This paper studies residential mortgage loss given default using a large set of historical loan-level default and recovery data of high loan-to-value mortgages from several private mortgage insurance companies. We show that loss given default can largely be explained by various characteristics associated with the loan, the underlying property, and the default, foreclosure, and settlement process. We find that the current loan-to-value ratio is the single most important determinant. More importantly, mortgage loss severity in distressed housing markets is significantly higher than under normal housing market conditions. Our empirical results have important policy implications for risk-based capital.

In housing crises, high mortgage debt can feed a vicious circle of falling housing prices and declining consumption and incomes, leading to higher mortgage defaults and deeper recessions. In such situations, resolution policies may need to be adapted to help contain negative feedback loops while minimizing overall loan losses and moral hazard. Drawing on recent experiences from Iceland, Ireland, Spain, and the United States, this paper discusses how economic trade-offs affecting mortgage resolution differ in crises. Depending on country circumstances, the economic benefits of temporary forbearance and loan modifications for struggling households could outweigh their costs.

In this ground-breaking new title, Risk Books brings together three prominent editors to provide a timely reference text on loss given default (LGD) measurement and management and the requirements of the Basel II Capital Accord.

Using a recent IMF survey and expanding on previous studies, we document the use of macroprudential policies for 119 countries over the 2000-13 period, covering many instruments. Emerging economies use macroprudential policies most frequently, especially foreign exchange related ones, while advanced countries use borrower-based policies more. Usage is generally associated with lower growth in credit, notably in household credit. Effects are less in financially more developed and open economies, however, and usage comes with greater cross-border borrowing, suggesting some avoidance. And while macroprudential policies can help manage financial cycles, they work less well in busts.

Provides an overview of the subprime mortgage securitization process and the seven key informational frictions that arise. Discusses the ways that market participants work to minimize these frictions and speculate on how this process broke down. Continues with a complete picture of the subprime borrower and the subprime loan, discussing both predatory borrowing and predatory lending. Presents the key structural features of a typical subprime securitization, documents how rating agencies assign credit ratings to mortgage-backed securities, and outlines how these agencies monitor the performance of mortgage pools over time. The authors draw upon the example of a mortgage pool securitized by New Century Financial during 2006. Illustrations.

The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.

Credit Scoring and Its Applications is recognized as the bible of credit scoring. It contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioral scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book contains a description of practical problems encountered in building, using, and monitoring scorecards and examines some of the country-specific issues in bankruptcy, equal opportunities, and privacy legislation. It contains a discussion of economic theories of consumers' use of credit, and readers will gain an understanding of what lending institutions seek to achieve by using credit scoring and the changes in their objectives. New to the second edition are lessons that can be learned for operations research model building from the global financial crisis, current applications of scoring, discussions on the Basel Accords and their requirements for scoring, new methods for scorecard building and new expanded sections on ways of measuring scorecard performance. And survival analysis for credit scoring. Other unique features include methods of monitoring scorecards and deciding when to update them, as well as different applications of scoring, including direct marketing, profit scoring, tax inspection, prisoner release, and payment of fines.

The use of credit scoring - the quantitative and statistical techniques to assess the credit risks involved in lending to consumers - has been one of the most successful if unsung applications of mathematics in business for the last fifty years. Now with lenders changing their objectives from minimising defaults to maximising profits, the saturation of the consumer credit market allowing borrowers to be more discriminating in their choice of which loans, mortgages and credit cards to use, and the Basel Accord banking regulations raising the profile of credit scoring within banks there are a number of challenges that require new models that use credit scores as inputs and extensions of the ideas in credit scoring. This book reviews the current methodology and measures used in credit scoring and then looks at the models that can be used to address these new challenges. The first chapter describes what a credit score is and how a scorecard is built which gives credit scores and models how the score is used in the lending decision. The second chapter describes the different ways the quality of a scorecard can be measured and points out how some of these measure the discrimination of the score, some the probability prediction of the score, and some the categorical predictions that are made using the score. The remaining three chapters address how to use risk and response scoring to model the new problems in consumer lending. Chapter three looks at models that assist in deciding how to vary the loan terms made to different potential borrowers depending on their individual characteristics. Risk based pricing is the most common approach being introduced. Chapter four describes how one can use Markov chains and survival analysis to model the dynamics of a borrower's repayment and ordering behaviour . These models allow one to make decisions that maximise the profitability of the borrower to the lender and can be considered as part of a customer relationship management strategy. The last chapter looks at how the new banking regulations in the Basel Accord apply to consumer lending. It develops models that show how they will change the operating decisions used in consumer lending and how their need for stress testing requires the development of new models to assess the credit risk of portfolios of consumer loans rather than a models of the credit risks of individual loans.