Large deviations asymptotics and the spectral theory of multiplicatively regular Markov processes

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We continue the investigation of the spectral theory and exponential asymptotics of Markov processes, following Kontoyiannis and Meyn (2003). We introduce a new family of nonlinear Lyapunov drift criteria, characterizing distinct subclasses of geometrically ergodic Markov processes in terms of inequalities for the nonlinear generator. We concentrate on the class of "multiplicatively regular" Markov processes, characterized via conditions similar to (but weaker than) those of Donsker-Varadhan. For any such process Phi(t) with transition kernel P on a general state space, the following are obtained. 1. SPECTRAL THEORY: For a large class of functionals F, the kernel Phat(x,dy) = eᶠ⁽ˣ⁾P(x,dy) has a discrete spectrum in an appropriately defined Banach space. There exists a "maximal" solution to the "multiplicative Poisson equation," defined as the eigenvalue problem for Phat. Regularity properties are established for Λ(F) = (λ), where λis the maximal eigenvalue, and for its convex dual. 2. MULTIPLICATIVE MEAN ERGODIC THEOREM: The normalized mean Eₓ[(Sₜ)] of the exponential of the partial sums Sₜ of the process with respect to any one of the above functionals F, converges to the maximal eigenfunction. 3. MULTIPLICATIVE REGULARITY: The drift criterion under which our results are derived is equivalent to the existence of regeneration times with finite exponential moments for Sₜ. 4. LARGE DEVIATIONS: The sequence of empirical measures of Phi(t) satisfies an LDP in a topology finer than the τ-topology. The rate function is Λ* and it coincides with the Donsker-Varadhan rate function. 5. EXACTR LARGE DEVIATIONS: The partial sums Sₜ satisfy an exact LD expansion, analogous to that obtained for independent random variables.