Changes In Credit Quality Research Articles

Pricing climate-related risks in the bond market

We develop a model for defaultable bonds incorporating both uncertainty about corporate earnings and uncertainty due to climate-related risks, which determine downward jumps in the firm value. In particular, we study how bond pricing is affected by transition risks, such as those coming from an abrupt change of climate policies. We show how the issuer’s credit quality changes as a result of its engagement in projects funded by green bonds and study the impact of green bonds on investors’ portfolio allocation. The way ‘green’ bonds may contribute to financial stability is also discussed.

CreditMetrics Methodology and Credit Value at Risk (Credit VaR)

Described by Hull (2011, 2012) as ‘a procedure for calculating credit value at risk’, CreditMetrics methodology (RiskMetrics Group 2007) is used for assessing portfolio risk due to changes in bond or debt value caused by credit quality changes including credit migration (upgrades and downgrades), as well as, default. It measures the uncertainty in forward value of the bond portfolio at the risk horizon caused by such credit events. Changes in debt value could be small in case of credit quality ratings change; however, they could be enormous, 50% to 90%, in case of default. Characterized by a long downside tail, credit-returns are highly-skewed and fat-tailed and thus far from the Gaussian normal distribution assumptions about market risk in VaR (Fig. 1). In the portfolio context, based upon correlation of credit quality moves across obligors, CreditMetrics assesses both value-at-risk (VaR), i.e., the volatility of value, as well as expected losses (EL). By distinguishing high quality well-diversified portfolios from low-quality concentrated portfolios, it offers better understanding of credit risk in terms of diversification benefits and concentration risk compared to mandated standard capital adequacy measures.

The Interaction between Profitability and Macroeconomic Factors for Future Examinations of European Banks Soundness – Theoretical Study

Any weakness in the financial institution is subject to the contagion mechanism. As result, the whole financial system will experience unpredictable financial risks and possible crisis, such as to a systemically relevant institution (e.g. Lehman Brothers’ default, 2008’s financial crises and Asian financial crises). The contagion mechanism (Quagliariello, M., 2009 [1] (Trapanese, M.) is a crucial element in the assessment of the cross-border dimension. The direct cross-border contagion risks (idiosyncratic risks) are: risks related to cross-border interbank links; money markets and cross-border ownership links; common shocks of foreign economies and global financial markets that can affect banks’ exposures due to changes in credit quality, market valuations and funding costs. Secondly, the indirect cross-border contagion risks (Indirect contagion) are caused by systematics risks that exclusively related to cross-border credit exposures (e.g. lending to non- financial institutions, credit risk transfer exposures as well as international syndicated lending), market risk exposures (by holdings of securities and off-balance sheet positions), common cross-border funding (by financing through market instruments and operational risk). From a theoretical point of view, said institutions are defined as risky banks, have unpredictable impacts on the smoothness of whole financial system. Moreover, these credit, market and liquidity risks represent the main triggers of crises. This paper is the second part of my theoretical study focused on the profitability and the soundness of European banks with an emphasis on the role of the Macro profile. In this paper, I also thoroughly investigated the macroeconomic determinants used to predict the Banking crises. Moreover, this paper analyzed the history and behavior of European banks during financial crises, and the corrective measures taken by authorities, governments and supervisory institutes to bail out the troubled banks, or to support the banking system as a whole. An extensive assessment of collected data resulted in detailed analysis of quantitative methodologies as well as the examination of the effectiveness of selected macroeconomic determinants to avoid the financial instability. This study shows that the macroeconomic adjustments were called upon during the crises. Keywords: bank profitability; soundness of banks, inflation, GDP, interest rates, macroeconomic determinants, Moody’s rating, exchange rates.

Procyclical ratings and market reactions

We investigate stock price reactions to credit rating changes during competing economic environments. Prior research has shown that credit rating assignments differ during times of economic expansion and economic contraction. We investigate if equity prices adjust differently to changes in credit quality in different economic environments. Our results show that markets react more strongly to negative ratings news during times of economic contraction. When the economy is expanding, markets also overreact by pushing prices higher than they otherwise would during an economic downturn.

Probabilistic prediction of credit ratings: a filtering approach

In a financial market, to analyze the credit quality of firms, this note proposes a dynamical model. The population of firms is divided into a finite number of classes, depending on their credit status. The cardinality of the population can increase during the time, since new firms can enter in the market. Due to changes in credit quality and to the defaults, each firm can move from a class to another, or can go to the class of the defaulted firms. Different rating agencies are considered, each of them defines its own partition of the population. Aim of this note is to find the probabilistic prediction of the actual partition of the population, and of the conditional distribution of the distance to defaults. In a partial observing setting, this topic is discussed using stochastic filtering techniques.

