Credit Risk Modeling using Excel and VBA, 2nd Edition by Gunter Löeffler, Peter N. Posch

By Gunter Löeffler, Peter N. Posch

This booklet presents practitioners and scholars with a hands-on advent to
modern credits danger modeling. The authors commence each one bankruptcy with an accessible
presentation of a given technique, sooner than delivering a step by step consultant to
implementation tools in Excel and visible simple for functions (VBA).
The publication covers default likelihood estimation (scoring, structural models,
and transition matrices), correlation and portfolio research, validation, as well
as credits default swaps and dependent finance. numerous appendices and videos
increase ease of access.
The moment variation contains new insurance of the real factor of how
parameter uncertainty could be handled within the estimation of portfolio possibility, as
well as accomplished new sections at the pricing of CDSs and CDOs, and
a bankruptcy on predicting borrower-specific loss given default with regression
models. In all, the authors current a number of functions - lots of which
go past regular Excel or VBA usages, for instance, find out how to estimate logit
models with greatest probability, or find out how to quick behavior large-scale Monte
Carlo simulations.
Clearly written with a large number of sensible examples, the hot version of
Credit threat Modeling utilizing Excel and VBA will turn out an indispensible resource
for an individual operating in, learning or getting to know this crucial field.

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Extra resources for Credit Risk Modeling using Excel and VBA, 2nd Edition

Sample text

Kocagil, A. 1 model, Moody’s KMV. Two academic papers that describe the estimation of a logit scoring model are Shumway, T. 2001, Forecasting bankruptcy more accurately: A simple hazard model, Journal of Business 74, 101–124 and Altman, R. , 2004, How rating agencies achieve rating stability, Journal of Banking and 9 For each variable, run a univariate logit regression in which default is explained by only this variable; the Pseudo-R2 s from these regressions give a good indication on the relative explanatory power of individual variables.

NOTES AND LITERATURE In the econometrics literature, the logit models we looked at are subsumed under the heading of ‘binary response or qualitative response models’. Statisticians, on the other hand, often speak of generalized linear models. , 2003, Econometric Analysis, Prentice Hall. J. , 1997, Fitting regression models to case-control data by maximum likelihood, Biometrika 84, 57–71. , 2000, RiskCalc for private companies. Moody’ default model. , Mikityanskaya, V. , 2000, Moody’s public firm risk model: A hybrid approach to modeling short term default risk.

1 Root-T-rule for scaling standard deviations of return The percentage price change over T periods from t PT P0 R0 T R1 R2 0 to t R3 T can be written as RT where P denotes price and R the simple, gross return. With logarithmic returns r ln(R) we have (recall ln(x y) ln(x) ln(y)) r0 T r1 r2 r3 rT If the returns are independent across periods, the T-period variance is just the sum of the one-period variances Var(r0 T ) Var(r1 ) Var(r2 ) Var(r3 ) If return variances are identical across time, Var(r1 ) Var(rt ), we can then write Var(r0 T ) T Var(r T ) Var(r2 ) Var(r T ) Var(rt ) For the standard deviation of returns, it follows that (r0 T ) T (rt ) This is the root-T-rule.

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