Credit Rating Transitions Research Articles

Time series properties of corporate credit rating transitions by regions and rating agencies

ABSTRACT The study confirms the time series properties of the credit rating transitions and finds commonalities and differences across regions and rating agencies. Significant differences were found between regions but not between rating agencies.

A new estimation of default probabilities based on non-performing loans

We model the one-year ahead probability of default of Spanish non-financial corporations. While most of the literature defines default based on bankruptcy filings, we define default as having non-performing loans during at least three months in a given year. This broader definition allows to predict firms’ financial distress at an earlier stage, before their financial conditions are too deteriorated. We also carry out two applications of our prediction models: we assess a program implemented by the Spanish government to provide direct aid to firms severely affected by the Covid-19 crisis and we construct credit rating transition matrices.

Time series properties of corporate credit rating transitions by regions and rating agencies

Time series properties of corporate credit rating transitions by regions and rating agencies

Composite Likelihood for Stochastic Migration Model with Unobserved Factor

Abstract We introduce the conditional maximum composite likelihood (MCL) estimation method for the stochastic factor ordered probit model of credit rating transitions of firms. This model is recommended for internal credit risk assessment procedures in banks and financial institutions under the Basel III regulations. Its exact likelihood function involves a high-dimensional integral, which can be approximated numerically before maximization. However, the estimated migration risk and required capital tend to be sensitive to the quality of this approximation, potentially leading to statistical regulatory arbitrage. The proposed conditional MCL estimator circumvents this problem and maximizes the composite log-likelihood of the factor ordered probit model. We present three conditional MCL estimators of different complexity and examine their consistency and asymptotic normality when n and T tend to infinity. The performance of these estimators at finite T is examined and compared with a granularity-based approach in a simulation study. The use of the MCL estimator is also illustrated in an empirical application.

Evaluating changes in credit rating quality of U.S. farmer cooperatives

The default of a cooperative has significant implications on cooperative members and the agricultural supply chain. Therefore, monitoring and even predicting future changes in creditworthiness is of value to cooperative managers and their lenders. However, we know little about farmer cooperatives' credit profiles and behavior because their financial statements are seldom shared. Using a Moody’s credit rating model and a unique data set, this article estimates Markov chains to evaluate changes in farmer cooperatives’ credit quality. The unconditional (one-size-fits-all) probability matrix, as is typically estimated, is shown to not be appropriate in describing credit rating transitions. Results also show cooperatives do not exhibit rating change momentum since a downgrade is not likely to be followed by another downgrade in the next period. Credit ratings of farmer cooperatives with less than $20 million in net sales follow a first-order Markov chain with stationary probabilities and the cooperatives with net sales of more than $250 million follow a zero-order Markov chain. This article adds to the limited research available on the credit rating behavior of farmer cooperatives. Cooperative managers, directors, and lenders can utilize these findings to make more informed decisions to impact future credit ratings.

An Analytical Expression for Credit Valuation Adjustment Pricing with Wrong-Way Risk

Recently, financial institutions were required to provide the financial derivatives instrument level credit valuation adjustment (CVA) by the new accounting standard. CVA trading desks are facing difficulties to calculate a netting-set level CVA with wrong-way risk (WWR) since the dynamics of the exposures and probability of default (PD) are separated and calculated by different counterparty credit risk (CCR) computing systems. Another difficult work is that the netting-set level CVA mixed the pricing models for all trades under a netting-set. It is significant to develop a new CVA model that is based on the credit adjustment to the existing pricing model under one risk-neutral framework. This paper presents the work on CVA with WWR under the credit deterioration dynamics in both normal and stressed economic conditions. In terms of the double-correlation structure that is constructed based on the Gaussian latent variable models we propose an analytical expression of CVA for the fundamental financial derivatives such as futures or forwards contracts. The double-correlation structure captures the market- and asset-credit correlations. The proposed CVA pricing framework is based on the credit deterioration dynamics rather than default dynamics. The credit deterioration index (CDI) is defined as the limit of the credit deterioration variable and calculated using the rating agency credit rating transition data. The proposed CVA with WWR model is a function of the correlations, CDI, counterparty probability of default, loss given default, interest rate and volatility of the traded derivatives. The market- and asset-credit correlation parameters are calibrated to either the normal or stressed market. Under the stressed market, the scenario design, shock variable selection and shock magnitude are discussed. The numerical results show that the CVA is an increasing function of the market-credit correlation and a decreasing function of the credit rating. The stressed CVA is about four times higher than the normal CVA.

A pricing model with dynamic credit rating transition matrixes

A pricing model with dynamic credit rating transition matrixes

Statistical Surveillance of Structural Breaks in Credit Rating Dynamics.

