We address the problem of portfolio management in the international bond markets. Interest rate risk in the local market, exchange rate volatility across markets, and decisions for hedging currency risk are integral p...
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We address the problem of portfolio management in the international bond markets. Interest rate risk in the local market, exchange rate volatility across markets, and decisions for hedging currency risk are integral parts of this problem. The paper develops a stochastic programming optimization model for integrating these decisions in a common framework. Monte Carlo simulation procedures, calibrated using historical observations of volatility and correlation data, generate jointly scenarios of interest and exchange rates. The decision maker's risk tolerance is incorporated through a utility function, and additional views on market outlook can also be incorporated in the form of user specified scenarios. The model prescribes optimal asset allocation among the different markets and determines bond-picking decisions and appropriate hedging ratios. Therefore, several interrelated decisions are cast in a common framework, while in the past these issues were addressed separately. Empirical results illustrate the efficacy of the simulation models in capturing the uncertainties of the Salomon Brothers international bond market index.
A new approximate proximal point method for minimizing the sum of two convex functions is introduced. It replaces the original problem by a sequence of regularized subproblems in which the functions are alternately re...
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A new approximate proximal point method for minimizing the sum of two convex functions is introduced. It replaces the original problem by a sequence of regularized subproblems in which the functions are alternately represented by linear models. The method updates the linear models and the prox center, as well as the prox coefficient. It is monotone in terms of the objective values and converges to a solution of the problem, if any. A dual version of the method is derived and analyzed. Applications of the methods to multistage stochastic programming problems are discussed and preliminary numerical experience is presented.
The main objects below are transferable-utility games in which each agent faces an optimization problem, briefly called production planning, constrained by his resource endowment. Coalitions can pool members' reso...
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The main objects below are transferable-utility games in which each agent faces an optimization problem, briefly called production planning, constrained by his resource endowment. Coalitions can pool members' resources. Such production games are here extended to accommodate uncertainty about events not known ex ante. Planning then takes the form of two-stage stochastic programming. Core solutions are sought, described, and computed via aggregate dual programs. The analysis is motivated by practical applications. Examples include stochastic production and regional distribution with random demand and supply, illustrated by a numerical example.
Executive *** investigate an optimal control approach to market timing strategy to assist property investors in deciding the allocation of investment funds between the risk-free savings deposit and the comparatively r...
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Executive *** investigate an optimal control approach to market timing strategy to assist property investors in deciding the allocation of investment funds between the risk-free savings deposit and the comparatively risky property investment. This strategy generates recommended investment actions, namely, buy, hold, sell and wait, with the objective to attain superior investment returns. The investment performance of this market timing strategy is compared to that of the buy-and-hold strategy. Results from the simulation study for the twenty-year period from 1977:1 to 1996:4 indicates that the proposed market timing strategy is capable of achieving superior investment returns in the Singapore property market.
MARKAL-Geneva is a system analysis model of energy and environment technology assessment for the Canton de Geneve in Switzerland. This model innovates by taking into account the uncertainties characterizing the scenar...
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MARKAL-Geneva is a system analysis model of energy and environment technology assessment for the Canton de Geneve in Switzerland. This model innovates by taking into account the uncertainties characterizing the scenarios through stochastic programming techniques. In stochastic programming, the whole set of scenarios is combined into an event tree, which describes the unfolding of uncertainties over the period of energy planning. The scenarios presented in this paper focus on the evaluation of efficient CO2 abatement policies and of the potential for demand-side management.
Practical scheduling problems typically require decisions without full information about the outcomes of those decisions. Yields, resource availability performance, demand, costs, and revenues may all vary. Incorporat...
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Practical scheduling problems typically require decisions without full information about the outcomes of those decisions. Yields, resource availability performance, demand, costs, and revenues may all vary. Incorporating these quantities into stochastic scheduling models often produces difficulties in analysis that may be addressed in a variety of ways. In this paper, we present results based on stochastic programming approaches to the hierarchy of decisions in typical stochastic scheduling situations. Our unifying framework allows us to treat all aspects of a decision in a similar framework. We show how views from different levels enable approximations that can overcome nonconvexities and duality gaps that appear in deterministic formulations. In particular, eve show that the stochastic program structure leads to a vanishing Lagrangian duality gap in stochastic integer programs as the number of scenarios increases.
In this paper, an interior-point based global filtering algorithm is proposed to solve linear programming problems with the right-hand-side and cost vectors being stochastic. Previous results on the limiting propertie...
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In this paper, an interior-point based global filtering algorithm is proposed to solve linear programming problems with the right-hand-side and cost vectors being stochastic. Previous results on the limiting properties of the Kalman filtering process have been extended to handle some non-stationary situations. A global Kalman filter, across all iterations of the interior-point method, is designed to filter out noises while improving the objective value and reducing the primal and dual infeasibilities. Under appropriate assumptions, the proposed algorithm is shown to be globally convergent to an optimal solution of the underlying "true value" system.
This paper presents a stochastic simulation based genetic algorithm for solving chance constrained integer programming and chance constrained integer goal programming as well as chance constrained integer multiobjecti...
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This paper presents a stochastic simulation based genetic algorithm for solving chance constrained integer programming and chance constrained integer goal programming as well as chance constrained integer multiobjective programming *** also provide some numerical examples to illustrate the effectiveness of the proposed genetic algorithm.
Remarkable progress has been made in the development of algorithmic procedures and the availability of software for stochastic programming problems. However, some fundamental questions have remained unexplored. This p...
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Remarkable progress has been made in the development of algorithmic procedures and the availability of software for stochastic programming problems. However, some fundamental questions have remained unexplored. This paper identifies the more challenging open questions in the field of stochastic programming. Some are purely technical in nature, but many also go to the foundations of designing models for decision making under uncertainty.
Single Premium Deferred Annuities (SPDAs) are investment vehicles, offered to investors by insurance companies as a means of providing income past their retirement age. They are mirror images of insurance policies. Ho...
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Single Premium Deferred Annuities (SPDAs) are investment vehicles, offered to investors by insurance companies as a means of providing income past their retirement age. They are mirror images of insurance policies. However, the propensity of individuals to shift part, or all, of their investment into different annuities creates substantial uncertainties for the insurance company. In this paper we develop a multiperiod, dynamic stochastic program that deals with the problem of funding SPDA liabilities. The model recognizes explicitly the uncertainties inherent in this problem due to both interest rate volatility and the behavior of individual investors. Empirical results are presented with the use of the model for the funding of an SPDA liability stream using government bonds, mortgage-backed securities and derivative products.
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