Credit Valuation

Our credit spread framework acts as a bridge across multiple markets when valuing credit instruments, utilizing information from a variety of sources, including equities, bonds, and CDS. Users can triangulate information from these markets, enabling a more comprehensive and in-depth understanding of credit spread drivers and identification of relative value opportunities.

In this paper we look at how institutions can streamline processes by focusing analytical attention on high EDF names, with resulting cost savings.

Presenter: David Munves, Yukyung Choi
Date: September 10, 2015

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In this Moody's Analytics webinar, Michael Infante, Chief Credit & Risk Officer at Cisco Capital shares his daily best practices, frameworks and processes. During his discussion with Mehna Raissi, Director at Moody's Analytics, Infante provides us with great insight on topics including: Counterparty on-boarding and portfolio monitoring and credit exposure management from a perspective of a global corporation.

Presenter: Mehna Raissi
Date: May 28, 2014

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In this paper, we construct long-short CDS portfolios and long-short equity portfolios using the information flow between these two markets. We show that these long-short portfolios are significantly profitable after controlling for known risk factors, survivorship biases, and other conditional variables. Results suggest that both credit and equity investors can use information from both markets to construct outperforming portfolios.

Central to the construction of these portfolios is a framework that links two commonly used risk metrics: default probabilities and credit spreads. Our framework generates equity-based Fair-Value CDS spreads (FVS) that can be compared directly with observed CDS spreads. This framework also provides credit default swap-implied (CDS-implied) EDF™ (Expected Default Frequency) credit measures that can be compared directly with equity-based EDF credit measures. Comparing these measures serve as the basis for forming the long-short portfolios. We find that the differences between these measures can be large, but they tend to converge over time. The convergence helps us anticipate price movements in both markets. Investors can take advantage of such predictability, producing meaningful excess returns using long-short portfolios.

Authors: Jian Du, Jing Zhang
Date: March 1, 2014
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This paper outlines the current framework and complexities related to transfer pricing. Based upon the unique needs in transfer pricing, the paper proposes a transparent tool that can easily satisfy governmental requirements for objectivity and standardization in transfer pricing transactions.

Author: Anuj Gupta, Arik Pelkey
Date: June 18, 2012

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This semiannual report examines credit risk in the otherwise opaque US private firm credit market. We report trends in four different types of risk measures: actual defaults, internal bank ratings, financial statement-based information, and model-based risk estimates. The statistics in this report are derived from Moody’s Analytics Credit Research Database® (CRD).

Author: Edward Atkinson
Date: May 23, 2012

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We develop a model-based approach to constructing investment grade and high yield corporate bond portfolios that both outperform their respective prevalent benchmark indices and popular Exchange Traded Funds (ETFs) with better risk-return profiles, i.e., higher returns with lower or similar risk. More importantly, the outperformance is obtained after controlling for credit risk, duration risk, and downside risk. The outperformance is robust across a number of different specifications of the strategy and over time. We also achieve outperformance using relatively smaller, more realistic portfolios. These model portfolios can be potentially converted into new fixed income indices or ETFs. Our model-based approach utilizes Moody’s Analytics’ EDF™ (Expected Default Frequency) credit measures and Fair-value Spread (FVS) valuation framework as powerful tools to control for credit risk and to exploit relative value in the bond market.

Author: Zan Li, Jing Zhang, Christopher Crossen
Date: April 19, 2012

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In addition to unbiased measures of Expected Default Frequency (EDF) and Expected Losses (EL), market participants also find it useful to estimate losses conditional upon a specific realization of the market environment. Scenario-based credit risk models are also becoming a business necessity, given increased regulatory and internal risk management requirements for periodic stress tests.

Author: Jun Chen; Kevin Cai
Date: December 16, 2011

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Presented by David Munves and David Hamilton at the Moody’s Analytics Munich Market Signals Event, 27 Sept 2011, this document illustrates how financial institutions can use fair value spread for their investment process.

Author: David Munves
Date: September 27, 2011

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In this slide presentation we look at credit market signals and explain market implied ratings (MIR) together with EDF ratings. We also show you how Moody’s Analytics’ unique modelling tools can be applied to many differentials to elicit past and future market trends.


Author: David Munves
Date: May 5, 2011

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This series of charts and graphics includes market performance analysis of Continental AG and Lehman’s, a look at the use of EDF “momentum” criteria to identify high risk situations and the consideration of higher default rates for firms with underperforming EDFs.

Author: David Munves
Date: May 5, 2011

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This presentation looks at what lessons can be learnt from the financial crisis and the structural credit risk models that Moody’s Analytics offers, in particular the ability to map fundamental credit concepts into default probability ratings; through balance sheet analysis and equity price data it is possible to present plausible estimations. The second half of the presentation considers credit spreads for European banks and peripheral sovereigns.

Author: David Munves, David Hamilton
Date: May 5, 2011

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Stringent credit risk management can increase credit profitability. This PowerPoint presentation investigates how lending can be used as an investment and provides analysis of how expected losses can be managed through limit setting and pricing. By reducing credit losses and maximising portfolio returns it is possible to improve baseline profit – here we explain how.

