UPMC

This is a joint work with V. Lemaire

(LPMA-UPMC). We propose and analyze a Multilevel Richardson-Romberg

(MLRR) estimator which combines the higher order bias cancellation of

the Multistep Richardson-Romberg ($MSRR$) method introduced

in~[Pag\`es 07] and the variance control resulting from the

stratification in the Multilevel Monte Carlo (MLMC) method (see~$e.g.$

[Heinrich 01, M. Giles 08]). Thus we show that in standard frameworks

like discretization schemes of diffusion processes, an assigned

quadratic error $\varepsilon$ can be obtained with our (MLRR)

estimator with a global complexity of

$\log(1/\varepsilon)/\varepsilon^2$ instead of

$(\log(1/\varepsilon))^2/\varepsilon^2$ with the standard (MLMC)

method, at least when the weak error $\E Y_h-\EY_0}$ induced by the

biased implemented estimator $Y_h$ can be expanded at any order in

$h$. We analyze and compare these estimators on several numerical

problems: option pricing (vanilla or exotic) using $MC$ simulation and

the less classical Nested Monte Carlo simulation (see~[Gordy \& Juneja

2010]).