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This study examines credit score migration rates of farm businesses, testing whether migration probabilities differ across business cycles. Results suggest that agricultural credit ratings are more likely to improve during expansions and deteriorate during recessions. The analysis also tests whether agricultural credit ratings depend on the previous period migration trends. The findings show that credit score ratings exhibit trend reversal where upgrades (downgrades) are more likely to be followed by downgrades (upgrades).

The migration approach to credit risk measurement is based on historic rates of movements of individual loans among the classes of a lender’s risk‐rating or credit‐scoring system. This article applies the migration concept to farm‐level data from Illinois to estimate migration rates for a farmer’s credit score and other performance measures under different time‐averaging approaches. Empirical results suggest greater stability in rating migrations for longer time‐averaging periods (although less stable than bond migrations), and for the credit score criterion versus ROE and repayment capacity.

Credit migration correlation is a critical assumption for the integration of market risk and credit risk within enterprise‐wide risk management. This article describes hypothesis testing performed on credit migration correlation, based on two models: 1) a factor model and 2) an asset‐value model. These tests involve both the correlation between obligors and the correlation between credit migration events and systematic market risk factors. The author concludes from the test results that over shorter risk horizons (e.g., biweekly or monthly) where all relevant underlying processes are distributed multi‐variate normal, non‐zero positive correlation weights overestimate risk capital requirements, on average.

This paper presents the statistical distribution of credit ratings and their migration in Israel, and shows that for 16 years the distribution of ranks has been skewed to the left. The purpose of this paper is to analyze why firms with average quality debt have not changed their tactics and consent to publishing their grade which would then differentiate an average quality debt from a riskier one.

The paper estimates the mean values of ranks and the diagonal of the migration matrix on the basis of data on 1,639 bond rankings listed on the Tel‐Aviv Stock Exchange and publications by the largest Israeli rating agency, Maalot.

From 1992 to 2004, one‐third of the Israeli firms that had initially requested ranking from a rating agency decided to prevent publication. The findings show the average bond rankings published by Israeli rating agencies tend to be relatively high, while bond rating migration is relatively slow. There was no change in the shape of the statistical distribution of ratings between 2004 and 2007. The strategy of borrowers has remained stable and shows no change over 16 years of credit ratings in Israel.

Debtors with an average quality debt view the publications of the credit agency as a weak signal and do not expect the investment community to give them better credit for an average grade. To obtain more detailed ratings, regulators along with the credit rating agencies should consider enforcement of the publication of the rank of firms that requested evaluation.

The paper offers insights into why credit ratings in Israel have remained stable over the last decade and explains why Israeli firms with average quality debt do not change their strategy and do not request credit rating agencies to issue their grade publically which could then distinguish them from firms with worse quality debt.

Credit ratings serve as an important input in several applications in risk management of the financial firms. The level of credit rating changes from time to time because of random credit risk and, thus, can be modeled by an appropriate stochastic process. Markov chain models have been widely used in the literature to generate credit migration matrices; however, emergent empirical evidences suggest that the Markov property is not appropriate for credit rating dynamics. The purpose of this article is to address the non-Markov behavior of the rating dynamics.

This paper proposes a model based on Markov regenerative process (MRGP) with subordinated semi-Markov process (SMP) to obtain the estimates of rating migration probability matrices and default probabilities. Numerical example is given to illustrate the applicability of the proposed model with the help of historical Standard & Poor’s (S&P) credit rating data.

The proposed model implies that rating of a firm in the future not only depends on its present rating, but also on its previous ratings. If a firm gets a rating lower than its previous ratings, there are higher chances of further downgrades, and the issue is called the rating momentum. The model also addresses the ageing problem of credit rating evolution.

The contribution of this paper is a more general approach to study the rating dynamics and overcome the issues of inappropriateness of Markov process applied in rating dynamics.

Loan migration analysis is conducted using a large data set of loan risk ratings in the Farm Credit System. We find path dependence and limited support for a trend reversal pattern. There is evidence that the magnitude of migrations reported in previous credit score proxy studies overstates trend reversal in agricultural loans rated by lenders. Our results indicate that retention rates of agricultural loan risk ratings are quite high. Small loans are less likely to migrate than medium and large‐sized loans, and unseasoned loans are more likely to migrate than seasoned farm loans

The credit migration process contains important information about the dynamics of a firm's credit quality, therefore, it has a significant impact on its relevant credit derivatives. We present a jump diffusion approach to model the credit rating transitions which leads to a partial integro-differential equation (PIDE) formulation, with defaults and rating changes characterized by barrier crossings. Efficient and reliable numerical solutions are developed for the variable coefficient equation that result in good agreement with historical and market data, across all credit ratings. A simple adjustment in the credit index drift converts the model to be used in the risk-neutral setting, which makes it a valuable tool in credit derivative pricing.

Tails probabilities are of paramount importance in shaping the risk profile of portfolios with credit risk sensitive securities. In this context, risk management tools require simulations that accurately capture the tails, and optimization models that limit tail effects. Ignoring tail events in the simulation or using inadequate optimization metrics can have significant effects and reduce portfolio efficiency. The resulting portfolio risk profile can be grossly misrepresented when long‐run performance is optimized without accounting for short‐term tail effects. This article illustrates pitfalls and suggests models to avoid them.

Recalling that the introductory chapter (Chapter 1) wanted to carry out similar types of analysis for the major states in India. Thus, the present chapter tries to examine the trends of a bank branch, deposit, credit, the credit–deposit ratio, sectoral shares of credit, magnitudes of banking transactions, credit concentration, etc., for the selected 15 states and Delhi as the only union territory for the period 1972–2019. The study period covers the pre-reform period from 1972 to 1992 and the post-reform period 1993–2019. The observations show that the branch, deposit and credit did not grow significantly during the post-reform period. As a result, the credit–deposit ratio did not increase significantly during the reform period. But, the magnitude of banking transactions increased in most of the states during the reform period. Regarding the sector-wise share of credit, AP, Maharashtra, UP and TN are the leading states in agricultural credit, WB, Gujarat and Maharashtra are in industrial credit and Kerala, Assam and Delhi are in the service sector. On the other hand, the study finds rising magnitudes credit concentrations of the states during the post-reform period in contrast to the declining concentration in the pre-reform period. Maharashtra is the state which holds around 25 per cent of all states’ credit throughout the entire period of 1972–2019. Hence, there are the notions of rising disparity and inequality in credit as well as incomes of the states and all India levels.