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	Stable distributions and their applications Volchenkova, Irina ; Klebanov, Lev (advisor) ; Beneš, Viktor (referee) The aim of this thesis is to show that the use of heavy-tailed distributions in finance is theoretically unfounded and may cause significant misunderstandings and fallacies in model interpretation. The main reason seems to be a wrong understanding of the concept of the distributional tail. Also in models based on real data it seems more reasonable to concentrate on the central part of the distribution not tails. Powered by TCPDF (www.tcpdf.org) Detailed record
	Empirical Estimates in Stochastic Optimization: Special cases Kaňková, Vlasta Classical optimization problems depending on a probability measure belong mostly to nonlinear deterministic optimization problems that are relatively complicated. On the other hand, these problems fulfil very often "suitable" mathematical properties guaranteing the stability (w.r.t. probability measure) and, moreover, giving a possibility to replace the "underlying" probability measure by an empirical one to obtain "good" stochastic estimates of the optimal value and the optimal solution. Properties of thess estimates have been investigated mostly for standard types of probability measures with suitable (thin) tails and independent random samples. However distributions with heavy tails correspond to many economic problems and, moreover, many applications do not correspond to the "classical" problems. The aim of the paper is, first, to try to recall stability results including also heavy tails and more general problems. Detailed record
	Poznámka k empirickým odhadům v ekonomických úlohách Kaňková, Vlasta Optimization problems depending on a probability measure correspond to many economic applications. Since the ``underlying" measure is usually unknown the decision is mostly determined on the data basis, it means on statistical (mostly empirical) estimates of the probability measure. Properties of the optimal value (and solution) estimates have been investigated many times. There were introduced assumptions under which the asymptotic distribution is normal and the convergence rate is at least exponential. We generalize the assertions concerning rate convergence. Especially we shall consider distribiotions with the Pareto tails. The introduced assertions are focus on optimal value estimates. Detailed record

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