坝基深层抗滑稳定可靠度分析蒙脱卡罗方法

通过非线性有限元分析具有软弱夹层的重力坝坝基滑动破坏过程和失稳机理，论证了尾岩抗力体第二滑裂面应向下游倾斜，而不宜简单地取为直立。按建议的失稳模式，导出了主滑动面处于极限平衡的功能函数和状态方程。鉴于功能函数的复杂性，推荐应用改进的抽样蒙脱卡罗方法求解失效概率和可靠度。     

Adjoint-based Monte Carlo calibration of financial market models

Adjoint methods have recently gained considerable importance in the finance sector, because they allow to quickly compute option sensitivities with respect to a large number of model parameters. In this paper we investigate how the efficiency of adjoint methods can be exploited to speed up the Monte Carlo-based calibration of financial market models. After analyzing the calibration problem both theoretically and numerically, we derive the associated adjoint equation and propose its application in combination with a multi-layer method, for which we prove convergence to a stationary point of the underlying optimization problem. Detailed numerical examples illustrate the performance of the method. In particular, the proposed algorithm reduces the calibration time for a typical equity market model with time-dependent model parameters from over three hours to less than ten minutes on a usual desktop PC.      

Transportation cost changes with statewide school district consolidation

This article studies the relationship between school district size and bus transportation costs, and estimates the change in such costs when a statewide policy of consolidation is pursued. To explore this relationship, we develop a multiple-objective model and solution procedure that combines a geographic aggregation and bus routing heuristic to generate consolidation scenarios. The heuristic was developed to explicitly consider efficiency, effectiveness, and equity objectives, and can be applied in both urban and rural states. The scenarios will generate average statewide bus transportation costs. As applied to the State of Iowa, within the legislature's proposed range of consolidation of 500-1000 students, it was found that transportation operational and capital cost increases range from 0.6 to 10.6 percent and 0.7 to 7.7 percent, respectively.     

A STATE-SPACE PARTITIONING METHOD FOR PRICING HIGH-DIMENSIONAL AMERICAN-STYLE OPTIONS

The pricing of American-style options by simulation-based methods is an important but difficult task primarily due to the feature of early exercise, particularly for high-dimensional derivatives. In this paper, a bundling method based on quasi-Monte Carlo sequences is proposed to price high-dimensional American-style options. The proposed method substantially extends Tilley's bundling algorithm to higher-dimensional situations. By using low-discrepancy points, this approach partitions the state space and forms bundles. A dynamic programming algorithm is then applied to the bundles to estimate the continuation value of an American-style option. A convergence proof of the algorithm is provided. A variety of examples with up to 15 dimensions are investigated numerically and the algorithm is able to produce computationally efficient results with good accuracy.     

Uncertainty Analysis of a Two-dimensional Hydrodynamic Model

First-Order Second Moment (FOSM) and Monte Carlo analysis were applied to characterize the uncertainty in selected water levels and velocities simulated by a two-dimensional hydrodynamic model of the Upper St. Lawrence River downstream from Lake Ontario. The analysis utilized an application of the Resource Management Associates’ RMA2 model. Both FOSM and Monte Carlo analysis provided similar estimates of uncertainty, with Monte Carlo analysis results being 15% less than FOSM. Based on the findings of this work, the FOSM is preferred. FOSM provides a conservative estimate of the uncertainty and it is simpler to apply than Monte Carlo analysis, requiring less information and fewer model executions. FOSM also provides an immediate indication of the primary contributors to the uncertainty in the output, where Monte Carlo analysis requires additional effort to do the same. Results indicate that the parameter describing bottom resistance using Manning's n contributed more to model uncertainty than other factors investigated. The uncertainty in and sensitivity in Manning's n is large which results in a significant amount of uncertainty in the model outputs is contributed by this parameter. The calculations described in this study show that uncertainty analysis is a practical addition to the two-dimensional hydrodynamic modelling process. It provides insight to the model developer, quantifying how good the model actually is. It also provides a measure of the accuracy of the model for future model developers or clients using hydrodynamic modeling outputs     

Estimating demand with distance functions: Parameterization in the primal and dual

Our purpose is to investigate the ability of different parametric forms to ‘correctly’ estimate consumer demands based on distance functions using Monte Carlo methods. Our approach combines economic theory, econometrics and quadratic approximation. We begin by deriving parameterizations for transformed quadratic functions which are linear in parameters and characterized by either homogeneity or which satisfy the translation property. Homogeneity is typical of Shephard distance functions and expenditure functions, whereas translation is characteristic of benefit/shortage or directional distance functions. The functional forms which satisfy these conditions and include both first- and second-order terms are the translog and quadratic forms, respectively. We then derive a primal characterization which is homogeneous and parameterized as translog and a dual model which satisfies the translation property and is specified as quadratic. We assess functional form performance by focusing on empirical violations of the regularity conditions. Our analysis corroborates results from earlier Monte Carlo studies on the production side suggesting that the quadratic form more closely approximates the ‘true’ technology or in our context consumer preferences than the translog.     

Testing for financial contagion based on a nonparametric measure of the cross‐market correlation

When contagion is defined as a significant increase in market comovement after a shock to one country, we propose a test for financial contagion based on a nonparametric measure of the cross-market correlation. Monte Carlo simulation studies show that our test has reasonable size and good power to detect financial contagion, and that Forbes and Rigobon's test (2002) is relatively conservative, indicating that their test tends not to find evidence of contagion when it does exist. Applying our test to investigate contagion from the 1997 East Asian crisis and the 2007 Subprime crisis, we find that there existed international financial contagion from the two financial crises.     

Monte-Carlo方法求解水动力学方程的原理及实践

介绍Monte Carlo方法的基本思想及求解Poisson方程的原理 ;利用Monte Carlo方法及图论建立河网计算的随机游动模型 ,并应用于实际的大型河网中 ,取得了较好的结果 .利用Monte Carlo方法计算河网可以避免解大型的稀疏矩阵 ,计算简单、灵活     

蒙特卡罗数值模拟法在随机参数转子动力学响应中的应用

本文运用模拟量输出卡和功率放大器生成符合正态分布的随机电流,根据已有的支撑刚度和随机电流之间的线性关系,得出支撑刚度的均值和标准差。在已有的转子系统模型上,利用蒙特卡罗数值模拟方法对随机参数进行模拟计算,得出支撑刚度参数的改变对转子动力学响应位移的影响。     

MCMC ESTIMATION OF LÉVY JUMP MODELS USING STOCK AND OPTION PRICES

We examine the performances of several popular Lévy jump models and some of the most sophisticated affine jump‐diffusion models in capturing the joint dynamics of stock and option prices. We develop efficient Markov chain Monte Carlo methods for estimating parameters and latent volatility/jump variables of the Lévy jump models using stock and option prices. We show that models with infinite‐activity Lévy jumps in returns significantly outperform affine jump‐diffusion models with compound Poisson jumps in returns and volatility in capturing both the physical and risk‐neutral dynamics of the S&P 500 index. We also find that the variance gamma model of Madan, Carr, and Chang with stochastic volatility has the best performance among all the models we consider.