Public Company Default Modeling Methodologies
Using EDF Momentum to Provide Insights into Credit Migration and Default Risk
Ozge Gokbayrak and Lee Chua, July 2010
This paper presents a study of momentum impact within the Moody’s Analytics
EDF™ (Expected Default Frequency) credit measure on patterns of default risk
and rating changes. We define momentum as the significant rise or fall of an issuer’s
one-year EDF level during a one-year Horizon. The main question motivating our study
is whether or not such momentum can provide useful information about future credit
events, thus improving the utility of EDF credit metric for risk managers and investors.
We find that deteriorating momentum in EDF credit measures signals a much higher
likelihood of default, whereas entities with improving momentum show lower levels
of default risk. In addition, we find that Moody’s rating changes are highly
correlated with momentum signals: Deteriorating momentum results in higher downgrade
rates, whereas improving momentum results in higher upgrades.
CDS-implied EDF™ Credit Measures and Fair-value Spreads
Douglas Dwyer, Zan Li, Shisheng Qu, Heather Russell and Jing Zhang, March 11, 2010
In this paper, we present a framework that links two commonly used risk metrics:
default probabilities and credit spreads. This framework provides credit default
swap-implied (CDS-implied) EDF (Expected Default Frequency) credit measures that
can be compared directly with equity-based EDF credit measures. The model also provides
equity-based Fair-value CDS spreads (FVS) that can be compared directly with observed
CDS spreads. CDS-implied EDF credit measures and fair-value spreads are powerful
tools that risk managers can use to extend coverage of credit risk measures, enhance
the assessment of default risk, and assess the relative value of various credits.
With CDS-implied EDF credit measures, we can provide default risk measures for the
population of entities without traded equity, such as private firms, subsidiaries
of public firms, and sovereigns, based on their CDS. For firms with both EDF credit
measures and CDS-implied EDF credit measures, risk managers can use both metrics
to enhance their assessments of credit risk at the entity level. That is, by comparing
information from both markets in a common metric and understanding the differences,
risk managers can gain valuable insights into the credit risk of these entities.
By using both measures, they can minimize the model risk of relying on one measure
alone and increase predictive power of credit risk measures. Additionally, fair-value
spreads can be used for mark-to-market valuation and as well as for portfolio management.
An Empirical Examination of the Power of Equity Returns vs. EDFsTM for Corporate
Default Prediction
Zhao Sun, PhD, CFA January 2010
In this paper we study the effectiveness of using equity returns for corporate default
prediction. Specifically, we analyze whether using equity return information alone
can yield similar performance to EDFs in default prediction. We find that the answer
is no. Key results from our study are as follows.
EDFs exhibit superior default predictive power to 6-month cumulative equity returns
over a one year horizon, with the accuracy ratio of the former 27 percentage points
higher than that of the latter. Shorter equity return windows lead to even larger
differences in default prediction power.
For 97% of the firms analyzed in our study, equity returns underperform EDFs by
large margins as default risk indicators, sometimes even providing signals opposite
to realized default rates. It is only for the 3% of the worst performing firms--where
financial distress is most obvious-- that equity returns exhibit comparable prediction
power to EDFs.
EDFs consistently outperform equity returns as default risk signals over time. The
cohort accuracy ratios of EDFs are also much more stable than that of the equity
returns, ranging between 80% and 90%, while those equity returns were between 24%
and 83%.
There is a weak relationship between equity returns and default risk. Both EDFs
and realized default rates show a "smirk" - shaped relationship to equity returns.
In addition, there is a wide variation in EDFs among stocks with similar past equity
returns. In addition, there is a wide variation in EDFs among stocks with similar
past equity returns, suggesting that EDFs and equity returns contain directionally
different information.
When firms with high EDFs and high equity returns are compared with those with low
EDFs and low equity returns (i.e., EDFs and equity returns provide distinctly opposite
default warning signals), the realized default rate of the former group is 16 times
higher than that of the latter group, suggesting EDFs are a much more accurate predictor
of default.