The 2007–2008 financial crisis had severe consequences on the global economy and an intriguing question related to the crisis is whether structural breaks in the credit market can be detected. To address this issue, we chose firms’ credit rating transition dynamics as a proxy of the credit market and discuss how statistical process control tools can be used to surveil structural breaks in firms’ rating transition dynamics. After reviewing some commonly used Markovian models for firms’ rating transition dynamics, we present several surveillance rules for detecting changes in generators of firms’ rating migration matrices, including the likelihood ratio rule, the generalized likelihood ratio rule, the extended Shiryaev’s detection rule, and a Bayesian detection rule for piecewise homogeneous Markovian models. The effectiveness of these rules was analyzed on the basis of Monte Carlo simulations. We also provide a real example that used the surveillance rules to analyze and detect structural breaks in the monthly credit rating migration of U.S. firms from January 1986 to February 2017.

Sequence-based clustering applied to long-term credit risk assessment

This paper studies the effectiveness of estimating credit rating transition matrices using sequence-based clustering on historical credit rating sequences. The data set used in this study consisted of monthly credit rating sequences from Korean companies from 1986 to 2018. The credit rating sequences were converted to sequence matrices and was clustered using PCA-guided K-means. Representative transition matrices of the resulting clusters were then generated to be used in the classification process. The proposed clustering model is evaluated under the 3 different long-term classification scenarios; 7 class credit rating prediction, credit rating transition direction (upgrade, stay, or downgrade) prediction, and default behaviour prediction. All three classification scenarios produced promising results suggesting that the representative transition matrix of the K clusters better describes future credit rating behaviour than a single transition matrix.

Credit Rating Downgrade Risk on Equity Returns

We develop a four-factor model intended to capture size, value, and credit rating transition patterns in excess returns for a panel of predominantly mid- and large-cap entities. Using credit transition matrices and rating histories from 48 US issuers, we provide evidence to support a statistically significant negative downgrade risk premium in excess returns, suggesting that stocks at higher risk of failure tend to deliver lower returns. The performance of the model remains robust across several estimation methods. Panel Granger causality test results indicate that there indeed is a Granger-causal relationship from credit rating transition probabilities to excess returns. Our paper thus provides a new methodology to generate firm-level downgrade probabilities and the basis for further empirical validation and development of Fama-French-type models under financial distress.

Firm’s Credit Risk in the Presence of Market Structural Breaks

The financial crises which occurred in the last several decades have demonstrated the significant impact of market structural breaks on firms’ credit behavior. To incorporate the impact of market structural break into the analysis of firms’ credit rating transitions and firms’ asset structure, we develop a continuous-time modulated Markov model for firms’ credit rating transitions with unobserved market structural breaks. The model takes a semi-parametric multiplicative regression form, in which the effects of firms’ observable covariates and macroeconomic variables are represented parametrically and nonparametrically, respectively, and the frailty effects of unobserved firm-specific and market-wide variables are incorporated via the integration form of the model assumption. We further develop a mixtured-estimating-equation approach to make inference on the effect of market variations, baseline intensities of all firms’ credit rating transitions, and rating transition intensities for each individual firm. We then use the developed model and inference procedure to analyze the monthly credit rating of U.S. firms from January 1986 to December 2012, and study the effect of market structural breaks on firms’ credit rating transitions.

An extended likelihood framework for modelling discretely observed credit rating transitions

The estimation of the parameters of a continuous-time Markov chain from discrete-time observations, also known as the embedding problem for Markov chains, plays in particular an important role for the modeling of credit rating transitions. This missing data problem boils down to a latent variable setting and thus, maximum likelihood estimation is usually conducted using the expectation-maximization (EM) algorithm. We illustrate that the EM algorithm is likely to get stuck in local maxima of the likelihood function in this specific problem setting and adapt a stochastic approximation simulated annealing scheme (SASEM) as well as a genetic algorithm (GA) to combat this issue. Above that, our main contribution is to extend our method GA by a rejection sampling scheme, which allows one to derive stochastic monotone maximum likelihood estimates in order to obtain proper (non-crossing) multi-year probabilities of default. We advocate the use of this procedure as direct constrained optimization (of the likelihood function) will not be numerically stable due to the large number of side conditions. Furthermore, the monotonicity constraint enables one to combine structural knowledge of the ordinality of credit ratings with real-life data into a statistical estimator, which has a stabilizing effect on far off-diagonal generator matrix elements. We illustrate our methods by Standard and Poor’s credit rating data as well as a simulation study and benchmark our novel procedure against an already existing smoothing algorithm.

Credit rating process

PurposeThe purpose of this paper is to analyze which key financial factors are appropriate for measuring a credit rating score for family firms. In the recent literature, there exists a vast number of studies which evaluates performance differences between family and non-family firms (NFF). However an analysis with regards to a distinction between credit rating scores of family-orientated businesses compared to their counterparts in Austria has not been examined so far.Design/methodology/approachIn order to bridge this research gap, an empirical model based on Moody’s credit rating methodology is used to address these issues. Therefore, the relevant data were taken from the 600 largest, both listed and non-listed, companies of Austria. The statistical measurements refer to a comparison of the means resulting from quantitative rating categories (profitability, leverage structure, liquidity development and firm size).FindingsThe results of this empirical research show that family firms achieve better values in profitability, leverage structure and liquidity development based on credit rating scores. Only firm size represents no significant differences between family and NFF.Originality/valueThis study will contribute to the existing literature in the academic area of family business research and offers a framework for future empirical analysis in this field. Furthermore, this paper provides important information that will help both family and NFF accomplish their financial strategies related to credit rating transitions.

