Credit Rating Migrations Research Articles

Research on the effect of multiple credit ratings from the perspective of financial regulatory systems in Chinese bond market.

This paper, starting from the effects of financial regulatory policies, considers the interaction between Chengxin_Moody and Lianhe_Fitch with the dual rating system and the multi-rating system, constructs a new ordered Logit model, and attempts to explore the impact of the Notice, the dual rating system and the multiple rating system on the probability of Chinese corporate bond defaults, rating upgrades, rating downgrades, and the magnitude of credit rating migrations. This study compares the effectiveness of different rating regulatory systems. Using nine thousand two hundred and sixty-two data of Chinese corporate bonds as the research samples. Empirical analysis and robustness test reveal the following findings: (1) The issuance of the Notice has a significant positive effect on the implementation of both the dual rating system and the multiple rating system, with a greater impact on the implementation of the multiple rating system; (2) The issuance of the Notice, along with the dual rating system and the multiple rating system, can all reduce the probability of corporate bond defaults, with the multiple rating system showing the best preventive effect against corporate bond defaults; (3) The dual rating system is more effective in promoting rating agencies to adjust rating behaviors, accurately correcting corporate bond ratings and effectively alleviating the issue of rating inflation. Competition among rating agencies intensifies rating shopping; (4) Under the dual rating system, rating agencies are more likely to expand the magnitude of credit rating upgrades and downgrades, enhancing the differentiation between high-quality corporate bonds and junk corporate bonds; (5) The selection of rating information from Chengxin_Moody and Lianhe_Fitch can appropriately adjust the degree of flexibility or tightening in response to rating regulatory systems. Chengxin_Moody demonstrates a more sensitive reaction compared to Lianhe_Fitch regarding the rating regulatory systems. This study provides valuable references for the formulation and evaluation of the effectiveness of Chinese corporate bond rating regulatory policies.

Variational inequalities arising from credit rating migration with buffer zone

AbstractIn Chen and Liang previous work, a model, together with its well-posedness, was established for credit rating migrations with different upgrade and downgrade thresholds (i.e. a buffer zone, also called dead band in engineering). When positive dividends are introduced, the model in Chen and Liang (SIAM Financ. Math.12, 941–966, 2021) may not be well-posed. Here, in this paper, a new model is proposed to include the realistic nonzero dividend scenarios. The key feature of the new model is that partial differential equations in Chen and Liang (SIAM Financ. Math.12, 941–966, 2021) are replaced by variational inequalities, thereby creating a new free boundary, besides the original upgrading and downgrading free boundaries. Well-posedness of the new model, together with a few financially meaningful properties, is established. In particular, it is shown that when time to debt paying deadline is long enough, the underlying dividend paying company is always in high grade rating, that is, only when time to debt paying deadline is within a certain range, there can be seen the phenomenon of credit rating migration.

Bond-CDS implied rating systems

Market-implied ratings gained importance as efficient early warnings of official credit rating migrations. We build a two-dimensional implied rating system based on bonds and CDS spreads, and test its forecast performances on a set of worldwide sovereign issuers. We show that the method efficiently uses the information from both markets, and is able to outperform implied ratings based on recent machine learning techniques.

Numerical estimates of risk factors contingent on credit ratings

Assuming a favorable or an adverse outcome for every combination of a credit class and an industry sector, a binary string, termed as a macroeconomic scenario, is considered. Given historical transition counts and a model for dependence among credit-rating migrations, a probability is assigned to each of the scenarios by maximizing a likelihood function. Applications of this distribution in financial risk analysis are suggested. Two classifications are considered: 7 non-default credit classes with 6 industry sectors and 2 non-default credit classes with 12 industry sectors. We propose a heuristic algorithm for solving the corresponding maximization problems of combinatorial complexity. Probabilities and correlations characterizing riskiness of random events involving several industry sectors and credit classes are reported.

