The New Basel Capital Accord (Basel II) provides added emphasis to the development of portfolio credit risk models. An important regulatory change in Basel II is the differentiated treatment in measuring capital requirements for the corporate exposures and retail exposures. Basel II allows agricultural loans to be categorized and treated as the retail exposures. However, portfolio credit risk model for agricultural loans is still in their infancy. Most portfolio credit risk models being used have been developed for corporate exposures, and are not generally applicable to agricultural loan portfolio. The objective of this study is to develop a credit risk model for agricultural loan portfolios. The model developed in this study reflects characteristics of the agricultural sector, loans and borrowers and designed to be consistent with Basel II, including consideration given to forecasting accuracy and model applicability. This study conceptualizes a theory of loan default for farm borrowers. A theoretical model is developed based on the default theory with several assumptions to simplify the model. An annual default model is specified using FDIC state level data over the 1985 to 2003. Five state models covering Iowa, Illinois, Indiana, Kansas, and Nebraska areestimated as a logistic function. Explanatory variables for the model are a three-year moving average of net cash income per acre from crops, net cash income per cwt from livestock, government payments per acre, the unemployment rate, and a trend. Net cash income generated by state reflects the five major commodities: corn, soybeans, wheat, fed cattle, and hogs. A simulation model is developed to generate the stochastic default rates by state over the 2004 to 2007 period, providing the probability of default and the loan loss distribution in a pro forma context that facilitates proactive decision making. The model also generates expected loan loss, VaR, and capital requirements. This study suggests two key conclusions helpful to future credit risk modeling efforts for agricultural loan portfolios: (1) net cash income is a significant leading indicator to default, and (2) the credit risk model should be segmented by commodity and geographical location.

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We analyze the nature of sovereign credit risk for selected Asian and European countries through a set of sovereign CDS data for an eighty-year period that includes the episode of the 2008-2009 financial crisis. Our principal component analysis results suggest that there is strong commonality in sovereign credit risk across countries after the crisis. The regression tests show that the commonality is linked to both local and global financial and economic variables. Besides, we also notice intriguing differences in the sovereign credit risk behavior of Asian and European countries. Specifically, we find that some variables, including foreign reserve, global stock market, and volatility risk premium, affect the of Asian and European sovereign credit risks in the opposite direction. Further, we assume that the arrival rates of credit events follow a square-root diffusion from which we build our pricing model. The resulting model is used to decompose credit spreads into risk premium and credit-event components.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 101-104). Also available in electronic version.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 84-90). Also available in electronic version.

This thesis is concerned with statistical aspects of credit scoring, the process of determining how likely an applicant for credit is to default with repayments. In Chapters 1-4 a detailed introduction to credit scoring methodology is presented, including evaluation of previous published work on credit scoring and a review of discrimination and classification techniques. In Chapter 5 we describe different approaches to measuring the absolute and relative performance of credit scoring models. Two significance tests are proposed for comparing the bad rate amongst the accepts (or the error rate) from two classifiers. In Chapter 6 we consider different approaches to reject inference, the procedure of allocating class membership probabilities to the rejects. One reason for needing reject inference is to reduce the sample selection bias that results from using a sample consisting only of accepted applicants to build new scorecards. We show that the characteristic vectors for the rejects do not contain information about the parameters of the observed data likelihood, unless extra information or assumptions are included. Methods of reject inference which incorporate additional information are proposed. In Chapter 7 we make comparisons of a range of different parametric and nonparametric classification techniques for credit scoring: linear regression, logistic regression, projection pursuit regression, Poisson regression, decision trees and decision graphs. We conclude that classifier performance is fairly insensitive to the particular technique adopted. In Chapter 8 we describe the application of the k-NN method to credit scoring. We propose using an adjusted version of the Eucidean distance metric, which is designed to incorporate knowledge of class separation contained in the data. We evaluate properties of the k-NN classifier through empirical studies and make comparisons with existing techniques.

In recent years, issues about credit risk attract more and more attentions. This thesis provides some empirical evidence for the behavior of credit spreads in Taiwan based on a Markov model proposed by Jarrow, Lando, and Turnbull (1997). Although the estimated risk premium adjustments increases as the credit rating level goes downward, it does not exist robust relations between credit yield spreads and credit ratings. Apparently, the model does not fit the real condition well because of some structural factors and limitations. I try to suggest some possible explanations for this phenomenon. Despites some poor performances of this model, these results still offer some directions to reconsider the valuation of straight corporate bonds in Taiwan.

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Credit risk models widely used in the financial market nowadays assume that losses are normally distributed and have linear dependence. Nevertheless it is well known that asset returns (loans included) are not normally distributed and present tail dependence. Therefore the traditional approaches are not able to capture possible stronger association among higher losses and tend to underestimate the probability of joint extreme losses. Copula functions are an alternative to overcome this drawback since they yield accurate dependence measures regardless of the distribution of the variables analysed. This technique was first applied to credit risk in 2000 but the studies in this field have been concentrated on corporate debt and derivatives. We filled this gap in the literature by employing copulas to estimate the dependence among consumer loans. In an empirical study based on a credit card portfolio of a large UK bank, we found evidence that standard models are misspecified as the dependence across default rates in the dataset is seldom expressed by the (Gaussian) copula implicit in those models. The comparison between estimations of joint high default rates from the conventional approach and from the best-fit copulas confirmed the superiority of the latter method. The initial investigation concerning pairs of credit segments was extended to groups of three segments with the purpose of accounting for potential heterogeneous dependence within the portfolio. To do so, we introduced vine copulas (combinations of bivariate copulas to form high-dimension copulas) to credit risk and the empirical estimations of simultaneous excessive defaults based on this technique were better than both the estimations from the pairwise copulas and from the conventional models. Another contribution of this work concerns the application of copulas to a method derived from the limited credit models: the calculation of the capital required to cover unexpected losses in financial institutions. Two models were proposed and, according to simulations, outperformed the current method (Basel) in most of the scenarios considered.

This thesis is devoted to UK Mortgage Performance Modelling. The research conducted uses an option pricing methodology to model theoretically the value of Mortgages, the Option to default and the probability to default and to compare the predictive accuracy of the latter with the predictive accuracy of data driven credit-scoring techniques. Theoretical models are constructed to represent the life cycles of loans collateralised by real property operating within a stochastic economic environment of house-price and interest rate. These realistic mortgage models provide a confirmation of recent research based upon a relaxation of the assumption of financially rational, 'ruthless' prepayment, bridge a potential oversight in existing research by an extension of existing modelling in the stochastic behaviour of the house price process and present a proposal for a straightforward approach utilising characteristic measures of borrower delinquency and insolvency that enables estimation of the default probabilities implicit in residential mortgages using a simple but enhanced optimising structural model. This model straightforwardly demonstrates that one can predict the probability of eventual default, beginning at the origination of the loan, the time when a lender would be most interested in making such a determination. Secondly the problem of mortgage loan default risk is empirically assessed in a number of different ways focusing upon analysis of the competing risks of early termination, the inclusion of macro-economic variables - time varying covariates and unobserved borrower heterogeneity. Key insight is provided by means of a multi-period model exploiting the potential of the survival analysis approach when both loan survival times and the various regressors are measured at discrete points in time. The discrete-time hazard model is used as an empirical framework for analysing the deterioration process leading to loan default and as a tool for prediction of the same event. Results show that the prediction accuracy of the duration model is better than that provided by a single period logistic model. The predictive power of the discrete time survival analysis is enhanced when it is extended to allow for unobserved individual heterogeneity (frailty).