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.     

遗传算法在飞机目标识别中的应用

针对雷达目标识别中特征维数压缩和神经网络的隐含层结点数的确定等关键问题 ,提出了一种基于遗传算法的目标识别方法 ,该方法首先构造了一个符合目标识别这一特定问题的初始种群 ,然后利用遗传算法模拟生物遗传迭代和自然选择的遗传机理 ,通过多次遗传迭代最终收敛于问题的一个满意解。最后将该方法用于对三类飞机目标的分类和识别 ,验证了该方法用于目标分类和识别的可行性和有效性。     

R&R Analysis for Measurement System Using Moving Range

1　IntroductionMetrology is an important aspect of manufacturing because measurements are necessaryfor monitoring and controlling processes. The quality of data depends on the gauges andother tools used for the measurementof measured variables.With the greater reliance onquantitative measurements in modern manufacturing industry,the criticality of gauge ca-pability analysis has increased.The goal of gauge capability analysis is the understandingand quantification of the sources of variability …     

基于无衍射光及虚拟探测器的三角测量系统

提出一种新型激光三角测量系统。该系统用无衍射光作测量光束，用虚拟探测器扩展探测器尺寸。与传统激光三角测量相比，该系统在保持高测量精度及高分辨率的同时，扩展了测量范围。且系统机构简单，测量速度快。     

浅谈篮球运动个人防守的控制范围

现代篮球运动竞争激烈,对抗性强性.提高运动员的防守能力,扩大其防守的控制范围,需要在防守的脚步动作、正确的选位、适时的干扰、伸缩及防守的基本姿势等方面,加强对运动员的训练与培养.     

高度场格点距平的长期统计预报试验

采用多通道奇异谱分析和相似预报方法，建立了长期统计预报模型．以７００ｈＰａ前期位势高度场显著时空主分量为预报因子，直接预报后期高度场格点距平的类别，由此获得了一定的预报技巧     

交错脉冲序列法测速公式应用的探讨

针对用于脉冲多普勒气象雷达的交错脉冲序列法的测速公式在使用上的限制条件作了理论推导，为进一步提高最大不模糊测速提供了思路．     

基于卷积神经网络的海面目标全极化高分辨距离像识别技术

针对雷达目标全极化高分辨距离像(high resolution range profile, HRRP)提取可分性特征时, 利用全部距离单元作为度量尺度无法保留各距离单元具体特征的问题, 在综合利用4个极化通道的舰船目标HRRP信息时选择单个距离单元作为度量尺度。在此基础上, 提出基于Pauli分解, H、α、A、α₁分解和结构相似性参数的特征提取方法对目标极化散射矩阵进行特征提取, 并将提取得到的特征与基于卷积神经网络(convolutional neural network, CNN)的舰船目标HRRP识别方法结合, 利用改进残差结构CNN从极化特征中进一步提取深层可分性特征进行目标识别。实验结果表明, 所提方法能够保留目标全极化HRRP更多特征, 提高目标识别的准确率。     

Nonlinear RCMC method for spaceborne/airborne forward-looking bistatic SAR

In the spaceborne/airborne forward-looking bistatic synthetic aperture radar (SA-FBSAR),due to the system platforms’ remarkable velocity difference and the forward-looking mode,the range cell migration (RCM) not only depends on the target’s twodimensional location,but also varies with the range location nonlinearly.And the nonlinearity is not just the slight deviation from the linear part,but exhibits evident nonlinear departure in the RCM trajectory.If the RCM is not properly corrected,nonlinear image distortions would occur.Based on the RCM model,a modified two-step RCM compensation (RCMC) method for SA-FBSAR is proposed.In this method,firstly the azimuth-dependent RCM is compensated by the scaling Fourier transform and the phase multiplication.And then the range-dependent RCM is removed through interpolation.The effectiveness of the proposed RCMC method is verified by the simulation results of both point scatterers and area targets.