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Credit Risk Modeling

1998-12-10 Business & Economics
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Release : 1998-12-10
Genre : Business & Economics
Kind : eBook
Book Rating : 382/5 ( reviews)

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Book Synopsis Credit Risk Modeling by : Elizabeth Mays

Download or read book Credit Risk Modeling written by Elizabeth Mays. This book was released on 1998-12-10. Available in PDF, EPUB and Kindle. Book excerpt: Covers: � Implementing an application scoring system � Behavior modeling to manage your portfolio � Incorporating economic factors � Statistical techniques for choosing the optimal credit risk model � How to set cutoffs and override rules � Modeling for the sub-prime market � How to evaluate and monitor credit risk models This is an indispensable guide for credit professionals and risk managers who want to understand and implement modeling techniques for increased profitability. In this one-of-a-kind text, experts in credit risk provide a step-by-step guide to building and implementing models both for evaluating applications and managing existing portfolios.

Market and Credit Risk Models and Management Report

2012
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Release : 2012
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Kind : eBook
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Book Synopsis Market and Credit Risk Models and Management Report by : Jing Qu

Download or read book Market and Credit Risk Models and Management Report written by Jing Qu. This book was released on 2012. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This report is for MA575: Market and Credit Risk Models and Management, given by Professor Marcel Blais. In this project, three different methods for estimating Value at Risk (VaR) and Expected Shortfall (ES) are used, examined, and compared to gain insightful information about the strength and weakness of each method. In the first part of this project, a portfolio of underlying assets and vanilla options were formed in an Interactive Broker paper trading account. Value at Risk was calculated and updated weekly to measure the risk of the entire portfolio. In the second part of this project, Value at Risk was calculated using semi-parametric model. Then the weekly losses of the stock portfolio and the daily losses of the entire portfolio were both fitted into ARMA(1,1)-GARCH(1,1), and the estimated parameters were used to find their conditional value at risks (CVaR) and the conditional expected shortfalls (CES).

Credit Risk Models and Management

2004 Business & Economics
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Release : 2004
Genre : Business & Economics
Kind : eBook
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Book Synopsis Credit Risk Models and Management by : David Shimko

Download or read book Credit Risk Models and Management written by David Shimko. This book was released on 2004. Available in PDF, EPUB and Kindle. Book excerpt: Building upon the seminal work established in the first best selling edition, this fully revised multi-author reference collection brings you up-to date with a complete and cohesive examination on the latest techniques for credit risk assessment and management

Credit Risk Analytics

2016-10-03 Business & Economics
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Release : 2016-10-03
Genre : Business & Economics
Kind : eBook
Book Rating : 985/5 ( reviews)

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Book Synopsis Credit Risk Analytics by : Bart Baesens

Download or read book Credit Risk Analytics written by Bart Baesens. This book was released on 2016-10-03. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Credit Risk Modeling and Management

2020-07-01 Business & Economics
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Release : 2020-07-01
Genre : Business & Economics
Kind : eBook
Book Rating : 605/5 ( reviews)

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Book Synopsis Advances in Credit Risk Modeling and Management by : Frédéric Vrins

Download or read book Advances in Credit Risk Modeling and Management written by Frédéric Vrins. This book was released on 2020-07-01. Available in PDF, EPUB and Kindle. Book excerpt: Credit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored.

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