Bayesian DEJD Model and Detection of Asymmetry in Jump Sizes

Abstract

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.