4,031 research outputs found
Multilevel Monte Carlo for stochastic differential equations with small noise
We consider the problem of numerically estimating expectations of solutions
to stochastic differential equations driven by Brownian motions in the commonly
occurring small noise regime. We consider (i) standard Monte Carlo methods
combined with numerical discretization algorithms tailored to the small noise
setting, and (ii) a multilevel Monte Carlo method combined with a standard
Euler-Maruyama implementation. Under the assumptions we make on the underlying
model, the multilevel method combined with Euler-Maruyama is often found to be
the most efficient option. Moreover, under a wide range of scalings the
multilevel method is found to give the same asymptotic complexity that would
arise in the idealized case where we have access to exact samples of the
required distribution at a cost of per sample. A key step in our
analysis is to analyze the variance between two coupled paths directly, as
opposed to their distance. Careful simulations are provided to illustrate
the asymptotic results.Comment: A section making a comparison with results in the jump process
setting has been added. We have also taken several opportunities to clarify
the presentation and interpret the results more full
Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation
This paper applies several well-known tricks from the numerical treatment of deterministic differential equations to improve the efficiency of the multilevel Monte Carlo (MLMC) method for stochastic differential equations (SDEs) and especially the Langevin equation. We use modified equations analysis as an alternative to strong-approximation theory for the integrator, and we apply this to introduce MLMC for Langevin-type equations with integrators based on operator splitting. We combine this with extrapolation and investigate the use of discrete random variables in place of the Gaussian increments, which is a well-known technique for the weak approximation of SDEs. We show that, for small-noise problems, discrete random variables can lead to an increase in efficiency of almost two orders of magnitude for practical levels of accuracy
From rough path estimates to multilevel Monte Carlo
New classes of stochastic differential equations can now be studied using
rough path theory (e.g. Lyons et al. [LCL07] or Friz--Hairer [FH14]). In this
paper we investigate, from a numerical analysis point of view, stochastic
differential equations driven by Gaussian noise in the aforementioned sense.
Our focus lies on numerical implementations, and more specifically on the
saving possible via multilevel methods. Our analysis relies on a subtle
combination of pathwise estimates, Gaussian concentration, and multilevel
ideas. Numerical examples are given which both illustrate and confirm our
findings.Comment: 34 page
Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
In this article, we propose a Milstein finite difference scheme for a
stochastic partial differential equation (SPDE) describing a large particle
system. We show, by means of Fourier analysis, that the discretisation on an
unbounded domain is convergent of first order in the timestep and second order
in the spatial grid size, and that the discretisation is stable with respect to
boundary data. Numerical experiments clearly indicate that the same convergence
order also holds for boundary-value problems. Multilevel path simulation,
previously used for SDEs, is shown to give substantial complexity gains
compared to a standard discretisation of the SPDE or direct simulation of the
particle system. We derive complexity bounds and illustrate the results by an
application to basket credit derivatives
Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes
When solving stochastic partial differential equations (SPDEs) driven by
additive spatial white noise, the efficient sampling of white noise
realizations can be challenging. Here, we present a new sampling technique that
can be used to efficiently compute white noise samples in a finite element
method and multilevel Monte Carlo (MLMC) setting. The key idea is to exploit
the finite element matrix assembly procedure and factorize each local mass
matrix independently, hence avoiding the factorization of a large matrix.
Moreover, in a MLMC framework, the white noise samples must be coupled between
subsequent levels. We show how our technique can be used to enforce this
coupling even in the case of non-nested mesh hierarchies. We demonstrate the
efficacy of our method with numerical experiments. We observe optimal
convergence rates for the finite element solution of the elliptic SPDEs of
interest in 2D and 3D and we show convergence of the sampled field covariances.
In a MLMC setting, a good coupling is enforced and the telescoping sum is
respected.Comment: 28 pages, 10 figure
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