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.     

Intensified Competition and The Impact on Credit Ratings in the RMBS market

In this paper, we empirically investigate the impact of intensified competition on rating quality in the credit rating market for residential mortgage-backed securities (RMBS) in the period 2017–2020. We provide evidence that competition between large credit rating agencies (CRAs) (Moody's and Standard & Poor's) and newer smaller ones (Dominion Bond Rating Service Morningstar and Kroll Bond Rating Agency) creates credit rating inconsistencies in the RMBS market. While a credit rating should solely represent the underlying credit risk of a RMBS, irrespective of the competition in the market, our results show that this is not the case. When competitive pressure is higher, both large and small CRAs tend to adjust their rating standards (smaller CRAs react to large CRAs and vice versa).     

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.     

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.     

Competition in the credit rating Industry: Benefits for investors and issuers

We empirically investigate the benefits of multiple ratings not only at issuance of debt instruments but also during the subsequent monitoring phase. Using a record of monthly credit rating migration data on all U.S. residential mortgage-backed securities rated by Standard & Poor's, Moody's, and Fitch between 1985 and 2012 (154,600 tranches), our results provide empirical evidence that rating agencies put more effort in rating and outlook revisions when tranches have assigned multiple ratings. Furthermore, we see that in the case of multiple ratings, agencies do a better job in discriminating tranches with respect to default risk. On the downside, we observe a shift in collateral towards senior tranches and incentives for issuers to engage in rating shopping activities, but find no evidence that rating agencies exploit such behavior to attract more rating business. Our results contribute to the literature on information production of credit ratings and extend the perspective to the monitoring period after issuance.     

Uncertainty avoidance,risk tolerance and corporate takeover decisions

In this paper, we examine the role of national culture in corporate takeover decisions, by arguing that managerial risk tolerance (a combination of risk aversion and risk perception), at the national level, is a cultural trait and affects the expected net synergies CEOs require. We propose a theoretical framework that links CEO risk tolerance to the expected net synergies. We empirically show that CEOs of firms located in countries with lower levels of risk tolerance, measured by Hofstede’s (1980, 2001) uncertainty avoidance score, require higher premiums on takeovers, and show that uncertainty avoidance plays a greater role in relatively large takeovers. Additional testing reveals that CEOs from high uncertainty avoiding nations engage less in cross-border/cross-industry takeovers, suggesting that uncertainty avoidance captures more the CEO’s risk perception than his/her risk aversion.     

New debt issues and earnings management

We empirically examine the influence and effects of real earnings management (REM) procedures on the debt market by investigating the bond rating and actual market price of a firm's new debt offerings. Extant research provides conflicting representations concerning the effects of REM techniques on equity shareholders and debt market participants. Our results indicate a negative association between all three REM manipulation methods and perceived credit risk resulting in a lower bond rating, and higher market yield of the firm's debt at issuance. Additional analyses exploring the use of REM techniques to achieve analyst's earnings forecasts indicates that this negative effect is particularly significant for firms who only achieve the earnings forecast by utilizing REM methods. Our research adds to the literature by empirically describing the effects of REM techniques on new debt issuances, and contributes to the ongoing debate regarding the efficacy of engaging in real earnings management to achieve known targets.     

The credit rating process and estimation of transition probabilities: A Bayesian approach

The Basel II Accord requires banks to establish rigorous statistical procedures for the estimation and validation of default and ratings transition probabilities. This raises great technical challenges when sufficient default data are not available, as is the case for low default portfolios. We develop a new model that describes the typical internal credit rating process used by banks. The model captures patterns of obligor heterogeneity and ratings migration dependence through unobserved systematic macroeconomic shocks. We describe a Bayesian hierarchical framework for model calibration from historical rating transition data, and show how the predictive performance of the model can be assessed, even with sparse event data. Finally, we analyze a rating transition data set from Standard and Poor's during 1981–2007. Our results have implications for the current Basel II policy debate on the magnitude of default probabilities assigned to low risk assets.     

Credit rating dynamics and Markov mixture models

Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on empirical evidence, we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firm’s ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical current credit ratings can have substantially different transition probability vectors. We also find that conditioning on the state of the business cycle or industry group does not remove the heterogeneity with respect to the rate of movement. We go on to compare the performance of mixture and Markov chain using out-of-sample predictions.     

Modeling Eurobond credit ratings and forecasting downgrade probability

This article proposes and empirically tests a two-step model to forecast the downgrade probability of sterling-denominated Eurobonds. In the first step, the conditional expectation of credit rating is estimated, employing an ordered probit. In the second step, the likelihood of downgrade is modeled using credit rating, as obtained from the conditional mean in the first step, alongside with traditional operating measures in a binary-probit framework. By parameterizing a system of two equations, we are able to accommodate the disentangled effect of credit quality and company financial information on the downgrade risk. We find evidence of a nonlinear response to shifts in both credit rating and leverage. The model's forecasting performance is ascertained by means of cross validation and is benchmarked against both a naive model and a neural network model.     