A Free Boundary Problem for Corporate Bond Pricing and Credit Rating Under Different Upgrade and Downgrade Thresholds

In this paper, a new model for corporate bond pricing and credit rating is proposed. In this model, credit rating migrations are assumed to depend on the ratio of debt and asset value of the underlying company, where debt is assumed to be a zero coupon corporate bond and asset value follows a geometric Brownian motion with volatility and dividend depending on credit rating. There is a buffer zone (called deadband in engineering) in credit rating migration, so upgrade and downgrade thresholds are different. Mathematically, this model is a system of partial differential equations with two free boundaries that correspond to the hitting boundaries in state space to upgrade and downgrade credit rating, respectively. The existence, uniqueness, regularity, and asymptotic behavior of the solution and free boundaries with zero dividend are obtained. Some nonzero dividend cases are discussed with numerical results.

Capturing model risk and rating momentum in the estimation of probabilities of default and credit rating migrations

We present two methodologies on the estimation of rating transition probabilities within Markov and non-Markov frameworks. We first estimate a continuous-time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computational time needed for the Wald confidence interval by a factor of a half. We provide an efficient procedure for transferring such uncertainties from the generator matrix of the Markov chain to the corresponding rating migration probabilities and, crucially, default probabilities. For our second contribution, we assume access to the full (continuous) data set and propose a tractable and parsimonious self-exciting marked point processes model able to capture the non-Markovian effect of rating momentum. Compared to the Markov model, the non-Markov model yields higher probabilities of default in the investment grades, but also lower default probabilities in some speculative grades. Both findings agree with empirical observations and have clear practical implications. We use Moody's proprietary corporate credit rating data set. Parts of our implementation are available in the R package ctmcd.

Statistical inference for Markov chains with applications to credit risk

The focus of this paper is on the derivation of confidence and credibility intervals for Markov chains when discrete-time, continuous-time or discretely observed continuous-time data are available. Thereby, our contribution is threefold: First, we discuss and compare multinomial confidence regions for the rows of discrete-time Markov transition matrices in the light of empirical characteristics of credit rating migrations. Second, we derive an analytical expression for the expected Fisher information matrix of a continuous-time Markov chain which is used to construct credibility intervals using a non-informative Jeffreys prior distribution and a Metropolis-Hastings Algorithm. Third, we concretize profile and estimated/pseudo likelihood based confidence intervals in the continuous-time data settings, which in contrast to asymptotic normality based intervals explicitly consider non-negativity constraints for the parameters. Furthermore, we illustrate the described methods by Moody’s corporate ratings data with exact continuous-time transitions.

Analysis of credit-rating migrations with genetic algorithms

Modelling dependent credit-rating migrations of assets classified into M credit classes and S industries, M × S + 2M×S parameters have to be estimated. For a realistic choice of M and S, this number is huge and it greatly exceeds the number of available observations. To avoid brute-force calculations, we suggest sequential and parallel genetic algorithms. Considering a practically important combination of M = 7 and S = 6, the approach is tested on Standard and Poor's data.

Identification of hidden Markov chains governing dependent credit-rating migrations

ABSTRACTThree models of dependent credit-rating migrations are considered. Each of them entails a coupling scheme and a discrete-time Markovian macroeconomic dynamics. Every credit-rating migration is modeled as a mixture of an idiosyncratic and a common component. The larger is the pool of debtors affected by the same common component, the stronger is the dependence among migrations. The distribution of the common component depends on macroeconomic conditions. At every time instant, the resulting allocation of debtors to credit classes and industries follows a mixture of multinomial distributions. Dealing with M non default credit classes, there are 2M theoretically possible macroeconomic outcomes. Only few of them occur with a positive probability. Restricting the macroeconomic dynamics to such outcomes simplifies estimation. A heuristics for identifying them is suggested. Using the maximum likelihood method, it was tested on a Standard and Poor's (S&P's) data set.

