70% 8.94%

All 0.66% 0.63% 1.08% 1.73% 1.73% 1.83% 2.24% 2.92% 3.13% 5.96% 2.16%

Firm

ED

F Le

vel

EDF Change Relative to Industry Peer Group Change

Default risk increases with poor performance vs. industry

Default risk

rises with

EDF level

46


A year before default, LEH underperformed its industry

Lehman Brothers EDF measure and decile of change vs. median industry change

47


Combining and synthesizing these signals is very difficult

Panel Probit Regression Results

Coef. Std. Err. z P>z [95% Conf. Interval]

EDF 0.061816 0.000874 70.77 0.000 0.060104 0.063528

EDF Change 0.006927 0.002413 7.01 0.000 0.012198 0.021657

ΔEDF-Δindustry -0.01679 0.002459 -6.83 0.000 -0.02161 -0.01197

Although EDF change and relative change are statistically significant

predictors of default beyond EDF level…

…the incremental effect on rank order power is not significant

Accuracy Ratio

EDF level 81.5%

Probit model 82.3%

48


Conclusions

» Using some basic rules of thumb, one may be able to more

effectively use public EDF measures for monitoring and

early warning

» However, it is difficult to find the right way to combine this

information that improves upon the rank ordering power of

EDF level alone

» The monitoring process could likely be improved by

bringing other measures (like CDS-implied EDF measures)

into the analysis

49

How to Use Market Signals in Your Risk Management Processes

October 2012David Munves, CFA, David Hamilton, PhD, Capital Markets Research Group


© 2012 Moody’s Analytics, Inc. and/or its licensors and affiliates (collectively, “MOODY’S”). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY

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of the information contained herein are, and must be construed solely as, statements of opinion and not statements of fact or recommendations to purchase, sell or hold any

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Moody‟s Analytics Workshop

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