Stefan Markus Giebel

Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.

2D-shape analysis of biological objects is described first in 2007 with MRI-data (magnetic resonance imaging) of renal tumours of infancy. For shape analysis the evaluation of landmarks is necessary (n >2). In this study 24 landmarks are selected. Every object is described by these landmarks. The shape is the standardised and centred object. The procedure is applied on transversal as well as on frontal images. The results for frontal and transversal images are compared. Tumours of different origin, topography and size can be analysed. The differentiation of relevant landmarks is important for statistical and medical reason. In this study, evaluation of landmarks and their possibility for tumour differentiation is demonstrated.

2D-shape analysis of biological objects is described first in 2007 with MRI-data (magnetic resonance imaging) of renal tumours of infancy. For shape analysis the evaluation of landmarks is necessary (n >2). In this study 24 landmarks are selected. Every object is described by these landmarks. The shape is the standardised and centred object. The procedure is applied on transversal as well as on frontal images. The results for frontal and transversal images are compared. Tumours of different origin, topography and size can be analysed. The differentiation of relevant landmarks is important for statistical and medical reason. In this study, evaluation of landmarks and their possibility for tumour differentiation is demonstrated.

Imprisonment and Recidivism of Young Offenders Stefan Markus Giebel, Verena Boxberg & Daniela Hosser 1. Introduction As in many other western industrialized nations, youth violence in Germany sharply increased during the mid-nineties (Heinz, 2008). Especially the rising ...

54 CP6: Biostatistics & Bio-Computing 1 Differentiation Tests for Three Dimensional Shape Analysis Stefan Markus Giebel1, Jens-Peter Schenk2 and Jang Schiltz1 1 Université du Luxembourg 4, rue Albert Borschette, L-1246 Luxembourg jang. schiltz@ uni. lu 2 Uniklinikum ...