Optimization of a Monte Carlo variance reduction method based on sensitivity derivatives

2013 ◽  
Vol 72 ◽  
pp. 160-171 ◽  
Author(s):  
Yaning Liu ◽  
M. Yousuff Hussaini ◽  
Giray Ökten
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method ◽  
Sensitivity Derivatives

scholarly journals Stratification as a General Variance Reduction Method for Markov Chain Monte Carlo

2020 ◽  
Vol 8 (3) ◽  
pp. 1139-1188
Author(s):  
Aaron R. Dinner ◽  
Erik H. Thiede ◽  
Brian Van Koten ◽  
Jonathan Weare
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method

Global variance reduction method based on multi-group Monte Carlo adjoint calculation

2021 ◽  
Vol 151 ◽  
pp. 107958
Author(s):  
Tao Shi ◽  
Hui Li ◽  
Qianxue Ding ◽  
Mengqi Wang ◽  
Zheng Zheng ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method ◽  
Global Variance Reduction

scholarly journals Monte Carlo model and variance reduction method based on lidar of ship wake

2013 ◽  
Vol 62 (1) ◽  
pp. 015205
Author(s):  
Liang Shan-Yong ◽  
Wang Jiang-An ◽  
Zhang Feng ◽  
Wu Rong-Hua ◽  
Zong Si-Guang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method ◽  
Monte Carlo Model ◽  
Ship Wake

Global variance reduction method for global Monte Carlo particle transport simulations of CFETR

2017 ◽  
Vol 28 (8) ◽  
Author(s):  
Xing-Chen Nie ◽  
Jia Li ◽  
Song-Lin Liu ◽  
Xiao-Kang Zhang ◽  
Ping-Hui Zhao ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Particle Transport ◽  
Variance Reduction ◽  
Reduction Method ◽  
Global Variance Reduction

Variance reduction method based on sensitivity derivatives, Part 2

2013 ◽  
Vol 74 ◽  
pp. 151-159 ◽  
Author(s):  
Edwin Jimenez ◽  
Yaning Liu ◽  
M. Yousuff Hussaini
Keyword(s):  
Variance Reduction ◽  
Reduction Method ◽  
Sensitivity Derivatives

scholarly journals ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

2021 ◽  
Vol 10 (4) ◽  
pp. 192
Author(s):  
IRENE MAYLINDA PANGARIBUAN ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Variance Reduction ◽  
Reduction Method ◽  
Random Numbers ◽  
Control Variates ◽  
Popular Method ◽  
Maximum Loss

Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR. The purpose of this study is to demonstrate control variates method as a variance reduction method that can be applied to estimate VaR. Moreover, it is to compare the results with the normal VaR method or analytical VaR calculation. Control variates method was used to find new returns from all stocks which are used as estimators of the control variates. The new returns were then used to define parameters needed to generate N random numbers. Furthermore, the generated numbers were used to find the VaR value. The method was then applied to estimate a portfolio of the game and esports company stocks that are EA, TTWO, AESE, TCEHY, and ATVI . The results show Monte Carlo simulation gives VaR of US$41.6428 within 1000 simulation, while the analytical VaR calculation  or  normal VaR method gives US$30.0949.

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