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Single Obligor Credit Risk | Portfolio Credit Risk | Portfolio Valuation | Model Validation & Testing |
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Basel II, Pillar 2: A Source of Competitive Advantage
Nov 7, 2007
(PDF document ~ 64KB)
January 1, 2008 was a date many regulatory agencies set for the introduction of the New Basel Capital Accord (Basel II). It is apparent that most of the effort has been applied to the Pillar 1 aspects of the Accord. Many banks made significant financial and human resource investments in data capture systems, risk grading tools, portfolio assessment tools, and other necessary elements to meet regulatory expectations.
We gain far greater advantages in the next phase, Pillar 2, the Supervisory Review Process (SRP). In this paper, we limit the discussion in relation to credit risk, but we also address important parallels for both market and operational risk.
An Overview of Modeling Credit Portfolios
Feb 14, 2008
(PDF document ~ 272KB)
This document provides a high-level overview of the modeling methodologies implemented in RiskFrontier™ to address the challenges faced by a credit risk manager or a credit portfolio manager. RiskFrontier attempts to accurately model the value of a credit investment at the analysis date and its value distribution at some investment horizon, as well as the correlation between two instruments in a portfolio. The approach is designed to explicitly analyze a wide range of credit investments and contingencies, including term loans with prepayment options and grid pricing, dynamic utilization in revolving lines of credit, bonds with put and call options, equities, credit default swaps, and structured instruments.
Incorporating Systematic Risk in Recovery: Theory and Evidence Introduction
Amnon Levy and Zhenya Hu, May 4, 2008
(PDF document ~ 258KB)
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.
A Brief History of Active Credit Portfolio Management
Brian Dvorak, March 26, 2008
(PDF document ~ 84KB)
The first commercial EDF™ credit measure model was released by KMV in 1990, although its foundations in extending the Merton model date from the early 1980s. The EDF model is now in use at hundreds of institutions worldwide, and Moody’s KMV EDF credit measures are produced daily on more than 30,500 listed firms in 58 countries.
This paper addresses the following questions: What are the origins of active credit portfolio management? How did the practice start, how has it evolved, and what can we see on the horizon?
The Distribution of Defaults and Bayesian Model Validation
Douglas W. Dwyer, March 11, 2008
(PDF document ~ 299KB)
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.
Asset Correlation, Realized Default Correlation, and Portfolio Credit Risk
Jing Zhang, Fanlin Zhu, and Joseph Lee, March 2008
(PDF document ~ 254KB)
Asset correlation is a critical driver in modeling portfolio credit risk. Despite its importance, there have been few studies on the empirical relationship between asset correlation and subsequently realized default correlation, and portfolio credit risk. This three three-way relationship is the focus of our study using U.S. public firm default data from 1981 to 2006.
Valuation of Corporate Loans: A Credit Migration Approach
Deepak Agrawal, Irina Korablev, and Douglas W. Dwyer, January 25, 2008
(PDF document ~ 306KB)
Banks and investors in loan assets have always had difficulty obtaining an unbiased and consistent value for the assets they hold. With the growth of liquidity in the loan market, the demand for a valuation method that can be consistently applied has been growing. However, the problems of loan valuation are complex. In large part this is because of the existence of embedded options and contractual conditions that can significantly affect the value of a loan. In this paper, we present the Moody’s KMV methodology for valuing corporate loans, taking into account both embedded options and credit state contingent cash flows.
Using Asset Values and Asset Returns for Estimating Correlations
Fanlin Zhu, Brian Dvorak, Amnon Levy, Jing Zhang, September 12, 2007
(PDF document ~ 145KB)
In the Moody?s KMV Vasicek-Kealhofer (VK) model, asset values and asset returns are
calculated separately. Moody?s KMV GCorr? uses weekly asset returns directly from the VK
model to calculate asset correlations. As an alternative, asset returns estimated from monthly
asset values from Credit Monitor® can be used to estimate asset correlations. This study
shows that the asset returns backed out from asset values are vulnerable to capital structure
changes and other corporate activities, especially for financial firms. The frequent capital
structure changes in financial firms make their correlations from asset values much smaller
than the correlations from VK asset returns. Moreover, it is demonstrated that confidence
intervals for correlation estimates from three years of monthly returns are much wider than
correlation estimates from three years of weekly VK asset returns.
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
(PDF document ~ 763KB)
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
(PDF document ~ 136KB)
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
(PDF document ~ 341KB)
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.
Humpbacks in Credit Spreads
Deepak Agrawal & Jeffrey R. Bohn, May 2006
(PDF document ~ 281KB)
Models of credit valuation generally predict a hump-shaped spread term structure for low quality issuers. This is understood to be driven by the shape of the underlying conditional default probabilities curve. We show that (a)recovery assumptions and (b) deviation of bond's price from par can also drive different term structure shapes. Our analysis resolves conflicting empirical evidence on the shape of speculative grade spread curves and explains the related existing theoretical results. On examining a large set of speculative grade bonds and credit default swaps, we find evidence that par-spread term structures are likely to be downward sloping as credit quality deteriorates sufficiently.
