Credit Risk Modeling Using Excel And Vba 2E +Cd 9780470660928 rb kindle 网盘 umd 极速 下载 pdf mobi

This book provides practitioners and students with a hands-on

introduction tomodern credit risk modeling. The authors begin each

chapter with an accessiblepresentation of a given methodology,

before providing a step-by-step guide toimplementation 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 wellas credit default swaps and structured finance.

Several appendices and videosincrease ease of access.

The second edition includes new coverage of the important issue of

howparameter uncertainty can be dealt with in the estimation of

portfolio risk, aswell as comprehensive new sections on the pricing

of CDSs and CDOs, anda chapter on predicting borrower-specific loss

given default with regressionmodels. In all, the authors present a

host of applications - many of whichgo beyond standard Excel or VBA

usages, for example, how to estimate logitmodels with maximum

likelihood, or how to quickly conduct large-scale MonteCarlo

simulations.

Clearly written with a multitude of practical examples, the new

edition ofCredit Risk Modeling using Excel and VBA will prove an

indispensible resourcefor anyone working in, studying or

researching this important field.

Preface to the 2nd edition. Preface to the 1st edition. Some

Hints for Troubleshooting. 1 Estimating Credit Scores with Logit.

Linking scores, default probabilities and observed default

behavior. Estimating logit coefficients in Excel. Computing

statistics after model estimation. Interpreting regression

statistics. Prediction and scenario analysis. Treating outliers in

input variables. Choosing the functional relationship between the

score and explanatory variables. Concluding remarks. Appendix.

Logit and probit. Marginal effects. Notes and literature. 2 The

Structural Approach to Default Prediction and Valuation. Default

and valuation in a structural model. Implementing the Merton model

with a one-year horizon. The iterative approach. A solution using

equity values and equity volatilities. Implementing the Merton

model with a T -year horizon. Credit spreads. CreditGrades.

Appendix. Notes and literature. Assumptions. Literature. 3

Transition Matrices. Cohort approach. Multi-period transitions.

Hazard rate approach. Obtaining a generator matrix from a given

transition matrix. Confidence intervals with the binomial

distribution. Bootstrapped confidence intervals for the hazard

approach. Notes and literature. Appendix. Matrix functions. 4

Prediction of Default and Transition Rates. Candidate variables for

prediction. Predicting investment-grade default rates with linear

regression. Predicting investment-grade default rates with Poisson

regression. Backtesting the prediction models. Predicting

transition matrices. Adjusting transition matrices. Representing

transition matrices with a single parameter. Shifting the

transition matrix. Backtesting the transition forecasts. Scope of

application. Notes and literature. Appendix. 5 Prediction of Loss

Given Default. Candidate variables for prediction.

Instrument-related variables. Firm-specific variables.

Macroeconomic variables. Industry variables. Creating a data set.

Regression analysis of LGD. Backtesting predictions. Notes and

literature. Appendix. 6 Modeling and Estimating Default

Correlations with the Asset Value Approach. Default correlation,

joint default probabilities and the asset value approach.

Calibrating the asset value approach to default experience: the

method of moments. Estimating asset correlation with maximum

likelihood. Exploring the reliability of estimators with a Monte

Carlo study. Concluding remarks. Notes and literature. 7 Measuring

Credit Portfolio Risk with the Asset Value Approach. A default-mode

model implemented in the spreadsheet. VBA implementation of a

default-mode model. Importance sampling. Quasi Monte Carlo.

Assessing Simulation Error. Exploiting portfolio structure in the

VBA program. Dealing with parameter uncertainty. Extensions. First

extension: Multi-factor model. Second extension: t -distributed

asset values. Third extension: Random LGDs. Fourth extension: Other

risk measures. Fifth extension: Multi-state modeling. Notes and

literature. 8 Validation of Rating Systems. Cumulative accuracy

profile and accuracy ratios. Receiver operating characteristic

(ROC). Bootstrapping confidence intervals for the accuracy ratio.

