Importance Sampling for Event Timing Models

This paper provides an ecient Monte Carlo method for estimating rare-event probabilities in point process models of correlated event timing, which have applications in nance, insurance, engineering, and many other areas. It develops an importance sampling scheme for the tail of the distribution of the total event count at a xed horizon, and provides conditions guaranteeing the asymptotic optimality of the resulting estimator. The change of measure diers from the widely used exponential twisting. The algorithm applies to point process models with arbitrary stochastic intensity dynamics. Numerical tests illustrate its performance.

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