scholarly journals Variance Reduction Techniques of Importance Sampling Monte Carlo Methods for Pricing Options

2013 ◽  
Vol 03 (04) ◽  
pp. 431-436 ◽  
Author(s):  
Qiang Zhao ◽  
Guo Liu ◽  
Guiding Gu
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Monte Carlo Methods ◽  
Variance Reduction ◽  
Variance Reduction Techniques ◽  
Pricing Options ◽  
Reduction Techniques

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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