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Weighted V@R and its Properties

机译:加权V @ R及其属性

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The paper deals with the study of a coherent risk measure, which we call Weighted V@R. It is a risk measure of the form $rho_mu(X)=intlimits_{[0,1]}hbox{TV}@hbox{R}_{uplambda}(hbox{X}) mu(hbox{d}uplambda),$ where μ is a probability measure on [0,1] and TV@R stands for Tail V@R. After investigating some basic properties of this risk measure, we apply the obtained results to the financial problems of pricing, optimization, and capital allocation. It turns out that, under some regularity conditions on μ, Weighted V@R possesses some nice properties that are not shared by Tail V@R. To put it briefly, Weighted V@R is “smoother” than Tail V@R. This allows one to say that Weighted V@R is one of the most important classes (or maybe the most important class) of coherent risk measures.
机译:本文涉及对连贯风险度量的研究,我们称其为加权V @ R。这是一种风险度量,格式为$ rho_mu(X)= intlimits _ {[0,1]} hbox {TV} @hbox {R} _ {uplambda}(hbox {X})mu(hbox {d} uplambda), $,其中,μ是[0,1]上的概率度量,TV @ R代表尾巴V @ R。在研究了该风险度量的一些基本属性之后,我们将获得的结果应用于定价,优化和资本分配的财务问题。事实证明,在某些关于μ的规则性条件下,加权V @ R具有一些不错的属性,而Tail V @ R无法共享这些属性。简而言之,加权V @ R比尾V @ R更“平滑”。可以这样说,加权V @ R是相关风险度量的最重要类别之一(或也许是最重要类别)。

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