估计风险值的重点抽样下的自助模拟

文章研究重点抽样下的估计风险值的自助模拟.指出应用重点抽样一方差缩减技术可改善自助模拟的效率.实际模拟分析结果表明,重点抽样下的蒙特卡洛自助模拟估计方差明显小于普通的蒙特卡洛自助模拟估计方差,重点抽样下的蒙特卡洛自助模拟方法比普通的蒙特卡洛自助模拟方法更有效.     

野战油料装备维修任务分配研究

分析了战时油料装备维修任务分配问题,利用蒙特卡洛方法对维修任务分配进行了计算机仿真模拟,并通过实例验证了该方法对解决战时油料装备维修任务分配问题的优越性。     

野战油料装备维修任务分配研究

分析了战时油料装备维修任务分配问题,利用蒙特卡洛方法对维修任务分配进行了计算机仿真模拟,并通过实例验证了该方法对解决战时油料装备维修任务分配问题的优越性.     

RSB-CWS算法解决CVRP问题

RSB-CWS算法结合了蒙特卡洛模拟和CWS节省算法,利用蒙特卡洛模拟对CWS算法节省列表中顾客对间的边进行模拟,模拟过程中采用基于类几何分布的直接抽样方法进行路径采样。对模拟得到的边序列应用CWS算法,所得解的质量能达到或者优于当前最优解.与其他CVRP问题算法相比具有高效、高质量的特点,且可以解决规模较大的CVRP问题。     

基于蒙特卡洛模拟的挖掘机铲斗结构优化研究

针对现有铲斗结构优化设计存在的容易陷入局部最优解、优化效率较低等问题,以"在GB9141-88规定的4种工况下铲斗结构最大应力不增加且体积最小"为目标,提出基于蒙特卡洛模拟的挖掘机铲斗结构优化方法,并以某斗容为1m3的挖掘机铲斗结构优化为例,验证了基于蒙特卡洛模拟的挖掘机铲斗结构优化方法的可行性和有效性。     

半参数变系数空间滞后模型的截面极大似然估计

在线性参数空间滞后模型中,解释变量的系数一般假设为固定常数,本文首先放松了这种假设,将解释变量的系数设定为某一变量的未知函数,提出一类全新的半参数变系数空间滞后模型;其次导出了该模型的截面极大似然估计,并证明了该估计的一致性;最后用蒙特卡洛数值模拟方法考察了该估计在小样本条件下的性质,数值模拟结果显示我们提出的估计方法在小样本条件下依然有优良的表现。     

非等间隔动态面板数据模型：估计方法与应用实例

非等间隔动态面板数据模型由于相邻两期观测之间的时间长度不尽相同使得传统动态面板数据模型的估计方法失效,本文提出使用非线性最小二乘、最短距离以及它们的一步估计量对该模型进行估计,证明了这四个估计量的一致性和渐进正态性,同时借助蒙特卡洛模拟的方法验证了它们在有限样本中的估计精度,并且进一步使用所提出的估计量讨论了以往文献由于缺乏相应的估计方法而没有被研究或者充分讨论的问题,得到了一些新的结论。     

基于蒙特卡洛方法对风力发电建设项目投资财务风险分析

项目投资的可行性分析和风险管理是项目可行性研究的重要内容。目前风电建设项目常用的经济可行性评价方法主要有确定性指标分析方法和不确定性指标分析方法。其中确定性指标包括净现值、内部收益率、静态投资回收期、投资利润率、资本金净利润率等;不确定性分析方法主要是敏感性分析。蒙特卡洛仿真属于不确定性分析,它建立在概率和随机抽样的基础上。本文将对具体风电建设项目进行经济可行性分析,并采用蒙特卡洛仿真方法对该项目中不确定因素引起的投资风险进行仿真模拟。     

利用VAR模型对股票期权中的风险度量

利用蒙特卡洛方法模拟方法对伊利股份股票期权VAR进行估计,得到不同置信水平下的风险值,对企业具有一定的参考。     

蒙特卡洛模拟在财务投资分析中的应用浅析

本文利用蒙特卡洛模拟方法对徐州技师学院的学生食堂项目进行财务投资分析、评价,并运用水晶球软件来具体实施.     

