Variance reduction methods for simulation of densities on Wiener space
AbstractWe 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.
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Bibliographic InfoPaper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 597.
Date of creation: Jan 2002
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Web page: http://www.econ.upf.edu/
Stochastic differential equations; weak approximation; variance reduction; kernel density estimation;
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- Moez Mrad & Nizar Touzi & Amina Zeghal, 2006. "Monte Carlo Estimation of a Joint Density Using Malliavin Calculus, and Application to American Options," Computational Economics, Society for Computational Economics, vol. 27(4), pages 497-531, June.
- Kebaier, Ahmed & Kohatsu-Higa, Arturo, 2008. "An optimal control variance reduction method for density estimation," Stochastic Processes and their Applications, Elsevier, vol. 118(12), pages 2143-2180, December.
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