Credit Default Prediction Based on Multivariate Regression

Valuation of portfolio credit derivatives and data - based default prediction

To my parents and to Diana Preface Credit derivatives play a major role in financial markets. To price these complex products objectively advanced mathematical models are needed. In this thesis we focus on the modelling and valuation of two portfolio credit derivatives: n th-to-default credit default swaps (CDSs) and collateralised debt obligations (CDOs). The key input parameters of these mode...

Credit Risk Modeling for Companies Default Prediction Using Neural Networks

The paper assesses the business default risk on a cross-national sample of 3000 companies applying for credit to an international bank operating in Romania. The structure of the sample replicates the structure of the general population of companies in Romania. Based on their past credit history, we have distributed the companies in seven classes plus the default, using and adapting the Standard...

Valuation of Credit Default Swaptions and Credit Default Index Swaptions

Default Prediction of Alternative Structural Credit Risk Models and Implications of Default Boundaries

While most of the empirical studies of structural credit risk models try to test the performance of structural models in bond and credit derivatives pricing, little results are provided for default prediction. Therefore, in this study, we empirically compare four structural credit risk models – for their default prediction capabilities. Our empirical results indicate that exogenous default boun...

On Segmented Multivariate Regression

This paper concerns segmented multivariate regression models, models which have different linear forms in different subdomains of the domain of an independent variable. Without knowing that number and their boundaries, we first estimate the number of these subdomains using a modified Schwarz criterion. The estimated number of regions proves to be weakly consistent under fairly general condition...