"Organizations and institutions are looking for implementing data-driven decision-making processes, business process improvement and methods for advancing faster in innovation. This book is about implementing the analytics process to develop a risk management practice that creates competitive...

While stochastic volatility models improve on the option pricing error when compared to theBlack-Scholes-Merton model, mispricings remain. This paper uses mixed normalheteroskedasticity models to price options. Our model allows for significant negative skewnessand time varying higher order...

Since little is known about the degree of bias in estimated fixed effects in panel data models, we run Monte Carlo simulations on a range of different estimators. We find that Anderson-Hsiao IV, Kiviet’s bias-corrected LSDV and GMM estimators all perform well in both short and long panels...

This paper introduces a new method for online estimation and predictionof states and parameters of nonlinear stochastic differential equations. Inthis setup parameters are considered as random variables in a Bayesiansense, which requires integration over parameter distributions. This...

In der vorliegenden empirischen Untersuchung erreichen Marktanteilsmodelle mit semi-parametrischen additiven Markenattraktionen bessere Anpassungsmaße sowohl nach einem Informationskriterium wie AIC, das ein Modell für die Anzahl verbrauchter Freiheitsgrade bestraft, als auch nach mittels...

This study deals with the determinants of factor demand in 27 industries of the manufacturing sector during the period 1978 to 1990. Using a quadratic cost function, six production factors are distinguished : capital, energy, three types of labour and intermediate materials. A parametric test of...