结构性油价波动对中国股市的非对称性影响分析

本文分别检验了不同驱动因素所致的油价波动(结构性油价波动)对我国股市收益波动的非对称性影响,并探寻了油价波动对我国股市产生非对称性影响的来源。首先利用SVAR模型将油价波动分解为供给、需求和预防性购买三种驱动因素所致的油价波动,发现三种驱动因素所致的油价波动对我国股市收益的影响存在显著差异;GARCH非对称模型检验及其信息影响曲线结果显示供给和预防性购买因素所致的油价波动是对我国股市的产生非对称性影响的主要原因。在应对油价波动策略时,区分油价波动背后的驱动因素有助于政府机构采取行之有效的宏观调控。     

Analyzing return asymmetry and quantiles through stochastic volatility models using asymmetric Laplace error via uniform scale mixtures

This paper proposes a new approach to analyze stock return asymmetry and quantiles. We also present a new scale mixture of uniform (SMU) representation for the asymmetric Laplace distribution (ALD). The use of the SMU for a probability distribution is a data augmentation technique that simplifies the Gibbs sampler of the Bayesian Markov chain Monte Carlo algorithms. We consider a stochastic volatility (SV) model with an ALD error distribution. With the SMU representation, the full conditional distribution for some parameters is shown to have closed form. It is also known that the ALD can be used to obtain the coefficients of quantile regression models. This paper also considers a quantile SV model by fixing the skew parameter of the ALD at specific quantile level. Simulation study shows that the proposed methodology works well in both SV and quantile SV models using Bayesian approach. In the empirical study, we analyze index returns of the stock markets in Australia, Japan, Hong Kong, Thailand, and the UK and study the effect of S&P 500 on these returns. The results show the significant return asymmetry in some markets and the influence by S&P 500 in all markets at all quantile levels. Copyright © 2014 John Wiley & Sons, Ltd.     

ON THE COMPUTATIONAL COMPLEXITY OF THE MAXIMUM TRADE PROBLEM

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Solving binary cutting stock problems by column generation and branch-and-bound

We present an algorithm for the binary cutting stock problem that employs both column generation and branch-and-bound to obtain optimal integer solutions. We formulate a branching rule that can be incorporated into the subproblem to allow column generation at any node in the branch-and-bound tree. Implementation details and computational experience are discussed.This research was supported by NSF and AFOSR grant DDM-9115768     

从股民素质分析中国股市

从股民素质的角度，利用博弈理论分析证监会、上市公司、股民之间的关系，得出股民素质的高低极大地制约了中国证券市场的健康发展的结论。提出在完善制度建设的同时，还应不断提高广大股民，特别是普通股民的素质。     

运用生存模型对上证指数涨跌天数的研究

本运用生存模型对上证指数连续上涨和下跌天数进行了研究,分析了“T 1”政策和“涨停板”政策对股市的影响。我们发现生存模型对于分析股市的变动是有效的。     

引入技术指标的BP网络在股市预测中的应用

本文使用股市分析中常用的一些技术指标构造BP网络的输入样本向量,在此基础上,对沪市股指的涨跌进行了预测.数值实验结果表明,该方法能够提高网络预测的正确率.使用跳跃学习及三种变学习率、批方式的学习算法对BP网络进行了训练,节省了预测时间.运用“在线预测”的方法对预测过程进行了跟踪.针对预测样本在预测性能及预测结果方面存在的差异,引入预测样本中心距离比的概念对其进行简单的划分,得到一些富有启发性的结果.     

运用生存模型对上证指数与成交量的研究——兼论股市的政策效应

本文首次运用生存模型对上证指数连续涨跌进行了研究,发现连涨和连跌的股指服从Gamma分布,当股指涨跌在不同的范围时其后下跌和上海的概率是不同的。我们还讨论了股指与成交量的关系。通过对各生存函数的分析,我们阐述了"T+1"政策和"涨停板"政策对股指产生的政策效应。     

Inventory placement in acyclic supply chain networks

By adding a set of redundant constraints, and by iteratively refining the approximation, we show that a commercial solver is able to routinely solve moderate-size strategic safety stock placement problems to optimality. The speed-up arises because the solver automatically generates strong flow cover cuts using the redundant constraints.     

股价指数的记忆性特征

本文通过ARFIMA模型(分整自回归滑动平均模型)分析了上证日指数、周指数、月指数序列的记忆性特征,说明股票价格日指数具有长记忆性、周指数具有中等记忆性、月指数具有短期记忆性,这一结论说明了中国股票市场是非有效的,其本身隐含一定的政策含义。