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The orthant convex and concave orders have been studied in the literature as extensions of univariate variability orders. In this paper, new results are proposed for bivariate orthant convex-type orders between vectors. In particular, we prove that these orders cannot be considered as dependence orders since they fail to verify several desirable properties that any positive dependence order should satisfy. Among other results, the relationships between these orders under certain transformations are presented, as well as that the orthant convex orders between bivariate random vectors with the same means are sufficient conditions to order the corresponding covariances. We also show that establishing the upper orthant convex or lower orthant concave orders between two vectors in the same Frechet class is not equivalent to establishing these orders between the corresponding copulas except when marginals are uniform distributions. Several examples related with concordance measures, such as Kendall’s tau and Spearman’s rho, are also given, as are results on mixture models.

CoVaR is a systemic risk measure proposed by Adrian and Brunnermeier (2011) able to measure a financial institution’s contribution to systemic risk and its contribution to the risk of other financial institutions. CoVaR stands for conditional Value-at-Risk, i.e. it indicates the Value at Risk for a financial institution that is conditional on a certain scenario. In this paper, two alternative extensions of the classic univariate Conditional Value-at-Risk are introduced in a multivariate setting. The two proposed multivariate CoVaRs are constructed from level sets of multivariate distribution functions ( resp. of multivariate survival distribution functions). These vector-valued measures have the same dimension as the underlying risk portfolio. Several characterizations of these new risk measures are provided in terms of the copula structure and stochastic orderings of the marginal distributions. Interestingly, these results are consistent with existing properties on univariate risk measures. Furthermore, comparisons between existent risk measures and the proposed multivariate CoVaR are developed. Illustrations are given in the class of Archimedean copulas. Estimation procedure for the multivariate proposed CoVaRs is illustrated in simulated studies and insurance real data.

In this paper, some new properties of the upper-corrected orthant of a random vector are proved. The univariate right-spread or excess wealth function, introduced by Fernandez-Ponce et al. (1996) , is extended to multivariate random vectors, and some properties of this multivariate function are studied. Later, this function was used to define the excess wealth ordering by Shaked and Shanthikumar (1998) and Fernandez-Ponce et al. (1998) . The multivariate excess wealth function enable us to define a new stochastic comparison which is weaker than the multivariate dispersion orderings. Also, some properties relating the multivariate excess wealth order with stochastic dependence are described.

We study the concept of multivariate dispersion order, defined as the existence of an expansion function that maps a random vector to another one, for multivariate distributions with the same dependence structure. As a particular case, we can order the multivariate t-distribution family in dispersion sense. Finally, we use these results in the problem of detection and characterization of influential observations in regression analysis. This problem can often be used to compare two multivariate t-distributions.

In this article, we introduce a new dispersion order weaker than the classic dispersion order discussed by Lewis and Thompson (1981). We study the equivalence of this order with the majorization order under the assumption of unimodality. Finally, we use this equivalence to characterize the IFR aging notion for unimodal distributions by means of the notion of decreasing in randomness.

In this paper we introduce a quantile dispersion measure. We use it to characterize different classes of ageing distributions. Based on the quantile dispersion measure, we propose a new partial ordering for comparing the spread or dispersion in two probability distributions. This new partial ordering is weaker than the well known dispersive ordering and it retains most of its interesting properties.

We characterize partial orderings between lifetime distributions by Ls-tailweight. We obtain some ageing properties by comparing distributions with the negative exponential distribution with unit mean. AMS classification: Primary 62N05; Secondary 62E10

Recently, Belzunce, Ortega, Pellerey, and Ruiz [3] have obtained stochastic comparisons in increasing componentwise convex order sense for vectors of random sums when the summands and number of summands depend on a common random environment, which prove how the dependence among the random environmental parameters influences the variability of vectors of random sums. The main results presented here generalize the results in Belzunce et al. [3] by considering vectors of parameters instead of a couple of parameters and the increasing directionally convex order. Results on stochastic directional convexity of families of random sums under appropriate conditions on the families of summands and number of summands are obtained, which lead to the convex comparisons between random sums mentioned earlier. Different applications in actuarial science, reliability, and population growth are also provided to illustrate the main results.

A multivariate dispersion ordering based on quantiles more widely separated is defined. This new multivariate dispersion ordering is a generalization of the classic univariate version. If we vary the ordering of the components in the multivariate random variable then the comparison could not be possible. We provide a characterization using a multivariate expansion function. The relationship among various multivariate orderings is also considered. Finally, several examples illustrate the method of this paper.