Pricing the American put options requires solving an optimal stopping problem and therefore is a challenge for the setting up of simulation parameters.This paper uses least square Monte Carlo（LSMC） simulation to pri...Pricing the American put options requires solving an optimal stopping problem and therefore is a challenge for the setting up of simulation parameters.This paper uses least square Monte Carlo（LSMC） simulation to price the American put options and output the optimal simulation steps and number of Hermite basis functions.The results suggest：with different time cost and error tolerance,investors can choose the optimal simulation steps and number of basis function individually to price American put options numerically.Generally,with the pre-limitation in the section ＂least square Monte Carlo simulation＂,a number of basis equals 4,15 000 simulation steps for Hermite basis function appear to be sufficient for the method.展开更多
A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the traini...A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.展开更多
This paper is concerned with the application of weighted least square method in change point analysis. Testing shift in the mean normal observations with time varying variances as well as of a GARCH time series are co...This paper is concerned with the application of weighted least square method in change point analysis. Testing shift in the mean normal observations with time varying variances as well as of a GARCH time series are considered. For both cases, the weighted estimators are given and their asymptotic behaviors are studied. It is also described that how the resampling methods like Monte Carlo and bootstrap may be applied to compute the finite sample behavior of estimators.展开更多
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona...In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...展开更多
提出基于贝叶斯理论的抗剪强度参数最优Copula函数识别方法,首先简要介绍了基于Copula函数的岩土体抗剪强度参数相关结构表征方法,给出常用的识别最优Copula函数的最小平方欧氏距离法和AIC(akaike information criterion)准则。其次,采...提出基于贝叶斯理论的抗剪强度参数最优Copula函数识别方法,首先简要介绍了基于Copula函数的岩土体抗剪强度参数相关结构表征方法,给出常用的识别最优Copula函数的最小平方欧氏距离法和AIC(akaike information criterion)准则。其次,采用蒙特卡洛模拟方法验证了贝叶斯理论识别最优Copula函数的有效性,比较了3种方法的最优Copula函数识别能力,并分析了影响贝叶斯理论识别精度的主要因素。最后,收集了实际工程共23组抗剪强度参数试验数据,研究了贝叶斯理论在抗剪强度参数最优Copula函数识别中的应用。结果表明,贝叶斯理论能够有效地识别表征抗剪强度参数间相关结构的最优Copula函数,且能有效考虑先验信息对识别结果的影响;与传统的最小平方欧氏距离法和AIC准则相比,贝叶斯理论的识别能力和识别精度都更高;抗剪强度参数的样本数目、相关性大小、真实Copula函数类型以及先验信息都对贝叶斯理论的识别精度具有重要的影响。此外,常用的Gaussian Copula函数并不总是表征抗剪强度参数间相关结构的最优Copula函数。展开更多
文摘Pricing the American put options requires solving an optimal stopping problem and therefore is a challenge for the setting up of simulation parameters.This paper uses least square Monte Carlo（LSMC） simulation to price the American put options and output the optimal simulation steps and number of Hermite basis functions.The results suggest：with different time cost and error tolerance,investors can choose the optimal simulation steps and number of basis function individually to price American put options numerically.Generally,with the pre-limitation in the section ＂least square Monte Carlo simulation＂,a number of basis equals 4,15 000 simulation steps for Hermite basis function appear to be sufficient for the method.
文摘A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.
文摘This paper is concerned with the application of weighted least square method in change point analysis. Testing shift in the mean normal observations with time varying variances as well as of a GARCH time series are considered. For both cases, the weighted estimators are given and their asymptotic behaviors are studied. It is also described that how the resampling methods like Monte Carlo and bootstrap may be applied to compute the finite sample behavior of estimators.
基金National High-tech Research and Development Pro-gram (2006AA04Z405)
文摘In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...
文摘提出基于贝叶斯理论的抗剪强度参数最优Copula函数识别方法,首先简要介绍了基于Copula函数的岩土体抗剪强度参数相关结构表征方法,给出常用的识别最优Copula函数的最小平方欧氏距离法和AIC(akaike information criterion)准则。其次,采用蒙特卡洛模拟方法验证了贝叶斯理论识别最优Copula函数的有效性,比较了3种方法的最优Copula函数识别能力,并分析了影响贝叶斯理论识别精度的主要因素。最后,收集了实际工程共23组抗剪强度参数试验数据,研究了贝叶斯理论在抗剪强度参数最优Copula函数识别中的应用。结果表明,贝叶斯理论能够有效地识别表征抗剪强度参数间相关结构的最优Copula函数,且能有效考虑先验信息对识别结果的影响;与传统的最小平方欧氏距离法和AIC准则相比,贝叶斯理论的识别能力和识别精度都更高;抗剪强度参数的样本数目、相关性大小、真实Copula函数类型以及先验信息都对贝叶斯理论的识别精度具有重要的影响。此外,常用的Gaussian Copula函数并不总是表征抗剪强度参数间相关结构的最优Copula函数。
