Minimizing a stochastic convex function subject to stochastic constraints and some applications

STOCHASTIC PROCESSES AND THEIR APPLICATIONS

卷:130

Article

Jacobovic, Royi¹  Kella, Offer¹ 

[1] Hebrew Univ Jerusalem, Dept Stat & Data Sci, IL-9190501 Jerusalem, Israel

关键词: Stochastic constrained minimization;    Minimizing a stochastic convex function;    Quadratic function with random coefficients;    Clearing process;    Constrained portfolio optimization;    Neyman-Pearson lemma;   

DOI  :  10.1016/j.spa.2020.07.006

来源: Elsevier

【 摘 要 】

In the simplest case, we obtain a general solution to a problem of minimizing an integral of a nondecreasing right continuous stochastic process from zero to some nonnegative random variable tau, under the constraints that for some nonnegative random variable T, tau is an element of [0, T] almost surely and E tau = alpha (or E tau <= alpha) for some alpha. The nondecreasing process and T are allowed to be dependent. In fact a more general setup involving sigma finite measures, rather than just probability measures is considered and some consequences for families of stochastic processes are given as special cases. Various applications are provided. (C) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

Free