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Generalisation of Hajek’s Stochastic Comparison Results to Stochastic Sums
Author(s) -
Jörg Kampen
Publication year - 2016
Publication title -
international journal of stochastic analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.19
H-Index - 28
eISSN - 2090-3340
pISSN - 2090-3332
DOI - 10.1155/2016/1018509
Subject(s) - univariate , mathematics , multivariate statistics , monotonic function , variance (accounting) , regular polygon , class (philosophy) , convex function , econometrics , statistics , computer science , mathematical analysis , artificial intelligence , geometry , accounting , business
Hajek’s univariate stochastic comparison result is generalised to multivariate stochastic sum processes with univariate convex data functions and for univariate monotonic nondecreasing convex data functions for processes with and without drift, respectively. As a consequence strategies for a class of multivariate optimal control problems can be determined by maximizing variance. An example is passport options written on multivariate traded accounts. The argument describes a narrow path between impossibilities of generalisations to jump processes or impossibilities of more general data functions

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