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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.

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).

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.

Logistic regression techniques for panel data are used to identify factors affecting farm credit transition probabilities. Results indicate that most farm‐specific factors do not have adequate explanatory influence on the probability of farm credit risk transition. Class upgrade probabilities are more significantly affected by changes in certain macroeconomic factors, such as economic growth signals (from changes in stock price indexes and farm real estate values) and larger money supply that relax the credit constraint. Increases in interest rates, on the other hand, negatively affect such probabilities.

The purpose of this paper is to examine the relative financial strength and endurance of several paired classes of farmers according to business maturity (beginning versus mature farm businesses), farm operators’ age/experience (young versus older, more experienced farm operators), and farm size (small vs large farm businesses) by utilizing random-effects ordered logistic techniques.

This study uses a credit migration approach to analyze the factors that impact the probability of farm credit migration rates. An ordered logit model is used to assess the influence that factors have on a farm upgrading, staying same, or downgrading in credit rating.

Results show that increasing farm size will lead to a higher probability of class upgrades. Being a young farm operator, meanwhile, decreases this probability. Positive changes in money supply and farm real estate values were found to increase the likelihood of credit upgrades. Results also show trend reversal of credit risk movement, where upgrades (downgrades) are more likely to be followed by downgrades (upgrades).

With farms being dependent on capital for growth, knowing what factors affect the ability of a farm to obtain credit lends insight in the agricultural credit markets. This paper is also the first to assess the impacts of these factors on small farms which constitute 92 percent of farms in the USA per the US Department of Agriculture.

The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector.

This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed.

Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models.

This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.

Agricultural credit risk migration is examined using loan records gathered from four agricultural lenders. Results indicate that lender risk ratings are much more stable than ratings based on credit scores estimated from financial statements, highlighting the importance played by nonfinancial factors such as management capacity, character, and collateral in assessing credit risk. Additionally, the borrower’s risk tier, personal characteristics, and the stage of the business life cycle provide useful information in predicting credit quality downgrades, while the primary agricultural enterprise does not impact the likelihood of a downgrade.

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 purpose of this paper is to examine empirical customer account data from 2006 through 2012 to review the probability of default (PD) rating methodology implemented by a FCS association for production agricultural accounts. This analysis provides insight into the migration of accounts across the association’s currently established PD rating categories with negative migration being a precursor to potential loan default.

The data set contained 17,943 observations from the years 2006 to 2012 and consisted of various fields of data including balance sheet date, earnings statement date, and PD rating as of the statement date. The methods include analysis on the dynamics of the PD ratings and component ratios. OLS regression was used to analyze the data to see how the current period PD rating and component ratios affected the PD rating one year, three years, and five years out. OLS regression examined the statistical significance of the PD ratings and ratio components for this analysis. The dependent variable, Future PD Rating, represents the assigned PD rating for the observed farm either one, three, or five years into the future. It is expected that the initial PD rating in any given year would have a positive relationship, and be statistically significant in estimating future PD ratings. The independent variables are the current PD rating and the various component ratios of the inverse current ratio (CR), the debt to asset ratio (D/A), the gross profit to total liabilities ratio, the inverse debt coverage ratio, working capital to gross profit, and funded debt to EBITDA.

Results indicate that financial ratio information gathered today can do a good job forecasting PD ratings up to three years in the future. CR information does not forecast five years into the future very well. Thus, there is an important need to update financial information on a regular basis. The results indicate that the D/A information is very important in predicting risk ratings. As the production agriculture sector has experienced difficult financial conditions during 2014 and 2015, agricultural finance institutions need to obtain up-to-date financial information from their clientele to effectively assess the risk of and manage their financial portfolio.

Several previous works have examined and established models to assess risk in agricultural lending. This research adds to this body of work by examining the migration of an account’s risk-rating class over time using actual lender account data.