Efficient rare-event simulation for multiple jump events in regularly varying random walks and compound Poisson processes

Bohan Chen, Jose Blanchet, Chang Han Rhee, Bert Zwart

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    We propose a class of strongly efficient rare-event simulation estimators for random walks and compound Poisson processes with a regularly varying increment/jump-size distribution in a general large deviations regime. Our estimator is based on an importance sampling strategy that hinges on a recently established heavy-tailed sample-path large deviations result. The new estimators are straightforward to implement and can be used to systematically evaluate the probability of a wide range of rare events with bounded relative error. They are “universal” in the sense that a single importance sampling scheme applies to a very general class of rare events that arise in heavy-tailed systems. In particular, our estimators can deal with rare events that are caused by multiple big jumps (therefore, beyond the usual principle of a single big jump) as well as multidimensional processes such as the buffer content process of a queueing network. We illustrate the versatility of our approach with several applications that arise in the context of mathematical finance, actuarial science, and queueing theory.

    Original languageEnglish
    Pages (from-to)919-942
    Number of pages24
    JournalMathematics of Operations Research
    Issue number3
    Publication statusPublished - 2019

    Bibliographical note

    Funding Information:
    Funding: The work of B. Chen, C-H. Rhee, and B. Zwart was supported by the Netherlands Orga-nisation for Scientific Research [Vici Grant 639.033.413]. The work of J. Blanchet was supported by the National Science Foundation [Grants 132055, 1538217, 1820942] and the Defense Advanced Research Projects Agency [Grant N660011824028].

    Publisher Copyright:
    © 2019 INFORMS


    • Compound Poisson processes
    • Large deviations results
    • Principle of multiple big jumps
    • Random walks
    • Rare-event simulation
    • Regularly varying distribution
    • Strong efficiency


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