This paper proposes a new multilevel Monte Carlo (MLMC) method for the ergodic SDEs which do not satisfy the contractivity condition. By introducing the change of measure technique, we simulate the path with contractivity and add the Radon–Nikodym derivative to the estimator. We can show the strong error of the path is uniformly bounded with respect to T. Moreover, the variance of the new level estimators increase linearly in T, which is a great reduction compared with the exponential increase in standard MLMC. Then the total computational cost is reduced to from of the standard Monte Carlo method. Numerical experiments support our analysis
We consider the application of multilevel Monte Carlo methods to elliptic PDEs with ran-dom coeffici...
With Monte Carlo methods, to achieve improved accuracy one often requires more expensive sampling (s...
We consider the numerical solution of elliptic partial differential equations with random coefficien...
In this paper we introduce a new multilevel Monte Carlo (MLMC) estimator for multi-dimensional SDEs ...
In this thesis, we focus on the numerical approximation of SDEs with a drift which is not globally ...
Abstract In this paper we develop antithetic multilevel Monte Carlo (MLMC) esti-mators for multidime...
In this work, the approximation of Hilbert-space-valued random variables is combined with the approx...
A standard problem in the field of computational finance is that of pricing derivative securities. T...
Previously the authors have presented MLMC algorithms exploiting Multiscale Finite Elements and Redu...
Abstract. We present an adaptive multilevel Monte Carlo (MLMC) method for weak approximations of sol...
The Euler–Maruyama scheme is known to diverge strongly and numerically weakly when applied to nonlin...
In this paper, we discuss the possibility of using multilevel Monte Carlo (MLMC) approach for weak a...
We develop a framework that allows the use of the multi-level Monte Carlo (MLMC) methodology (Giles2...
In this work the approximation of Hilbert-space-valued random variables is combined with the approxi...
The efficient numerical simulation of models described by partial differential equations (PDEs) is a...
We consider the application of multilevel Monte Carlo methods to elliptic PDEs with ran-dom coeffici...
With Monte Carlo methods, to achieve improved accuracy one often requires more expensive sampling (s...
We consider the numerical solution of elliptic partial differential equations with random coefficien...
In this paper we introduce a new multilevel Monte Carlo (MLMC) estimator for multi-dimensional SDEs ...
In this thesis, we focus on the numerical approximation of SDEs with a drift which is not globally ...
Abstract In this paper we develop antithetic multilevel Monte Carlo (MLMC) esti-mators for multidime...
In this work, the approximation of Hilbert-space-valued random variables is combined with the approx...
A standard problem in the field of computational finance is that of pricing derivative securities. T...
Previously the authors have presented MLMC algorithms exploiting Multiscale Finite Elements and Redu...
Abstract. We present an adaptive multilevel Monte Carlo (MLMC) method for weak approximations of sol...
The Euler–Maruyama scheme is known to diverge strongly and numerically weakly when applied to nonlin...
In this paper, we discuss the possibility of using multilevel Monte Carlo (MLMC) approach for weak a...
We develop a framework that allows the use of the multi-level Monte Carlo (MLMC) methodology (Giles2...
In this work the approximation of Hilbert-space-valued random variables is combined with the approxi...
The efficient numerical simulation of models described by partial differential equations (PDEs) is a...
We consider the application of multilevel Monte Carlo methods to elliptic PDEs with ran-dom coeffici...
With Monte Carlo methods, to achieve improved accuracy one often requires more expensive sampling (s...
We consider the numerical solution of elliptic partial differential equations with random coefficien...