Before revision:  We derive the resulting endogenous jump-diffusion , and show that it is a solution to the stochastic filtering problem associated with the Markov-modulated multivariate information process . We give a Feynman-Ka\'c representation for the endogenous jump-diffusion, and produce an explicit expression for the size of the jumps . The jump-size distribution is a function of a weighted sum of those information processes that are activated at the jump time.
After revision:  We derive the resulting endogenous jump-diffusion dynamics, where the jumps are governed by information source switches. The dynamic representation gives rise to a set of martingales on random time intervals that capture evolving information states as well as to a state-dependent stochastic volatility evolution with jumps. The nature of information flows usually exhibits complex behaviour, however, we succeed in maintaining analytic tractability by introducing what we term the effective and complementary information processes. The effective information captures in a univariate process all information provided by the active sources, and the complementary information is a weighted sum of those information processes that are activated at the jump time.