Paper #597

Títol:

Variance reduction methods for simulation of densities on Wiener space

Autors:

Arturo Kohatsu i Roger Pettersson

Data:

Gener 2002

Resum:

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.

Paraules clau:

Stochastic differential equations, weak approximation, variance reduction, kernel density estimation

Codis JEL:

G13

Àrea de Recerca:

Estadística, Econometria i Mètodes Quantitatius

Publicat a:

SIAM Journal of Numerical Analysis, 12, (2002) pp.423-476

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