Interpreting caps and ROCs. Brier score. Testing the calibration of

rating-specific default probabilities. Validation strategies.

Testing for missing information. Notes and literature. 9 Validation

of Credit Portfolio Models. Testing distributions with the

Berkowitz test. Example implementation of the Berkowitz test

Representing the loss distribution. Simulating the critical

chi-square value. Testing modeling details: Berkowitz on

subportfolios. Assessing power. Scope and limits of the test. Notes

and literature. 10 Credit Default Swaps and Risk-Neutral Default

Probabilities. Describing the term structure of default: PDs

cumulative, marginal and seen from today. From bond prices to

risk-neutral default probabilities. Concepts and formulae.

Implementation. Pricing a CDS. Refining the PD estimation. Market

values for a CDS. Example. Estimating upfront CDS and the 'Big

Bang' protocol. Pricing of a pro-rata basket. Forward CDS spreads.

Example. Pricing of swaptions. Notes and literature. Appendix.

Deriving the hazard rate for a CDS. 11 Risk Analysis and Pricing of

Structured Credit: CDOs and First-to-Default Swaps. Estimating CDO

risk with Monte Carlo simulation. The large homogeneous portfolio

(LHP) approximation. Systemic risk of CDO tranches. Default times

for first-to-default swaps. CDO pricing in the LHP framework.

Simulation-based CDO pricing. Notes and literature. Appendix.

Closed-form solution for the LHP model. Cholesky decomposition.

Estimating PD structure from a CDS. 12 Basel II and Internal

Ratings. Calculating capital requirements in the Internal

Ratings-Based (IRB) approach. Assessing a given grading structure.

Towards an optimal grading structure. Notes and literature.

Appendix A1 Visual Basics for Applications (VBA). Appendix A2

Solver. Appendix A3 Maximum Likelihood Estimation and Newton's

Method. Appendix A4 Testing and Goodness of Fit. Appendix A5

User-defined Functions. Index.

GUNTER L?FFLER is Professor of finance atthe University of Ulm

in Germany. His currentresearch interests are on credit risk and

empiricalfinance. Previously, Gunter was Assistant Professorat

Goethe University Frankfurt, and served asan internal consultant in

the asset managementdivision of Commerzbank. His Ph.D. in financeis

from the University of Mannheim. Gunter hasstudied at Heidelberg

and Cambridge Universities.

PETER N. POSCH is Assistant Professor of financeat the University

of Ulm in Germany. Previously,Peter was with the credit treasury of

a large bank,where he also traded credit derivatives and otherfixed

income products for the bank's proprietarybooks. His Ph.D. in

finance on the dynamics ofcredit risk is from the University of

Ulm. Peterhas studied economics, philosophy and law at

theUniversity of Bonn.

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媒体评论

This book provides

practitioners and students with an intuitive, hands-on introduction

to modern credit risk modeling. A typical chapter starts with an

approachable presentation of the methodology. Step by step, the

authors then show how to implement the methods in Excel and Visual

Basic for Applications. Focusing on risk management issues, 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 authors present a host of applications – many of which go

beyond standard Excel or VBA usages. For example, they show how to

estimate logit models with maximum likelihood, or how to conduct

large-scale Monte Carlo simulations in little time. Even to

experienced modelers the book can serve as a toolbox and source of

inspiration.  

"In one place, L?ffler and Posch provide all that is needed to

install state-of-the-art risk management system, including a broad

understanding of different risk management frameworks, detailed

estimation techniques for deriving PD, LGD, and correlation

parameters, and programing tools for putting these methods into

practice."

—

Richard Cantor, Managing Director, Credit Policy

Research, Moody’s Investors Service

"I read this book cover-to-cover and recommend it heartily. For

each topic, there is straightforward explanation, practical

examples, and implementable coding. This book would have saved me

months of effort many times over with its full ‘toolset’ of

Excel/VBA code. I have immediate plans to reread sections and

incorporate sections of code into my own spreadsheets."

—Greg M. Gupton, Fitch Ratings & DefaultRisk.com

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.