A non-linear time series approach to modelling asymmetry in stock market indexes

In this paper we analyse the performances of a novel approach to modelling non-linear conditionally heteroscedastic time series characterised by asymmetries in both the conditional mean and variance. This is based on the combination of a TAR model for the conditional mean with a Constrained Changing Parameters Volatility (CPV-C) model for the conditional variance. Empirical results are given for the daily returns of the S&P 500, NASDAQ composite and FTSE 100 stock market indexes.     

我国县级以上城市规模等级分布的省际差异性分析

本文分析并计算了表征省际城市规模、城市规模等级分布差异性特征的指标;运用聚类分析法,对我国各个省区的城市规模等级分布进行分析,考察了各省区城市规模等级分布的动态变化,并对其进行了一些分析,提出了一些建议。     

基于分布模型的层次聚类算法

提出了一种新的层次聚类算法,先对数据集进行采样,以采样点为中心吸收邻域内的数据点形成子簇,再根据子簇是否相交实现层次聚类。在层次聚类过程中,重新定义了簇与簇之间的距离度量,并以此为基础建立堆结构。利用估计数据点总体分布的思想,证明该算法将逼近最优解。实验结果表明,算法的聚类效果大大优于现有的聚类算法。     

辽宁省产业金融的发展现状及问题分析

在产业转型升级和产业与金融集聚融合的背景下,如何正确选择辽宁产业金融发展之路成为社会各界关注的焦点。从产业发展状况、金融服务能力、产融结合三个方面分析辽宁省发展产业金融过程中存在的问题,结合辽宁省自身的资源状况和历史特点探究其成因,据此提出适合辽宁省产业金融发展的思路及对策。     

The linked survival prospects of siblings: Evidence for the Indian states

This paper reports an analysis of micro-data for India that shows a high correlation in infant mortality among siblings. In 13 of 15 states, we identify a causal effect of infant death on the risk of infant death of the subsequent sibling (a scarring effect), after controlling for mother-level heterogeneity. The scarring effects are large, the only other covariate with a similarly large effect being mother's (secondary or higher) education. The two states in which evidence of scarring is weak are Punjab, the richest, and Kerala, the socially most progressive. The size of the scarring effect depends upon the sex of the previous child in three states, in a direction consistent with son-preference. Evidence of scarring implies that policies targeted at reducing infant mortality will have social multiplier effects by helping avoid the death of subsequent siblings. Comparison of other covariate effects across the states offers some interesting new insights.     

交叉销售下基于支持向量聚类的数据驱动多产品库存鲁棒优化模型

针对单周期环境下考虑交叉销售的多产品库存决策问题,在市场需求不确定条件下,建立了带有预算约束的交叉销售多产品库存鲁棒优化模型。针对不确定市场需求,采用支持向量聚类(SVC)方法构建了满足一定置信水平的数据驱动不确定集。进一步,运用拉格朗日对偶方法将所建模型等价转化为易于求解的线性规划问题。最后,通过数值计算对比分析了SVC不确定集下及传统不确定集下的零售商利润绩效,并评估了SVC数据驱动鲁棒优化方法导致的绩效损失,进而分析了预算及交叉销售系数对零售商利润绩效的影响。结果表明,SVC数据驱动鲁棒优化方法具有良好的鲁棒性,能够有效抑制需求不确定性对从事多产品销售的零售商利润绩效的影响。特别地,需求分布信息的缺失虽然会给零售商带来一定的绩效损失,但损失值很小,表明文中提出的基于SVC的数据驱动鲁棒优化方法可以为管理者在需求不确定性环境下制定库存策略提供有效决策借鉴。     

Estimating class-specific parametric models using finite mixtures: an application to a hedonic model of wine prices

Hedonic price models are commonly used in the study of markets for various goods, most notably those for wine, art, and jewelry. These models were developed to estimate implicit prices of product attributes within a given product class, where in the case of some goods, such as wine, substantial product differentiation exists. To address this issue, recent research on wine prices employs local polynomial regression clustering (LPRC) for estimating regression models under class uncertainty. This study demonstrates that a superior empirical approach – estimation of a mixture model – is applicable to a hedonic model of wine prices, provided only that the dependent variable in the model is rescaled. The present study also catalogues several of the advantages over LPRC modeling of estimating mixture models.     

基于门限双幂变差的资产价格时点波动非参数估计

估计带跳资产价格的时点波动时,需要用门限过滤方法消除跳的影响。在有限样本下,门限过滤会产生错滤偏误和漏虑偏误,降低估计精度。跳错滤产生的偏误可通过对错滤样本进行补足的方法进行纠偏,但由于发生时点未知,跳漏滤产生的偏误无法纠正,只能通过估计量设计来减少漏滤偏误。本文首次提出基于门限双幂变差的时点波动估计量,采用核平滑方法对资产价格时点波动进行非参数估计,有效减少跳错滤导致的偏误。采用随机阵列极限理论,本文证明了估计量的一致性和渐进正态性,在分析有限样本偏误的基础上,给出估计量的纠偏方法。蒙特卡洛模拟表明,本文给出的估计量,漏滤偏误明显小于基于二次变差构造的估计量,对时点波动估计的性质具有实质改进。采用Kupiec动态VaR精度检验对沪深300指数的实证分析表明,本文给出的时点波动估计更能描述资产收益的波动特征。     

The Clustering of Categorical Data: A Comparison of a Model-based and a Distance-based Approach

For clustering multivariate categorical data, a latent class model-based approach (LCC) with local independence is compared with a distance-based approach, namely partitioning around medoids (PAM). A comprehensive simulation study was evaluated by both a model-based as well as a distance-based criterion. LCC was better according to the model-based criterion and PAM was sometimes better according to the distance-based criterion. However, LCC had an overall good and sometimes better distance-based performance as PAM, although this was not the case in a real data set on tribal art items.     

Periodic Autoregressive Conditional Heteroscedasticity

Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class of periodic autoregressive moving average (ARMA) models for the mean. The implicit relation between periodic generalized ARCH (P-GARCH) structures and time-invariant seasonal weak GARCH processes documents how neglected autoregressive conditional heteroscedastic periodicity may give rise to a loss in forecast efficiency. The importance and magnitude of this informational loss are quantified for a variety of loss functions through the use of Monte Carlo simulation methods. Two empirical examples with daily bilateral Deutschemark/British pound and intraday Deutschemark/U.S. dollar spot exchange rates highlight the practical relevance of the new P-GARCH class of models. Extensions to discrete-time periodic representations of stochastic volatility models subject to time deformation are briefly discussed.