Before revision:  A state space representation of the model is constructed using both financial returnsand non-parametric measures of integrated volatility and price jumps as the observable quantities. Bayesian inference , based on a Markov chain Monte Carlo algorithm , is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities .
After revision:  A state space representation of the model is constructed using both financial returnsand non-parametric measures of integrated volatility and price jumps , to the specified model components; with Bayesian inference conducted using a Markov chain Monte Carlo algorithm , is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities .