山东省及各主要城市保险需求的收入弹性分析

对保险需求的收入弹性做了分析,利用山东省的数据对山东省及各城市的保险需求分别做了计算和经济分析,旨在为我省保险市场的扩容量提供一个方便易行的宏观经济预测手段。     

Fisher and Inference for Scores

This paper examines the work of Fisher and Bartlett on discriminant analysis, ordinal response regression and correspondence analysis. Placing these methods with canonical correlation analysis in the context of the singular value decomposition of particular matrices, we use explicit models and vector space notation to unify these methods, understand Fisher's approach, understand Bartlett's criticisms of Fisher and relate both to modern thinking. We consider in particular the formulation of certain hypotheses and Fisher's arguments to obtain approximate distributions for tests of these hypotheses (without assuming multivariate normality) and put these in modern notation. Using perturbation techniques pioneered by G.S. Watson, we give an asymptotic justification for Fisher's test for assigned scores and thereby resolve a long standing conflict between Fisher and Bartlett.     

Market insurance and endogenous saving with multiple loss states

The standard one-period model for insurance demand does not consider the interaction between the present and the future. Reflecting this observation, we analyze intertemporal insurance demand and saving in a two-period model with multiple loss states. When an individual has no access to a capital market, we first find that an actuarially fair premium does not guarantee full insurance in general, unlike in the standard approach. Income stream and discount factors are also important in determining insurance demand. Second, insurance is neither an inferior good nor a Giffen good. Third, an increase in concavity of the utility function does not always lead to an increase in insurance demand. The current income level and changes in downside risk aversion affect insurance demand. When the individual has access to a capital market, we further have the following observations. Fourth, an actuarially fair premium leads to full insurance. Fifth, insurance is an inferior good and can be a Giffen good under decreasing absolute risk aversion (DARA). An increase in the interest rate leads to a lower insurance demand and a higher saving when the relative risk aversion is less than unity. Lastly, an increase in concavity of the utility function leads to an increase in insurance demand and a decrease in saving. In conjunction, our findings point to the fact that the standard results are not obtainable if insurance demand is considered in isolation from the capital market.     

Endogenous censoring in the mixed proportional hazard model with an application to optimal unemployment insurance

We examine the sensitivity of estimates of the MPH model with respect to assumptions on the censoring mechanism in the context of an economic model of optimal unemployment insurance. We assume a parametric model for the duration of interest and leave the distribution of censoring unrestricted, allowing it to be correlated with observed and unobserved characteristics. We provide a practical characterization of the identified set with moment inequalities and suggest methods for estimating this set. We apply this approach to estimate the elasticity of unemployment exit rate with respect to unemployment benefit. Finally, we investigate welfare consequences of our estimates.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

The use of multivariate methods in economics with reference to regional analysis

The purpose of this paper is to contrasts four different multivariate methods; multiple regression using principal components, factors analysis, discriminant analysis involving another use of principal components, and canonical correlation. The method adopted to compare and constrast the techniques is to make careful note of the assumptions involved in each model and to apply each form of analysis to variables drawn from the same set of data.     

On the use of grouping methods in the analysis of residential housing markets

In this article we employ four different statistical techniques (geographic, AID, cluster and discriminant analysis) to define homogeneous groupings of houses within an urban area. Analysis of a sample of data from Fayette Country, Kentucky indicates that each of these methods produces distinguishable homogeneous groupings of properties. Predictions of house values are compared using data from Lane County, Oregon, San Mateo County, California, and Fayette County. The major conclusions of the study are that there are no discernible differences among the four methods and that predictions made ignoring the grouping information are as accurate as those obtained by grouping.     

Network effects in influenza spread: The impact of mobility and socio-economic factors

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs, including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016–2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors.     

Applications of Statistics in the Field of General Insurance: An Overview

This is an expository paper on applications of statistics in the field of general insurance, also called non‐life insurance. Unlike life insurance where advanced statistical techniques have long been part of financial mathematics and actuarial applications, their use is only relatively recent in non‐life insurance. The business model of insurance companies, especially those active in non‐life insurance, has seen dramatic changes over the last 15 years. The aim of this paper is to convince the readers that especially today non‐life insurance is not only an exciting ground to apply existing modern statistical tools but also a fertile environment for new and challenging statistical developments. The activities of an insurance company can be viewed as an industrial process where data management and data analysis play a key role. That is why a fundamental understanding of data‐related issues (such as data quality, variability, analysis and correct interpretation) is so essential to the insurance business. These are exactly the tasks where professional statisticians excel. Also, a better understanding of the field of general insurance by statisticians will promote fruitful exchanges between actuaries and statisticians, thereby helping to bring actuarial and statistical professional societies closer to each other. Selected examples are used to cover the essential aspects of general insurance, and all of them are based on the author's experience. The paper concludes with some remarks on the role of statisticians working in general insurance.     

Bayesian inference in a sample selection model

This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment.     

