Invasive Aquatic Workshop: Taxonomic Group Michael Moody, Donald Les, Joe DiTomaso.
Moody‟s Analytics Workshop
Transcript of Moody‟s Analytics Workshop
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Moody‟s Analytics Workshop:Balancing risk and reward: using market-based signals to
strengthen your risk management and investment
processes
October 2012
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How to Use Market Signals in Your Risk Management Processes
October 2012David Munves, CFA, David Hamilton, PhD, Capital Markets Research Group
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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
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How to Use Market Signals in Your Risk Management Processes October 2012
Market Implied Ratings (MIR) and Public Firm Expected Default Frequency (EDF) Metrics Explained1
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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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
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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
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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)
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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
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How to Use Market Signals in Your Risk Management Processes October 2012
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%
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How to Use Market Signals in Your Risk Management Processes October 2012
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.
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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
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Credit spreads track default rates reasonably well in high yield…Average US HY spread and 1yr HY default rate
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…But this is often not the case in investment grade
Average US Baa spread and the 5-year Baa default rate
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Agency ratings have successfully rank ordered default risk over many years, and for long horizons
5-year default rates by rating category: 1926-2011
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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!
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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
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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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
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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
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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
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Accuracy Ratios for EDFs are consistently high across time as wellCAP Curves and Accuracy Ratios for EDFs and 6-month equity returns
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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
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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
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…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
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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
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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
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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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
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How to Use Market Signals in Your Risk Management Processes October 2012
Credit Market Signals: A Deep Dive1. Credit spreads and default signals2. Equity prices and credit risk3. Spreads and ratings for confidence-sensitive
entities4. Credit model outputs vs. levels of credit exposure
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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
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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
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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Effective Monitoring and Early Warning with EDF Measures3
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Can we detect potential LEH outcomes early enough?
Lehman Brothers Holdings EDF Levels, 2000-2008
EDF = 1 bp
EDF = 5 bp
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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
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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
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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
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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LEH EDF-implied ratings hinted at risk before default
Lehman Brothers Holdings EDF Measure and EDF-Implied Rating
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Negative EDF momentum signals higher default risk
One-Year default rates conditioned on EDF momentum
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EDF momentum signals future rating changes
One-year rating upgrade and downgrade rates conditioned on EDF momentum
Rating Downgrade Rates Rating Upgrade Rates
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EDFs exhibit mean reverting behavior over the cycle
Caterpillar Corp.’s EDF and Through-the-Cycle EDF measures
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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
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A year before default, LEH underperformed its industry
Lehman Brothers EDF measure and decile of change vs. median industry change
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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%
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How to Use Market Signals in Your Risk Management Processes October 2012
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
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How to Use Market Signals in Your Risk Management Processes
October 2012David Munves, CFA, David Hamilton, PhD, Capital Markets Research Group
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How to Use Market Signals in Your Risk Management Processes October 2012
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