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.     

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.     

信道估计偏差对空时编码的OFDM系统性能的影响

本文研究了采用空时编码的OFDM系统的性能，通过Monte Carlo仿真分析了系统性能和信道估计偏差对系统的影响。     

Spillovers in Exchange Rates and the Effects of Global Shocks on Emerging Market Currencies

This paper provides an investigation into the spillover effects of exchange rate returns and volatility for developed and emerging market currencies, using data from 1997 to 2011. The results suggest that spillovers in exchange rate returns have increased steadily over time, in moderate reaction to economic events. In contrast, spillovers in total observed volatility (measured by squared returns) react more strongly to economic events, and this transmission has remained at a relatively high level since the global financial crisis. Furthermore, over the course of time, global shocks would appear to account for a larger proportion of aggregate exchange rate volatility (and the relative importance of domestic shocks has declined). The paper also considers whether the increase in volatility spillover is due to sudden shocks, or whether it is due to changes in the stochastic trend of the underlying volatility process. The results suggests that in most cases, this increase is due to sudden shocks, however, in certain instances country‐specific events may perpetuate changes to the trend of the underlying volatility spillover.     

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.     

分式布朗运动模型下的金融市场风险度量--以上证指数为例

金融市场风险度量在金融风险度量乃至于整个金融风险管理过程中都具有十分重要的地位。本文基于2013年至2014年上证指数的收盘数据,通过对分式布朗运动驱动的随机微分方程进行离散化处理,用其解（分式几何布朗运动）模拟上证指数的未来走势。在此基础上,采用Monte Carlo模拟法模拟出上证指数的价值分布与损益分布。最后,在95％的置信水平下计算出上证指数的在险价值（ VaR）。相对于传统股票价格预测模型---布朗运动模型,分式布朗运动模型更符合金融问题本身;利用Monte Carlo模拟法不再借助于股票价格历史数据。故而本模型对市场金融风险的预测精度更高。     

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。     

On the determination of an optimal sample size

Abstract

In works on sample survey theory and methods the sample size is usually regarded as determined by the sampling procedure and the total cost of the survey.     

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.