关于参数型copula函数的拟合检验

在金融和保险中,copula函数是一种构造多元相关分布函数的有力工具．然而,怎样选择一个适当的copula函数用于拟合数据,并没有找到统一的方法．因此,基于copula函数的经验分布,我们提出了一种用于检验具有某种特定参数结构的copula函数拟合数据优良性的方法,并得到了此检验的渐近性质．由于该检验统计量的极限分布依赖未知参数,我们采用非参数蒙特卡罗方法确定临界值．我们做了一个简单的模拟来验证本文提出的检验方法的功效．     

由某些函数构造的状态变权

研究状态变权的构造问题,给出一种由多元函数和已知状态变权构造新状态变权和三种由多元函数直接构造状态变权的方法。特别地,证明了文[5]由状态均值构造状态变权的方法都可看作是函数构造状态变权的特例,并进一步给出一种基于几何均值的状态变权构造方法。     

拟Bent函数的性质和构造

本文将基本2-群中拟Bent函数的概念推广到一般的有限Abel群中,统一了目前几乎所有的Bent函数概念,完全刻画了一类拟Bent函数和Bent函数的本质联系,给出了几种拟Bent函数的构造方法,拟Bent函数和相对差集的一种关系以及一种用拟Bent函数构造Bent函数的方法.最后,利用Galois环和组合集,找到一类拟Bent函数.     

一类周期紧小波框架的构造

从L2(Rd)上构造紧小波框架的一般方法(即延拓原理)出发,利用函数的周期化过程,给出了L2([0,1]d)上一类紧小波框架的构造方法.     

小波的传递函数构造法

本文从小波与尺度函数的传递函数出发 ,给出了构造小波母函数及尺度函数的构造方法 .根据此方法 ,首先以小波与其尺度函数的传递函数为起点 ,构造了一个非正交小波 ,随后以此小波和一个已有的非正交小波为基准 ,进一步推广得到了一类非正交小波及尺度函数类 .在非正交小波的基础上 ,利用将尺度函数正交化的方法 ,构造出了相应正交小波的函数族 .     

n维散乱数据带自然边界条件多元多项式样条插值

考虑n维散乱数据Hermit-Birkhoff型插值问题,在使给定的目标泛极小的条件下,构造了一种带自然边界条件的多元多项式样条函数插值方法.重点研究了插值问题解的特征,存在唯一性和构造方法,并讨论了收敛性及误差,最后给出了一些数值算例对方法进行验证.     

二元数字期权定价与copula的关系

首先简要介绍了 copula的一些基本概念和性质 ,然后探讨了一种多元未定权益——二元数字期权的价格与 copula的关系 .结论表明 ,一个二元数字期权的价格恰好是一个 copula函数 ,由此结论进而给出了根据实际数据获得二元数字期权价格的基本思路和步骤 .     

多元函数的高阶微分中值定理

对凸区域DRn上的二次可微函数,本文采用构造“混合函数”的方法,将多元函数微分中值定理推广到了高阶的情形,并给出了应用示例.     

非线性l1问题的光滑近似算法

为非线性l1问题的求解构造了光滑逼近函数.首先将非线性l1问题转化为等价的不可微优化问题;其次通过两步提出光滑逼近函数的一般性构造方法;最后进行了数值仿真.文中介绍了光滑逼近函数的有关性质,指出相关文献已有的光滑函数方法是本文的特例,并证明了方法的收敛性及有效性.     

正交多小波的构造

1 引言 在小波的构造和应用中,对于2尺度单一小波已有相当成熟的理论,特别是在小波构造方面,若知道正交单一尺度函数,相应的单一小波是很容易构造出的。对于a尺度紧支撑多小波,如何从已知的a尺度紧支撑多重尺度函数构造出相应的多小波,到目前为止尚没有一般的构造方法。W.Lanton等用仿酉矩阵扩充的方法构造出相应的多小     

Distorted Mix Method for constructing copulas with tail dependence

This paper introduces a method for constructing copula functions by combining the ideas of distortion and convex sum, named Distorted Mix Method. The method mixes different copulas with distorted margins to construct new copula functions, and it enables us to model the dependence structure of risks by handling the central and tail parts separately. By applying the method we can modify the tail dependence of a given copula to any desired level measured by tail dependence function and tail dependence coefficients of marginal distributions. As an application, a tight bound for asymptotic Value-at-Risk of order statistics is obtained by using the method. An empirical study shows that copulas constructed by this method fit the empirical data of SPX 500 Index and FTSE 100 Index very well in both central and tail parts.     