Author: Andreas Kalenteridis
Date: May 3, 2011

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How can high risk situations and default frequencies be correctly identified? Through case studies including that of Lehman Brothers and an examination of EDF risk techniques, this presentation identifies forward thinking scenario tests that help firms to make performance predictions and prepare for unexpected market volatility.

Author: David Munves
Date: May 2, 2011

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Moody’s Analytics considers different modelling techniques for emerging and developed markets in order to determine credit quality. This is driven largely by a lack of data or unreliable financial statements, as well as unstable operating environments and inherently differing levels of risk. However in essence, the bottom line credit quality of the borrower is the determining factor when creating effective models. This presentation provides a detailed breakdown of how such models are created, and the factors that need to be considered.

Author: Andro Cecuk
Date: April 4, 2011

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The government guarantee, explicit or implicit, of the so-called “Too-Big-to-Fail” (TBTF) institutions has a market value and represents a cost to tax payers. In this study, we quantify the values of these guarantees with a market value-based approach. These government guarantees reduce investor's expected losses and should have price implications, reflecting value transfer from tax payers to these TBTF institutions and their stake holders. By nature of the support, we postulate that it should primarily affect large financial institutions. By our estimates, the value of these government guarantees can be very significant. Compared with other studies on the subject, our approach has a number of advantages, as it is a market-based approach that can be used on both an ex ante and ex post basis.

Author: Zan Li, Shisheng Qu, Jing Zhang
Date: January 14, 2011

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There are advantages to measuring credit risk quantitatively, when possible. Nevertheless, qualitative factors may add information, because some credit risk determinants cannot be captured by quantitative measures. We present a framework for producing an internal rating system by overlaying additional factors onto a quantitative model, such as Moody’s Analytics RiskCalc™ EDF™ (Expected Default Frequency).

Authors: Douglas Dwyer, Heather Russell
Date: November 15, 2010

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This paper presents Moody’s Analytics methodology for valuing corporate loans using RiskFrontier™, taking into account both embedded options and credit state contingent cash flows. We find that our valuation and risk measurement methodologies compare extremely well with quotes from the secondary loan market, making their use in broad portfolios with limited secondary market prices valuable.

Authors: Sunny Kanugo, Tiago Pinheiro, Yashan Wang
Date: January 25, 2008

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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.

Authors: Deepak Agrawal, Jeffrey R. Bohn
Date: May 2006

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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.

Authors: Navneet Arora, Jeffrey R. Bohn, Fanlin Zhu
Date: February 17, 2005

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In recent years, the Moody’s KMV Expected Default Frequency™ (EDF) credit measure has become a standard measure of corporate credit risk among traders and managers of credit risk. Beyond predicting defaults, one other important application of any quantitative credit risk measure is to value credit risky claims such as corporate bonds, loans and credit derivatives. The goal of this paper is to provide evidence on the valuation performance of an EDF-based valuation model on a comprehensive sample of corporate bond data.

Authors: Deepak Agrawal, Navneet Arora, Jeffrey Bohn
Date: April 29, 2004

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The option-pricing framework first introduced by Fischer Black, Myron Scholes, and Robert Merton in the 1970s facilitates the development of valuation models to use equity market information to price corporate bonds and credit default swaps. This paper reviews a particular implementation of an option-pricing model developed by Moody’s KMV to relate equity and debt markets. The out-of-sample testing suggests that this structural model can be used effectively in pricing debt when only equity prices are available.

Authors: Deepak Agrawal, Navneet Arora, Jeffrey Bohn, Kehong Wen, Bin Zeng
Date: January 29, 2004

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In 2002, Moody's Investors Service started using RiskCalcTM Japan for private companies1 in its ratings of CLOs backed by loans to smalland medium-size enterprises (SMEs). As of end-January 2003, Moody’s had used this analytical tool to rate 7 CLO deals, including 6 publicly rated transactions.

Author: Yusuke Seki
Date: February 17, 2003

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Recently, 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.

Author: Oldrich Alfons Vasicek
Date: November 21, 2001

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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 KMV’s Expected Default Frequency™ (EDF™) credit measure. Specifically, King discusses the relationship of the EDF value (based on a firm’s equity) to credit spreads (based on the same firm’s traded bonds).

Author: Jeffrey R Bohn
Date: November 19, 2001

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Current debt markets are plagued by liquidity crunches (e.g., fall, 1998 when, at one point, market makers were unwilling to give bids for corporate bonds1) which at least in part result from the lack of price transparency coupled with the lack of commonly accepted approaches to valuing a risky bond. Although many researchers are working to address this need, the results have been mixed. While financial research in the area of risky debt valuationhas been theoretically satisfying, it has also been an empirically frustrating enterprise.

Author: Jeffrey R. Bohn
Date: Summer 2000

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Building a simple, yet comprehensive model for risky debt valuation continues to be both an elusive and alluring venture. We sacrifice tractability at the altar of realism. In contrast, we sacrifice usefulness at the altar of simplicity. We are left with Occam’s razor to determine how much complexity we need in practice. This razor must be sharpened with empirical results.

Author: Jeffrey R Bohn
Date: Spring 2000

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This paper characterizes credit spreads for corporate bonds reflected in a large and comprehensive dataset from Bridge/EJV.

Author: Jeffrey R. Bohn
Date: April 1999

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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.

Author: Oldrich Alfons Vasicek
Date: March 22, 1984

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