空盘量变动对我国期货市场期货价格收益波动性的影响

空盘量是除交易之外刻画期货市场交易活跃程度的另一个重要指标。将空盘量分解为可预期和不可预期两部分,研究空盘量的变动对期货价格收益波动性的影响。实证结果表明,空盘量对期货价格收益的波动性具有负向影响,即总体而言,空盘量的增加对期货价格收益波动性的影响小于空盘量的减少对期货价格收益波动性的影响;并且,不可预期空盘量对期货价格收益的影响比可预期空盘量对价格收益的影响大许多。同时,从信息经济学的角度对实证结果分别进行了解释。     

基于波动率测量误差的波动率预测模型

本文研究了一种基于波动率测量误差的波动率预测模型,并做了非线性扩展,期望改进预测效果.考虑到文献中关于波动率可能长记忆性和非线性并存的观点,本文以具有长记忆特征的HAR(heterogeneous autoregressive)模型为基础,加入波动率测量误差后模型持续性有所提高,结合非线性的时变参数模型则达到结构变化和减弱异方差的效果.本文用2652天的沪深300高频数据计算的已实现极差波动率来验证模型效果.固定参数下,在HAR型模型中加入测量误差作为调节变量可以较显著地改善样本外预测效果.时变参数下,加入测量误差的HARQ型模型预测效果大多优于对应的HAR型模型.时变参数模型总体上可以改善固定参数模型的预测效果,尤其在预测期较长的情况下改善均是显著的.     

新闻报道倾向性对上市公司股东利益的影响:媒体互动的中介效应

针对近些年上市公司从"被"媒体关注到战略性"媒体倾斜"转变中媒体的公司治理动因分析不足的问题,提出了结合公司治理、信息经济学理论及媒体互动中介效应分析新闻报道倾向性对股东利益影响的内在关联性的新方法.基于中国A股上市公司财务和媒体相关数据,采用单变量多因素耦合方法,分析了倾向性报道对公司股东每股收益、净资产收益率与市净率等因素的经济压力,揭示了上市公司"媒体关联"的真正动机.研究表明:1)高强度的新闻报道倾向性有助于媒体信息中介效应的发挥,从而改善股东的利益及其与上市公司的关系;2)媒体激励作用在积极的、正面的媒体报道倾向样本中更明显;3)上市公司所在地区的高市场化程度会强化两者间的关系.在控制了宏观环境行政处罚变量后,实证结果依然显著.文章加深了对媒体的公司治理效应和关联动机的认知,有助于上市公司价值的实现及其与主要利益相关者关系的改善.     

人民币汇率市场联动特征分析

融合Copula理论和GARCH有偏T模型,构建了汇率波动的t-Copula-DCC-GARCH-skewT模型,对中、美、欧3大经济体货币汇率USD-CNY和EUR-USD进行实证研究.该模型能够捕捉到人民币汇率之间的动态相关性.相关曲线展现了3大经济体在汇率制度上的阶段性联动特征,表明中、美、欧等经济体货币汇率联动性有利于人民币国际化的汇率制度建设以及世界经济的共同治理和系统性风险防范.     

Combining multivariate volatility forecasts using weighted losses

The ability to improve out-of-sample forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out-of-sample forecast performance across varying dimensions, loss functions, sub-samples and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the individual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.     

An improved traitor tracing scheme based on bilinear map

T6 et al presented a bilinear-map-based traitor tracing scheme（TSZ scheme） with revocation, but it is a symmetric scheme because it does not provide non-repudiation. In this paper, an improved TSZ scheme was proposed by using oblivious polynomial evaluation （OPE） protocol and service parameters. Under the recondition of general sameness capabilities of both TSZ and improved TSZ scheme, the new scheme adds some advantages such as providing multi-service capability, user＇s non-repudiation and data provider＇s no-framing innocent users. Furthermore, it is also proved to be semantically secure under the decisional bilinear Diffie-Hellman （DBDH problem） assumption.     

实际伽利略搜救信号参数估计的Cramer-Rao界

考虑调制不对称度和信息位宽的不确定度,研究了实际伽利略搜救信号信息位宽、到达频率(FOA)、到达时间(TOA)估计的Cramer-Rao界(CRB).推导了伽利略搜救信号Fisher信息矩阵中各元素的计算表达式,利用单位冲激函数的性质和Parseval定理,将单位冲激函数的平方和转换到频域进行计算,得到了信息位宽、FOA和TOA估计的CRB的解析结果.数值分析和Monte Carlo仿真结果表明,CRB具有有效性,可以用于评估上述参数估计算法的性能.     

使用Bayes方法识别股市变结构模型

为了使用边际似然函数进行模型变点的有效识别,通过使用变结构模型和Monte Carlo方法,对波动率模型中的变点进行了判断。通过对中国股票市场的波动性进行分析,识别出中国证券市场的4个变点。实证结果表明:中国股票市场从成立以来,一共经历了5个阶段,分别是1990年12月到1991年秋、1991年秋到1992年中、1992年中到1997年中、1997年中到2002年春、2002年春至今。研究发现,中国证券市场结构发生变化与股票市场价格波动无关,而与中国证券市场不断发展和完善息息相关。     

太行山猕猴掌骨和跖骨的不对称性

了解太行山猕猴掌骨和蹠骨长度的不对称性.对掌骨(35例)和蹠骨(33例)的长度变量进行测量,SPSS13.0统计软件进行正态分布检验.结果表明:5根掌骨有明显的左侧方向不对称趋势,5根跖骨没有发现方向不对称.结果表明左侧掌骨有大于右侧掌骨的趋势,跖骨作为对照组有右侧大于左侧的优势,但没有达到统计学的差异水平.提示上肢骨...     

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.