Construction of asymmetric copulas and its application in two-dimensional reliability modelling

Copulas offer a useful tool in modelling the dependence among random variables. In the literature, most of the existing copulas are symmetric while data collected from the real world may exhibit asymmetric nature. This necessitates developing asymmetric copulas that can model such data. In the meantime, existing methods of modelling two-dimensional reliability data are not able to capture the tail dependence that exists between the pair of age and usage, which are the two dimensions designated to describe product life. This paper proposes a new method of constructing asymmetric copulas, discusses the properties of the new copulas, and applies the method to fit two-dimensional reliability data that are collected from the real world.     

A class of multivariate copulas with bivariate Fréchet marginal copulas

In this paper, we present a class of multivariate copulas whose two-dimensional marginals belong to the family of bivariate Fréchet copulas. The coordinates of a random vector distributed as one of these copulas are conditionally independent. We prove that these multivariate copulas are uniquely determined by their two-dimensional marginal copulas. Some other properties for these multivariate copulas are discussed as well. Two applications of these copulas in actuarial science are given.     

Hierarchical Archimedean copulas through multivariate compound distributions

In this paper, we propose a new hierarchical Archimedean copula construction based on multivariate compound distributions. This new imbrication technique is derived via the construction of a multivariate exponential mixture distribution through compounding. The absence of nesting and marginal conditions, contrarily to the nested Archimedean copulas approach, leads to major advantages, such as a flexible range of possible combinations in the choice of distributions, the existence of explicit formulas for the distribution of the sum, and computational ease in high dimensions. A balance between flexibility and parsimony is targeted. After presenting the construction technique, properties of the proposed copulas are investigated and illustrative examples are given. A detailed comparison with other construction methodologies of hierarchical Archimedean copulas is provided. Risk aggregation under this newly proposed dependence structure is also examined.     

非参数核密度估计与Copula

本文提出非参数核密度估计-ML方法来估计Copula函数中的未知参数;再由统计检验推断得到能较好描述金融资产之间非线性相关结构的Copula。实证分析表明:可以利用Clayton Copula、Gumbel Copula来描述A股市场上证指数与深证成指之间的非线性相关结构.     

Multivariate copulas, quasi-copulas and lattices

We investigate some properties of the partially ordered sets of multivariate copulas and quasi-copulas. Whereas the set of bivariate quasi-copulas is a complete lattice, which is order-isomorphic to the Dedekind-MacNeille completion of the set of bivariate copulas, we show that this is not the case in higher dimensions.     

Characterizations of Copulas Attaining the Bounds of Multivariate Kendall’s Tau

Kendall’s tau is one of the most popular measures of concordance, and even in the multivariate case exact upper and lower bounds of Kendall’s tau are known. The present paper provides characterizations of the copulas attaining the bounds of multivariate Kendall’s tau, mainly in terms of the copula measure, but also via Kendall’s distribution function and for shuffles of copulas.     

Joint distributions of random sets and their relation to copulas

Random sets are set-valued random variables. They have been applied in various fields like stochastic geometry, statistics, economics, engineering or computer science, and are often used for modeling uncertainty. In an earlier paper the author has defined joint capacity and joint containment functionals which are multivariate set functions describing the joint distribution of random sets. This paper is concerned with the question how copulas can be used to describe or model the dependence of random sets. It is demonstrated that a joint containment functional can be related to its margins by a family of copulas. Furthermore, the paper provides a first insight how copulas can be used to define joint containment functionals.     

Construction of asymmetric multivariate copulas

In this paper we introduce two methods for the construction of asymmetric multivariate copulas. The first is connected with products of copulas. The second approach generalises the Archimedean copulas. The resulting copulas are asymmetric and may have more than two parameters in contrast to most of the parametric families of copulas described in the literature. We study the properties of the proposed families of copulas such as the dependence of two components (Kendall’s tau, tail dependence), marginal distributions and the generation of random variates.     

Multivariate negative binomial models for insurance claim counts

It is no longer uncommon these days to find the need in actuarial practice to model claim counts from multiple types of coverage, such as the ratemaking process for bundled insurance contracts. Since different types of claims are conceivably correlated with each other, the multivariate count regression models that emphasize the dependency among claim types are more helpful for inference and prediction purposes. Motivated by the characteristics of an insurance dataset, we investigate alternative approaches to constructing multivariate count models based on the negative binomial distribution. A classical approach to induce correlation is to employ common shock variables. However, this formulation relies on the NB-I distribution which is restrictive for dispersion modeling. To address these issues, we consider two different methods of modeling multivariate claim counts using copulas. The first one works with the discrete count data directly using a mixture of max-id copulas that allows for flexible pair-wise association as well as tail and global dependence. The second one employs elliptical copulas to join continuitized data while preserving the dependence structure of the original counts. The empirical analysis examines a portfolio of auto insurance policies from a Singapore insurer where claim frequency of three types of claims (third party property damage, own damage, and third party bodily injury) are considered. The results demonstrate the superiority of the copula-based approaches over the common shock model. Finally, we implemented the various models in loss predictive applications.