Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex

The primary objective of the paper is to forecast the beta values of companies listed on Sensex,Bombay Stock Exchange(BSE).The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies.To reach out the predefined objectives of the research,Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017.Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years.Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement.The results revealed that out of 30 listed companies in the BSE Sensex,10 companies’exhibits high beta values,12 companies are with moderate and 8 companies are with low beta values.Further,it is to note that Housing Development Finance Corporation(HDFC)exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study.A mixed trend is found in forecasted beta values of the BSE Sensex.In this analysis,all the p-values are less than the F-stat values except the case of Tata Steel and Wipro.Therefore,the null hypotheses were rejected leaving Tata Steel and Wipro.The values of actual and forecasted values are showing the almost same results with low error percentage.Therefore,it is concluded from the study that the estimation ARIMA could be acceptable,and forecasted beta values are accurate.So far,there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data.But,hardly there are very few studies which attempt to forecast the returns on the basis of their beta values.Certainly,the attempt so made is a novel approach which has linked risk directly with return.On the basis of the present study,authors try to through light on investment decisions by linking it with beta values of respective stocks.Further,the outcomes of the present study undoubtedly useful to academicians,researchers,and policy makers in their respective area of studies.

经济政策不确定性、投资者情绪与股价同步性——基于TVP-VAR模型的时变参数

基于时变参数向量自回归(time-varying parameter-vector auto regression,TVPVAR)模型,考察了经济政策不确定性、投资者情绪与股价同步性之间的时变关联性.模型估计结果表明,经济政策不确定性对股价同步性主要表现为中短期的正向影响,且波动比较明显,长期影响则相对较弱;投资者情绪对股价同步性表现为负向影响,且短期影响最为明显,长期影响则较弱.时点脉冲函数结果显示,在不同时间点上,股价同步性对经济政策不确定性的冲击具有正向响应,对投资者情绪的冲击具有负向响应,且不同时间点的响应程度和响应时间均存在差异.这些结论为进一步完善政策调控体系,规范和引导投资者行为,促进市场理性化提供了思路.

基于卡尔曼滤波的四旋翼飞行器姿态估计和控制算法研究(英文)

四旋翼飞行器作为无人机的一种,由于其简单气动布局和复杂的动力学模型,在控制领域获得了越来越多的学术关注;本文首先分析了微机电系统惯性测量单元(MEMS IMU)传感器的误差,给出了基于自回归(autoregressive,AR)噪声模型的卡尔曼滤波算法设计;然后根据加速度计和陀螺仪长短周期测量的不同特性,进一步对姿态数据做互补融合,实验表明此算法可以实现良好的滤波效果;基于上面的姿态估计,本文又提出了一种双增益的PD控制算法对飞行器进行姿态控制;最后将姿态估计算法和控制算法应用到实验平台中,可以实现四旋翼在支架上的自主悬停等功能.

开放经济条件下我国游资规模的测算研究

自2005年汇率改革以来，在人民币预期升值驱动下，国际游资大规模流入中国，对我国货币政策的独立性和实施效果产生了较大的冲击和影响。结合AUtO—Regressive模型、PLS回归和GM（1，1）模型对2005-2009年的我国国际游资规模进行测算，结果表明，我国的国际游资主要是通过贸易顺差流入我国的，FDI中隐藏的国际游资相对要少很多。测算至2009年底，我国的国际游资规模已经达到了12623．31亿美元。

Demonstration Analysis of Relationship Between R&D Investment and GDP

To reveal the quantitative relationship between research and development (R&D) investment and gross domestic product (GDP) in China, we have demonstrated and analyzed the relationship between R&D investment and science and technology (S&T) progress, and based on a mount of S&T statistical data, have proceeded demonstration research of the relationship between R&D investment and GDP in China with Solow and vector auto regression (VAR) models. Cubic curve fitting and cross-correlation analysis of them with SPSS have shown that there is a strong synchronic relationship between R&D investment and GDP.

国际石油价格之残差自回归模型短期预测

国际石油市场上,油价围绕国际石油价值这个轴心随供求关系的变化而不断上下波动。而石油是现代工业的血液,预测油价变化,制定相关石油战略,具有重要的意义。文章运用非平稳序列的残差自回归模型方法对以往油价建立模型进行短期预测,模型拟合度比较理想,并和ARIMA模型及GARCH模型结果比较,残差自回归明显优于其他模型。

A Hybrid Methodology for Short Term Temperature Forecasting

Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

渐变附着式起重机风致响应及疲劳分析

起重机风载特性的研究有助于确定起重机的薄弱环节、制定附着杆的附着方案,从而提高其安全性。文章以SP7525型平头式起重机为原型,建立ABAQUS有限元模型,通过自回归Auto Regressive改进模型模拟了风速时程,采用雨流计数法并引入应力-寿命曲线处理应力时程,探索了渐变附着式起重机的风致动力响应及疲劳损伤规律。结果表明:塔吊附着杆的风致应力数值随塔吊高度的增加而增大,不等长附着状态下,FZG7-1、FZG7-2、FZG7-3的风致应力值分别降低了5.70%、5.17%、7.78%;平衡臂、起重臂以及接近塔顶处塔身的疲劳损伤度量级为10^(-9)~10^(-8);FZG n-1、FZG n-3相对FZG n-2的风致应力值分别增大了10.10%、23.15%,且疲劳损伤度增加了1~2个数量级;塔吊采用不等长附着形式具有更高的安全性。

Edge computing-Based mobile object tracking in internet of things

Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.

A Truncated SVD-Based ARIMA Model for Multiple QoS Prediction in Mobile Edge Computing

In the mobile edge computing environments,Quality of Service(QoS)prediction plays a crucial role in web service recommendation.Because of distinct features of mobile edge computing,i.e.,the mobility of users and incomplete historical QoS data,traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments.In this paper,we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices.By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition(SVD)with the classical ARIMA model,we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently.Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.