Credit rating dynamics in the presence of unknown structural breaks

In many credit risk and pricing applications, credit transition matrix is modeled by a constant transition probability or generator matrix for Markov processes. Based on empirical evidence, we model rating transition processes as piecewise homogeneous Markov chains with unobserved structural breaks. The proposed model provides explicit formulas for the posterior distribution of the time-varying rating transition generator matrices, the probability of structural break at each period and prediction of transition matrices in the presence of possible structural breaks. Estimating the model by credit rating history, we show that the structural break in rating transitions can be captured by the proposed model. We also show that structural breaks in rating dynamics are different for different industries. We then compare the prediction performance of the proposed and time-homogeneous Markov chain models.     

Variations in sovereign credit quality assessments across rating agencies

We investigate agency variation in credit quality assessment (Standard and Poor’s vs. Moody’s vs. Fitch) employing sovereign ratings data for 129 countries, spanning the period 1990–2006. While we find that the credit rating agencies often disagree about credit quality, it is usually confined to one or two notches on the finer scale. We find that several variables have varying importance in explaining ratings across agencies which leads us to conclude that material heterogeneity exists between them. Also, while watch and outlook procedures are generally strong predictors of rating changes relative to other public data, additional significant variables suggest that it might be possible to augment these agency data to provide better forecasts of future rating changes.     

The adjustment of credit ratings in advance of defaults

This paper assesses biases in credit ratings and lead–lag relationships for near-to-default issuers with multiple ratings by Moody’s and S&P. Based on defaults from 1997 to 2004, we find evidence that Moody’s seems to adjust its ratings to increasing default risk in a timelier manner than S&P. Second, credit ratings by the two US-based agencies are not subject to any home preference. Third, given a downgrade (upgrade) by the first rating agency, subsequent downgrades (upgrades) by the second rating agency are of greater magnitude in the short term. Fourth, harsher rating changes by one agency are followed by harsher rating changes in the same direction by the second agency. Fifth, rating changes by the second rating agency are significantly more likely after downgrades than after upgrades by the first rating agency. Additionally, we find evidence for serial correlation in rating changes up to 90 days subsequent to the rating change of interest after controlling for rating changes by the second rating agency.     

Split bond ratings and rating migration

This paper examines the relationships between split ratings and ratings migration. We find that bonds with split ratings are more likely to have future rating changes. A one-notch (more-than-one-notch) split rating increases the probability of rating change within one year of initial issuance by about 3% (6%). Furthermore, we find that about 30% of split rated bonds have their two ratings converge after four years of initial issuance. The rating convergence tapers off after three years, and the rating agency with a higher (lower) initial rating generally maintains a higher (lower) rating in subsequent years if the two ratings do not converge. We also show that rating transition estimation can be improved by taking into consideration split ratings. We find that one-year rating transition matrices are significantly different between non-letter-split rated bonds and letter-split rated bonds, and we show that the difference has an economically significant impact on the pricing of credit spread options and VaR-based risk management models. Overall, our results suggest that split ratings contain important information about subsequent rating changes.     

Do Rating Agencies Benefit from Providing Higher Ratings? Evidence from the Consequences of Municipal Bond Ratings Recalibration

We ask whether credit rating agencies receive higher fees and gain greater market share when they provide more favorable ratings. To investigate this question, we use the 2010 rating scale recalibration by Moody's and Fitch, which increased ratings absent any underlying change in issuer credit quality. Consistent with prior research, we find that the recalibration allowed the clients of Moody's and Fitch to receive better ratings and lower yields. We add to this evidence by showing that the recalibration also led to larger fees and to increases in the market shares of Moody's and Fitch. These results are consistent with critics’ concerns about the effects of the issuer‐pay model on the credit ratings market.     

Timeliness of Spread Implied Ratings

Rating agencies are known to be prudent in their approach to rating revisions, which results in delayed rating adjustments. For a large set of eurobonds we derive credit spread implied ratings and compare them with agency ratings. Our results indicate that spread implied ratings often anticipate the future movement of agency ratings and hence can help track credit risk in a more timely manner. This finding has important implications for risk managers in banks who, under the new Basel 2 regulations, have to rely more on credit ratings for capital allocation purposes, and for portfolio managers who face rating‐related investment restrictions.     

Managerial ability and bond rating changes

This study examines the relation between managerial ability and bond credit rating changes. We attempt to add to the credit rating agency literature by exploring the role managerial ability plays in the initial bond rating assignments and in rating changes. We predict firms with more‐able managers are more likely to have higher bond ratings and to be more able to have a positive influence on rating changes. We find a significant and positive relation between managerial ability and change in credit ratings, suggesting that more‐able managers can take effective actions to improve their credit ratings.     

