Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Cross‐Functional Integration: Concept Clarification and Scale Development

The centrality of cross‐functional integration (CFI) to supply chain theory and practice has long been recognized. Yet researchers continue to struggle with consistently defining or measuring the CFI construct, thus limiting the utility of CFI research. This research develops (1) a comprehensive definition of CFI that synthesizes previous supply chain research and (2) a valid set of scale items that measure the conceptual domain outlined by this definition. The goal is to build a common foundation for extending knowledge on CFI's antecedents and consequences, and ultimately to improve scholars’ ability to guide a broader practitioner community still struggling to achieve integration in their organizations.     

改革开放以来广州经济增长因素分析

本文从实证角度建立计量模型,利用主成分分析方法和最小二乘回归的方法,对广州改革开放30多年以来的经济增长因素进行了具体分析,研究表明改革开放30多年来,广州经济增长的促进因素主要是劳动力数量的增长,经济制度的变革,产业结构的升级,以及人力资本增长的作用,物质资本不是促进广州经济增长的主要因素。     

Use of minimum risk approach in the estimation of regression models with missing observations

This article considers a linear regression model with some missing observations on the response variable and presents two estimators of regression coefficients employing the approach of minimum risk estimation. Small disturbance asymptotic properties of these estimators along with the traditional unbiased estimator are analyzed and conditions, that are easy to check in practice, for the superiority of one estimator over the other are derived. Received May 2001     

中国外商直接投资的区位决定因素分析:城市数据

本文以全国210个地级以上城市为对象,分析了外商直接投资的区位决定因素。本文采用OLS和逐步回归分析方法以及2002年的横截面数据,在以往研究的基础上选择四项区位决定因素包括三种成本因素、三种市场因素、四种集聚因素和四种制度因素进行实证研究。结果表明,劳动力成本对各城市的外商直接投资的区位选择影响不明显,而市场规模、基础设施、教育和研究开发条件以及地方金融规模对外商直接投资的区位选择的影响具有显著性。本文的逐步回归分析结果还表明,就东部地区而言,能源条件和市场规模是较重要的区位决定因素;就中西部地区而言,市场规模、基础设施、教育和研究开发条件以及地方金融成为较重要的区位决定因素。     

Pseudo empirical likelihood method in the presence of missing data

In this paper, we propose an estimator for the population mean when some observations on the study and auxiliary variables are missing from the sample. The proposed estimator is valid for any unequal probability sampling design, and is based upon the pseudo empirical likelihood method. The proposed estimator is compared with other estimators in a simulation study.     

商业银行盈利能力影响因素的实证分析——基于偏最小二乘回归（PLSR）算法

运用偏最小二乘回归模型及其辅助分析手段对商业银行盈利能力影响因素进行一次深入、细致、系统的梳理。研究表明,影响商业银行盈利能力的前六大因素依次为净息差、资产质量、成本收入比、风险承受能力、金融创新及存贷比,而银行资产规模对盈利能力的贡献度最低。为提升商业银行盈利能力,必须强化金融创新能力、防控商业银行风险、完善金融监管体制。     

回归分析法在经济预测中的应用浅析

回归分析法是在经济预测中常用的方法之一，它是在观察和分析经济发展的历史和现状的基础上，按照一定的方式建立反映其关系的数学模型，然后根据自变量在未来的变化来计算预测变量的变化，从而对未来的经济发展趋势进行预测。其关键是建立回归模型，并进行相关分析和结果预测。     

现金流对上市公司投资行为的影响分析

本文采用实证方法，检验我国上市公司融资约束、现金流与投资决策之间的关系。以沪深300指数样本上市公司为基础，结合面板数据和时间数据，利用线性回归方法检验公司投资行为与现金流量、营业收入、负债率、托宾Q等财务指标的关系，并进一步分析上市公司的投资与现金流量等指标的敏感性。