Traces of business cycles in credit-rating migrations.

Using migration data of a rating agency, this paper attempts to quantify the impact of macroeconomic conditions on credit-rating migrations. The migrations are modeled as a coupled Markov chain, where the macroeconomic factors are represented by unobserved tendency variables. In the simplest case, these binary random variables are static and credit-class-specific. A generalization treats tendency variables evolving as a time-homogeneous Markov chain. A more detailed analysis assumes a tendency variable for every combination of a credit class and an industry. The models are tested on a Standard and Poor’s (S&P’s) dataset. Parameters are estimated by the maximum likelihood method. According to the estimates, the investment-grade financial institutions evolve independently of the rest of the economy represented by the data. This might be an evidence of implicit too-big-to-fail bail-out guarantee policies of the regulatory authorities.

Numerical Modeling of Dependent Credit Rating Transitions with Asynchronously Moving Industries

Two models of dependent credit rating migrations governed by industry-specific Markovian matrices, are considered. Caused by macroeconomic factors, positive and negative unobserved tendencies, encoded as values “1” or “0” of the corresponding variables, modify the transition probabilities and render the evolutions dependent. They are neither synchronized across industry sectors, nor over credit classes: an upswing in some of them can coexist with a decline of the rest. The models are tested on Standard and Poor’s data. MATLAB optimization software and maximum likelihood estimators are used. Obtained distributions of the hidden variables demonstrate that the considered industries migrate asynchronously trough credit classes. Since downgrading probabilities are less affected by the unobserved tendencies, estimated by Monte-Carlo simulations distributions of defaults, exhibit lighter, than for the known coupling models, tails for schemes with asynchronously moving industries. Moreover, the lightest tails were obtained in the case of industry-specific transition matrices.

A Novel Credit Rating Migration Modeling Approach Using Macroeconomic Indicators

Modeling credit rating migrations conditional on macroeconomic conditions allows financial institutions to assess, analyze, and manage the risk related to a credit portfolio. Existing methodologies to model credit rating migrations conditional on the business cycle suffer from poor accuracy, difficult readability, or model inconsistencies. The modeling methodology proposed in this paper extends ordinal logistic regression to estimate the complete migration matrix including default rates as a function of rating dynamics and macroeconomic indicators. The gradient and Hessian derivations show efficient optimization within the Levenberg–Marquardt algorithm. The proposed modeling methodology is applied to model corporate rating migrations using historical data from 1984 to 2011. It is shown that the resulting model captures the cyclical behavior of credit rating migrations and default rates, and is able to approximate historic migration levels with good precision. The model therefore permits analysis of the impact of economical downturn conditions on a credit portfolio. Copyright © 2013 John Wiley & Sons, Ltd.

Monounireducible Nonhomogeneous Continuous Time Semi-Markov Processes Applied to Rating Migration Models

Monounireducible nonhomogeneous semi- Markov processes are defined and investigated. The mono- unireducible topological structure is a sufficient condition that guarantees the absorption of the semi-Markov process in a state of the process. This situation is of fundamental importance in the modelling of credit rating migrations because permits the derivation of the distribution function of the time of default. An application in credit rating modelling is given in order to illustrate the results.

Portfolio Credit Derivatives Based on Rating Migration

This thesis discusses portfolio credit derivatives which offer rating migration protection on a portfolio of assets. Credit rating migrations are interesting, both from an originator's and an investor's point of view. After discussing the possibilities and problems of expressing a view on rating migration through default-sensitive instruments, we introduce a new portfolio credit derivative. Applications and payoff examples are provided, as well as a chapter on the modelling and pricing of the product.

