ABSTRACT
Besides their probabilistic behavior and nondeterministic general dynamics, an additional factor that may appear in real data variation is “jumps”. Jumps are formed by the relatively large fluctuation (positive or negative variation) with
amplitude stochastic process. The jump-diffusion process description and estimation method of the process parameters are given. In the section 3 case studies are represented, giving the technical consideration of the calibration method. Real data evolution and simulated data from the chosen model are compared.
