Credit Risk Management in Absence of Financial and Market Data
Credit risk management is a significant fragment in financial institutions' security precautions against the downside of their investments. A major quandary within the subject of credit risk is the modeling of simultaneous defaults. Globalization causes economises to be affected by innumerous external factors and companies to become interdependent, which in turn enlarges the complexity of establishing reliable mathematical models. The precarious situation is exacerbated by the fact that managers often suffer from the lack of data. The default correlations are most often calibrated by either using financial and/or market information. However, there exists circumstances where these types of data are inaccessible or unreliable. The problem of scarce data also induces diculties in the estimation of default probabilities. The frequency of insolvencies and changes in credit ratings are usually updated on an annual basis and historical information covers 20-25 years at best. From a mathematical perspective, this is considered as a small sample and standard statistical models are inferior in such situations.
The first part of this thesis specifies the so-called entropy model which estimates the impact of macroeconomic fluctuations on the probability of defaults, and aims to outperform standard statistical models for small samples. The second part specifies the CIMDO, a framework for modeling correlated defaults without financial and market data. The last part submits a risk analysis framework for calculating the uncertainty in the simulated losses.
It is shown that the entropy model will reduce the variance of the regression coefficients but increase its bias compared to the OLS and Maximum Likelihood. Furthermore there is a significant difference between the Student's t CIMDO and the t-Copula. The former appear to reduce the model uncertainty, however not to such extent that evident conclusions were carried out.
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