Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates

Hawkes processes are used in statistical modeling for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices and demographic covariates in the maximization step. We validate the approach on both short-term and long-term forecasting tasks, showing that the Hawkes process outperforms several models currently used to track the pandemic, including an ensemble approach and an SEIR-variant. We also investigate which covariates and mobility indices are most important for building forecasts of COVID-19 in the U.S.     

Identifying crime generators and spatially overlapping high-risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain)

The behavior and spatial distribution of crime events can be explained through the characterization of an area in terms of its demography, socioeconomy, and built environment. In particular, recent studies on the incidence of crime in a city have focused on the identification of features of the built environment (specific places or facilities) that may increase crime risk within a certain radius. However, it is hard to identify environmental characteristics that consistently explain crime occurrence across cities and crime types. This article focuses on the assessment of the effect that certain types of places have on the incidence of property crime, robbery, and vandalism in three cities of the Valencian region (Spain): Alicante, Castellon, and Valencia. A nonlinear effects model is used to identify such places and to construct a risk map over the three cities considering the three crime types under research. The results obtained suggest that there are remarkable differences across cities and crime types in terms of the types of places associated with crime outcomes. The identification of high-risk areas allows verifying that crime is highly concentrated, and also that there is a high level of spatial overlap between the high-risk areas corresponding to different crime types.     

Marked point process hotspot maps for homicide and gun crime prediction in Chicago

Crime hotspot maps are a widely used and successful method of displaying spatial crime patterns and allocating police resources. However, hotspot maps are often created over a single timescale using only one crime type. In the case of short-term hotspot maps that utilize several weeks of crime data, risk estimates suffer from a high variance, especially for low frequency crimes such as homicide. Long-term hotspot maps that utilize several years of data fail to take into account near-repeat effects and emerging hotspot trends. In this paper we show how point process models of crime can be extended to include leading indicator crime types, while capturing both short-term and long-term patterns of risk, through a marked point process approach. Several years of data and many different crime types are systematically combined to yield accurate hotspot maps that can be used for the purpose of predictive policing of gun-related crime. We apply the methodology to a large, open source data set which has been made available to the general public online by the Chicago Police Department.     

EVT and tail-risk modelling: Evidence from market indices and volatility series

Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.     

Predicting the portfolio risk of high-dimensional international stock indices with dynamic spatial dependence

The spatial dependence of assets, which relates to similarities in economic, political, or cultural systems and other aspects, has been confirmed through empirical research; however, spatial dependence has rarely been applied to financial risk measurement. To fill this gap in the literature, a dynamic spatial GARCH-copula (sGC) model is proposed in this paper to evaluate the portfolio risk of international stock indices. In this model, a spatial GARCH is used as the marginal distribution and vine copula is adopted as the joint distribution of indices. Then, the proposed model is applied empirically to assess portfolio risk. Results show that, first, the proposed risk prediction model with spatial dependence outperforms a model neglecting spatial effects per the Kupiec test, Z test and Christoffersen test. Risk prediction during periods of economic stability is also more accurate than during times of crisis. Second, risk measures for models with spatial dependence are higher than those without such dependence but lower than for vine copula models. Third, models including either spatial dependence or vine copulas alone exhibit relatively poor performance. Fourth, the model involving extreme value theory (EVT) generates the greatest value at risk to pass the Kupiec test, Z test and Christoffersen test; however, this model is not suitable for characterizing international indices with EVT based on negative values of the shape parameters of estimates. Findings offer important implications for personal investors, institutional investors, and national regulatory authorities.     

基于买卖强度的交易策略及其实证分析

文章引入了点过程中的Hawkes过程来进行股票买卖强度的拟合与预测,并提供了基于该有效预测的交易策略。文中首先对所使用的Hawkes过程进行了介绍,并从理论上说明其在高频金融数据拟合中的优势;之后叙述了极大似然方法在Hawkes模型参数估计中的具体应用;最后,结合由wind数据库中选取的内地股票市场中的股票实例,使用Hawkes过程进行强度预测、策略构建与盈利情况分析,证实了该模型在实际拟合中的优势与策略的有效性。     

Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data

This study examines the theoretical and empirical perspectives of the symmetric Hawkes model of the price tick structure. Combined with the maximum likelihood estimation, the model provides a proper method of volatility estimation specialized in ultra-high-frequency analysis. Empirical studies based on the model using the ultra-high-frequency data of stocks in the S&P 500 are performed. The performance of the volatility measure, intraday estimation, and the dynamics of the parameters are discussed. A new approach of diffusion analogy to the symmetric Hawkes model is proposed with the distributional properties very close to the Hawkes model. As a diffusion process, the model provides more analytical simplicity when computing the variance formula, incorporating skewness and examining the probabilistic property. An estimation of the diffusion model is performed using the simulated maximum likelihood method and shows similar patterns to the Hawkes model.     