Does it matter who pays for bond ratings? Historical evidence

We test whether Standard and Poor's (S&P) assigns higher bond ratings after it switches from investor-pay to issuer-pay fees in 1974. Using Moody's rating for the same bond as a benchmark, we find that when S&P charges investors and Moody's charges issuers, S&P's ratings are lower than Moody's. Once S&P adopts issuer-pay, its ratings increase and no longer differ from Moody's. More importantly, S&P only assigns higher ratings for bonds that are subject to greater conflicts of interest, measured by higher expected rating fees or lower credit quality. These findings suggest that the issuer-pay model leads to higher ratings.     

Bayesian inference for issuer heterogeneity in credit ratings migration

Rating transition matrices for corporate bond issuers are often based on fitting a discrete time Markov chain model to homogeneous cohorts. Literature has documented that rating migration matrices can differ considerably depending on the characteristics of the issuers in the pool used for estimation. However, it is also well known in the literature that a continuous time Markov chain gives statistically superior estimates of the rating migration process. It remains to verify and quantify the issuer heterogeneity in rating migration behavior using a continuous time Markov chain. We fill this gap in the literature. We provide Bayesian estimates to mitigate the problem of data sparsity. Default data, especially when narrowing down to issuers with specific characteristics, can be highly sparse. Using classical estimation tools in such a situation can result in large estimation errors. Hence we adopt Bayesian estimation techniques. We apply them to the Moodys corporate bond default database. Our results indicate strong country and industry effects on the determination of rating migration behavior. Using the CreditRisk+ framework, and a sample credit portfolio, we show that ignoring issuer heterogeneity can give erroneous estimates of Value-at-Risk and a misleading picture of the risk capital. This insight is consistent with some recent findings in the literature. Therefore, given the upcoming Basel II implementation, understanding issuer heterogeneity has important policy implications.     

Looking Beyond Credit Ratings: Factors Investors Consider In Pricing European Asset‐Backed Securities

In this paper, we empirically investigate what credit factors investors rely upon when pricing the spread at issue for European asset‐backed securities. More specifically, we investigate how credit factors affect new issuance spreads after taking into account credit rating. We do so by investigating primary market spreads for tranches of non‐mortgage‐related asset‐backed securities issued from 1999 to the year prior to the subprime mortgage crisis, 2007. We find that although credit ratings play a major role in determining spreads, investors appear to not rely exclusively on these ratings. Our findings strongly suggest that investors do not ignore other credit factors beyond the assigned credit rating.     

Credit rating model development: An ordered analysis based on accounting data

In this paper we propose and test a methodology for constructing a credit rating model. We follow a polytomous ordered probit analysis leading to the specification of statistically significant credit rating intervals. We test our model with accounting data of Greek listed firms over the years 2004–2013, a period which includes both the pre-crisis growth and the crisis phase of the Greek economy and the stock market. Using the empirically—based rating categories that the model generates endogenously, we observe not only a clear and timely response of ratings to the changing economic environment, but we also obtain significant predictive ability over a period of one, two and three years.     

Do multiple credit ratings reduce money left on the table? Evidence from U.S. IPOs

Using credit ratings as an uncertainty-reducing mechanism, we provide evidence of the beneficial impact of multiple credit ratings on reducing IPO underpricing and filing price revision. We find that the acquisition of multiple ratings in the pre-IPO period mitigates uncertainty more than the acquisition of a single rating. Multi-rated firms also have higher probabilities of survival than those with a single rating, whereas credit rating levels matter only for IPOs with more than one rating. The IPOs that are awarded the first rating on the borderline between investment and non-investment grades are more likely to seek an additional rating.     

How did increased competition affect credit ratings?

The credit rating industry has historically been dominated by just two agencies, Moody's and Standard & Poor's, leading to long-standing legislative and regulatory calls for increased competition. The material entry of a third rating agency (Fitch) to the competitive landscape offers a unique experiment to empirically examine how increased competition affects the credit ratings market. What we find is relatively troubling. Specifically, we discover that increased competition from Fitch coincides with lower quality ratings from the incumbents: Rating levels went up, the correlation between ratings and market-implied yields fell, and the ability of ratings to predict default deteriorated. We offer several possible explanations for these findings that are linked to existing theories.     

The Effect of Business and Financial Market Cycles on Credit Ratings: Evidence from the Last Two Decades

We analyze the effect of business and financial market cycles on credit ratings using a sample of firms from the Russell 3000 index that are rated by Standard and Poor's over the period 1986–2012. We also examine investor reaction to credit rating actions in different stages of business and financial market cycles. We document that credit rating agencies are influenced by business and financial market cycles; they assign lower credit ratings during downturns of business and financial market cycles and higher ratings during upturns. Our study is the first to find strong evidence of pro‐cyclicality in credit ratings using a long window. We also document stronger investor reaction to negative credit rating actions during downturns. Our results confirm theoretical predictions and inform regulators.     