不确定条件下对称企业R&D项目的期权博弈分析

为了对市场不确定性条件下的企业项目价值进行评价,文章通过构建期权博弈模型对对称企业的研究与开发(R&D)项目投资行为进行了研究,并采用了最小二乘法的蒙特卡罗模拟(LSM)方法对模型进行了仿真求解,得到了两企业R&D项目的价值。研究结果显示,LSM方法与期权博弈相结合做法具有可行性,对企业进行项目价值的评估具有参考价值和指导意义。     

Efficient Monte Carlo pricing of European options¶using mean value control variates

We describe in this paper a variance reduction method based on control variates. The technique uses the fact that, if all stochastic assets but one are replaced in the payoff function by their mean, the resulting integral can most often be evaluated in closed form. We exploit this idea by applying the univariate payoff as control variate and develop a general Monte Carlo procedure, called Mean Monte Carlo (MMC). The method is then tested on a variety of multifactor options and compared to other Monte Carlo approaches or numerical techniques. The method is of easy and broad applicability and gives good results especially for low to medium dimension and in high volatility environments.     

蒙特卡罗方法在核类本科专业的教学初探

蒙特卡罗方法是以概率统计理论为基础、通过计算机编程而实现的一种数值计算方法,在核科学与技术等领域有着广泛的应用。新时期下,初步尝试面向核类本科专业开设蒙特卡罗方法这门课程,将概率统计思想和编程实践训练融入到蒙特卡罗方法教学中,教学取得了一定成效。     

Randomized quasi-Monte Carlo methods in pricing securities

Today quasi-Monte Carlo methods are used successfully in computational finance and economics as an alternative to the Monte Carlo method. One drawback of these methods, however, is the lack of a practical way of error estimation. To address this issue several researchers introduced the so-called randomized quasi-Monte Carlo methods in the last decade. In this paper we will present a survey of randomized quasi-Monte Carlo methods, and compare their efficiencies with the efficiency of the Monte Carlo method in pricing certain securities. We will also investigate the effects of Box–Muller and inverse transformation techniques when they are applied to low-discrepancy sequences.     

Advanced Multilevel Monte Carlo Methods

This article reviews the application of some advanced Monte Carlo techniques in the context of multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations, which can be biassed in some sense, for instance, by using the discretization of an associated probability law. The MLMC approach works with a hierarchy of biassed approximations, which become progressively more accurate and more expensive. Using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider some Markov chain Monte Carlo and sequential Monte Carlo methods, which have been introduced in the literature, and we describe different strategies that facilitate the application of MLMC within these methods.     

A Monte Carlo filtering application for systematic sensitivity analysis of computable general equilibrium results

ABSTRACT

Parameter uncertainty has fuelled criticisms on the robustness of results from computable general equilibrium models. This has led to the development of alternative sensitivity analysis approaches. Researchers have used Monte Carlo analysis for systematic sensitivity analysis because of its flexibility. But Monte Carlo analysis may yield biased simulation results. Gaussian quadratures have also been widely applied, although they can be difficult to apply in practice. This paper applies an alternative approach to systematic sensitivity analysis, Monte Carlo filtering and examines how its results compare to both Monte Carlo and Gaussian quadrature approaches. It does so via an application to rural development policies in Aberdeenshire, Scotland. We find that Monte Carlo filtering outperforms the conventional Monte Carlo approach and is a viable alternative when a Gaussian quadrature approach cannot be applied or is too complex to implement.     

Een toepassing van "importance sampling"

Summary  This paper describes an experiment with "importance sampling", to show how much reduction of the computation time and sample size can be achieved in comparison with the usual Monte Carlo method. A comparison is made between each of the three methods of "importance sampling" and the usual Monte Carlo method by the determination of the expression

Of the three methods A, B and C the first one uses the shifted exponential distribution, the second one uses the gamma distribution, and the third one uses the exponential distribution with modified parameter. These three methods have all smaller variances, ranges and sample sizes than the usual Monte Carlo method. Their order of preference is A, B, C. With respect to computing time only the method A is significantly better. So only the method A is an improvement in respect of both the sample size and the computing time.     

A classified bibliography of Monte Carlo studies in econometrics

The Monte Carlo method of exploring the properties of econometric estimators and significance tests has yielded a considerable amount of information that has practical value in guiding choice of technique in applied research. This paper presents a bibliography of such Monte Carlo studies over the period 1948–1972. About 150 citations are listed alphabetically by author, and also under a detailed subject-matter classification scheme.     

Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies Using Resampling Methods

Good statistical practice dictates that summaries in Monte Carlo studies should always be accompanied by standard errors. Those standard errors are easy to provide for summaries that are sample means over the replications of the Monte Carlo output: for example, bias estimates, power estimates for tests and mean squared error estimates. But often more complex summaries are of interest: medians (often displayed in boxplots), sample variances, ratios of sample variances and non‐normality measures such as skewness and kurtosis. In principle, standard errors for most of these latter summaries may be derived from the Delta Method, but that extra step is often a barrier for standard errors to be provided. Here, we highlight the simplicity of using the jackknife and bootstrap to compute these standard errors, even when the summaries are somewhat complicated. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute