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2015 | OriginalPaper | Chapter

72. Rating Dynamics of Fallen Angels and Their Speculative Grade-Rated Peers: Static vs. Dynamic Approach

Author : Huong Dang

Published in: Handbook of Financial Econometrics and Statistics

Publisher: Springer New York

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Abstract

This study adopts the survival analysis framework (Allison, P. D. (1984). Event history analysis. Beverly Hills: Sage) to examine issuer-heterogeneity and time-heterogeneity in the rating migrations of fallen angels (FAs) and their speculative grade-rated peers (FA peers). Cox’s hazard model is considered the preeminent method to estimate the probability that an issuer survives in its current rating grade at any point in time t over the time horizon T. In this study, estimation is based on two Cox’s hazard models, including a proportional hazard model (Cox, Journal of Royal Statistical Society Series B (Methodological) 34:187–220, 1972) and a dynamic hazard model. The first model employs a static estimation approach and time-independent covariates, whereas the second uses a dynamic estimation approach and time-dependent covariates. To allow for any dependence among rating states of the same issuer, the marginal event-specific method (Wei et al., Journal of The American Statistical Association 84:1065–1073, 1989) was used to obtain robust variance estimates. For validation purpose, the Brier score (Brier, Monthly Weather Review 78(1):1–3, 1950) and its covariance decomposition (Yates, Organizational Behaviour and Human Performance 30:132–156, 1982) were applied to assess the forecast performance of estimated models in forming time-varying survival probability estimates for issuers out of sample.

It was found that FAs and their peers exhibit strong but markedly different dependences on rating history, industry sectors, and macroeconomic conditions. These factors jointly, and in several cases separately, are more important than the current rating in determining future rating changes. A key finding is that past rating behaviors persist even after controlling for the industry sector and the evolution of the macroeconomic environment over the time for which the current rating persists. Switching from a static to a dynamic estimation framework markedly improves the forecast performance of the upgrade model for FAs. The results suggest that rating history provides important diagnostic information and different rating paths require different dynamic migration models.

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See, for example, Altman and Kao (1992b), Carty and Fons (1994), Lando and Skodeberg (2002), Hamilton and Cantor (2004), and Figlewski et al. (2012).

See Carty and Fons (1994) and Lando and Skodeberg (2002).

See, for example, Altman and Kao (1991, 1992a, b).

See Altman (1998) and Figlewski et al. (2012).

See Mann et al. (2003), Vazza et al. (2005a), and Figlewski et al. (2012).

Samuelson and Rosenthal (1986), Bessler and Ruffley (2004), Yao et al. (2005), Grunert et al. (2005), and Dang (2010) are among the few studies in finance that applied this scoring rule to assess the predictive accuracy of estimated models. Johnstone (2002) suggested that the Brier score performs better than categorical measures in accurately assessing forecast performance.

Vazza et al. (2005a) defined FA peers as those originally rated in speculative grades and have identical rating distribution characteristics as FAs. Mann et al. (2003) defined FA peers as speculative grade-rated issuers that were of the same ratings as FAs at the time they lost investment grade status and never rated in the investment grade spectrum.

To get enough observations to make meaningful inference of the effect of rating history, the study considers lag-one and lag-two rating states. By definition, all FAs and FA peers in the study experienced a downgrade at lag-one rating state.

The notation was changed from \( {\widehat{S}}_q\left[t,Z\right] \) Eq. 72.5 or \( {\widehat{S}}_q\left[t,Z,Z(t)\right] \) Eq. 72.12 to f _t ^state_q to provide a compact presentation of the formula in a form consistent with the literature review on the Brier score.

The covariance decomposition proposed by Yates (1982) provides components of forecast accuracy that are more basic than the Sander (1963) and Murphy (1973) decompositions (Yates 1982, p. 141).

http://www.standardandpoors.com/ratings/definitions-and-faqs/en/us/ (Accessed 17 August 2012)

Seventeen macroeconomic candidate variables were considered and those that exhibited strong multicollinearity were eliminated.

By definition, all FAs and FA peers in this study experienced a downgrade at lag-one rating state.

Rating withdrawals bear negative credit implications if there is a lack of information to accurately assess debt issues (Carty 1997, p. 10). Issuers are likely to withdraw from being rated when they expect a downgrade. In this case, being unrated (censored) substitutes for being downgraded. The characteristics of issuers lost to non-independent (informative) censoring are often associated with the [migration] process under study (Blossfeld and Rohwer 1995; Kalbfleisch and Prentice 1980). There is no statistical test to check for and no standard methods for handling informative censoring (Allison 1995, p. 14). In this study, two sensitivity tests suggested by Allison (1995, pp. 249–252) to examine the effect of informative censoring on the main results have been applied to the proportional hazard models for FAs and their peers. It is found that being unrated is not informative.

Number prior FA does not take into account the FA event FAs experienced at lag-one rating state. By definition, none of FA peers in this study experienced a FA event at lag-one rating state.

Substantial rating changes of more than one letter grade (i.e., three rating notches) were more frequently observed in the ratings B through C (Lucas and Lonski 1992) and were less frequent than rating revisions of small magnitude (Carty and Fons 1994; Carty 1997). Downgrades involved a much bigger change in credit rating than upgrades (Jorion and Zhang 2007).

According to Lando and Skodeberg (2002), most financial institutions were assigned investment rating grades. As confidence- and capital-sensitive entities, it is difficult for financial institutions to run business with a poor credit profile or low credit rating. Lando and Skodeberg (2002) found that the duration dependence and the downward momentum are less pronounced for issuers in the financial institution sector than for issuers in other sectors. As this study examines the question of rating history dependence in the rating dynamics of speculative grade-rated issuers, financial institution sector was excluded from this study.

The year 1984 was selected as the starting point for several reasons. 1982 is as far back as all macro data are available, and Standard & Poor’s rating scales were changed in 1983. The growth of the US high-yield bond market and rating migrations from 1984 also constitute a significant source of events to this study.

The FA sample and the universe of FA-peer candidates in the estimation/holdout period have markedly different distribution of issuers in the upper speculative rating classes (BB, BB+). Thus, it is impossible to construct from the candidate pool a FA-peer sample with the same current rating distribution and the same sample size as the FA sample for either the estimation or the holdout period.

The effect of a previous rating change decays as time passes (Hamilton and Cantor 2004, p. 10). Thus, the shorter the lag-one rating state, the more influential the rating change at lag-two state (dummy lag2 down). Additional analysis (not reported) indicates that FA peer down states have a shorter lag one than FAs down states. Consequently, the effect of dummy lag2 down on the probability of a subsequent downgrade persists on FA peers but does not hold on FAs (as discussed earlier).

In forming the survival forecasts for holdout FAs/FA peers, the approach of Chen et al. (2005) is followed. As the time horizon unfolds, Chen et al. (2005) deleted from the holdout sample at time t those cases which are censored, or have experienced the event, before time t. The approach of Chen et al. (2005) results in a holdout sample that reduces with the passage of time. The number of survival forecasts N _t Eq. 72.13 accordingly reduces as the forecast time t gets longer.

The pro-cyclicality in rating actions may be attributed to the possibility that business cycle fluctuations coincide with permanent changes in credit quality (Loffler 2012).

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Title: Rating Dynamics of Fallen Angels and Their Speculative Grade-Rated Peers: Static vs. Dynamic Approach
Author: Huong Dang
Publisher: Springer New York
Book: Handbook of Financial Econometrics and Statistics
Print ISBN: 978-1-4614-7749-5

Electronic ISBN: 978-1-4614-7750-1

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-1-4614-7750-1_72