Although stochastic models driven by latent Markov processes are widely used, the classical importance sampling methods based on the exponential tilting for these models suffer from difficulties in computing associated eigenvalues and eigenfunctions and the plausibility of the indirect asymptotic large deviation regime for the variance of the estimator. We propose an importance sampling framework that twists the observable and latent processes separately using a link function that directly minimizes the estimator's variance. An optimal link function is chosen within the locally asymptotically normal family. We show the logarithmic efficiency of the estimator. As applications, we estimate an overflow probability under a pandemic model and the CoVaR, a measurement of the co-dependent financial systemic risk; both are beyond the scope of traditional importance sampling methods due to nonlinear features.

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