A second-order discretization with Malliavin weight and Quasi-Monte Carlo method for option pricing

2018 ◽  
Vol 20 (11) ◽  
pp. 1825-1837 ◽  
Author(s):  
Toshihiro Yamada ◽  
Kenta Yamamoto
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Option Pricing ◽  
Second Order ◽  
Quasi Monte Carlo

A second-order discretization for degenerate systems of stochastic differential equations

2020 ◽  
Author(s):  
Yuga Iguchi ◽  
Toshihiro Yamada
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Second Order ◽  
Weak Approximation ◽  
Hörmander Condition ◽  
Quasi Monte Carlo ◽  
Control Variate ◽  
The Monte Carlo Method

Abstract The paper proposes a new second-order weak approximation scheme for hypoelliptic diffusions or degenerate systems of stochastic differential equations satisfying a certain Hörmander condition. The scheme is constructed by a Gaussian process and a stochastic polynomial weight through a technique based on Malliavin calculus, and is implemented by a Monte Carlo method and a quasi-Monte Carlo method. A variance analysis for the Monte Carlo method is discussed, and further control variate methods are introduced to reduce the variance. The effectiveness of the proposed scheme is illustrated through numerical experiments for some hypoelliptic diffusions.

Monte Carlo & Quasi-Monte Carlo approach in option pricing

2012 ◽  
Author(s):  
M. A. Maasar ◽  
N. A. M. Nordin ◽  
M. Anthonyrajah ◽  
W. M. W. Zainodin ◽  
A. M. Yamin
Keyword(s):  
Monte Carlo ◽  
Option Pricing ◽  
Monte Carlo Approach ◽  
Quasi Monte Carlo

scholarly journals A quasi-Monte Carlo method for computing areas of point-sampled surfaces

2006 ◽  
Vol 38 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Yu-Shen Liu ◽  
Jun-Hai Yong ◽  
Hui Zhang ◽  
Dong-Ming Yan ◽  
Jia-Guang Sun
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Quasi Monte Carlo
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