Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/30176 
Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
Frankfurt School - Working Paper Series No. 136
Verlag: 
Frankfurt School of Finance & Management, Frankfurt a. M.
Zusammenfassung: 
Credit risk measurement and management become more important in all financial institutions in the light of the current financial crisis and the global recession. This particularly applies to most of the complex structured financing forms whose risk cannot be quantified with com-mon rating methods. This paper explains the risk associated with leveraged buyout (LBO) transactions and demon-strates the implementation of a new rating method based on a logistic regression (logit func-tion), a rating system commonly used by banks. The system estimates probabilities of default for various time horizons between three months and two years. Input variables contain information about the transaction (based on financial covenants) as well as macroeconomic parameters. The most important factor is a firm’s cyclicality. Leve-rage and capital structure are statistically significant and are also utilized in this ratings sys-tem, however they are far less important compared to cyclicality when this method is em-ployed. The validation results demonstrate a very good calibration and discriminatory power between defaulting and non-defaulting LBO transactions.
Schlagwörter: 
Logistic Regression
Logit
Credit Risk
Credit Risk Modeling
Rating
Probabili-ty of Default
PD
Basel II
Rating Validation
Rseudo-R-Square
Alpha Error
Beta Error
Minimum Classification Error
Cumulative Accuracy Profile Curve
CAP
Receiver Operating Characteristic
ROC
Area Under the Curve
AUC
Brier Score
Bootstrapping
Leveraged Buyout
LBO
Buyout
Leveraged Finance
Private Equity
JEL: 
C01
C02
C12
C22
C52
G01
G11
G21
G24
G32
G33
Dokumentart: 
Working Paper

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