Liquidity basis between credit default swaps and corporate bonds markets

Liquidity risk has drawn much attention among academic researchers, institutional professionals and financial regulators in various financial markets. This paper empirically investigates the difference and relationship between the liquidities of CDS and corporate bond markets. The liquidity basis which is defined as the difference of liquidity between CDS and corporate bond are negative most of the time across different rating categories, implying more illiquid corporate bond market and the fact that CDS market moves quickly in reflecting credit quality changes. There exists significant Granger-causality from CDS liquidity to bond liquidity, and some bidirectional Granger-causality for some investment grade reference entities. The empirical tests are performed in the VAR system including the monetary policy variables and financial market variables. The relative bid-ask spread adopted by many researchers turns out to be less reliable as a measure of liquidity for CDS and corporate bond where the credit spread and liquidity risk are positively correlated.

Credit Risk in an Economy with New Firms Arrivals

We propose a dynamic model to analyze the credit quality of firms. In the market in which they operate, the firms are divided into a finite number of classes representing their credit status. The cardinality of the population can increase, since new firms can enter the market and the partition is supposed to change over time, due to defaults and changes in credit quality, following a class of Markov processes. Some conditional probabilities related to default times are investigated and the role of occupation numbers is highlighted in this context. In a partial information setting at discrete time, we present a particle filtering technique to numerically compute by simulation the conditional distribution of the number of firms in the credit classes, given the information up to time t.

Financial Stress in Indian Corporates

We characterize the changes in credit quality of a large sample of listed Indian corporates. Multiple indicators suggest that credit quality declines sharply between 2010 and 2015, creating a thick tail of vulnerable corporate debt. Stress is primarily due to a sharp contraction in aggregate corporate growth, a modest drop in profitability, and imbalanced financing patterns. Market models of default risk show that the stress has an asymmetric pass-through with greater effects on state-owned banks than on private ones. Remedies for corporates hinge on reviving growth and external equity cycles while those for banks pose more difficult choices.

Asymptotic Properties of the Markov Chain Model method of finding Markov chains Generators of Empirical Transition Matrices in Credit Ratings Applications

Credit quality changes need to be analysed from time to time. A good model for analysis needs to determine the Capital and reserves needed to support Credit Instruments portfolios as well as individual Credits. Conditions under which the Series method of finding Markov chains generators of Empirical Transition Matrices in Credit Ratings applications are identified in this article. Searching for valid Generators especially when a true generator does not exist and the properties of the series method is shown. Credit exposures Transition from one rating to another as well as Historical information are used to model estimation that pro- vide a description of Credit quality evolution probability. Time Homogeneous Markov Model specification is popularly used but it is only good in providing a description of portfolio risk changes in the short run hence restrictive in long run Credit processes. I propose a test which is simple and is of time homogeneity null hypothesis performed on all types of data reported often. The data used in the test is Sovereign debt, Municipal Bonds and Commercial paper. I find that transitions on municipal Bond ratings are described adequately for a period of up to five years. The Commercial paper assumes Markovian characteristics for a period of up to Six Months on a scale of 30 days transitions and the Sovereign debt transitions are described adequately by the Markov model using small data sample sizes.

Assessment of Credit Risk in Project Finance

In project finance, raising sufficient funds via the debt channel is a key task for all project companies and sponsors. Before furnishing a loan, lenders typically need to ascertain the ability of the project company to service principal payments plus interest. This paper aims to establish a quantitative model to analyze default risks and loan losses in infrastructure projects. Acting as an assessment system, the model will help lenders evaluate their exposure to default risk by monitoring the changes in credit quality of the project company. The model uses a conditional credit rating transition matrix to predict the probability of default and the net present value technique to estimate the maximum default loss. The Hong Kong-Canton highway project is used as a case study to illustrate the techniques and output of the proposed credit risk model. The model can be used to assist lenders and investors in making sound investment decisions, price contracts, and allocate capital. Similarly, it can also help project sponsors evaluate those critical measures that they must control in order to secure favorable loan terms by minimizing the risk of default and improving the bankability of a project.

