Variance reduction methods for simulation of densities on Wiener space
Arturo Kohatsu and
Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra
We develop a general error analysis framework for the Monte Carlo simulation of densities for functionals in Wiener space. We also study variance reduction methods with the help of Malliavin derivatives. For this, we give some general heuristic principles which are applied to diffusion processes. A comparison with kernel density estimates is made.
Keywords: Stochastic differential equations; weak approximation; variance reduction; kernel density estimation (search for similar items in EconPapers)
JEL-codes: G13 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:597
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