Modelling security market events in continuous time: Intensity based,multivariate point process models

A continuous time econometric modelling framework for multivariate financial market event (or ‘transactions’) data is developed in which the model is specified via the vector conditional intensity. Generalised Hawkes models are introduced that incorporate inhibitory events and dependence between trading days. Novel omnibus specification tests based on a multivariate random time change theorem are proposed. A bivariate point process model of the timing of trades and mid-quote changes is then presented for a New York Stock Exchange stock and related to the market microstructure literature. The two-way interaction of trades and quote changes in continuous time is found to be important empirically.     

Trivariate non‐Gaussian copulas to analyze the spatial behavior of rat sightings

The brown rat lives with man in a wide variety of environmental contexts and adversely affects public health by transmission of diseases, bites, and allergies. Understanding behavioral and spatial correlation aspects of pest species can contribute to their effective management and control. Rat sightings can be described by spatial coordinates in a particular region of interest defining a spatial point pattern. In this paper, we investigate the spatial structure of rat sightings in the Latina district of Madrid (Spain) and its relation to a number of distance‐based covariates that relate to the proliferation of rats. Given a number of locations, biologically considered as attractor points, the spatial dependence is modeled by distance‐based covariates and angular orientations through copula functions. We build a particular spatial trivariate distribution using univariate margins coming from the covariate information and provide predictive distributions for such distances and angular orientations.     

Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators break down when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non‐uniform intervals and no solution is currently available when time‐varying covariates are included in the model. In this paper, we propose two new estimators for dynamic panel data models when data are irregularly spaced and compare their finite‐sample performance to the näive application of existing estimators. We illustrate the practical importance of this issue in an application concerning early childhood development. Copyright © 2016 John Wiley & Sons, Ltd.     

“入室抢劫”浅议

刑法规定了"入户抢劫"等八种抢劫罪的加重处罚情形。其实,"入室抢劫"也具有比一般抢劫行为更严重的社会危害性。然而,由于刑法还没有相关的规定,目前对此种行为的处罚仅与一般抢劫行为相同。这不利于准确、有效地惩罚抢劫犯罪,保护公民、法人等的合法权益。     

Searching for synchrony

Authorities working on economic-crime investigation in Finland are trying to change their form of collaboration from the sequential passing of documents towards parallel, interorganizational collaboration: the on-line investigation of an ongoing crime. The synchronization of events and the outputs of various participants proved to be difficult in this emerging process. This new model of crime investigation also requires a new kind of time management. This article explores how the change is being constructed in everyday practice by examining three economic-crime-investigation cases. It is claimed that individual efforts to manage time allocation suffice only in terms of co-ordination of events. It is suggested that a successful shift to parallel, interorganizational collaboration requires more than the common marking of calendars. The object of the work and the forms of interaction should be taken as subjects of reflective negotiation. New kinds of collective time-management tools are needed in this effort.     

Diffusion and spatial equilibrium of a social norm: voting participation in the United States, 1920–2008

Social conformity can spread social norms and behaviors through a society. This research examines such a process geographically and over time for voting, which is strongly influenced by the norm that citizens should vote. A mathematical model for the spread of voting participation under the influence of social conformity is developed based on the diffusion equation, and predictions are tested with spatial analysis of state-level voter turnout in American presidential elections from 1920 to 2008. Results show that voter turnout has converged to a stable equilibrium in its geographical distribution across the states—but it is an equilibrium that results in persistent differences at the state level. Results are compared to individual-level and agent-based models. The model may be applied to other types of social diffusion that depend on specific geographical location.     

