Exact Sampling for the Maximum of Infinite Memory Gaussian Processes

Jose Blanchet (), Lin Chen () and Jing Dong ()
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Jose Blanchet: Stanford University
Lin Chen: Columbia University
Jing Dong: Columbia University

A chapter in Advances in Modeling and Simulation, 2022, pp 41-63 from Springer

Abstract: Abstract We develop an exact sampling algorithm for the all-time maximum of Gaussian processes with negative drift and general covariance structures. In particular, our algorithm can handle non-Markovian processes even with long-range dependence. Our development combines a milestone-event construction with rare-event simulation techniques. This allows us to find a random time beyond which the running time maximum will never be reached again. The complexity of the algorithm is random but has finite moments of all orders. We also test the performance of the algorithm numerically.

Keywords: Perfect simulation; Infinite memory; Rare event sampling (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10193-9_3

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DOI: 10.1007/978-3-031-10193-9_3

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