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.

Modeling dependent credit rating transitions: a comparison of coupling schemes and empirical evidence

Three coupling schemes for generating dependent credit rating transitions are compared and empirically tested. Their distributions, the corresponding variances and default correlations are characterized. Using Standard and Poor’s data for OECD countries, parameters of the models are estimated by the maximum likelihood method and MATLAB optimization software. Two pools of debtors are considered: with 5 and with 12 industry sectors. They are classified into two non-default credit classes. First portfolio mimics the Dow Jones iTraxx EUR market index. The default correlations evaluated for 12 industry sectors are confronted with their counterparts known for the US economy.

Numerical analysis of rating transition matrix depending on latent macro factor via nonlinear particle filter method

We propose a new nonlinear filtering model for a better estimation of credit rating transition matrix consistent with the hypothesis that rating transition intensities as well as dynamics of financial asset prices depend on some unobservable macroeconomic factor. We attempt a branching particle filter method to numerically obtain the conditional distribution of the latent factor. For an illustration, we analyze a rating transition history of Japanese enterprises. As a result, we realize that our model can capture some contagion effect of credit events and an interpolative role of financial market information on the rating transition intensities.

Maximum likelihood estimation for left-censored survival times in an additive hazard model

Motivated by an application from finance, we study randomly left-censored data with time-dependent covariates in a parametric additive hazard model. As the log-likelihood is concave in the parameter, we provide a short and direct proof of the asymptotic normality for the maximal likelihood estimator by applying a result for convex processes from Hjort and Pollard (1993). The technique also yields a new proof for right-censored data. Monte Carlo simulations confirm the nominal level of the asymptotic confidence intervals for finite samples, but also provide evidence for the importance of a proper variance estimator. In the application, we estimate the hazard of credit rating transition, where left-censored observations result from infrequent monitoring of rating histories. Calendar time as time-dependent covariates shows that the hazard varies markedly between years.

Predictive Effect of Economic and Market Variations on Structural Breaks in Credit Rating Dynamics

Recent studies have shown that firms credit rating transition process is not stationary and may have structural breaks. To study the predictability of structural breaks, we develop a predictive model for latent structural breaks in firms rating transition dynamics, using historical records of (highdimensional) economic and market fundamentals. As a large number of economic and market variables are sometimes involved in the study, we also introduce an inference procedure that select and estimate important economic factors at the same time from the high-dimensional factor space. Based on an empirical study using the U.S. firms’ credit rating transition records and the history of economic and market variations from 1986 to 2013, we demonstrate that not all structural breaks are black-swan events and some of them can be estimated and predicted up to certain extent.

A Stochastic Model of Corporate Lifespan Based on Corporate Credit Ratings

Credit rating agencies and corporate lifecycles have been a subject of interest for practitioners and academics during the recent period of worldwide economic and debt crises. In this article, we examine what corporate lifespan the credit rating agencies predict. We employ the reliability theory commonly used in engineering and solve a Markov model based on the credit rating transition matrices issued by the Standard & Poor's rating agency. The results show that every company will eventually default in the long-term. However, the mean time to default differs according to the initial conditions of the model, which are represented by the initial credit rating. We considered a company as having initial speculative grades of B and CCC/C and calculated the mean time to default and the time after which the business can be considered safe, with a probability of only 50%. We also determined the probabilities of the individual rating grades. We suggest assessing corporate business cycles in probabilistic terms, taking into account all possible states and initial conditions.

Modelling tail credit risk using transition matrices

Innovative transition matrix techniques are used to compare extreme credit risk for Australian and US companies both prior to and during the global financial crisis (GFC). Transition matrix methodology is traditionally used to measure Value at Risk (VaR), a measure of risk below a specified threshold. We use it to measure Conditional Value at Risk (CVaR) which is the risk beyond VaR. We find significant differences in VaR and CVaR measurements in both the US and the Australian markets. We also find a greater differential between VaR and CVaR for the US as compared to Australia, reflecting the more extreme credit risk that was experienced in the US during the GFC. Traditional transition matrix methodology assumes that all borrowers of the same credit rating transition equally, whereas we incorporate an adjustment based on industry share price fluctuations to allow for unequal transition among industries. Our revised model shows greater change between Pre-GFC and GFC total credit risk than the traditional model, meaning that those industries that were riskiest during the GFC are not the same industries that were riskiest Pre-GFC. Overall, our analysis finds that our innovative modelling techniques are better able to account for the impact of extreme risk circumstances and industry composition than traditional transition matrix techniques.