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pro vyhledávání: '"Embrechts, Paul"'
We study the optimal decisions of agents who aim to minimize their risks by allocating their positions over extremely heavy-tailed (i.e., infinite-mean) and possibly dependent losses. The loss distributions of our focus are super-Pareto distributions
Externí odkaz:
http://arxiv.org/abs/2403.20171
We find the perhaps surprising inequality that the weighted average of independent and identically distributed Pareto random variables with infinite mean is larger than one such random variable in the sense of first-order stochastic dominance. This r
Externí odkaz:
http://arxiv.org/abs/2208.08471
We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk measurement related optimization problem is robust, which we call "robustness against
Externí odkaz:
http://arxiv.org/abs/1809.09268
Publikováno v:
Annals of Applied Probability 2016, Vol. 26, No. 3, 1636-1658
The tail-dependence compatibility problem is introduced. It raises the question whether a given $d\times d$-matrix of entries in the unit interval is the matrix of pairwise tail-dependence coefficients of a $d$-dimensional random vector. The problem
Externí odkaz:
http://arxiv.org/abs/1606.08212
Autor:
Embrechts, Paul, Kirchner, Matthias
Publikováno v:
Theory of Probability and Its Applications, 62(1):163-193 (2017)
This paper introduces the Hawkes skeleton and the Hawkes graph. These objects summarize the branching structure of a multivariate Hawkes point process in a compact, yet meaningful way. We demonstrate how graph-theoretic vocabulary (`ancestor sets', `
Externí odkaz:
http://arxiv.org/abs/1601.01879
Natural disasters may have considerable impact on society as well as on (re)insurance industry. Max-stable processes are ideally suited for the modeling of the spatial extent of such extreme events, but it is often assumed that there is no temporal d
Externí odkaz:
http://arxiv.org/abs/1507.07750
Akademický článek
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A fundamental problem in risk management is the robust aggregation of different sources of risk in a situation where little or no data are available to infer information about their dependencies. A popular approach to solving this problem is to formu
Externí odkaz:
http://arxiv.org/abs/1410.0852
Publikováno v:
In Journal of Computational Physics 1 July 2019 388:601-623
