Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to...

In this study we forecast the term structure of FIBOR/EURIBOR swap rates by means of recursive vector autoregressive (VAR) models. In advance, a principal components analysis (PCA) is adopted to reduce the dimensionality of the term structure. To evaluate ex–ante forecasting performance for...

The purpose of this work is to introduce one of the most promising among recentlydeveloped statistical techniques – the support vector machine (SVM) –to corporate bankruptcy analysis. An SVM is implemented for analysing suchpredictors as financial ratios. A method of adapting it to default...

The paper proposes a data driven adaptive model selection strategy. The selection criterionmeasures economic ex-ante forecasting content by means of trading implied cash flows.Empirical evidence suggests that the proposed strategy is neither exposed to selection biasnor to the risk of choosing...