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

合作研究：依赖建模和风险管理中的 Copulas、Tail Copulas、Garch 和极值

• 批准号: 0631608
• 负责人: Liang Peng
• 金额: $ 15.21万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0631608&HistoricalAwards=false
• 关键词: Collaborative; Research; Copulas; Tail; Garch

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).

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变量数据序列具有典型的非线性、非正态分布、非线性协同运动和相互作用。然而，现有的计量经济学和统计学方法在多变量时间序列数据分析中大多局限于多变量正态或条件正态框架。这项跨学科的合作研究项目将考察基于Copula的广泛类别的半参数模型的估计、测试和经验应用，这些模型用于分析呈现非线性协同运动、不对称、重尾、可能被删失和/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(或半参数)指定边际分布和以参数(或半参数)指定捕捉多变量之间相关性的Copula函数来部分地解决“维度灾难”问题。这项研究将涉及几种现代统计方法的新的修改和应用，如经验似然方法、筛选最大似然方法、Bootstrap方法、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，以及非线性和可能的重尾时间序列数据的半参数动态模型的估计和检验的论文。该项目将制作新的估算和测试软件，可从作者的网页上免费下载。这个项目的结果将对计量经济学和统计学文献中基于Copulas、尾部Copulas和带重尾的GARCH的半参数多变量模型的估计和检验做出重大贡献，并将在经济学(如产业组织、收入不平等、卫生经济学)、保险、生物统计学、医学和其他非线性相关性非常重要的领域中非常有用。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相关性，并通过对经济、保险、金融、统计和其他科学的实证应用来了解人类和社会互动的复杂动力学。由于金融和保险从业者一直在将非线性时间序列模型与Copula和/或尾部Copula相结合，以启发式地建模多变量期权定价、投资组合在险价值、相关的违约和信用风险，以及不同序列之间的时变非对称非线性协动，因此该项目的结果将指导应用研究人员进行统计上可靠的经济政策评估、财务预测和风险管理。主要的教育计划是培养经济学、统计学和金融学的博士生，使他们成为相关学科的有能力的研究人员，并为学生开发经济学、管理学、统计学和定量金融学的新课程。该奖项作为2006财政年度数学科学优先领域数学社会科学和行为科学特别竞赛的一部分得到支持。