The impact of sovereign rating actions on bank ratings in emerging markets

This paper analyses the effects of sovereign rating actions on the credit ratings of banks in emerging markets, using a sample from three global rating agencies across 54 countries for 1999–2009. Despite widespread attention to sovereign ratings and bank ratings, no previous study has investigated the link in this manner. We find that sovereign rating upgrades (downgrades) have strong effects on bank rating upgrades (downgrades). The impact of sovereign watch status on bank rating actions is much weaker and often insignificant. The sensitivity of banks’ ratings to sovereign rating actions is affected by the countries’ economic and financial freedom and by macroeconomic conditions. Ratings of banks with different ownership structures are all influenced strongly by the sovereign rating, with some variation depending on the countries’ characteristics. Emerging market bank ratings are less likely to follow sovereign rating downgrades during the recent financial crisis period.     

The impact of credit rating announcements on credit default swap spreads

We document the ability of the credit default swap (CDS) market to anticipate favorable as well as unfavorable credit rating change (RC) announcements based on more extensive samples of credit rating events and CDS spreads than previous studies. We obtain four new results. In contrast to prior published studies, we find that corporate RC upgrades do have a significant impact on CDS spreads even though they are still not as well anticipated as downgrades. Second, CreditWatch (CW) and Outlook (OL) announcements, after controlling for prior credit rating events, lead to significant CARs at the time positive CW and OL credit rating events are announced. Third, we extend prior results by showing that changes in CDS spreads for non-investment-grade credits contain information useful for estimating the probability of negative credit rating events. Fourth, we find that the CDS spread impact of upgrades but not downgrades is magnified during recessions and that upgrades and downgrades also differ as to the impact of simultaneous CW/OL announcements, investment-grade/speculative-grade crossovers, current credit rating, market volatility, and industry effects.     

Sovereign ceilings “lite”? The impact of sovereign ratings on corporate ratings

Although credit rating agencies have gradually moved away from a policy of never rating a corporation above the sovereign (the ‘sovereign ceiling’), it appears that sovereign credit ratings remain a significant determinant of corporate credit ratings. We examine this link using data for advanced and emerging economies over the period of 1995–2009. Our main result is that a sovereign ceiling continues to affect the rating of corporations. The results also suggest that the influence of a sovereign ceiling on corporate ratings remains particularly significant in countries where capital account restrictions are still in place and with high political risk.     

ARE WATCH PROCEDURES A CRITICAL INFORMATIONAL EVENT IN THE CREDIT RATINGS PROCESS? AN EMPIRICAL INVESTIGATION

The Boot, Milbourn, and Schmeits (2006) model (Boot model) predicts certain credit rating events are likely to be more informative than others and that credit watch procedures are an important driver of such differences. We test the core empirical predictions of their model. Our sample comprises U.S. corporate issuer credit ratings provided by Moody's, 1990–2006. Our findings fail to uncover compelling evidence for the empirical predictions of the Boot model in relation to the role of watch procedures as coordinating mechanisms. Rather, our findings are more supportive of the view that rating agencies are always at an informational advantage relative to investors.     

Economic policy uncertainty and sovereign credit rating decisions: Panel quantile evidence for the Eurozone

We employ a panel quantile framework that quantifies the relative importance of quantitative and qualitative factors across the conditional distribution of sovereign credit ratings in the Eurozone area. We find that regulatory quality and competitiveness have a stronger impact for low rated countries whereas GDP per capita is a major driver of high rated countries. A reduction in the current account deficit leads to a rating or outlook upgrade for low rated countries. Economic policy uncertainty impacts negatively on credit ratings across the conditional distribution; however, the impact is stronger for the lower rated countries. In other words, the creditworthiness of low rated countries takes a much bigger ‘hit’ than that of high rated countries when European policy uncertainty is on the rise.     

A comparative study of the probability of default for global financial firms

This article presents a modification of Merton’s (1976) ruin option pricing model to estimate the implied probability of default from stock and option market prices. To test the model, we analyze all global financial firms with traded options in the US and focus on the subprime mortgage crisis period. We compare the performance of the implied probability of default from our model to the expected default frequencies based on the Moody’s KMV model and agency credit ratings by constructing cumulative accuracy profiles (CAP) and the receiver operating characteristic (ROC). We find that the probability of default estimates from our model are equal or superior to other credit risk measures studied based on CAP and ROC. In particular, during the subprime crisis our model surpassed credit ratings and matched or exceeded KMV in anticipating the magnitude of the crisis. We have also found some initial evidence that adding off-balance-sheet derivatives exposure improves the performance of the KMV model.