arXiv releases v3 of a general importance sampling framework for Markov random walks
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Cheng-Der Fuh, Yanwei Jia, and Steven Kou have published the third version of a paper on arXiv, proposing a general importance sampling framework for stochastic models driven by hidden Markov processes. The framework achieves logarithmically efficient estimation by separately distorting observable and implicit processes, and using a link function that directly minimizes estimator variance. This framework can be used to estimate spillover probabilities under epidemic models and the systemic financial risk co-dependence measure CoVaR, overcoming the nonlinear characteristic limitations of traditional importance sampling methods.
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- arXiv q-finA General Importance Sampling Framework Based on Markov Random Walks
For stochastic models driven by hidden Markov processes, this paper proposes a general importance sampling framework that separately distorts observable and hidden processes, achieving logarithmic-efficient estimation through a link function that directly minimizes estimator variance. This framework can be used to estimate overflow probabilities under epidemic models and the systemic risk co-dependence measure CoVaR, overcoming the nonlinearity limitations of traditional importance sampling methods.
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