风险分配与风险预算管理

本文首先引入风险度量定义,分析了无组合效应风险与可互换风险这两种具有特殊性质的个体风险特征,在此基础上我们讨论了风险分配应该满足的原则.我们证明并比较了在标准差风险度量下,协方差风险分配函数与相对风险分配函数性质上的差异,并证明风险预算也是一种特殊形式的风险分配函数.最后我们给出不同风险分配函数之间的联系.     

流动性风险的度量

VaR模型忽略了流动性风险,到目前为止还没有统一的指标度量流动性风险.本文分析了最高成交价与最低成交价之差模型度量流动性风险存在偏差,同时给出了度量流动性风险一种新的修正模型.最后,结合实证对两种模型进行对比,修正模型从更加微观的层面上充分考虑到各价位的实际成交的价、量分布对总交易金额的作用,计算流动性风险值比较客观、精确.     

CVaR风险度量模型在投资组合中的运用

风险价值(VaR)是近年来金融机构广泛运用的风险度量指标，条件风险价值(CVaR)是VaR的修正模型，也称为平均超额损失或尾部VaR，它比VaR具有更好的性质。在本中，我们将运用风险度量指标VaR和CVaR，提出一个新的最优投资组合模型。介绍了模型的算法，而且利用我国的股票市场进行了实证分析，验证了新模型的有效性，为制定合理的投资组合提供了一种新思路。     

与TSD准则一致的组合证券投资分析

基于预先给定的目标收益率,利用投资者对低于目标收益率的风险损失和高于目标收益率的风险报酬之间的权衡,给出了一些非对称风险度量模型,特别其中一种风险度量是低于参考点的方差和高于参考点的方差的加权和,它利用二阶上偏矩来修正二阶下偏矩,进一步建立了在该非对称风险度量下的组合投资优化模型,并证明了该模型在三阶随机占优的意义下是有效的.此外,还给出了其它3个模型与三阶随机占优准则是否一致的结论,并对所给出的几个组合证券投资模型的求解方法及其应用进行了分析.以上研究和分析为投资者在选择投资模型时避免盲目性、任意性提供了有益的决策参考.     

基于改进的Fishburn测度的一致性风险度量

一致性风险度量在金融风险管理中是十分重要的风险度量.下偏矩度量是位于目标收益(率)下方的风险损失,通过引入新目标收益率对Fishburn风险测度进行了改进,在Fischer基于单边矩风险度量所建立的一致性风险度量的基础上得到了一个新的一致性风险度量,适用于任何类型的收益(率)分布,更加符合投资者的心理实际,最后通过实例验证了所得的一致性风险度量的有效性和合理性.     

基于可接受集的多维风险度量北大核心

在多维框架下提出了基于可接受集的两种风险度量概念,讨论了一些相应的性质,给出了这两种风险度量在满足一定条件下的表示定理.最后给出了几个实例.     

聚合风险模型下Esscher风险度量的估计及其大样本性质

Esscher度量是一种重要的风险度量,在金融风险管理、保险精算中有广泛的应用,然而大部分关于Esscher风险度量的文献均在个体风险模型下考虑的.本文建立了聚合风险模型下Esscher度量的估计模型,得到了相应的非参数估计,并证明了估计的强相合性和渐近正态性,最后,通过数值模拟的方法验证了估计的大样本性质.     

点式p．度量的等价公理及其应用

给出了点式 p.度量在L格上的一组等价公理,通过定义O-nbd映射簇对它进行了刻画,同时还给出了它的一些其它性质及其它在L-实直线上的应用.     

基于风险网络的大型工程项目风险度量方法研究

风险度量是风险管理的基础,提出适合大型工程项目风险的风险度量方法.针对大型工程项目风险因素、风险信息、风险损失之间的复杂联系,构建大型工程项目风险网络,分别采用贝叶斯网络推理和网络层次分析法获得风险发生概率和风险量的估计,从而提出基于风险网络的大型工程项目风险度量方法.方法将风险损失量和风险损失发生概率进行了明确合理的结合,既可用于度量客观风险,也可用于度量主观风险.最后以槽菁头隧道施工风险管理为例说明该方法的具体应用步骤和效果.     

一致风险度量和锥优化分析

一致风险理论的公理系统为风险分析建立了坚实的基础,然而它背后的数学却和凸优化理论思想密切相关,特别是对偶理论. 本文在有限维空间中,利用锥优化的对偶定理给出了一致风险度量的一般表达式的简单证明. 分析了可接受集的概念在一致风险度量中的中心作用,根据锥优化的对偶关系,探索了常用风险度量的性质. 尽管可接受集的大小能够表达风险控制的强弱,但是我们不知道如何定量地表示. 本文提出用相对熵控制风险度量松紧度的方法和意义. 另外,根据一致风险度量的灵活的结构,给出了无套利条件的一种放松,这一结果可用于不完全市场中的期权定价问题.     

Asymptotic theory for the empirical Haezendonck–Goovaerts risk measure

Haezendonck–Goovaerts risk measures is a recently introduced class of risk measures which includes, as its minimal member, the Tail Value-at-Risk (T-VaR)—T-VaR arguably the most popular risk measure in global insurance regulation. In applications often one has to estimate the risk measure given a random sample from an unknown distribution. The distribution could either be truly unknown or could be the distribution of a complex function of economic and idiosyncratic variables with the complexity of the function rendering indeterminable its distribution. Hence statistical procedures for the estimation of Haezendonck–Goovaerts risk measures are a key requirement for their use in practice. A natural estimator of the Haezendonck–Goovaerts risk measure is the Haezendonck–Goovaerts risk measure of the empirical distribution, but its statistical properties have not yet been explored in detail. The main goal of this article is to both establish the strong consistency of this estimator and to derive weak convergence limits for this estimator. We also conduct a simulation study to lend insight into the sample sizes required for these asymptotic limits to take hold.     

