Estimating Correlated Jumps and Stochastic Volatilities

We formulate a bivariate stochastic volatility jump-diffusion model with correlated jumps and volatilities. An MCMC Metropolis-Hastings sampling algorithm is proposed to estimate the model's parameters and latent state variables (jumps and stochastic volatilities) given observed returns. The methodology is successfully tested on several artificially generated bivariate time series and then on the two most important Czech domestic financial market time series of the FX (CZK/EUR) and stock (PX index) returns. Four bivariate models with and without jumps and/or stochastic volatility are compared using the deviance information criterion (DIC) confirming importance of incorporation of jumps and stochastic volatility into the model

Case Study of Mercury Dispersion from Point Source

On measured concentrations of gazeous Hg from point source point source the ATEM model was applied (Gaussian dispersion model). Space distribution of Hg for 1.Real metorological data at 25.3.2005 and for 2.Lower flow velocity are presented in figures

Large Eddy Simulation of stratified flows over structures

We tested the ability of the LES model CLMM (Charles University Large-Eddy Microscale Model) to model the stratified flow around three dimensional hills. We compared the quantities, as the height of the dividing streamline, recirculation zone length or length of the lee waves with experiments by Hunt and Snyder[3] and numerical computations by Ding, Calhoun and Street[5]. The results mostly agreed with the references, but some important differences are present