基于多维动态模型的中国股指相关性预测研究

运用时间序列的ADCC(Asymmetric Dynamic Conditional Correlation)多维GARCH模型和CCC(Constant Conditional Correlation)多维GARCH模型对中国主要股指之间的相关性进行预测,并对预测结果进行评价和比较,结果表明ADCC多维GARCH模型拟合和预测中国股指相关性较好,这为投资组合管理和风险管理提供了理论支持。     

事件营销在汽车市场中的应用剖析

文章通过有选择性的事例归纳事件营销的特点,结合汽车业实际,提出了在汽车业开展事件营销的注意点,并最终提出了发展希望。     

Multivariate stable ARMA processes with time dependent coefficients

This paper considers the class of m-variate autoregressive moving average (ARMA) processes with stable innovations and time dependent coefficients. A set of suitable AR and MA regularity conditions is given to ensure existence and uniqueness of valid solutions. A simple form of the above solution is expressed in terms of one sided Green's matrix functions associated with the AR operator. We solve the prediction problem arising in this class of models. A few examples are added to support the general theory.     

Detecting Abnormal Bid-Ask Spread: A Comparison of Event Study Methods

This study examines empirical issues associated with the use of bid-ask spreads in event studies. The simulation results indicate that the distribution of average standardized abnormal spread shows little deviation from normality. Simulation results also indicate that the widely used percent spread metric results in test statistics with low power. In contrast, use of a standardized raw spread metric and a simple mean-adjusted expectation model results in well specified and reasonably powerful Patell and Brown-Warner type test statistics. As the abnormal spread series is characterized by high first order serial correlation, it is important to adjust for this serial correlation when using multi-day event windows.     

A Complete Nonparametric Event Study Approach

Event studies have been used to examine the direction, magnitude, and speed of security price reactions to various phenomenon. Concerns over the lack of normality in stock return distributions motivated the introduction of nonparametric test statistics in the event study literature. A parametric procedure (OLS), however, has been extensively employed in the estimation of parameters for the market model. This paper, in contrast, applies Theil's nonparametric regression in the estimation of abnormal returns; an approach which is distribution free and provides a complete nonparametric approach for the detection of abnormal performance. Simulation results indicate Theil's estimation procedure offers a slight improvement in power in the detection of abnormal performance over the traditionally employed methodology. The results suggest employing Theil's nonparametric estimation procedure combined with the rank statistic. This complete nonparametric combination offers similar power with fewer underlying assumptions.     

Smooth Transition ARCH Models: Estimation and Testing

In this paper, we suggest an extension of the ARCH model, the smooth-transition autoregressive conditional heteroskedasticity (STARCH) model. STARCH models endogenously allow for time-varying shifts in the parameters of the conditional variance equation. The most general form of the model that we consider is a double smooth-transition model, the STAR-STARCH model, which permits not only the conditional variance, but also the mean, to be a function of a smooth-transition term. The threshold ARCH model, the Markov-ARCH model and the standard ARCH model are special cases of our STARCH model. We also develop Lagrange multiplier tests of the hypothesis that the smooth-transition term in the conditional variance is zero. We apply our STARCH model to excess Treasury bill returns. We find some evidence of a smooth transition in excess returns, but in contrast to previous studies, we find almost no evidence of volatility persistence once we allow for smooth transitions in the conditional variance. Thus, the apparent persistence in the conditional variance reported by many researchers could be a mere statistical artifact. We conduct in-sample tests comparing STARCH models to nested competitors; these suggest that STARCH models hold promise for improved predictions. Finally, we describe further extensions of the STARCH model and suggest issues in finance to which they might profitably be applied.     

On The Log-Linear Analysis Of Multiple Response Data

Log-linear procedures have been recommended for analyzing data that are in the form of counts or frequencies (Iacobucci and McGill 1990). In its current form, the recommended procedure does not deal with multiple response data (i.e., more than one thought per response category). The paper proposes analytical modifications that researchers need to consider when fitting models with this type of data. The problems and recommended modifications are illustrated with two distinct data sets. The first involves consumers' attribution responses to negative word-of-mouth communication about a brand whereas the second pertains to consumers' cognitive responses toward an advertisement. Results in each case indicate that researchers may generate erroneous conclusions about the significance of estimated parameters if the recommendations are ignored.     

林业上市公司资本结构及其影响因素的实证研究

利用主成分分析方法将反映林业上市公司资本结构影响因素的总资产、总资产增加率等15个指标归纳为两个主成分(“公司规模与盈利性”、“股权结构与成长性”),并以这两个主成分为解释变量,以资本结构度量指标为被解释变量进行多元线性回归分析,以测度这两个主成分影响林业上市公司资本结构的方向和程度。     

TRAMO/SEATS在危机事件中对旅游影响研究的应用

国内外对旅游危机事件的研究集中于管理方面,近年相关热点转向对旅游影响的研究.以往定性研究多,从经济数量模型进行定量分析研究的较少.本文使用在经济金融领域使用广泛的ARIMA改进版--TRAMO/SEATS方法评估危机事件,如疾病、地震等突发事件对中国旅游业的影响.作为欧盟各国统计局主要统计方法,TRAMO/SEATS方法能够最大程度地反映旅游业季节性波动的特点.通过该方法对2003年在中国内地和香港地区爆发的SARS进行实证研究,分析评估该危机事件的长期影响.在此基础上提出,国家短期内应当通过提高产品质量和拓展高质量的细分市场实现旅游效益型增长;恢复后期才可以推出更为直接的促销手法实现旅游数量型增长,采取刺激旅游人数快速增长的政策和措施.