Inference for intermediate Haezendonck–Goovaerts risk measure

Recently Haezendonck–Goovaerts (H–G) risk measure has received much attention in actuarial science. Nonparametric inference has been studied by Ahn and Shyamalkumar (2014) and Peng et al. (2015) when the risk measure is defined at a fixed level. In risk management, the level is usually set to be quite near one by regulators. Therefore, especially when the sample size is not large enough, it is useful to treat the level as a function of the sample size, which diverges to one as the sample size goes to infinity. In this paper, we extend the results in Peng et al. (2015) from a fixed level to an intermediate level. Although the proposed maximum empirical likelihood estimator for the H–G risk measure has a different limit for a fixed level and an intermediate level, the proposed empirical likelihood method indeed gives a unified interval estimation for both cases. A simulation study is conducted to examine the finite sample performance of the proposed method.     

Haezendonck–Goovaerts risk measure with a heavy tailed loss

Recently Haezendonck–Goovaerts (H–G) risk measure has received much attention in (re)insurance and portfolio management. Some nonparametric inferences have been proposed in the literature. When the loss variable does not have enough moments, which depends on the involved Young function, the nonparametric estimator in Ahn and Shyamalkumar (2014) has a nonnormal limit, which challenges interval estimation. Motivated by the fact that many loss variables in insurance and finance could have a heavier tail such as an infinite variance, this paper proposes a new estimator which estimates the tail by extreme value theory and the middle part nonparametrically. It turns out that the proposed new estimator always has a normal limit regardless of the tail heaviness of the loss variable. Hence an interval with asymptotically correct confidence level can be obtained easily either by the normal approximation method via estimating the asymptotic variance or by a bootstrap method. A simulation study and real data analysis confirm the effectiveness of the proposed new inference procedure for estimating the H–G risk measure.     

Characterization of upper comonotonicity via tail convex order

In this paper, we show a characterization of upper comonotonicity via tail convex order. For any given marginal distributions, a maximal random vector with respect to tail convex order is proved to be upper comonotonic under suitable conditions. As an application, we consider the computation of the Haezendonck risk measure of the sum of upper comonotonic random variables with exponential marginal distributions.     

Tail subadditivity of distortion risk measures and multivariate tail distortion risk measures

In this paper, we extend the concept of tail subadditivity (Belles-Sampera et al., 2014a; Belles-Sampera et al., 2014b) for distortion risk measures and give sufficient and necessary conditions for a distortion risk measure to be tail subadditive. We also introduce the generalized GlueVaR risk measures, which can be used to approach any coherent distortion risk measure. To further illustrate the applications of the tail subadditivity, we propose multivariate tail distortion (MTD) risk measures and generalize the multivariate tail conditional expectation (MTCE) risk measure introduced by Landsman et al. (2016). The properties of multivariate tail distortion risk measures, such as positive homogeneity, translation invariance, monotonicity, and subadditivity, are discussed as well. Moreover, we discuss the applications of the multivariate tail distortion risk measures in capital allocations for a portfolio of risks and explore the impacts of the dependence between risks in a portfolio and extreme tail events of a risk portfolio in capital allocations.     

Portfolio optimization under entropic risk management

framework in the risk uniqueness In this paper, properties of the entropic risk measure are examined rigorously in a general This risk measure is then applied in a dynamic portfolio optimization problem, appearing management constraint. By considering the dual problem, we prove the existence and of the solution and obtain an analytic expression for the solution.     

从管理风险的角度看金融风险度量

本文得出了连续时间下均值-VaR模型的最优投资策略。在这个最优解的基础上,我们比较说明了概率和分位数作为风险度量方法在管理风险中发挥的作用。我们的分析结果表明:从管理风险的角度出发控制损失发生的概率要比控制损失的水平更为有意义;并且选择的VaR置信度水平越高,监管的效果会越好。     

信息熵度量风险的探究

本文分析了风险的本质后指出,风险是某一特定行为主体对某一金融投资中损失的不确定性和收益的不确定性的认识。在众多风险度量的方法中,熵函数法有着其独特的度量风险的优势,因此,在本文中重点讨论了熵函数作为风险度量的合理性。同时提出一个新的风险度量模型,剖析其主要的数学特性,阐明该模型可以针对不同行为主体能有效地度量金融风险,并且计算量小,易于操作。     

Estimations and asymptotic behaviors of coherent entropic risk measure for sums of random variables

In this article, we provide an estimation and several asymptotic behaviors for the coherent entropic risk measure of compound Poisson process. We also establish an estimation for the coherent entropic risk measure of sum of i.i.d. random variables in virtue of Log-Sobolev inequality. As an application, we provide two deviation estimations of the tail probability for compound Poisson process. Finally, several simulation results are given to support our results.     

基于鞅测度的流动性风险溢价的测算

研究了在一般市场条件下流动性风险的定价问题.首先借助金融数学和金融工程的无套利思想在鞅测度下对市场风险和流动性风险进行定价,通过等价测度变换,使可交易资产的贴现价值过程转化为鞅过程,得到了市场风险和流动性风险的市场价格,进而给出了流动性风险溢价的计算公式.得到的风险的市场价格在同一市场中对于所有可交易资产都是相同的,并且这一价格对于所有投资者也都是相同的,不会因投资者的风险厌恶水平的不同而不同.