‘Old age’ and Ageism in Urban Research: The Case of Fear of Crime

A critique has recently been made of research and theory on ageing in spatial research, arguing for the development of approaches which are informed by critical theoretical perspectives. Perhaps the most significant of these is the recognition that ‘old age’ is culturally constructed. This paper illustrates the value of such an approach with reference to understanding of fear of crime. It is suggested that many difficulties with past research result from epistemological problems, including ageism. A number of assumptions about elderly people and crime can be contested if scrutiny is informed by humanistic, feminist and social constructionist perspectives. Drawing on in depth interviews with elderly people, some of the problems and prospects of work on old age are discussed. Age is only one dimension by which people situate themselves and are situated by others in relation to the risk of crime. Local contexts, life course experiences and other social identities are involved in the constitution of fear for each individual. While the role of ageing can be understood within a framework of power relations, its positive as well as negative impacts on reactions to crime require representation. Similar analysis could profitably be developed in other areas of urban research.     

Criminal incident prediction using a point-pattern-based density model

Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or “hot spots” observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space–time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA.     

一心创业,一路坚持:青年创业者创业韧性影响因素的扎根研究

近年来,国家不断部署深入推进"大众创业、万众创新",重点支持高校毕业生等青年群体就业创业。然而,创业是一项高风险活动,我国青年创业平均成功率仅有约2%。创业韧性有助于青年创业者在创业过程中应对各种困难和挑战,从而获取创业成功。尽管如此,"青年创业者的创业韧性如何产生,受哪些因素影响,其形成机理是什么"这一问题还少有考察。本研究以我国青年创业的主力军大学生创业者为研究对象,运用扎根理论方法 ,通过对30个青年创业大学生的深度访谈,构建出"重大事件激活—动机形成—动态能力形成"的理论模型,包括重大事件等13个子范畴以及创业期望的形成等7个主范畴,并运用事件系统理论、自我差异理论、社会转型理论和场理论分析形成此模式的深层次原因。在此基础上探讨促使其增强创业韧性行为的外部干预措施,研究结论对发展创新创业教育的理论和实践都有一定的促进作用。     

The uncertainty in extreme risk forecasts from covariate-augmented volatility models

For a GARCH-type volatility model with covariates, we derive asymptotically valid forecast intervals for risk measures, such as the Value-at-Risk or Expected Shortfall. To forecast these, we use estimators from extreme value theory. In the volatility model, we allow for leverage effects and the inclusion of exogenous variables, e.g., volatility indices or high-frequency volatility measures. In simulations, we find coverage of the forecast intervals to be adequate for sufficiently extreme risk levels and sufficiently large samples, which is consistent with theory. Finally, we investigate if covariate information from volatility indices or high-frequency data improves risk forecasts for major US stock indices. While—in our framework—volatility indices appear to be helpful in this regard, intra-day data are not.     

论网络犯罪及其防治对策

网络犯罪是现代社会的一种新型犯罪,它包括以网络为对象的犯罪和以网络为犯罪现场的犯罪。它具有技术含量高、危害程度重、涉及面广、隐蔽性强等特征。因此,打击该种犯罪,要完善法律法规,加强网络犯罪立法的国际协调与合作。     

Industrial and Spatial Spillovers and Productivity Growth: Evidence from Taiwan High-Technology Plant Level Data

The purpose of this paper is to recognize the importance of innovation networks and incorporate various types of knowledge and spatial spillovers and their associated adjustments on productivity growth. We explore the temporal, spatial, and industrial/sectoral spillovers using a dynamic external spillover model that assumes each firm derives an optimal plan such that the expected present value of current and future cost streams is minimized. The goal is to measure and evaluate various types of spillover mechanisms, which allow us both to quantify their cost effects and evaluate the contribution of such inter-dependencies on productive performance.JEL Classification: D2, L6, O3     

Inference on Self‐Exciting Jumps in Prices and Volatility Using High‐Frequency Measures

Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state‐space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified model components, with Bayesian inference conducted using a Markov chain Monte Carlo algorithm. An evaluation of marginal likelihoods for the proposed model relative to a large number of alternative models, including some that have featured in the literature, is provided. An extensive empirical investigation is undertaken using data on the S&P 500 market index over the 1996–2014 period, with substantial support for dynamic jump intensities—including in terms of predictive accuracy—documented. Copyright © 2016 John Wiley & Sons, Ltd.