Now showing items 1-8 of 8
The Sample Average Approximation Method for Stochastic Discrete Optimization
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and consequently the expected value function ...
Concavity and Efficient Points of Discrete Distributions in Probabilistic Programming
We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent ...
On Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs
In this paper we discuss Monte Carlo simulation based approximations of a stochastic programming problem. We show that if the corresponding random functions are convex piecewise smooth and the distribution is discrete, ...
Creating Synthetic Option Strategies for Asset Allocation with Transaction Costs Using Multi-Period Stochastic Programming
We discuss a new approach to asset allocation with transaction costs. A multi-period stochastic linear programming model is developed where the risk is based on the worst case payoff which is endogenously determined by the ...
The Application of Operations Research Techniques to Financial Markets
This paper reviews the application of OR to financial markets. After considering reasons for the attractiveness of general finance problems to OR researchers, the main types of financial market problem amendable to OR are ...
A Dynamic Asset Allocation Model with Downside Risk Control
This paper presents a new stochastic model for investment. The investor's objective is to maximize the expected growth rate while controlling for downside risk. Assuming lognormally distributed prices, the strategy that ...