Time series patterns in credit ratings

This article offers a substitute setting to simulate credit rating migrations. The internal correlations model tracks time-series movements within credit rating entries, rather than cross-ratings correlations. The proposed nonhomogeneous process is authenticated through the likelihood ratio Dickey–Fuller test, and is found to be statistically and economically significant, by better fitting observed cumulative default rates. Several nonlinear regression models assist to better identify these time-related patterns. The economic structure underlying the time dependency often corresponds to changes in GDP, business cycles, and market risk. Furthermore, significant positive autocorrelation is detected mostly among downgrade probabilities.

The multi-state latent factor intensity model for credit rating transitions

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semi-Markov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model.

The Multi-State Latent Factor Intensity Model for Credit Rating Transitions

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions between investment grade, subinvestment grade, and default ratings for U.S. corporates. The model strongly suggests the presence of a common dynamic component that can be interpreted as the credit cycle. We also show that the impact of this credit cycle is asymmetric with respect to downgrade and upgrade probabilities.

Testing Conflicts of Interest at Bond Rating Agencies with Market Anticipation: Evidence that Reputation Incentives Dominate

This paper presents the first comprehensive test of whether well-known conflicts of interest at bond rating agencies importantly influence their actions. This hypothesis is tested against the alternative that rating agency actions are primarily influenced by a countervailing incentive to protect their reputations as delegated monitors. These two hypotheses generate a number of testable predictions regarding the anticipation of credit-rating downgrades by the bond market, which we investigate using a new data set of about 2,000 credit rating migrations from Moody's and Standard & Poor's, and matching issuer-level bond prices. The findings strongly indicate that rating changes do not appear to be importantly influenced by rating agency conflicts of interest but, rather, suggest that rating agencies are motivated primarily by reputation-related incentives.

Testing Conflicts of Interest at Bond Rating Agencies with Market Anticipation: Evidence that Reputation Incentives Dominate

This paper presents the first comprehensive test of whether well-known conflicts of interest at bond rating agencies importantly influence their actions. This hypothesis is tested against the alternative that rating agency actions are primarily influenced by a countervailing incentive to protect their reputations as delegated monitors. These two hypotheses generate a number of testable predictions regarding the anticipation of credit-rating downgrades by the bond market, which we investigate using a new data set of about 2,000 credit rating migrations from Moody's and Standard & Poor's, and matching issuer-level bond prices. The findings strongly indicate that rating changes do not appear to be importantly influenced by rating agency conflicts of interest but, rather, suggest that rating agencies are motivated primarily by reputation-related incentives.

Credit Risk and Risk Neutral Default Probabilities: Information About Rating Migrations and Defaults

Default probabilities are important to the credit markets. Changes in default probabilities may forecast credit rating migrations to other rating levels or to default. Such rating changes can affect the firm’s cost of capital, credit spreads, bond returns, and the prices and hedge ratios of credit derivatives. While rating agencies such as Moodys and Standard & Poors compute historical default frequencies, option models can also be used to calculate forward looking or expected default frequencies. In this paper, we compute risk neutral probabilities or default (RNPD) using the diffusion models of Merton (1974) and Geske (1977). It is shown that the Geske model produces a term structure of RNPD’s, and the shape of this term structure may forecast impending credit events. Next, it is shown that these RNPD’s serve as bounds to estimates of actual default probabilities. Furthermore, the RNPD’s exhibit the same comparative statics as the estimates of actual default probabilities. Also, the risk neutral default probabilities may be more accurately estimated than actual default probabilities because they do not require an estimate of the firm’s drift. Given these similarities and advantages of RNPD’s, their estimates may possess significant information about credit events. To confirm this an event study of the relation between RNPD and rating migrations is conducted. We first show that these RNPD’s from both the Merton and Geske models do possess significant and very early information about credit rating migrations.While the sample of firms that actually default during this time period is small, changes in the shape of the term structure of default probabilities appears to detect impending migrations to default. This is shown to be consistent with an inverted term structure of default probabilities, where prior to an impending default, the short term default probability is higher than the forward default probability. Finally, since rating migrations to either lower ratings or to default can be detected months in advance these credit events may not be a surprise.