Properties of the sample autocorrelations of non-linear transformations in long memory stochastic volatility models

The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility. This article shows that, in the context of long-memory stochastic volatility models, these autocorrelations are smaller than the autocorrelations of the log volatility and so is the rate of decay for squared and absolute returns. Furthermore, the corresponding sample autocorrelations could have severe negative biases, making the identification of conditional heteroscedasticity and long memory a difficult task. Finally, we show that the power of some popular tests for homoscedasticity is larger when they are applied to absolute returns.Publicad

Determination of local material properties of OSB sample by coupling advanced imaging techniques and morphology-based FEM simulation

This is the publisher’s final pdf. The published article is copyrighted by Walter de Gruyter & Co. and can be found at: http://www.degruyter.com/.The goal was to determine local mechanical properties inside of oriented strand board (OSB) based on a realistic morphology-based finite element (FE) model and data acquired from a physical test performed on the same material. The spatial information and local grayscale intensity from CT-scans obtained from small OSB sample was transformed into a 2D regular morphology-based FE mesh with corresponding material properties. The model was then used to simulate the actual compression test performed on the specimen using simplified boundary conditions. The simulated strain fields from the model were compared with the actual strain field measured on the specimen surface during the compression test by means of a full-field optical method, named digital image correlation (DIC). Finally, the original set of material properties was adjusted by an iterative procedure to minimize the difference between the simulated and the measured strain data. The results show that the developed procedure is useful to find local material properties as well as for morphological modeling without the need of segmentation of the image data. The achieved results serve as a prerequisite for full 3D analyses of the complex materials