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Credit Risk Modeling Using Excel And Vba 2E +Cd 9780470660928书籍详细信息
- ISBN：9780470660928
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2011-02
- 页数：360
- 价格：648.60
- 纸张：胶版纸
- 装帧：精装
- 开本：16开
- 语言：未知
- 丛书：暂无丛书
- TAG：暂无
- 豆瓣评分：暂无豆瓣评分
内容简介：
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.
书籍目录：
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.
作者介绍：
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.
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