Displaying similar documents to “Optimal mean-variance robust hedging under asset price model misspecification.”

Dynamic portfolio optimization with risk management and strategy constraints

Csilla Krommerová, Igor Melicherčík (2014)



We investigate the problem of power utility maximization considering risk management and strategy constraints. The aim of this paper is to obtain admissible dynamic portfolio strategies. In case the floor is guaranteed with probability one, we provide two admissible solutions, the option based portfolio insurance in the constrained model, and the alternative method and show that none of the solutions dominate the other. In case the floor is guaranteed partially, we provide one admissible...

Algorithm for turnpike policies in the dynamic lot size model

Stanisław Bylka (1996)

Applicationes Mathematicae


This article considers optimization problems in a capacitated lot sizing model with limited backlogging. Nothing is assumed about the cost function in the case of finite restrictions of the size on the stock and backlogs. The holding and backlogging costs are functions assumed to be stationary or nearly stationary in time. In both cases, it is shown that there exists an optimal infinite inverse policy and a periodical turnpike policy. Some forward and backward procedures are adopted...

Strong and weak solutions to stochastic inclusions

Michał Kisielewicz (1995)

Banach Center Publications


Existence of strong and weak solutions to stochastic inclusions x t - x s s t F τ ( x τ ) d τ + s t G τ ( x τ ) d w τ + s t n H τ , z ( x τ ) q ( d τ , d z ) and x t - x s s t F τ ( x τ ) d τ + s t G τ ( x τ ) d w τ + s t | z | 1 H τ , z ( x τ ) q ( d τ , d z ) + s t | z | > 1 H τ , z ( x τ ) p ( d τ , d z ) , where p and q are certain random measures, is considered.

Bayes optimal stopping of a homogeneous poisson process under linex loss function and variation in the prior

Marek Męczarski, Ryszard Zieliński (1997)

Applicationes Mathematicae


A homogeneous Poisson process (N(t),t ≥ 0) with the intensity function m(t)=θ is observed on the interval [0,T]. The problem consists in estimating θ with balancing the LINEX loss due to an error of estimation and the cost of sampling which depends linearly on T. The optimal T is given when the prior distribution of θ is not uniquely specified.