Collaborative Research: Copulas, Tail Copulas, Garch and Extreme Values in Dependence Modelling and Risk Management

经济学、生存分析、医学、生物统计学、保险、金融等社会科学领域的多元数据序列通常是非线性的、非正态分布的，并且具有非线性的协同运动和相互作用。 然而，现有的多元时间序列数据分析的计量经济学和统计学方法大多局限于多元正态或条件正态框架。 这个跨学科的合作研究项目将研究基于copula的半参数模型的广泛类别的估计，测试和实证应用，用于分析表现出非线性共同运动，不对称，重尾，可能删失和/或其他非正态模式的多变量数据序列。 这类模型将通过非参数化（或半参数化）地指定边际分布，并指定参数化（或半参数化）地捕捉多变量之间的依赖关系的copula函数，部分解决“维数灾难”问题。研究将涉及几种现代统计方法的新修改和应用，如经验似然法，筛分最大似然法，自助法，极值理论，基于残差的加权经验过程理论等。研究成果将包括几篇关于独立数据和删失数据的基于copula的半参数多变量模型的估计和检验的原创论文，以及非线性和可能重尾时间序列数据的半参数动态模型的估计和检验。该项目将产生新的估算和测试软件，可从作者的网页上免费下载。 本项目的成果将对基于copula、tail copula和Garch的半参数多变量模型的估计和检验的计量经济学和统计学文献做出重大贡献，并将在经济学（如产业组织、收入不平等、健康经济学）、保险、生物统计学、医学科学和其他非线性相关性很重要的领域非常有用。该项目将开发新的方法，并应用现代统计方法来探索多变量序列之间的非线性依赖关系，并了解人类和社会相互作用的复杂动态，并将其应用于经济学，保险学，金融学，统计学和其他科学。 由于金融和保险从业人员一直将非线性时间序列模型与copula和/或尾copula相结合，以建立多元期权定价，投资组合风险价值，相关违约和信用风险以及不同序列之间随时间变化的非对称非线性协动模型，因此本项目的结果将指导应用研究人员进行统计可靠的经济政策评估，财务预测，和风险管理。 主要教育计划是培养经济学，统计学和金融学博士生成为相关主题的有能力的研究人员，并为经济学，管理学，统计学和定量金融学的学生开发新课程。 该奖项是作为2006财政年度数学科学优先领域数学社会和行为科学（MSBS）特别竞赛的一部分获得支持的。

Multivariate data series in economics, survival analysis, medical science, biostatistics, insurance, finance, and other fields in social sciences are typically nonlinear, non-normally distributed, and have nonlinear co-movements and interactions. However, the existing econometrics and statistics methods in multivariate time series data analysis are largely confined to multivariate normal or conditional normal framework. This interdisciplinary collaborative research project will examine the estimation, testing, and empirical applications of broad classes of copula-based semiparametric models for analyzing multivariate data series that exhibit nonlinear co-movements, asymmetric, heavy-tailed, possibly censored and/or other non-normal patterns. These classes of models will partially solve the "curse-of-dimensionality" problem by specifying the marginal distributions nonparametrically (or semiparametrically) and specifying the copula functions that capture the dependence among the multivariate variables parametrically (or semiparametrically). The research will involve novel modifications and applications of several modern statistical methods such as empirical likelihood method, sieve maximum likelihood method, bootstrap method, extreme value theory, residual-based weighted empirical process theory, etc. Research outputs will include several original papers on estimation and testing of copula-based semiparametric multivariate models for independent data and for censored data, as well as estimation and testing of semiparametric dynamic models for nonlinear and possibly heavy-tailed time series data. The project will produce new estimation and testing software that can be freely downloaded from the authors' web pages. The results from this project will make significant contributions to econometrics and statistics literature on estimation and testing of semiparametric multivariate models based on copulas, tail copulas, and Garch with heavy tails, and will be very useful in economics (such as industrial organization, income inequality, health economics), insurance, biostatistics, medical science, and other fields where nonlinear dependence is important. This project will develop new methodologies and apply modern statistical methods to explore nonlinear dependence among multivariate series and to understand complicated dynamics of human and social interactions with empirical applications to economics, insurance, finance, statistics, and other sciences. Since practitioners in finance and insurance have been combining nonlinear time series models with copulas and/or tail copulas to heuristically model multivariate option pricing, portfolio Value-at-Risk, correlated default and credit risk, and the time-varying asymmetric nonlinear co-movements among different series, the results from this project will guide applied researchers to perform statistically reliable economic policy evaluations, financial forecasts, and risk managements. The main educational plans are to train PhD students in economics, statistics, and finance to become capable researchers in related topics, and to develop new courses for students in economics, management, statistics, and quantitative finance. This award was supported as part of the fiscal year 2006 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).