When the one-year distance to default, or DD1 (a monotonic transformation of EDFs),
is pitted against equity returns in a regression setting, the coefficient estimate
of DD1 is of the expected sign and is statistically significant, while equity returns
provide no additional default prediction power in the presence of DD1.
Validating the Public EDF™ Model During the Credit Crisis in Asia and Europe
Ozge Gokbayrak and Lee Chua November 19, 2009
In this paper, we validate the performance of the Moody’s Analytics EDF™
(Expected Default Frequency) model during the recent credit crisis. We analyze the
model’s performance during the past two and a half years, and compare this
performance with the model’s longer history (2001-2006). We focus on the model’s
ability to differentiate between good and bad firms, the timeliness of its default
prediction, and accuracy of levels for two primary samples: Asian and European non-financial
firms. The current credit crisis has elevated default rates in both samples. We
measure performance with predictive power, early warning, and level validation.
A separate, recent Moody’s Analytics study looks at North American non-financial
firms as well as global financial firms. Overall, the EDF model’s predictive
power is as good as or better than the previous period. The model provides an early
warning signal a few years before default occurs; EDF levels were conservative (higher
than subsequently realized default rate) before the crisis compared with later-realized
default rates, and levels were statistically consistent with later-realized default
rates. We find that EDF credit measures perform consistently well across different
time horizons and across regions. Our tests indicate that EDF credit measures provide
a very useful measure of credit risk that can be applied throughout the world.
Validating
the Public EDF™ Model Performance during the Credit Crisis
Irina Korablev, Shisheng Qu, June 26, 2009
In this paper, we validate the performance of the Moody's KMV EDF™ (Expected
Default Frequency) model during the recent credit crisis. We analyze the model performance
during the past two years, and compare this performance to the model's longer
history (1996-2006). We focus on the model's ability to differentiate between
good and bad firms, the timeliness of its default prediction, and accuracy of levels
for two primary samples: North American non-financial firms and global financial
firms. The current credit crisis has elevated default rates in both samples. Defaults
during the current crisis are somewhat unique because added complexities involving
government bailouts created, in effect, ambiguous defaults. We measure performance
with predictive power, early warning, and level validation. We also compare the
performance of EDF credit measures with agency ratings and credit default swap (CDS)
spreads.
Overall, the EDF model's predictive power is as good as or better than in the
previous ten years, and is comparable with CDS spreads on their respective samples.
The model provides an early warning signal a few years before default occurs; EDF
levels were conservative (higher than subsequently realized default rate) before
the crisis compared with later-realized default rates, and levels were statistically
consistent with later-realized default rates.
The Distribution of Defaults and Bayesian Model Validation
Douglas W. Dwyer, March 11, 2008
Quantitative rating systems are increasingly being used for the purposes of capital
allocation and pricing credits. For these purposes, it is important to validate
the accuracy of the probability of default (PD) estimates generated by the rating
system and not merely focus on evaluating the discriminatory power of the system.
The validation of the accuracy of the PD quantification has been a challenge, fraught
with theoretical difficulties (mainly, the impact of correlation) and data issues
(eg, the infrequency of default events).Moreover, models - even “correct“
models - will over-predict default rates most of the time. Working within the standard
single-factor framework, we present two Bayesian approaches to the level validation
of a PD model. The first approach provides a set of techniques to facilitate risk
assessment in the absence of sufficient historical default data. It derives the
posterior distribution of a PD, given zero realized defaults, thereby providing
a framework for determining the upper bound for a PD in relation to a low default
portfolio. The second approach provides a means for monitoring the calibration of
a rating system. It derives the posterior distribution of the aggregate shock in
the macro-economic environment, given a realized default rate. By comparing this
distribution to the institution's view of the stage of the credit cycle its
borrowers are in, this approach provides useful insight for whether an institution
should revisit the calibration of its rating system. This method allows one to determine
that a calibration needs to be revisited even when the default rate is within the
95% confidence level computed under the standard approach.
