同单调相依结构下两重生命模型的概率分布

在寿险实务中,在处理涉及到多个生命的问题时往往假设各个生命之间是独立的,但事实上,因为受某些相同因素影响的生命之间总是存在一定的正相依性．本文证明了在给定边际分布的二维随机向量中,同单调相依结构是在相关序意义下最强的正相依结构,研究了在此相依结构下的两重生命模型的概率分布,并给出了随机序意义下两个状态消亡时间的随机上界和随机下界．     

相依情形下多生命状态年金现值的比较北大核心CSCD

在多生命状态各个体余寿相依的情形下,通过比较边际分布或相依结构,研究终身年金趸缴保费精算现值的差异.采用Copula函数作为相依结构的表示,分别研究:(1)假设各投保集团个体余寿之间相依结构相同的情形下,根据余寿向量在随机序意义下的大小,比较各投保集团终身年金趸缴保费精算现值的大小;(2)假设两个投保集团个体余寿有相同的分布,但个体余寿之间相依结构不相同的情形下,比较其终身年金趸缴保费精算现值的差异.     

Comparing present values of life annuity of multi-life status under different dependent structure

在多生命状态各个体余寿相依的情形下,通过比较边际分布或相依结构,研究终身年金趸缴保费精算现值的差异.采用Copula函数作为相依结构的表示,分别研究:(1)假设各投保集团个体余寿之间相依结构相同的情形下,根据余寿向量在随机序意义下的大小,比较各投保集团终身年金趸缴保费精算现值的大小;(2)假设两个投保集团个体余寿有相同的分布,但个体余寿之间相依结构不相同的情形下,比较其终身年金趸缴保费精算现值的差异.     

时间相依更新风险模型中无限时绝对破产概率的渐近性

本文考虑了两类时间相依且带常利率和常值保费收入率的更新风险模型的无限时绝对破产概率, 其中索赔额及其到达时间间隔构成独立同分布的随机对列, 以及每个随机对遵循某种相依结构. 基于此, 当索赔额分布属于R_-∞∩J(γ), γ > 0 分布族时, 我们分别得到了两类时间相依结构下的无限时绝对破产概率的渐近公式和渐近上界.     

随机多个随机变量之和的期望公式的一种新证法

用随机变量之和的分布的卷积公式直接给出随机多个随机变量之和的期望公式的证明 ,避免了原有的证明过程需引入条件期望和全期望公式的麻烦 .     

一类具有相依结构的离散时间风险模型的赤字分布

研究一类具有相依结构的离散时间风险模型的破产赤字问题.其中,保费和利率过程假设为两个不同的自回归移动平均模型.利用更新递归技巧,首先得到了该模型下破产赤字分布的递推公式.然后,根据该递推公式得到了赤字分布的上下界估计.     

考虑随机投资收益和单边线性索赔的时间依赖更新风险模型的破产概率

本文主要研究一类考虑随机投资收益和相依索赔额的时间依赖的更新风险模型.在该模型中,保险投资收益服从指数Lévy过程,而索赔额服从具有独立同分布步长的单边线性过程.该单边线性过程的步长与索赔到达时间构成独立同分布的随机向量序列,并且该随机向量的分量之间具有运用步长关于索赔到达时间间隔的条件尾概率渐近性刻画的相依关系.当单边线性过程的步长服从重尾分布时,本文得到该更新风险模型破产概率在时间域内的一致渐近估计.     

参数弱向量平衡问题解集的似Holder性和相依导数

本文研究了一类参数弱向量平衡问题解集的似Hölder性和相依导数. 首先, 讨论了该问题的一类实值间隙函数的Lipschitz连续性和Hadamard方向可微性. 然后, 借助这些性质, 建立了该问题解集的似Hölder性和Hölder连续性以及相依导数的具体表达式.     

关于任意非负随机序列随机和的一类随机偏差定理

本文引入任意随机变量序列随机极限对数似然比概念,作为任意相依随机序列联合分布与其边缘乘积分布“不相似”性的一种度量,利用构造新的密度函数方法来建立几乎处处收敛的上鞅,在适当的条件下,给出了任意受控随机序列的一类随机偏差定理.     

