NDLI: Asymptotic Multivariate Dominance: A Financial Application

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Asymptotic Multivariate Dominance: A Financial Application

Content Provider	Springer Nature Link
Author	Petronio, Filomena Lando, Tommaso Lozza, Sergio Ortobelli Tichý, Tomáš
Copyright Year	2016
Abstract	We propose a multivariate stochastic dominance relation aimed at ranking different financial markets/sectors from the point of view of a non-satiable risk averse investor. In particular, we assume that the vector of returns of a given market is in the domain of attraction of a symmetric stable Paretian law in order to take into account the asymptotic behaviour of the financial returns. We determine the stochastic dominance rule for stable symmetric distributions, where the stability parameter plays a crucial role. Consequently, the multivariate rule for ordering markets is based on a comparison between i) location parameters, ii) dispersion parameters, and iii) stability indices. Finally, we apply the method to the equity markets of the four countries with the highest gross domestic product in 2013, namely, the US, China, Japan and Germany. In this empirical comparison we examine the ex ante and ex post dominance between stock markets, either assuming that the returns are jointly (or conditionally, for a robust approach) Gaussian distributed, or in the domain of attraction of a stable sub-Gaussian law.
Ending Page	1115
Page Count	19
Starting Page	1097
File Format	PDF
ISSN	13875841
e-ISSN	15737713
Journal	Methodology and Computing in Applied Probability
Issue Number	4
Volume Number	18
Language	English
Publisher	Springer US
Publisher Date	2016-06-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Life Sciences Economics Stochastic dominance Business and Management Multivariate preferences Management decision making, including multiple objectives Infinitely divisible distributions; stable distributions Financial markets comparison Inequalities; stochastic orderings Statistics Stable distribution Electrical Engineering
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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