A review of DEA methods to identify and measure congestion

Congestion is an economic phenomenon of overinvestment that occurs when excessive inputs decrease the maximally possible outputs. Although decision-makers are unlikely to decrease outputs by increasing inputs, congestion is widespread in reality. Identifying and measuring congestion can help decision-makers detect the problem of overinvestment. This paper reviews the development of the concept of congestion in the framework of data envelopment analysis (DEA), which is a widely accepted method for identifying and measuring congestion. In this paper, six main congestion identification and measurement methods are analysed through several numerical examples. We investigate the ideas of these methods, the contributions compared with the previous methods, and the existing shortcomings. Based on our analysis, we conclude that existing congestion identification and measurement methods are still inadequate. Three problems are anticipated for further study: maintaining the consistency between congestion and overinvestment, considering joint weak disposability assumption between desirable outputs and undesirable outputs, and quantifying the degree of congestion.     

保险公司的资产负债管理中的特殊利率风险分析——“嵌入条款”的期权定价分析

保险公司作为金融企业的一种,其资产负债管理一般而言都采用久期和凸性的方法。但是保险公司的业务独特性使其资产负债管理不能仅仅局限于对资产和负债匹配的管理。寿险公司“的嵌入条款”,实际上是加大了公司的利率风险。尤其当宏观经济的波动比较大,比如利率处于上升的周期中,这种嵌入条款的存在使得公司对于同等的利率波动的损失可能行加大,也就是公司的风险程度上升。本文基于对“嵌入条款”的本质的分析,运用期权(surrenderoption)的定价方法来测算当前经济周期内这种风险的大小;并基于模型和当前的经济环境分析,给出一定的管理政策建议。     

Classification and Discrimination Problems with Applications, Part IIa

Abstract  In Part I exact results for univariate (" p = 1") two-group ("k = 2") classification problems were derived assuming normality and equality of the variances. In Part IIa asymptotic results for multivariate (" p > I") two-group classification and discrimination problems are based on the corresponding assumptions of multivariate normality and equality of the covariance matrices. The results (4.6.5), (4.6.6) and (4.6.7) are believed to be new.
The asymptotic results in Section 4.6, together with results presented elsewhere in the literature, constitute the basis of various detailed proposals to deal with problems from actual statistical practice. Most of these proposals are modifications or specifications of existing ones. We shall pay some attention to (I) testing whether differences exist. But we are mainly interested in: (II) constructing a discriminant function, (III) assigning the individual under classification, and in (IV) constructing a confidence interval for "the" posterior probability that the individual under classification belongs to Population 2.
An important part in our theory is played by various techniques for selecting variables in discriminant analysis. The need for such techniques follows from Section 4.10. The consequences of building-in a selection technique are discussed in Section 4.12. One of our proposals motivates the theory presented in Chapter 3 and is mentioned here for that reason: employ a large part of the data, say 70%, in order to construct a discriminant function (via a selection of variables); by applying this function to the rest of the data, the exact univariate theory of Part I becomes of application. Part IIb will contain a chapter on applications.     

Comparison of dimension reduction methods for DEA under big data via Monte Carlo simulation

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.     

基于现金流测量标准的财务预警实证研究

本文突破以传统绩效类指标为预警指标的模式,选取现金流指标并采用判别分析法,对财务危机发生前一年和前两年的制造行业样本数据进行实证研究,建立了以现金流指标为测量标准、针对具体行业的中长期财务预警模型。模型能够从现金流角度判断企业是否存在财务困境,具有较好的预警作用。     

对美国非寿险业分销渠道的探析

系统地探讨了美国非寿险业各种分销渠道的发展状况。现今分销主渠道是保险中介,中介的主要两种形式是保险代理人和经纪人,在提高保险营销效率方面发挥着重要的作用。也探讨了在上世纪90年代后期出现的一些新型的分销渠道对于近几年财产和意外保险分销渠道的影响,包括网络、银行保险等,从而启示我国财险分销体系结构调整的必要性。     

The three Cs for cultivating organizational culture in a hybrid world

For years we have known that effective organizational culture is critical to ensuring revenue growth, employee retention, and even stock price acceleration. Yet, culture change is hard. Today, in the hybrid world, most employees still spend the majority of their time working within small work teams, but team members are often remote from one another. These employee interactions are essential to sharing and modeling the norms and behaviors necessary to cultivate the desired culture. Combining behavioral data with the use of organizational network analysis helps to overcome these limitations and provides a deeper understanding of the employee cultural experience—even in the hybrid context. In our research, we evaluated 10 attributes critical to an organization’s culture from more than 50,000 employees across three years. Using passive data, we also re-constructed the employee network based on primary interactions. Our research highlighted three critical cultural patterns that will help to more deeply evaluate culture in a hybrid context, but even more importantly,help target local solutions that can be quickly implemented. We call these the three“C’s” of cultivating culture. We show that organizational culture clusters in the employee network, is contagious among employees, and is contextually dependent.We discuss our key insights on the three C’s, concluding with practical applications for leaders applying these insights in their organization.     

Risk measures for variable annuities: A hermite series expansion approach

In this study, we propose an efficient approach to the calculation of risk measures for an insurer's liability from writing a variable annuity with guaranteed benefits. Our approach is based on a novel application of the Hermite series expansions on the transition density of a diffusion process to the insurance setting. We compare our method with existing methods in the literature, including the analytical method, spectral method and Green's function method, and illustrate its substantial advantages in calculating risk measures for variable annuities with different guarantee structures. The improved efficiency makes our method flexible to practical implementation in reporting risk measures on a daily basis. We also conduct a sensitivity analysis of the risk measures with respect to key parameters.