Inferring the Default Rate in a Population by Comparing Two Incomplete Default Databases
Douglas W. Dwyer & Roger M. Stein, Revised October 14, 2005
(PDF document ~ 168KB)
It is often the case in default modeling that the need arises to calibrate a model to some prior probability of default. In many situations, a researcher may not know the true prior default rate for the population because the data set at hand is itself incomplete either with respect to default identification (hidden defaults) or default under reporting. In situations where a researcher has access to two incomplete default data sets, it is possible to infer the number of "missing" defaults, which we demonstrate in this short note. We discuss an approach to estimating this quantity and show an example in which we infer the number of missing defaults in the combined legacy databases of the former Moody's Risk Management Services and Moody's KMV. While calibration is one application of this approach, the method is quite general and can be applied in other settings as well.
Reduced Form vs. Structural Models of Credit Risk: A Case Study of Three Models
Navneet Arora, Jeffrey R. Bohn, Fanlin Zhu, February 17, 2005
(PDF document ~ 402KB)
In this paper, we empirically compare two structural models (basic Merton and Vasicek-Kealhofer (VK)) and one reduced-form model (Hull-White (HW)) of credit risk. We propose here that two useful purposes for credit models are default discrimination and relative value analysis. We test the ability of the Merton and VK models to discriminate defaulters from non-defaulters based on default probabilities generated from information in the equity market. We test the ability of the HW model to discriminate defaulters from non-defaulters based on default probabilities generated from information in the bond market. We find the VK and HW models exhibit comparable accuracy ratios on both the full sample and relevant sub-samples and substantially outperform the simple Merton model. We also test the ability of each model to predict spreads in the credit default swap (CDS) market as an indication of each models strength as a relative value analysis tool. We find the VK model tends to do the best across the full sample and relative sub-samples except for cases where an issuer has many bonds in the market. In this case, the HW model tends to do the best. The empirical evidence will assist market participants in determining which model is most useful based on their purpose in hand. On the structural side, a basic Merton model is not good enough; appropriate modifications to the framework make a difference. On the reduced-form side, the quality and quantity of data make a difference; many traded issuers will not be well modeled in this way unless they issue more traded debt.
Examples of Overfitting Encountered When Building Private Firm Default Prediction Models
Douglas W. Dwyer, April 12, 2005
(PDF document ~ 245KB)
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 Internal Rating Platform and the Basel II IRB Approaches
March 31, 2005
(PDF document ~ 350KB)
In June 2004, the Basel Committee on Banking Supervision issued the long-awaited "International Convergence of Capital Measurement and Capital Standards: a Revised Framework" describing changes to the regulatory capital requirements for banks. A key element of the new Accord is greater reliance on banks? internal rating systems in the calculation of regulatory capital charges.
The Moody?s KMV internal rating platform can accommodate a wide variety of risk rating models. Specifically, it is used to deploy risk models across a bank?s local and global lending network and to manage the data requirements of the credit rating process. Banks around the world are making the platform a critical component of their credit risk process as they prepare for Basel II IRB compliance.
Power and Level Validation of the EDF? Credit Measure in the U.S. Market
Jeff Bohn, Navneet Arora, & Irina Korablev, March 18, 2005
(PDF document ~ 393KB)
The new Basel Capital Accord states that ?The methodology for assigning credit assessments must be rigorous, systematic, and subject to some form of validation based on historical experience.? There are two important components to this validation process: the ability to predict defaults and the accuracy of the default predictive measure.
The Relationship Between Default Prediction and Lending Profits: Integrating ROC Analysis and Loan Pricing
Roger M. Stein, May 2005, Journal of Banking and Finance, Vol. 29, No. 5.
(PDF document ~ 350KB)
The use of credit scoring models by financial institutions has increased dramatically. With this increase has come a need among users to understand the economic value of the models and to use this information to integrate them into traditional lending practices in a profitable manner. For example, many institutions find it helpful to define a lending cut-off or threshold as a guideline for either more junior credit officers or for pre-screening in loan underwriting.
Evidence on the Incompleteness of Merton-type Structural Models for Default Prediction
Roger M. Stein, February 9, 2005
(PDF document ~ 180KB)
In this short paper we provide some evidence that unmodified, Merton-type models are not, in fact, complete in the sense that additional information provides better discrimination between defaulters and non-defaulters even when conditioned on Merton-based variables. Using Moody's extensive database of corporate defaults, we first show heuristically that partitioning a standard Merton model by a second variable provides more information about default. We then show that econometric tests of significance refute the assertion that additional information does not help explain default. Finally, we show that even a simple regression-based multi-factor model appears to outperform its single-factor (basic Merton-only) counterpart in rigorous (out-of-sample and out-of-time) validation. This suggests merit to exploring enhancements to the Merton framework such as, for example, those introduced by the Vasicek-Kealhofer model.
LossCalc V2 : Dynamic Prediction of LGD
Greg M. Gupton & Roger M. Stein, January 2005
(PDF document ~ 1.13MB)
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.
FINANCIAL EDF MEASURES A New Model of Dual Business Lines
Martha Sellers & Navneet Arora
(PDF document ~ 2MB)
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.
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