Selecting copulas for risk management

Copulas offer financial risk managers a powerful tool to model the dependence between the different elements of a portfolio and are preferable to the traditional, correlation-based approach. In this paper, we show the importance of selecting an accurate copula for risk management. We extend standard goodness-of-fit tests to copulas. Contrary to existing, indirect tests, these tests can be applied to any copula of any dimension and are based on a direct comparison of a given copula with observed data. For a portfolio consisting of stocks, bonds and real estate, these tests provide clear evidence in favor of the Student’s t copula, and reject both the correlation-based Gaussian copula and the extreme value-based Gumbel copula. In comparison with the Student’s t copula, we find that the Gaussian copula underestimates the probability of joint extreme downward movements, while the Gumbel copula overestimates this risk. Similarly we establish that the Gaussian copula is too optimistic on diversification benefits, while the Gumbel copula is too pessimistic. Moreover, these differences are significant.     

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.     

Reading between the ratings: Modeling residual credit risk and yield overlap

Credit ratings group firms by risk, yet yields are shown to overlap between firms of adjacent ratings. We model this by considering the residual risk arising from differences in the parameters of each firm's value process for firms with the same rating. To do so, our framework simultaneously incorporates jump default with Markov-governed likelihoods and continuous defaults in a default-barrier framework. We provide closed-form approximations for expected default time and tail probabilities, and empirically fit the S-shaped yield curve, intra-rating spread, and inter-rating overlap. Results are robust to time period, rating system, sub-rating, and common characteristics such as liquidity.     

Risk structure of interest rates: an empirical analysis for Deutschemark‐denominated bonds

This paper empirically studies the risk structure of interest rates for Deutschemark‐denominated bonds. For this purpose, we estimate term structures of interest rates using the parsimonious fitting function of Nelson and Siegel (1987) for virtually risk free Government bonds and five different rating categories classified by Moody's ratings (Aaa, Aa, A, Baa, Ba). The sample period covers the time interval from July 1990 to December 1996. We investigate the pricing errors resulting from our estimation procedure and analyse credit spreads over the term structure of Government bonds.     

Firm Opacity Lies in the Eye of the Beholder

We classify and test empirical measures of firm opacity and document theoretical and empirical inconsistencies across these proxies by testing the relative opacity of banks versus non‐banks. We evaluate the effectiveness of these proxies by observing the effect of two cleanly identified shocks to firm‐specific information: credit rating initiation and inclusion in the S&P 500 index. Using a difference‐in‐difference approach, we compare firms that are newly rated and firms that are included in the S&P 500 index with a propensity matched sample of “unchanged” firms. We find that only the number of analysts and Amihud's illiquidity ratio provide consistent patterns across different estimation specifications and different econometric settings. These two proxies show that banks are more opaque than non‐banks. Based on our tests, we recommend that these proxies be used as the primary measures of firm opacity.     

公司透明度与股价波动同步性的相关性研究

本文选取2001-2007年在深交所上市的所有A股上市公司为研究样本,以深交所的信息披露评级指标作为公司信息透明度的度量指标,在此基础上,考察公司透明度对股价波动同步性的影响。实证结果表明,随着公司信息透明度的改善,股价同波动性不断下降,但各信用评级等级之间的股价同波动性差异并不高。本文给出了可能的分析解释,并指出了下一步深入研究的方向。     

The impact of terrorism on financial markets: An empirical study

The main focus of this paper is to study empirically the impact of terrorism on the behavior of stock, bond and commodity markets. We consider terrorist events that took place in 25 countries over an 11-year time period and implement our analysis using different methods: an event-study approach, a non-parametric methodology, and a filtered GARCH-EVT approach. In addition, we compare the effect of terrorist attacks on financial markets with the impact of other extreme events such as financial crashes and natural catastrophes. The results of our analysis show that a non-parametric approach is the most appropriate method among the three for analyzing the impact of terrorism on financial markets. We demonstrate the robustness of this method when interest rates, equity market integration, spillover and contemporaneous effects are controlled. We show how the results of this approach can be used for investors’ portfolio diversification strategies against terrorism risk.     

Testing for explosive bubbles in the presence of autocorrelated innovations

We analyze an empirically important issue with recursive right-tailed unit root tests for bubbles in asset prices. First, we show that serially correlated innovations, which is a feature that is present in most financial series used to test for bubbles, can lead to severe size distortions when using either fixed or automatic (based on information criteria) lag-length selection in the auxiliary regressions underlying the test. Second, we propose a sieve-bootstrap version of these tests and show that this results in tests which control size well across a number of simulation designs both with and without highly autocorrelated innovations. We also find that these improvements in size come at a relatively low cost for the power of the tests. Finally, we apply the bootstrap tests on the housing market of OECD countries, and generally find much weaker evidence of housing bubbles compared to existing evidence.     

Testing the local volatility assumption: a statistical approach

In practice, the choice of using a local volatility model or a stochastic volatility model is made according to their respective ability to fit implied volatility surfaces. In this paper, we adopt a different point of view. Indeed, using a purely statistical methodology, we design new procedures aiming at testing the assumption of a local volatility model for the price dynamics, against the alternative of a stochastic volatility model. These test procedures are based only on historical data and do not require any calibration procedures via option prices. We also provide a convincing simulation study and an empirical analysis on future contracts on interest rates.     

How does capital affect bank performance during financial crises?

This paper empirically examines how capital affects a bank’s performance (survival and market share) and how this effect varies across banking crises, market crises, and normal times that occurred in the US over the past quarter century. We have two main results. First, capital helps small banks to increase their probability of survival and market share at all times (during banking crises, market crises, and normal times). Second, capital enhances the performance of medium and large banks primarily during banking crises. Additional tests explore channels through which capital generates these effects. Numerous robustness checks and additional tests are performed.