Overview

Credit risk refers to the risk that a borrower may default on any type of debt by failing to make required payments. MATLAB Financial Toolbox provides a collection of tools for modeling credit risk analysis. These include modeling transition probabilities from credit rating data, converting transition probabilities to credit quality thresholds, and credit scorecard modeling. Transition probabilities offer one way to characterize the past changes in credit quality of obligors (typically firms), and are cardinal inputs to many risk management applications. Financial Toolbox supports the estimation of transition probabilities using both cohort and duration (also known as hazard rate or intensity) approaches using transprob and related functions. An equivalent way to represent transition probabilities is by transforming them into credit quality thresholds. These are critical values of a standard normal distribution that yield the same transition probabilities. Financial Toolbox supports the transformation between transition probabilities and credit quality thresholds. Credit scoring is one of the most widely used credit risk analysis tools. The goal of credit scoring is ranking borrowers by their credit worthiness. In the context of retail credit (credit cards, mortgages, car loans, etc.), credit scoring is performed using a credit scorecard. Credit scorecards represent different characteristics of a customer (age, residential status, time at current address, time at current job, and so on) translated into points and the total number of points becomes the credit score. The credit worthiness of customers is summarized by their credit score; high scores usually correspond to low-risk customers, and conversely. Scores are also used for corporate credit analysis of small and medium enterprises, and, large corporations. A credit scorecard is a lookup table that maps specific characteristics of a borrower into points. The total number of points becomes the credit score. Credit scorecards are a widely used type of credit scoring model. As such, the goal of a credit scorecard is to distinguish between customers who repay their loans ( good customers), and customers who will not ( bad customers). Like other credit scoring models, credit scorecards quantify the risk that a borrower will not repay a loan in the form of a score and a probability of default. Normaly, the parameters of the regression model are estimated via m ximum likelihood estimation. For multiple series, this requires iteration until convergence. The complication due to the possibility of missing data is incorporated into the analysis with a variant of the EM algorithm known as the ECM algorithm. This book takes into account the work with portfolios in the presence of missing data.

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