从SPVⅡ分布生成的新的多元偏态t分布的有关性质

随机向量的t分布属于椭球等高分布族,然而,它是对称分布.在许多诸如经济学、生理学、社会学等领域中,有时回归模型中的随机误差不再满足对称性,通常表现出高度的偏态性(skewness).于是就有了偏态椭球等高分布族.本文在已有的多元偏态t分布的基础上,着重研究它的分布性质,包括线性组合分布、边缘分布、条件分布及各阶矩.     

Second order tail asymptotics for the sum of dependent, tail-independent regularly varying risks

In this paper we consider dependent random variables with common regularly varying marginal distribution. Under the assumption that these random variables are tail-independent, it is well known that the tail of the sum behaves like in the independence case. Under some conditions on the marginal distributions and the dependence structure (including Gaussian copula’s and certain Archimedean copulas) we provide the second-order asymptotic behavior of the tail of the sum.     

Asymptotic Results for the Sum of Dependent Non-identically Distributed Random Variables

In this paper we extend some results about the probability that the sum of n dependent subexponential random variables exceeds a given threshold u. In particular, the case of non-identically distributed and not necessarily positive random variables is investigated. Furthermore we establish criteria how far the tail of the marginal distribution of an individual summand may deviate from the others so that it still influences the asymptotic behavior of the sum. Finally we explicitly construct a dependence structure for which, even for regularly varying marginal distributions, no asymptotic limit of the tail of the sum exists. Some explicit calculations for diagonal copulas and t-copulas are given. Dominik Kortschak was supported by the Austrian Science Fund Project P18392.     

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.     

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.     

On the distribution of the (un)bounded sum of random variables

We propose a general treatment of random variables aggregation accounting for the dependence among variables and bounded or unbounded support of their sum. The approach is based on the extension to the concept of convolution to dependent variables, involving copula functions. We show that some classes of copula functions (such as Marshall-Olkin and elliptical) cannot be used to represent the dependence structure of two variables whose sum is bounded, while Archimedean copulas can be applied only if the generator becomes linear beyond some point. As for the application, we study the problem of capital allocation between risks when the sum of losses is bounded.     

On rank correlation measures for non-continuous random variables

For continuous random variables, many dependence concepts and measures of association can be expressed in terms of the corresponding copula only and are thus independent of the marginal distributions. This interrelationship generally fails as soon as there are discontinuities in the marginal distribution functions. In this paper, we consider an alternative transformation of an arbitrary random variable to a uniformly distributed one. Using this technique, the class of all possible copulas in the general case is investigated. In particular, we show that one of its members—the standard extension copula introduced by Schweizer and Sklar—captures the dependence structures in an analogous way the unique copula does in the continuous case. Furthermore, we consider measures of concordance between arbitrary random variables and obtain generalizations of Kendall's tau and Spearman's rho that correspond to the sample version of these quantities for empirical distributions.     

Second order risk aggregation with the Bernstein copula

We analyze the tail of the sum of two random variables when the dependence structure is driven by the Bernstein family of copulas. We consider exponential and Pareto distributions as marginals. We show that the first term in the asymptotic behavior of the sum is not driven by the dependence structure when a Pareto random variable is involved. Consequences on the Value-at-Risk are derived and examples are discussed.     

Operator Tail Dependence of Copulas

A notion of tail dependence based on operator regular variation is introduced for copulas, and the standard tail dependence used in the copula literature is included as a special case. The non-standard tail dependence with marginal power scaling functions having possibly distinct tail indexes is investigated in detail. We show that the copulas with operator tail dependence, incorporated with regularly varying univariate margins, give rise to a rich class of the non-standard multivariate regularly varying distributions. We also show that under some mild conditions, the copula of a non-standard multivariate regularly varying distribution has the standard tail dependence of order 1. Some illustrative examples are given.     

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.     

Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model

This paper combines copula functions with GARCH-type models to construct the conditional joint distribution, which is used to estimate Value-at-Risk (VaR) of an equally weighted portfolio comprising crude oil futures and natural gas futures in energy market. Both constant and time-varying copulas are applied to fit the dependence structure of the two assets returns. The findings show that the constant Student t copula is a good compromise for effectively fitting the dependence structure between crude oil futures and natural gas futures. Moreover, the skewed Student t distribution has a better fit than Normal and Student t distribution to the marginal distribution of each asset. Asymmetries and excess kurtosis are found in marginal distributions as well as in dependence. We estimate VaR of the underlying portfolio to be 95% and 99%, by using the Monte Carlo simulation. Then using backtesting, we compare the out-of-sample forecasting performances of VaR estimated by different models.