Credit Risk Modeling using Excel and VBA mobi 下载 网盘 caj lrf pdf txt 阿里云

This book provides practitioners and students with a hands-on introduction to

modern credit risk modeling. The authors begin each chapter with an accessible

presentation of a given methodology, before providing a step-by-step guide to

implementation methods in Excel and Visual Basic for Applications (VBA).

The book covers default probability estimation (scoring, structural models,

and transition matrices), correlation and portfolio analysis, validation, as well

as credit default swaps and structured finance. Several appendices and videos

increase ease of access.

The second edition includes new coverage of the important issue of how

parameter uncertainty can be dealt with in the estimation of portfolio risk, as

well as comprehensive new sections on the pricing of CDSs and CDOs, and

a chapter on predicting borrower-specific loss given default with regression

models. In all, the authors present a host of applications - many of which

go beyond standard Excel or VBA usages, for example, how to estimate logit

models with maximum likelihood, or how to quickly conduct large-scale Monte

Carlo simulations.

Clearly written with a multitude of practical examples, the new edition of

Credit Risk Modeling using Excel and VBA will prove an indispensible resource

for anyone working in, studying or researching this important field.

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书籍介绍

This book provides practitioners and students with a hands-on introduction to

modern credit risk modeling. The authors begin each chapter with an accessible

presentation of a given methodology, before providing a step-by-step guide to

implementation methods in Excel and Visual Basic for Applications (VBA).

The book covers default probability estimation (scoring, structural models,

and transition matrices), correlation and portfolio analysis, validation, as well

as credit default swaps and structured finance. Several appendices and videos

increase ease of access.

The second edition includes new coverage of the important issue of how

parameter uncertainty can be dealt with in the estimation of portfolio risk, as

well as comprehensive new sections on the pricing of CDSs and CDOs, and

a chapter on predicting borrower-specific loss given default with regression

models. In all, the authors present a host of applications - many of which

go beyond standard Excel or VBA usages, for example, how to estimate logit

models with maximum likelihood, or how to quickly conduct large-scale Monte

Carlo simulations.

Clearly written with a multitude of practical examples, the new edition of

Credit Risk Modeling using Excel and VBA will prove an indispensible resource

for anyone working in, studying or researching this important field.