  News might trigger jump arrivals in financial time series. The "bad" and
"good" news seems 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 two empirical studies, employing both
simulated and real-world data (the KGHM index). News might trigger jump
arrivals in financial time series. The "bad" and "good" news seems 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 two empirical studies, employing both simulated and real-world
data (the KGHM index).
