Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

It is investigated whether Euro-area variables can be forecast better based on synthetic time series for the pre-Euro period or by using just data from Germany for the pre-Euro period. Our forecast comparison is based on quarterly data for the period 1970Q1 - 2003Q4 for ten macroeconomic...

State price densities (SPD) are an important element in applied quantitativefinance. In a Black-Scholes model they are lognormal distributions with constant volatility parameter. In practice volatility changes and the distribution deviates from log-normality. We estimate SPDs using EUREX option...

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...