Capital rationing problems under uncertainty and risk

Authors:
Patrizia Beraldi;Maria Elena Bruni;Antonio Violi
Affiliations:
Dipartimento di Elettronica, Informatica, Sistemistica, Università della Calabria, Rende (Cosenza), Italy 87030;Dipartimento di Elettronica, Informatica, Sistemistica, Università della Calabria, Rende (Cosenza), Italy 87030;Dipartimento di Elettronica, Informatica, Sistemistica, Università della Calabria, Rende (Cosenza), Italy 87030
Venue:
Computational Optimization and Applications
Year:
2012

Citing 8
Cited 1

Stochastic dominance and expected utility: survey and analysis

Management Science
Dual Stochastic Dominance and Related Mean-Risk Models

SIAM Journal on Optimization
Capital Budgeting Under Uncertainty--An Integrated Approach Using Contingent Claims Analysis and Integer Programming

Operations Research
The Probabilistic Set-Covering Problem

Operations Research
Convexity and decomposition of mean-risk stochastic programs

Mathematical Programming: Series A and B
An Exact Solution Approach for Portfolio Optimization Problems Under Stochastic and Integer Constraints

Operations Research
An integer programming approach for linear programs with probabilistic constraints

Mathematical Programming: Series A and B
Optimal project selection when borrowing and lending rates differ

Mathematical and Computer Modelling: An International Journal

The Express heuristic for probabilistically constrained integer problems

Journal of Heuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Capital rationing is a major problem in managerial decision making. The classical mathematical formulation of the problem relies on a multi-dimensional knapsack model with known input parameters. Since capital rationing is carried out in conditions where uncertainty is the rule rather than the exception, the hypothesis of deterministic data limits the applicability of deterministic formulations in real settings. This paper proposes a stochastic version of the capital rationing problem which explicitly accounts for uncertainty. In particular, a mathematical formulation is provided in the framework of stochastic programming with joint probabilistic constraints and a novel solution approach is proposed. The basic model is also extended to include specific risk measures. Preliminary computational results are presented and discussed.