IFRS Accounting Effects on Public
EDF
Adam Rapp & Shisheng Qu, April 18, 2007
In July 2002, the International Accounting Standards Board (IASB) published a new
set of accounting policies to be adopted by all firms incorporated in the European
Union as of January 1, 2005. The new rubric, called the International Financial
Reporting Standard (IFRS), replaced a variety of preexisting standards in France,
Germany, the United Kingdom, and other European countries, each of which had a slightly
different set of Generally Accepted Accounting Principles (GAAP) standards.With
the imposition of mandatory IFRS filing beginning with fiscal year 2005, all firms
in the European Union were required to file under these standards with their 2005
year-end figures, though some companies did publish IFRS-compliant figures at earlier
dates. Some changes in reporting standards required the reclassification of previously
off-balance sheet liabilities and their subsequent marking to market, as well as
reclassification of sales figures between lines of business. Other changes resulting
from IFRS adoption include recognition of extraordinary items and revenue based
on the transfer of risk and reward on the income statement.
Power and Level Validation of Moody's KMV EDF™ Credit Measures in North America,
Europe, and
Asia
Douglas W. Dwyer & Irina Korablev, September 10, 2007
In this paper, we validate the performance of Moody's KMV EDF credit measures in
its timeliness of default prediction, ability to discriminate good firms from bad
firms, and accuracy of levels in three regions: North America, Europe, and Asia.
We focus on the period 1996-2006 for most of our tests. Wherever possible, we compare
the performance to that of other popular alternatives, such as agency ratings, Moody's
KMV RiskCalc® EDF credit measures, Altman's Z-Scores, and a simpler version
of the Merton model. We find that EDF credit measures perform consistently well
across different time horizons, and different subsamples based on firm size and
credit quality. Our tests indicate that EDF credit measures provide a very useful
measure of credit risk that can be applied throughout the world.
Analyzing the Subprime Market Fallout Using EDF™ Credit Measures
Douglas W. Dwyer & Sarah Woo, April 16, 2007
Recent turmoil in the subprime mortgage market claimed several victims, notably
New Century Financial Corporation (NEWC), which filed for bankruptcy on April 2,
2007. In a review of the credit risk of a group of over two hundred REITs and mortgage
lenders, we found several firms with high EDF credit measures, which is the one-year
probability of default. A look at the EDF credit measure for the group as a whole,
however, reveals that credit risk has not changed much for less-risky companies,
i.e., those falling within the 75th percentile and below. Clearly, market players
are able to distinguish between companies holding onto subprime mortgages and those
with higher-quality or highly-diversified portfolios. Prior to its default on April
2, 2007, NEWC had been materially deteriorating since 2006, when its EDF credit
measure crossed the 90th percentile of its peer group. Most significant in NEWC’s
spiral towards bankruptcy was its liquidity crisis, as the company received notices
of default from its lenders alongside accelerated demands that it complies with
mortgage loan repurchase obligations. This report provides an analysis as to how
a subprime mortgage lender such as NEWC entered into default.
EDF 8.0 MODEL ENHANCEMENTS
Douglas W. Dwyer & Shisheng Qu, January, 2007
The Moody's KMV Expected Default Frequency model of public firms is the pioneering
implementation of a structural model that gives investors the ability to monitor
credit risk across a broad range of firms. The release of the EDF 8.0 model represents
a major recalibration of the model, which incorporates both a larger default dataset
and improved estimation techniques that derive the EDF term structure from credit
migration.
The EDF 8.0 model refines the mapping of the Distance-to-Default (DD) to the EDF
level. The resulting EDF value is a superior measure of both absolute and relative
risk. The EDF 8.0 model also provides improved granularity with a wider range of
EDF credit measures so that fewer credits hit the model cap and floor.
These refinements meet the increased demands that market participants are placing
on quantitative credit risk models. Market participants demand models that provide
more granular measure of credit risk. They also want models they can use to determine
the fair value spread on a given exposure. Models need to be transparent and validated.
