Moody's KMV - The Leading Provider of Credit Risk Management Solutions

Moody's KMV Research

Overview

EDF Credit Risk Measures

White Papers

FAQ

Default Case Studies

New Research | Single Obligor Credit Risk | Portfolio Credit Risk | Model Validation & Testing

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.

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.

Introduction to CreditMark
Alec McAndrew, February 11, 2004
(PDF document ~ 290KB)

In a world where loan, bond, and credit default swap markets are converging, mark-to-market is gaining importance in the management of loan portfolios. A rigorous mark-to-market process focuses all participants in the credit risk management process on the contribution of individual loans and the loan portfolio as a whole to the shareholder value of their financial institution.

Moody’s KMV CreditMark™ (CreditMark) is a software tool that provides a comprehensive solution for the accurate, timely, and secure valuation of loans and loan portfolios. Moody's KMV provides CreditMark subscribers with valuation models and with EDF values and other derived data that allow information across credit markets to be used in loan valuation.

The Economics of the Bank and of the Loan Book
Stephen Kealhofer, May 2002
(PDF document ~ 929KB)

Banks were once viewed as originating relatively safe assets, and earning money by the difference between their short term funding rate and their lending rates. Today, banks are viewed as originating riskier assets. In this new view, banks earn money from loans by their underwriting or distribution activities. It is now better understood that the profitability of a loan can be measured more accurately and more straightforwardly by decomposing the performance of the loan into two parts, one attributable to the underwriting activity, and one attributable to the subsequent performance of the loan. The value of the loan is based primarily upon external market valuations of similar instruments, adjusted to reflect the particular characteristics of the loan. These loans are then placed in a portfolio where diversification facilitates better management of the risk of these loans. The appropriate standard for evaluating the profitability of the portfolio is relative to the performance of a well-constructed portfolio formed from the same universe of potential assets. This paper explores this new paradigm for banking and suggests a methodology for valuing and managing loans in this context.

EDF™ Credit Measure and Corporate Bond Pricing
Oldrich Alfons Vasicek, November 2001
(PDF document ~ 70KB)

Recently, Moody's KMV has performed a simple analysis of the relationship of corporate bond pricing to Expected Default Frequency™ as a measure of default probability, and a simulation of bond investment strategies based on the differences between actual and theoretical prices. While this work is by all criteria a preliminary effort, based on limited data and utilizing naive methodology, the results are sufficiently encouraging to warrant further investigation. The findings are reported here.

A Survey of Contingent-Claims Approaches to Risky Debt Valuation
Jeffrey R. Bohn, Revised June 1999
(PDF document ~ 229KB)

This paper surveys available research on the contingent-claims approach to risky debt valuation. Structural and reduced-form versions of contingent claims models are described. Both the theoretical and empirical research in this area is summarized. Relative to the progress made in the theory of risky debt valuation, empirical validation of these models lags far behind. This survey highlights the widening gap between the theoretical valuation and the empirical understanding of risky debt.

Empirical Assessment of a Simple Contingent-Claims Model for the Valuation of Risky Debt
Jeffrey R. Bohn, Revised June 1999
(PDF document ~ 1.67MB)

Using a simple contingent-claims risky debt valuation model in the spirit of Black and Scholes (1973) and Merton (1974) (BSM), I fit actual term structures of credit spreads derived from data provided by Bridge/EJV. An essential component to fitting this model is the use of EDF™ credit measures (expected default frequency: a measure provided by Moody's KMV which provides an estimate of a firm's expected default probability over a specific time horizon). While the data are noisy, robust estimation techniques applied to median credit spread data result in a reasonable modeling picture. Iteratively re-weighted, non-linear least squares are used to dampen the impact of outliers and ensure convergence in each cross-sectional estimation, monthly spanning 1992 to 1999. The market price of risk exhibits stability over time and typically EDF credit measures explain 60% of credit spread volatility.

Credit Valuation
Oldrich A. Vasicek, March 22, 1984
(PDF document ~ 156KB)

Credit valuation is a necessary prerequisite to lending. It insures a desired quality of the asset portfolio, and results in loan pricing that corresponds to the risks assumed. It also provides means to reduce the likelihood of substantive losses through portfolio diversification.

A Comment on the Formation of Bank Stock Prices
John Andrew McQuown & Stephen Kealhofer, April 1997
(PDF document ~ 143KB)

To understand bank stock prices is to understand the economics of banking. We are not close to understanding the formation of bank stock prices in an empirically convincing way. Thus, the following should be construed as a story about which hypotheses we should be willing to entertain and why.

Response to JP Morgan's Paper "Using Equities to Price Credit"
Jeffrey R. Bohn, November 2001
(PDF document ~ 71KB)

The relationship of the equity market to the debt market has intrigued financial researchers for decades. A recent article written by Matt King of JP Morgan’s Credit Strategy places this discussion in the context of Moody's KMV Expected Default Frequency™ (EDF™) credit measure. While King agrees that the EDF value can be useful for evaluating illiquid debt securities, he implies that the measure is not generally useful to traders of credit. He also asserts that EDF values are unlikely to lead credit spreads. This note responds to King's conclusions. Moody's KMV and others have found evidence on larger samples of corporate bonds that EDF values do lead credit spreads. However, this characteristic of EDF values is not as important as the fact that equity-based expected default probabilities are not contaminated by other sources of risk. Credit spreads reflect compensation for more than just probability of default such as interest rates, liquidity, and pre-payment. Moreover, a credit spread embeds an expected recovery rate. For credit risk portfolio modeling, an equity-based measure of expected default probability provides a higher quality estimate of this key component of credit portfolio modeling. This note also highlights the use of equity-based measures in calculating correlations. Overall EDF values prove themselves useful for modeling efforts ranging from stand-alone credit assessment to credit portfolio analyses.

Characterizing Credit Spreads
Jeffrey R. Bohn, Revised June 1999
(PDF document ~ 1.8MB)

This paper characterizes credit spreads for corporate bonds reflected in a large and comprehensive dataset from Bridge/EJV. While the data are clearly noisy, robust measures of central tendency combined with graphical analysis produce term structures of credit spreads that conform with the qualitative predictions of Black and Scholes (1973) and Merton (1974) (BSM). The theoretical prediction of BSM regarding lower credit quality firms has been controversial. Their model suggests lower credit quality firms will exhibit humped-shape or downward sloping credit spread term structures. While some empirical researchers (Sarig and Warga (1989)) confirm this feature of the BSM modeling framework, other researchers (Helwege and Turner (1998)) dispute this feature arguing it is an artifact of the method of constructing the term structures. Comparing results using both agency ratings and EDF^TM (expected default frequency: a measure provided by Moody's KMV which provides an estimate of a firm's expected default probability over a specific time horizon), I present a resolution to this controversy. Properly controlling for credit quality in the cross-section, I show that low credit quality issuers tend to exhibit humped-shape and downward sloping credit spread term structures.

Back to Top