Add to ORKGMultilevel Monte Carlo (MLMC) methods have been used by the information-based complexity community in order to improve the computational efficiency of parametric integration. We extend this approach by relaxing the assumptions on differentiability of the simulation output. Relaxing the assumption on the differentiability of the simulation output makes the MLMC method more widely applicable to stochastic simulation metamodeling problems in industrial engineering. The proposed scheme uses a sequential experiment design which allocates effort unevenly among design points in order to increase its efficiency. The procedure’s efficiency is tested on an example of option pricing in the Black-Scholes model.
AbstractOne-way coupling often occurs in multi-dimensional stochastic models in finance. In this pap...
In this work the approximation of Hilbert-space-valued random variables is combined with the approxi...
We show that multigrid ideas can be used to reduce the computational complexity of estimating an exp...
With Monte Carlo methods, to achieve improved accuracy one often requires more expensive sampling (s...
Monte Carlo methods are a very general and useful approach for the estima-tion of expectations arisi...
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...
In Monte Carlo path simulations, which are used extensively in computational fi-nance, one is intere...
A standard problem in mathematical finance is the calculation of the price of some financial derivativ...
Monte Carlo is a simple and flexible tool that is widely used in computational finance. In this cont...
Monte Carlo path simulations are common in mathematical and computational finance as a way of estima...
In this thesis, we center our research around the analytical approximation of American put options w...
One-way coupling often occurs in multi-dimensional stochastic models in finance. In this paper, we d...
We introduce a Multilevel Monte Carlo method for approximating the transitiondensity for discretely ...
Multilevel Monte Carlo is a novel method for reducing the computational cost when computing conditio...
AbstractOne-way coupling often occurs in multi-dimensional stochastic models in finance. In this pap...
In this work the approximation of Hilbert-space-valued random variables is combined with the approxi...
We show that multigrid ideas can be used to reduce the computational complexity of estimating an exp...
With Monte Carlo methods, to achieve improved accuracy one often requires more expensive sampling (s...
Monte Carlo methods are a very general and useful approach for the estima-tion of expectations arisi...
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...
In Monte Carlo path simulations, which are used extensively in computational fi-nance, one is intere...
A standard problem in mathematical finance is the calculation of the price of some financial derivativ...
Monte Carlo is a simple and flexible tool that is widely used in computational finance. In this cont...
Monte Carlo path simulations are common in mathematical and computational finance as a way of estima...
In this thesis, we center our research around the analytical approximation of American put options w...
One-way coupling often occurs in multi-dimensional stochastic models in finance. In this paper, we d...
We introduce a Multilevel Monte Carlo method for approximating the transitiondensity for discretely ...
Multilevel Monte Carlo is a novel method for reducing the computational cost when computing conditio...
AbstractOne-way coupling often occurs in multi-dimensional stochastic models in finance. In this pap...
In this work the approximation of Hilbert-space-valued random variables is combined with the approxi...
We show that multigrid ideas can be used to reduce the computational complexity of estimating an exp...