投资者信心和中国宏观经济波动——基于MIDAS混频模型

将投资者信心指数引入MIDAS混频模型之中,可以考察新冠疫情背景下投资者信心对于中国宏观经济波动的影响。基于投资者信心指数和“三驾马车”对我国季度GDP增长率进行预测,通过实证发现:引入投资者信心指数的MIDAS混频模型在预测精准度方面和基准模型进行比较,预测精准度高于未加入投资者信心的模型,均方根残差比值更小;在多元MIDAS混频模型之中,加入投资者信心指数的回归模型对我国GDP的实时预报和短期预测结果更加稳定,可为决策者提供更加精确的参考区间;宏观经济的波动对投资者信心指数变化反应,和投资、消费和进出口相比是最灵敏的。这为定量追踪宏观经济波动提供了一个新的视角。     

Modeling and forecasting realized covariance matrices with accounting for leverage

The existing dynamic models for realized covariance matrices do not account for an asymmetry with respect to price directions. We modify the recently proposed conditional autoregressive Wishart (CAW) model to allow for the leverage effect. In the conditional threshold autoregressive Wishart (CTAW) model and its variations the parameters governing each asset's volatility and covolatility dynamics are subject to switches that depend on signs of previous asset returns or previous market returns. We evaluate the predictive ability of the CTAW model and its restricted and extended specifications from both statistical and economic points of view. We find strong evidence that many CTAW specifications have a better in-sample fit and tend to have a better out-of-sample predictive ability than the original CAW model and its modifications.     

混频投资者情绪与股票价格行为

采用混频数据抽样模型(MIDAS)研究了混频投资者情绪对中国股市收益率及其波动的影响.通过构建日度、周度及月度这三种不同频率的投资者情绪,实证结果发现,混频情绪对当期收益率及其波动都存在显著的正向影响,并且与传统回归模型相比,MIDAS模型具有更强的解释能力.本文进一步使用GARCH-MIDAS模型研究了混频情绪对收益率波动长期成分的影响,发现混频情绪能够显著影响收益的长期波动.     

A self-normalizing approach to the specification test of mixed-frequency models

In econometrics and finance, variables are collected at different frequencies. One straightforward regression model is to aggregate the higher frequency variable to match the lower frequency with a fixed weight function. However, aggregation with fixed weight functions may overlook useful information in the higher frequency variable. On the other hand, keeping all higher frequencies may result in overly complicated models. In literature, mixed data sampling (MIDAS) regression models have been proposed to balance between the two. In this article, a new model specification test is proposed that can help decide between the simple aggregation and the MIDAS model.     

混凝土收缩徐变对简支变连续桥梁的影响

利用MIDAS／civil 6．71软件，针对先简支后连续桥的特性，运用具体工程实例，建立模型，计算出桥梁从开始施工到成桥以及成桥后十年的任意时刻任意截面的内力以及变形图形，通过整理主梁恒载弯矩数据的变化，分析了混凝土收缩徐变对简支变连续桥梁的受力和变形的影响，结果发现徐变对跨中的挠度影响最大，两边跨的挠度大于中间两跨的挠度，徐变对中间固定支座负弯矩的影响比跨中正弯矩的影响要大很多．     

我国季度GDP实时数据预测与评价

传统实证研究中使用的当期特定数据存在滞后信息和噪音信息缺陷,导致模型估计结果存在偏误。应用宏观经济实时数据可以有效的剔除造成模型偏误的滞后信息和噪音信息,得到更为准确的估计结果。MIDAS模型可将低频的关键经济数据与高频数据同时估计,较好的解决了应用一般模型存在的高频数据信息损失问题。本文应用M-MIDAS-DL模型与季度GDP实时数据建立我国季度GDP预测模型,实证表明,应用实时数据与组合预测方法,能及时准确预测出2008年以来中国经济增长率的下滑与反弹走势,能起到较好的提前预警作用,是当前较为有效的经济预测手段之一。     

集成DCOM和CORBA的应用服务器设计与实现

采用Inprise的MIDAS技术，设计并实现了一个集成DCOM／CORBA的数据库应用程序服务器，能同时支持DCOM／CORBA用户对后台数据库的访问，并构造了DCOM和CORBA客户应用程序与其进行交互测试．该集成提供的这种新思路能充分利用Windows平台上丰富的软件资源和CORBA畀种平台的易通讯性．     

MIDAS Regressions: Further Results and New Directions

We explore mixed data sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider applications in macroeconomics and finance, among other areas. The regressions combine recent developments regarding estimation of volatility and a not-so-recent literature on distributed lag models. We study various lag structures to parameterize parsimoniously the regressions and relate them to existing models. We also propose several new extensions of the MIDAS framework. The paper concludes with an empirical section where we provide further evidence and new results on the risk-return trade-off. We also report empirical evidence on microstructure noise and volatility forecasting.     

Should Macroeconomic Forecasters Use Daily Financial Data and How?

We introduce easy-to-implement, regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of mixed data sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about 1000 daily financial assets. Our analysis is designed to elucidate the value of daily financial information and provide real-time forecast updates of the current (nowcasting) and future quarters of real GDP growth.     

MIDAS模型与EQW模型预测精度的比较——以资产价格的经济增长效应为例

深入剖析混频数据模型（MIDAS）的内部结构,推导出MIDAS模型与传统同频率EQW模型的区别与联系,并且证明EQW模型参数估计的偏误,在此基础上,进一步结合多种不同形式的权重函数,推导出MIDAS类模型非线性最小二乘估计方法的具体机理及理论演绎过程,并且以资产价格对中国经济增长效应的样本内预测为例,对MIDAS模型的精度进行了实证检验。实证结果表明:多元EQW模型的拟合效果及样本内预测精度均低于最优滞后阶数下ExpAlmon-AR-M-MIDAS模型,ExpAlmon-AR-M-MIDAS模型能够提取更多高频解释变量的有效信息。