scholarly journals The model of insurance premium rates of motorcycle payment futures contract by using quasi Monte Carlo simulation method and spot future Parity theorem

2020 ◽  
Author(s):  
Faizal Hafiz Fadilah ◽  
Dodi Devianto
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Method ◽  
Insurance Premium ◽  
Monte Carlo Simulation Method ◽  
Quasi Monte Carlo ◽  
Futures Contract ◽  
Premium Rates

scholarly journals A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains

2008 ◽  
Vol 56 (4) ◽  
pp. 958-975 ◽  
Author(s):  
Pierre L'Ecuyer ◽  
Christian Lécot ◽  
Bruno Tuffin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chains ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Quasi Monte Carlo

Application of Quasi-Monte Carlo Method Based on Good Point Set in Tolerance Analysis

2016 ◽  
Vol 16 (2) ◽  
Author(s):  
Yanlong Cao ◽  
Huiwen Yan ◽  
Ting Liu ◽  
Jiangxin Yang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Tolerance Analysis ◽  
Simulation Method ◽  
Analysis Tool ◽  
Monte Carlo Simulation Method ◽  
Worst Case ◽  
Quasi Monte Carlo ◽  
Good Point

Tolerance analysis is increasingly becoming an important tool for mechanical design, process planning, manufacturing, and inspection. It provides a quantitative analysis tool for evaluating the effects of manufacturing variations on performance and overall cost of the final assembly. It boosts concurrent engineering by bringing engineering design requirements and manufacturing capabilities together in a common model. It can be either worst-case or statistical. It may involve linear or nonlinear behavior. Monte Carlo simulation is the simplest and the most popular method for nonlinear statistical tolerance analysis. Monte Carlo simulation offers a powerful analytical method for predicting the effects of manufacturing variations on design performance and production cost. However, the main drawbacks of this method are that it is necessary to generate very large samples to assure calculation accuracy, and that the results of analysis contain errors of probability. In this paper, a quasi-Monte Carlo method based on good point (GP) set is proposed. The difference between the method proposed and Monte Carlo simulation lies in that the quasi-random numbers generated by Monte Carlo simulation method are replaced by ones generated by the method proposed in this paper. Compared with Monte Carlo simulation method, the proposed method provides analysis results with less calculation amount and higher precision.

scholarly journals Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Author(s):  
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Method ◽  
Electricity Sector ◽  
Cost Functions ◽  
Density Estimator ◽  
Monte Carlo Simulation Method ◽  
Commercial Building ◽  
Complementary Method ◽  
The Cost

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

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