scholarly journals A Computational Approach to Financial Option Pricing Using Quasi Monte Carlo Methods via Variance Reduction Techniques

2012 ◽  
Vol 02 (02) ◽  
pp. 195-198 ◽  
Author(s):  
Farshid Mehrdoust ◽  
Kianoush Fathi Vajargah
Keyword(s):  
Monte Carlo ◽  
Option Pricing ◽  
Monte Carlo Methods ◽  
Variance Reduction ◽  
Computational Approach ◽  
Quasi Monte Carlo ◽  
Variance Reduction Techniques ◽  
Reduction Techniques ◽  
Financial Option Pricing

scholarly journals Monte Carlo methods for backward equations in nonlinear filtering

2009 ◽  
Vol 41 (01) ◽  
pp. 63-100 ◽  
Author(s):  
G. N. Milstein ◽  
M. V. Tretyakov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Nonlinear Filtering ◽  
Error Control ◽  
Variance Reduction ◽  
Stochastic Partial Differential Equation ◽  
Numerical Algorithms ◽  
Variance Reduction Techniques ◽  
Reduction Techniques ◽  
Filtering Problem

We consider Monte Carlo methods for the classical nonlinear filtering problem. The first method is based on a backward pathwise filtering equation and the second method is related to a backward linear stochastic partial differential equation. We study convergence of the proposed numerical algorithms. The considered methods have such advantages as a capability in principle to solve filtering problems of large dimensionality, reliable error control, and recurrency. Their efficiency is achieved due to the numerical procedures which use effective numerical schemes and variance reduction techniques. The results obtained are supported by numerical experiments.

scholarly journals Monte Carlo methods for backward equations in nonlinear filtering

2009 ◽  
Vol 41 (1) ◽  
pp. 63-100 ◽  
Author(s):  
G. N. Milstein ◽  
M. V. Tretyakov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Nonlinear Filtering ◽  
Error Control ◽  
Variance Reduction ◽  
Stochastic Partial Differential Equation ◽  
Numerical Algorithms ◽  
Variance Reduction Techniques ◽  
Reduction Techniques ◽  
Filtering Problem

We consider Monte Carlo methods for the classical nonlinear filtering problem. The first method is based on a backward pathwise filtering equation and the second method is related to a backward linear stochastic partial differential equation. We study convergence of the proposed numerical algorithms. The considered methods have such advantages as a capability in principle to solve filtering problems of large dimensionality, reliable error control, and recurrency. Their efficiency is achieved due to the numerical procedures which use effective numerical schemes and variance reduction techniques. The results obtained are supported by numerical experiments.

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