Change points in heavy‐tailed multivariate time series: Methods using precision matrices

We propose two robust data‐driven techniques for detecting network structure change points between heavy‐tailed multivariate time series for situations where both the placement and number of change points are unknown. The first technique utilizes the graphical lasso method to estimate the change points, whereas the second technique utilizes the tlasso method. The techniques not only locate the change points but also estimate an undirected graph (or precision matrix) representing the relationship between the time series within each interval created by pairs of adjacent change points. An inference procedure on the edges is used in the graphs to effectively remove false‐positive edges, which are caused by the data deviating from normality. The techniques are compared using simulated multivariate t‐distributed (heavy‐tailed) time series data and the best method is applied to two financial returns data sets of stocks and indices. The results illustrate the method's ability to determine how the dependence structure of the returns changes over time. This information could potentially be used as a tool for portfolio optimization.     

广西经济增长、金融发展与城乡收入差距的实证研究

探讨了经济增长及金融发展与城乡收入差距之间互动影响,刻画了三者间的逻辑关系,并基于广西1990-2017年的统计数据,运用状态空间模型及卡尔曼滤波算法对三者间动态关系进行了实证分析.结果显示:经济增长对城乡收入差距呈现倒U型曲线形态,而金融发展则显示出具有不断缩小城乡收入差距的趋势.     

Computational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity

Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clinical trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually <1/5000), IDT is often life threatening and is one of the major reasons new drugs never reach the market or are withdrawn post marketing. Computational methodologies, like the computer-based approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant analysis (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D molecular structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chemicals. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, respectively in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability analysis was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and pharmacologically related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, respectively. The computational approach proposed here can be considered as a useful tool in early IDT prognosis.     

基于人工神经网络和随机游走模型的汇率预测

由于金融数据具有随机性特征,使得建模和预测变得极其困难.提出一种组合预测方法,即假定任何金融时序数据由线性和非线性两部分组成,将其中线性部分的数据通过随机游走(RW)模型进行模拟,剩余的非线性残差部分由前馈神经网络(FANN)和诶尔曼神经网络(EANN)协同处理.从实证结果可知,该组合方法相比单独使用RW、FANN或EANN模型有更高的预测精度.     

基于光纤振动安全预警系统的振源识别算法研究

光纤振动预警系统可自动采集周边振动信号,面对大量复杂的振动信号,如何准确识别目标振源是系统研究的难点。针对光纤振动安全预警系统采集到的振动信号进行属性特征分析,建立相应的特征模型,并建立振源属性特征模型,包括识别下雨振源的能量信息熵模型,以及区分机械施工和车辆经过振源的基频稳定性模型等。通过振源识别算法,提高了振源类型识别的准确性。测试结果表明,特征模型的设计和选择合理,识别准确率高。     

奇异协方差阵及不同借贷利率下均值—方差模型的解析解

随着金融资产种类的增加,特别是考虑大规模投资组合问题时,很可能出现资产间的多重共线性或相关性,从而出现协方差阵奇异的情况。然而,目前关于投资组合的均值—方差分析大都是在协方差阵正定的条件下得到的,因此,不适用于奇异协方差阵的情形。针对这一问题,利用广义逆矩阵研究了协方差阵奇异时的均值—方差投资组合模型,在不同借贷利率条件下得到了前沿组合和组合前沿的解析解,突破了传统方法中要求协方差阵可逆的限制,推广了经典Markowitz模型。     

某印染废水的水质指纹特征

水质指纹携带了水样的发光有机物组成的信息,可以作为一种新方法来弥补COD和TOC等传统理化参数只能给出有机物总量信息的不足。印染废水是我国水量最大的工业废水之一,其特点是水量大、污染重,含有大量发光有机物质,且难降解,其对自然水体的危害尤为明显。对我国某印染废水处理厂废水进行了连续采样,对水样的水质指纹特征进行了研究。结果表明,该厂印染废水水质指纹特征比较明显,水质指纹上主要有激发波长/发射波长分别为230/340和280/310 nm左右的两个峰,其中230/340 nm处的强度最强,280/310 nm处的强度次之,各峰波长和强度比较稳定。该印染废水的两个水纹峰的峰强呈正相关关系,线性相关系数为0.910 8,斜率为1.506。激发波长/发射波长为280/310 nm的强度与230/340 nm强度比值的平均值为0.777,范围为0.712～0.829。对该印染污水水质指纹的可能荧光物质进行分析,结果表明生活污水和苯胺类物质的水质指纹与该印染污水的水质指纹不同,而几种大量使用的染料的水质指纹与该印染污水的水质指纹大致相同,所以染料的大量使用可能是导致印染废水产生上述典型水质指纹特征的主要原因。水质指纹可以用于水体水质预警。     

Boom and bust in continuous time evolving economic model

We show that a simple model of a spatially resolved evolving economic system, which has a steady state under simultaneous updating, shows stable oscillations in price when updated asynchronously. The oscillations arise from a gradual decline of the mean price due to competition among sellers competing for the same resource. This lowers profitability and hence population but is followed by a sharp rise as speculative sellers invade the large un-inhabited areas. This cycle then begins again.     

基于弹道的紫外告警系统探测方位角及规避时间仿真分析

提出基于比例导引规律的弹道三维仿真方法,利用弹道仿真法计算了紫外告警系统的探测方位角以及规避时间,分析了来袭导弹距离、威胁方向对探测方位角的和规避时间的影响。结果表明:当导弹在一定距离外,探测方位角随导弹距离的减小而增大、随威胁方向的增大先增大后减小,并且飞机以某威胁方向飞行时,告警系统对某距离范围内的导弹探测方位角几乎没有变化,规避时间随威胁方向的增大而减小。     

First passage time for multivariate jump‐diffusion processes in finance and other areas of applications

The first passage time (FPT) problem is an important problem with a wide range of applications in science, engineering, economics, and industry. Mathematically, such a problem can be reduced to estimating the probability of a stochastic process first to reach a boundary level. In most important applications in the financial industry, the FPT problem does not have an analytical solution and the development of efficient numerical methods becomes the only practical avenue for its solution. Most of our examples in this contribution are centered around the evaluation of default correlations in credit risk analysis, where we are concerned with the joint defaults of several correlated firms, the task that is reducible to a FPT problem. This task represents a great challenge for jump‐diffusion processes (JDP). In this contribution, we develop further our previous fast Monte Carlo method in the case of multivariate (and correlated) JDP. This generalization allows us, among other things, to evaluate the default events of several correlated assets based on a set of empirical data. The developed technique is an efficient tool for a number of financial, economic, and business applications, such as credit analysis, barrier option pricing, macroeconomic dynamics, and the evaluation of risk, as well as for a number of other areas of applications in science and engineering, where the FPT problem arises. Copyright © 2008 John Wiley & Sons, Ltd.