Results 261 to 270 of about 897,818 (297)

Multivariate Stochastic Dominance and Moments

Mathematics of Operations Research, 1991
The sequence ≥nd of nth degree stochastic dominances for d-dimensional distribution functions is defined. It is shown that, under some regularity conditions, ≥nd implies ≥nd−1 for the (d − 1)-dimensional marginals. Also some necessary conditions for ≥nd are established.
G. L. O'Brien, SCARSINI, MARCO
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Operational asymptotic stochastic dominance

European Journal of Operational Research, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rachel J. Huang, Larry Y. Tzeng, Jr-Yan Wang, Lin Zhao 0014   +3 more
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METRIZATION OF STOCHASTIC DOMINANCE RULES [PDF]

International Journal of Theoretical and Applied Finance, 2012
We consider a new approach towards stochastic dominance rules which allows measuring the degree of domination or violation of a given stochastic order and represents a way of describing stochastic orders in general. Examples are provided for the n-th order stochastic dominance and stochastic orders based on a popular risk measure.
Stoyanov, S. V., Rachev, S. T., Fabozzi, F. J.   +2 more
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Standard Stochastic Dominance

European Journal of Operational Research, 2014
We propose a new Stochastic Dominance (SD) criterion based on standard risk aversion, which assumes decreasing absolute risk aversion and decreasing absolute prudence. To implement the proposed criterion, we develop linear systems of optimality conditions for a given prospect relative to a discrete or polyhedral choice opportunity set in a general ...
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Stochastic Dominance and Moment Inequalities

Mathematics of Operations Research, 1984
For any distribution function (df) F, define F1 = F and Fn+1 (x) = ∫−∞x Fn(y) dy. For two df's F and G, we obtain a relationship between the behaviour of Gn(x) − Fn(x) for large x and certain inequalities involving the moments of F and G. In particular, we generalize Fishburn's theorem, which deduces such inequalities from the condition that Gn(x ...
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ON STOCHASTIC DOMINANCE OF NILPOTENT OPERATORS

Infinite Dimensional Analysis, Quantum Probability and Related Topics, 2013
In this paper, motivated by Nica and Speicher [Lectures on the Combinatorics of Free Probability, London Mathematical Society Lecture Note Series, Vol. 335 (Cambridge University Press, 2006)] and Kubo and Kuo [MRM-factors for the probability measures in the Meixner class, Infin. Dimens. Anal. Quantum Probab. Relat.
Mudakkar, Syeda Rabab, Utev, Sergey
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Marginal Conditional Stochastic Dominance

Management Science, 1994
This paper introduces the concept of Marginal Conditional Stochastic Dominance (MCSD), which states the conditions under which all risk-averse individuals, when presented with a given portfolio, prefer to increase the share of one risky asset over that of another. MCSD rules also answer the question of whether all risk-averse individuals include a new
Haim Shalit, Shlomo Yitzhaki
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Stochastic dominance with nonadditive probabilities

ZOR Zeitschrift f�r Operations Research Methods and Models of Operations Research, 1993
Summary: Choquet expected utility which uses capacities (i.e. nonadditive probability measures) in place of \(\sigma\)-additive probability measures has been introduced to decision making under uncertainty to cope with observed effects of ambiguity aversion like the Ellsberg paradox.
Rainer Dyckerhoff, Karl Mosler
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Stochastic Dominance of Pension Plans

Metroeconomica, 2003
We compare different possibilities to reform a funded pension plan, whose balance is threatened by a decrease in mortality rates. Since the plan is mandatory, the welfare of employees might be reduced if contributions increase or if the retirement age is raised. An empirical study of Israeli data shows that a reform which decreases the pension benefits
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Multi-Period Stochastic Dominance

Management Science, 1974
First degree stochastic dominance rules for uncertain options (distributions of returns) have been developed for the following two cases: (a) multi-period additive utility functions, (b) univariate utility functions and compound distributions of returns.
Haim Levy, Jacob Paroush
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