The ordered qualitative model for credit rating transitions

Information on the expected changes in credit quality of obligors is contained in credit migration matrices which trace out the movements of firms across ratings categories in a given period of time and in a given group of bond issuers. The rating matrices provided by Moody's, Standard & Poor's and Fitch became crucial inputs to many applications, including the assessment of risk on corporate credit portfolios (CreditVar) and credit derivatives pricing. We propose a factor probit model for modeling and prediction of credit rating matrices that are assumed to be stochastic and driven by a latent factor. The filtered latent factor path reveals the effect of the economic cycle on corporate credit ratings, and provides evidence in support of the PIT (point-in-time) rating philosophy. The factor probit model also yields the estimates of cross-sectional correlations in rating transitions that are documented empirically but not fully accounted for in the literature and in the regulatory rules established by the Basle Committee.

The Debt Contracting Value of Accounting Information and Loan Syndicate Structure

In this paper, we investigate how the fraction of the loan retained by the lead arranger of a syndicated loan deal is affected by the informativeness of the borrower's accounting information relative to credit quality, after controlling for the direct use of accounting variables in the formal loan contract. We hypothesize that as the ability of publicly reported accounting numbers to capture changes in credit quality on a timely basis increases (i.e., debt-contracting value of accounting increases), lead arrangers will hold a smaller proportion of the syndicated loan deal. The idea is that when debt-contracting value of accounting increases, information asymmetry between the lead arranger and other syndicate participants is reduced, mitigating ex-ante adverse selection problems and reducing the incentive pressure necessary to induce lead arrangers to exert unobservable monitoring effort ex-post. Further, we hypothesize that accounting information with high debt-contracting value is relatively more important in reducing the proportion of the loan retained when the borrower is not rated, the lead arranger's reputation is low, and when the lead arranger has not previously served as a lead arranger for the same borrower. We estimate a direct proxy for the debt-contracting value of accounting and document evidence consistent with both of our hypotheses. Finally, we exploit the existence of performance pricing provisions, a unique feature of syndicated loan contracts, to investigate how the debt-contracting value of accounting influences the choice of the performance measure used in these provisions. We predict and find that for loans that include performance pricing provisions, the likelihood that the single performance measure used in the provision will be an accounting ratio rather than a credit rating increases as the debt-contracting value of accounting information increases.

Default Correlation Dynamics with Business Cycle and Credit Quality Changes

The distinction between structural and reduced form credit risk models is now well known. Structural models arise from more compelling theoretical principles, but reduced form models are easier to implement in practice. As derivative instruments tied to portfolios of credit–sensitive securities, such as collateralized debt obligations (CDOs) and basket credit derivatives, become more common, the need to understand and model default correlations along with default probabilities also becomes important. But structural models have had a hard time producing the amount of correlation needed to match market prices for these products, partly because default intensities are typically modeled as being constant, or at most, functions of largely firm–specific variables. In this article, Kim and Kim introduce a tractable structural model in which default intensities also depend on broad macroeconomic variables that are assumed to follow correlated diffusions. This joint dependence on macro factors can induce strong default correlations, and can also help to explain certain results from the literature, like the fact that credit risk seems to be more highly correlated among high–grade firms than among low–grade firms, even though the latter have higher individual probabilities of default. The model can also produce a rich dynamic in credit risk exposure.

Understanding Default Risk and Ttc Ratings Through Hazard Rates

This paper investigates the dynamics of default probabilities implied by Through-The-Cycle (TTC) ratings and default events. Using a Standard & Poor's database, we show that aging e®ects determine probability changes in the short term after a rating assignment, while the business cycle dominates in the long run. We examine to what extent ratings echo changes in credit quality without stabilizing intervention from rating agencies. Documented non-Markovian patterns can be attributed to the TTC perspective described as a four to six-year assessment of the default likelihood. Results do not support possible delays in rating revisions, but rather indicate small reactions in the magnitudes of the rating reviews. These conclusions are drawn from a new probability estimation technique focusing on hazard rates. It allows us to consistently estimate probabilities of default for all horizons at once, making all results robust. Our estimator is a smooth estimator of hazard rates which is free of bias and outperforms commonly used methodologies. It avoids the Markovian framework and takes care of censoring.

Are the Standards Too Poor? An Empirical Analysis of the Timeliness and Predictability of Credit Rating Changes

In this paper we investigate the dynamics of credit rating changes by Standard & Poor's (S&P) for all 583 companies that were included in the S&P 500 index between July 1998 and June 2003. Our analysis shows that KMV Corporation, a 'new' entrant to the credit rating industry that uses a quantitatively oriented, market-based approach to credit risk measurement, provides a significantly timelier insight into companies' credit quality changes than the qualitatively oriented, accounting-based approaches used by S&P. Based on a sample of 690 rating changes reported by S&P during the sample period, we show that 75% of the S&P rating changes are significantly anticipated by KMV more than a year in advance. Furthermore, we test for the predictability of S&P's rating changes. We show that KMV's Expected Default Frequencies possess considerable predictive power to forecast future rating changes by S&P.