The validity of the EDF level needs to be demonstrated given its importance in computing
both required economic and regulatory capital.
In this document, we describe the process of mapping the DD to an EDF credit measure.
Further, we show the impact of the mapping changes on the EDF level, required economic
capital, required regulatory capital, and the EDF Credit Categories, as well as
the implications for the EDF-implied spreads for both Bonds and CDS contracts. Finally,
we provide more details regarding the inner workings of the model.
The Relationship Between Default Prediction and Lending Profits: Integrating ROC
Analysis and Loan Pricing
Roger M. Stein, May 2005
In evaluating credit risk models, it is common to use metrics such as power curves
and their associated statistics. However, power curves are not necessarily easily
linked intuitively to common lending practices. Bankers often request a specific
rule for defining a cut-off above which credit will be granted and below which it
will be denied. In this paper we provide some quantitative insight into how such
cut-offs can be developed. This framework accommodates real-world complications
(e.g., "relationship" clients). We show that the simple cut-off approach can be
extended to a more complete pricing approach that is more flexible and more profitable.
We demonstrate that in general more powerful models are more profitable than weaker
ones and we provide a simulation example. We also report results of another study
that conservatively concludes a mid-sized bank might generate additional profits
on the order of about $4.8 million per year after adopting a moderately more powerful
model.
FINANCIAL EDF
MEASURES A New Model of Dual Business Lines
Martha Sellers & Navneet Arora, August 2004
Recent financial institution research at MKMV has focused on the implications of
the fact that certain large banks and non-bank financial institutions behave as
combinations of two quite different businesses: a financial asset portfolio and
franchise or service business.
Modeling Default Risk
Peter Crosbie & Jeffrey R. Bohn, Revised December 18, 2003
Default risk is the uncertainty surrounding a firm's ability to service its debts
and obligations. Prior to default, there is no way to discriminate unambiguously
between firms that will default and those that won't. At best we can only make probabilistic
assessments of the likelihood of default. As a result, firms generally pay a spread
over the default-free rate of interest that is proportional to their default probability
to compensate lenders for this uncertainty.
Private Company Default Modeling Methodologies
Bank Failures Past and Present: Validating the RiskCalc V3.1 U.S. Banks Model
Douglas W. Dwyer and Daniel Eggleton, October 8, 2009
This document outlines the validation results for the RiskCalc v3.1 U.S. Banks model,
and highlights the deteriorating financial ratios present in the banking sector.
We contrast trends of key risk measures to those of the savings and loan crisis
of the late 1980s and early 1990s. We also explore the speed and nature of recent
bank failures and demonstrate the model’s strong performance in light of this rapidly
changing environment.
Level and
Rank Order Validation of RiskCalc v3.1 United States
Douglas Dwyer, Daniel Eggleton, September 2, 2009
In this paper, we validate the Moody's KMV RiskCalc v3.1 United States private firm
default model. We show that the EDF™ (Expected Default Frequency) produced
by the model continues to rank order risk effectively by providing substantial discriminatory
power across multiple cuts of the data.
We also validate the EDF level produced by the model. For most development datasets
used to build private firm default models, direct level validation is not possible
due to multiple issues encountered when working with private firm data. The U.S.
is an exception, because we have been collecting loan accounting system data for
the past nine years from multiple banks. This data enables direct measurement of
the default rate on a cohort of active borrowers. Using the loan accounting system
data, we find that the EDF level is consistent with the realized default rates observed
over the last nine years.
Examples of Overfitting Encountered When Building
Private Firm Default Prediction Models
Douglas W. Dwyer, April 2005
The key to building default prediction models, if they are to be incorporated
into credit risk management systems, is to build the most powerful model possible
subject to the constraints that it is transparent, usable, and intuitive. In this
process, we must constantly be on guard for whether or not we have overfit the data.
In this paper, we present two examples of overfitting that we encountered while
building private firm models. These issues, if not detected, would have reduced
the usability of the model and overstated the true predictive power of the model
in real credit decision-making.
