News might trigger jump arrivals in financial time series. The “bad” news and “good” news seem to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable estimation of the model’s parameters, detection of jumps and analysis of jump frequency. The methodology, founded upon the idea of latent variables, is illustrated with simulated data.

In the paper we present and apply a Bayesian jump-diffusion model and stochastic volatility models with jumps. The problem of how to classify an observation as a result of a jump is addressed, under the Bayesian approach, by introducing latent variables. The empirical study is focused on the time series of gas forward contract prices and EUA futures prices. We analyse the frequency of jumps and relate the moments in which jumps occur to calendar effects or political and economic events and decisions. The calendar effects explain many jumps in gas contract prices. The single jump is identified in the EUA futures prices under the SV-type models. The jump is detected on the day the European Parliament voted against the European Commission’s proposal of backloading. The Bayesian results are compared with the outcomes of selected non-Bayesian techniques used for detecting jumps.

This paper presents results of the study devoted to analysis of impact of upper extremities' momentum on the jump length and analysis of selected kinematic data changes during the standing long jump. Four young sportsmen participated in the initial study. They have performed standing long jump in two measuring conditions: with and without arms swinging. Motion was captured using a 3D opto-electronic camera system SMART (BTS) and selected kinematic data were evaluated using software packages and data processing: trajectory of body centre of gravity (COG), velocity of COG, maximal vertical distance of COG, take-off angle together with momentum of upper extremities were analyzed. The data were statistically evaluated using descriptive statistics and analysis of variance. Statistical significance of the kinematic data and jump length were analyzed using the Kruskal-Wallis test and post-hoc test (p<0.05) in Statistics toolbox of Matlab program. Statistically significant differences were assessed within intraindividual and intraclass comparison of data.

Second law analysis (entropy generation) for the steady two-dimensional laminar forced convection flow, heat and mass transfer of an incompressible viscous fluid past a nonlinearly stretching porous (permeable) wedge is numerically studied. The effects of viscous dissipation, temperature jump, and first-order chemical reaction on the flow over the wedge are also considered. The governing boundary layer equations for mass, momentum, energy and concentration are transformed using suitable similarity transformations to three nonlinear ordinary differential equations (ODEs). Then, the ODEs are solved by using a Keller’s box algorithm. The effects of various controlling parameters such as wedge angle parameter, velocity ratio parameter, suction/injection parameter, Prandtl number, Eckert number, temperature jump parameter, Schmidt number, and reaction rate parameter on dimensionless velocity, temperature, concentration, entropy generation number, and Bejan number are shown in graphs and analyzed. The results reveal that the entropy generation number increases with the increase of wedge angle parameter, while it decreases with the increase of velocity ratio parameter. Also, in order to validate the obtained numerical results of the present work, comparisons are made with the available results in the literature as special cases, and the results are found to be in a very good agreement.

A Bayesian stochastic volatility model with a leverage effect, normal errors and jump component with the double exponential distribution of a jump value is proposed. The ready to use Gibbs sampler is presented, which enables one to conduct statistical inference. In the empirical study, the SVLEDEJ model is applied to model logarithmic growth rates of one month forward gas prices. The results reveal an important role of both jump and stochastic volatility components.

The stability and positivity of linear positive Markovian jump systems with respect to part of the variables is considered. The methodologies of stability of positive systems with known transition probabilities based on common linear copositive Lyapunov function and stability of linear systems with respect to part of the variables are combined to find sufficient conditions of the stochastic stability and positivity of Markovian jump systems with respect to part of the variables. The results are extended for a class of nonlinear positive Markovian jump systems with respect to part of the variables. An example is given to illustrate the obtained results.