The role of volume in order book dynamics: a multivariate Hawkes process analysis

We show that multivariate Hawkes processes coupled with the nonparametric estimation procedure first proposed in Bacry and Muzy [IEEE Trans. Inform. Theory, 2016, 62, 2184–2202] can be successfully used to study complex interactions between the time of arrival of orders and their size observed in a limit order book market. We apply this methodology to high-frequency order book data of futures traded at EUREX. Specifically, we demonstrate how this approach is amenable not only to analyse interplay between different order types (market orders, limit orders, cancellations) but also to include other relevant quantities, such as the order size, into the analysis, showing also that simple models assuming the independence between volume and time are not suitable to describe the data.     

Multivariate Prediction with Nonlinear Principal Components Analysis: Theory

We propose the notion of multivariate predictability as a measure of goodness-of-fit in data reduction techniques which are useful for visualizing and screening data. For quantitative variables this leads to the usual sums-of-squares and variance accounted for criteria. For categorical variables we show how to predict the category-levels of all variables associated with every point (case). The proportion of predictions which agree with the true categories gives the measure of fit. The ideas are very general; as an illustration we use nonlinear principal components analysis (NLPCA) in association with ordered categorical variables. A detailed example using data from the International Social Survey Program (ISSP) will be given in Blasius and Gower (quality and quantity, 39, to appear). It will be shown that the predictability criterion suggests that the fits are rather better than is indicated by “percentage of variance accounted for”.This article was written while John Gower was a visiting professor at the ZA-Eurolab, at the Zentralarchiv für Empirische Sozialforschung, University of Cologne, Germany. The ZA is a Large Scale Facility funded by the Training and Mobility of Researchers program of the European Union.     

影响居民消费需求的因素分析

阐释了在扩大消费需求中起至关重要作用的因素,指出应通过产品结构调整、增加国民收入、建立和健全社会保障体系来消除消费者的后顾之忧。     

基于出游频次的乡村旅游消费行为分异研究

乡村旅游对社会主义新农村建设、构建和谐社会、统筹城乡发展等意义巨大.乡村旅游消费行为在不同职业群体中的分异状况,是一个值得特别关注的问题.文章通过对以抽样调查方法所获得的长沙市居民乡村旅游消费行为的丰富基础数据,按照出游频次分群统计分析,发现乡村旅游在交通方式、花费水平、出游距离上表现出出游频次的分异特征;在停留时间、信息渠道上呈现出出游频次的同一性;在出游方式、住宿选择表现出出游频次的分民异与同一性并存色彩.     

国内外财务危机预警研究综述

经济全球化为企业带来了机遇,也潜伏着许多危机,这些危机最终表现为财务危机。在经历了如世界通、安然等大企业倒闭的惨痛教训之后,如何协助企业及早有效地规避财务危机逐渐成为国内外专家学者关注的焦点。对国内外财务危机预警研究进行了综述,并对财务危机预警研究的发展趋势进行了探讨。     

A comparison of monthly global indicators for forecasting growth

This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world real GDP growth using mixed-frequency models. It shows that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecasting accuracy, while other monthly measures have more mixed success. Specifically, the best-performing model yields impressive gains with MSPE reductions of up to 34% at short horizons and up to 13% at long horizons relative to an autoregressive benchmark. The global economic conditions indicator also contains valuable information for assessing the current and future state of the economy for a set of individual countries and groups of countries. This indicator is used to track the evolution of the nowcasts for the U.S., the OECD area, and the world economy during the COVID-19 pandemic and the main factors that drive the nowcasts are quantified.     

企业短期贷款违约预测Bayes模型构建

目前,企业违约预测模型距离实际应用还具有一定差异,表现在:(1)模型所使用的样本基本都是配对模式,与现实情况不符;(2)模型没有考虑到误判成本的非对称性.针对以上问题,本文运用SAS统计软件对某国有商业银行的2003年全部短期贷款企业的财务数据进行分析,摒弃以往配对模式,采用全样本进行分析,筛选出11个财务比率指标作为企业信用风险评价函数的计量参数.应用Bayes判别原理,引入误判成本和先验概率,构建了一个简明的违约判别模型,经检验模型是统计有效的,判别结果也是较好的.     

射频识别技术在物流全程跟踪中的应用

本文结合射频自动识别的技术特点,设计研制了射频识别技术在物流全程跟踪中的应用系统,并对物流动态跟踪和物流静态跟踪分别进行了阐述.     

A testing of the purchasing power parity hypothesis using a vector autoregressive model

The paper examines the hypothesis of purchasing power parity relations and the hypothesis of interest rates parity relations between the Japanese yen and the US dollar in a four-dimensional VAR (4-VAR) model, using the statistical technique developed by Johansen and Juselius (1992). The paper demonstrates that the so-called symmetry restriction on the PPP relationship holds not in a 3-VAR model but in the 4-VAR model, which indicates that a correct specification of the sampling distribution of data is important. The interest rates parity relation also holds in the 4-VAR model. The one-step prediction based on the ECM representation with such long-run relations outperforms the random walk model. These results are similar to those under the exchange rate control period (January 1974 to December 1980), which support the inclusion of this period in a whole sample period (January 1974 to December 2001).The author is very grateful to the referees and the editor of the journal for their variable and helpful comments. The first draft of the paper was presented at the autumn meeting of Japan Society of Monetary Economics in Oct 1999. The author is thankful to Eiji Ogawa for his helpful comments. The research was supported by Grant-in-Aid 13630104 of the Ministry of Education, Science and Culture of Japan and the Nomura Foundation for Social Science in 2000.First version received: March 2001/Final version received: April 2003     

基于Stacking模型融合的用户购买行为预测研究

在大数据时代背景下,如何利用大量的销售数据精准预测顾客未来需求,成为企业制定客户管理和库存管理决策的一个重要问题。目前关于用户购买行为预测的研究中很少能够预测用户具体的购买时间。基于已有的销售数据,提出了基于机器学习和Stacking集成的综合预测模型预测用户的购买行为,即未来是否购买及其购买时间。将模型应用在一家大型连锁零售企业的需求预测中,并对方法的有效性进行评估。结果表明,基于Stacking集成的融合模型对预测用户未来是否购买具有最佳性能,准确率达85%,AUC值达到0.928;LightGBM集成算法在预测用户购买时间时具有最优性能,相比于融合模型提升了5.5%的预测性能;融合模型+LightGBM算法的组合相比于均使用融合模型提升了9.4%的预测性能。