24 research outputs found
Bounds on Integrals with Respect to Multivariate Copulas
Finding upper and lower bounds to integrals with respect to copulas is a
quite prominent problem in applied probability. In their 2014 paper, Hofer and
Iaco showed how particular two dimensional copulas are related to optimal
solutions of the two dimensional assignment problem. Using this, they managed
to approximate integrals with respect to two dimensional copulas. In this
paper, we will further illuminate this connection, extend it to d-dimensional
copulas and therefore generalize the method from Hofer and Iaco to arbitrary
dimensions. We also provide convergence statements. As an example, we consider
three dimensional dependence measures
Extremal Dependence Concepts
The probabilistic characterization of the relationship between two or more random variables calls for a notion of dependence. Dependence modeling leads to mathematical and statistical challenges, and recent devel- opments in extremal dependence concepts have drawn a lot of attention to probability and its applications in several disciplines. The aim of this paper is to review various concepts of extremal positive and negative dependence, including several recently established results, reconstruct their history, link them to probabilistic optimization problems, and provide a list of open ques- tions in this area. While the concept of extremal positive dependence is agreed upon for random vectors of arbitrary dimensions, various notions of extremal negative dependence arise when more than two random variables are involved. We review existing popular concepts of extremal negative de- pendence given in literature and introduce a novel notion, which in a gen- eral sense includes the existing ones as particular cases. Even if much of the literature on dependence is focused on positive dependence, we show that negative dependence plays an equally important role in the solution of many optimization problems. While the most popular tool used nowadays to model dependence is that of a copula function, in this paper we use the equivalent concept of a set of rearrangements. This is not only for historical reasons. Re- arrangement functions describe the relationship between random variables in a completely deterministic way, allow a deeper understanding of dependence itself, and have several advantages on the approximation of solutions in a broad class of optimization problems
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Copulae: On the Crossroads of Mathematics and Economics
The central focus of the workshop was on copula theory as well as applications to multivariate stochastic modelling. The programme was intrinsically interdisciplinary and represented areas with much recent progress. The workshop included talks and dynamic discussions on construction, estimation and various applications of copulas to finance, insurance, hydrology, medicine, risk management and related fields
Current Open Questions in Complete Mixability
Complete and joint mixability has raised considerable interest in recent few
years, in both the theory of distributions with given margins, and applications
in discrete optimization and quantitative risk management. We list various open
questions in the theory of complete and joint mixability, which are
mathematically concrete, and yet accessible to a broad range of researchers
without specific background knowledge. In addition to the discussions on open
questions, some results contained in this paper are new