Moody's KMV RiskCalc® V3.1 United States
Douglas W. Dwyer, Ahmet E. Kocagil, June 1, 2004
Moody's KMV RiskCalc® is the Moody's KMV model for predicting private company
defaults. It covers over 80% of the world's GDP, has more than 20 geographic specific
models, and is used by hundreds of institutions worldwide. While using the same
underlying framework, each model reflects the domestic lending, regulator, and accounting
practices of its specific region.
In January 2004, Moody's KMV introduced its newest RiskCalc modeling framework,
Moody's KMV RiskCalc® v3.1. By incorporating both market (systematic) and company
specific (idiosyncratic) risk factors, RiskCalc v3.1 is in the forefront of modeling
middle-market default risk. This modeling approach substantially increases the model's
predictive powers.
This document outlines the underlying research, model characteristics, data, and
validation results for the RiskCalc v3.1 United States model.
Moody's KMV RiskCalc® V3.1 Model
Douglas W. Dwyer, Ahmet E. Kocagil, Roger M. Stein, April 5, 2004
This white paper outlines the methodology, performance, and key economic benefits
of the Moody's KMV EDF™ (Expected Default Frequency™) RiskCalc model,
which powers the next-generation of default prediction technology for middle market,
private firms. With EDF RiskCalc v3.1, Moody's KMV answers an important challenge
faced by our customers: "How can we support our decision-making process for
extending loans, managing portfolios and pricing debt securities when there is little
available market insight into a firm's prospects, as is the case for middle market
credits.
Recovery Modeling Methodologies
Incorporating Systematic Risk in Recovery: Theory and Evidence Introduction
Amnon Levy and Zhenya Hu, May 4, 2008
This paper proposes a theoretical framework to account for systematic risk in recovery
and to address the correlation between the firm's underlying asset process and recovery.
Under the proposed framework, the expected value in default under the risk-neutral
measure can be expressed as a linear function of the expected value under the physical
measure. This allows for a simple mapping between expected recovery observed in
the data and a measure that can be applied when using risk-neutral valuation methods.
V2: Dynamic Prediction of LGD
Greg M. Gupton & Roger M. Stein, January 2005
LossCalc is a statistical model that incorporates information at different levels:
collateral, instrument, firm, industry, country, and the macroeconomy to predict
LGD. It significantly improves on the use of historical recovery averages to predict
LGD, helping institutions to better price and manage credit risk.
Archive
Inferring the Default Rate in a Population by Comparing Two Incomplete Default Databases
W. Dwyer & Roger M. Stein, Revised October 14, 2005
Japanese EDF™ Credit Measure: Validation with the New Asset Volatility Model
Navneet Arora, Jeff Bohn, Min Ding & Shisheng Qu, June 15, 2005
Power and Level Validation of the EDF Credit Measure in the U.S. Market
Jeff Bohn, Navneet Arora, & Irina Korablev, March 18, 2005
Evidence on the Incompleteness of Merton-type Structural Models for Default Prediction
Roger M. Stein, February 9, 2005
Are the probabilities right?: A First Approximation to the Lower Bound on the Number
of Observations Required to Test for Default Rate Accuracy
Roger M. Stein, Revised May 22, 2003
Systematic and Idiosyncratic Risk in Middle-Market Default Prediction: A Study of
the Performance of the RiskCalc and PFM Models
Roger M. Stein, Ahmet E. Kocagil, Jeff Bohn & Jalal Akhavein,February 2003
Moody's Approach to Rating SME CLOs with RiskCalc Japan
Yusuke Seki, February 17, 2003
Methodology
for Testing the Level of the EDF Credit Measure
Matthew Kurbat & Irina Korablev, Revised August 08, 2002
Moody's RiskCalc for Private US Banks Model
Ahmet E. Kocagil, Alexander Reyngold, Roger M. Stein & Eduardo Ibarra, July 2002
Benchmarking Default Prediction Models: Pitfalls and Remedies in Model Validation
Roger M. Stein, July 2002
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