This paper describes a continuous-time state-process, discrete-time observation, Interacting Multiple Model (IMM) tracking algorithm, and its applications to financial market modeling and asset allocation. The system state is modeled as a continuous-time, affine-Gaussian stochastic dynamical process driven by a white process noise, as well as by structural changes modeled by a finite-state, continuous-time, Markov process. The system generally assumes multiple models with different state space dimensions, and an affine-Gaussian state jump whenever a model transition occurs. The underlying problem is a standard filtering problem for estimating the system state based on a sequence of discrete-time, linear-Gaussian observations of partial system states. To demonstrate the new method, we apply the IMM algorithm to financial market modeling for dynamic asset allocation. The resulting performance shows the potential applicability of the proposed method.