Estimating Markov Transition Matrices Using Proportions Data: An Application to Credit Risk

This paper outlines a way to estimate transition matrices for use in credit risk modeling with a decades-old methodology that uses aggregate proportions data. This methodology is ideal for credit-risk applications where there is a paucity of data on changes in credit quality, especially at an aggregate level. Using a generalized least squares variant of the methodology, this paper provides estimates of transition matrices for the United States using both nonperforming loan data and interest coverage data. The methodology can be employed to condition the matrices on economic fundamentals and provide separate transition matrices for expansions and contractions, for example. The transition matrices can also be used as an input into other credit-risk models that use transition matrices as a basic building block.

Estimating Markov Transition Matrices Using Proportions Data: An Application to Credit Risk

This paper outlines a way to estimate transition matrices for use in credit risk modeling with a decades-old methodology that uses aggregate proportions data. This methodology is ideal for credit-risk applications where there is a paucity of data on changes in credit quality, especially at an aggregate level. Using a generalized least squares variant of the methodology, this paper provides estimates of transition matrices for the United States using both nonperforming loan data and interest coverage data. The methodology can be employed to condition the matrices on economic fundamentals and provide separate transition matrices for expansions and contractions, for example. The transition matrices can also be used as an input into other credit-risk models that use transition matrices as a basic building block.

Integrating Interest Rate Risk in Credit Portfolio Models

I. INTRODUCTION A typical shortcoming of most current credit portfolio models is the lack of a stochastic modeling of risk factors, such as interest rates or credit spreads, during the revaluation process at the risk horizon. For example, fixed income instruments, such as bonds or loans, are revalued at the risk horizon using the current forward rates and (rating class specific) forward credit spreads for discounting future cash flows. Hence, the stochastic nature of the instrument's value in the future which results from changes in factors other than credit quality is ignored, and the riskiness of the credit portfolio at the risk horizon is underestimated. A further consequence is that correlations between changes of the debtor's default probability and changes of market risk factors and, hence, the exposure at default cannot be integrated into the credit portfolio model. This drawback is especially relevant for portfolios of defaultable market‐driven derivatives. One reason why risk factors not directly related to credit risk are neglected in most current credit portfolio models is that there is still no commonly accepted approach for modeling the credit quality of a debtor and the dependencies between the credit quality changes of different debtors. Hence, it might be over‐ambitious to incorporate correlations between market risk factors and credit quality changes. Even empirical evidence on the sign of the correlation remains inconclusive. Additionally, introducing stochastic market risk factors and modeling the correlation between these risk factors and credit quality changes would significantly increase the computational burden for calculating robust risk measures of credit portfolios.

A multi-factor, credit migration model for sovereign and corporate debts

This paper develops a multi-factor, Markov chain model for rating migrations and credit spreads that is applicable to both sovereign and corporate debts. The model’s central feature is to allow transition matrices to be time-varying and driven by rating specific latent variables which encompass economic factors like the business cycle. There are three main contributions. First, the model incorporates well-documented empirical properties of transition matrices such as their dependence on business/credit cycles, and it also allows for inter-rating variations in credit quality changes. Second, instead of focusing solely on empirical modeling of rating transitions, the paper also shows how the empirical model can be implemented for actual valuations. Third, the estimation and calibration procedures are easy to follow and implement.

Metrics for Comparing Credit Migration Matrices

Credit migration or transition matrices, which characterize the expected changes in credit quality of obligors, are cardinal inputs to applications such as asset pricing and risk management. We propose a new metric for comparing these matrices (a mobility index) by first subtracting the identity matrix, focusing the analysis on the dynamics, and then taking the average of the singular values for the resulting matrix. This yields a metric which has an intuitively-appealing size related to the average probability of migration of the original matrix. We also propose a new mobility index performance criterion which is particularly relevant for credit migration matrices, namely that it be distribution discriminatory, i.e. sensitive to the distribution of off-diagonal probability mass. We demonstrate the advantages of the proposed metric over more traditional cell-by-cell distance metrics and eigenvalue-based mobility indices. We then apply these metrics to credit rating histories of S&P rated U.S. obligors from 1981-2001.