InterCon Travel Health: Case B

InterCon provides services to health insurers of foreign tourists who travel to the United States and Canada. Management wants to implement a new information system that will deal with several operational problems, but it is having difficulty securing the capital resources to fund the system’s development. After an initial failure, the chief information officer tries a second time with a modified approach referred to as real options valuation. Real options valuation methods are well suited when valuing assets that present discretion or flexibility in how asset implementation is structured in terms of amount or timing. The efficacy of real options valuation to information systems development projects is explored as the company’s management applies the valuation method to the proposed information system

A robust asset–liability management framework for investment products with guarantees

This paper suggests a robust asset–liability management framework for investment products with guarantees, such as guaranteed investment contracts and equity-linked notes. Stochastic programming and robust optimization approaches are introduced to deal with data uncertainty in asset returns and interest rates. The statistical properties of the probability distributions of uncertain parameters are incorporated in the model through appropriately selected symmetric and asymmetric uncertainty sets. Practical data-driven approaches for implementation of the robust models are also discussed. Numerical results using generated and real market data are presented to illustrate the performance of the robust asset–liability management strategies. The robust investment strategies show better performance in unfavorable market regimes than traditional stochastic programming approaches. The effectiveness of robust investment strategies can be improved by calibrating carefully the shape and the size of the uncertainty sets for asset returns

A robust optimization approach to finance

Thesis (Ph.D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2002.Includes bibliographical references (p. 137-141).An important issue in real-world optimization problems is how to treat uncertain coefficients. Robust optimization is a modeling methodology that takes a deterministic view: the optimal solution is required to remain feasible for any realization of the uncertain coefficients within prescribed uncertainty sets. The focus of this thesis is on robust linear programming problems in which the uncertainty sets are polytopes. The assumption of polyhedral uncertainty leads to compact, efficiently solvable linear formulations. In the first part of the thesis, we study special types of polyhedral uncertainty sets that allow for incorporating moment information about the distribution of the uncertain coefficients, and for controlling the tradeoff between robustness and optimality. We provide probabilistic guarantees on the feasibility of optimal solutions obtained with such uncertainty sets for any realization of the uncertain coefficients. We then illustrate the versatility of robust polyhedral formulations by studying three financial applications: single period portfolio optimization, multiperiod portfolio management, and credit risk estimation. In the area of single period portfolio optimization, we propose ways of modeling inaccuracy in parameter estimates, and explore the benefits of robust optimal strategies through computational experiments with the statistical estimation of a particular measure of portfolio risk - sample shortfall. We emphasize the advantages of linear, as opposed to nonlinear, robust formulations in large portfolio problems with integrality constraints.(cont.) In the area of multiperiod portfolio management, we propose robust polyhedral formulations that use some minimal information about long-term direction of movement of asset returns to make informed decisions about portfolio rebalancing over the short term. The suggested formulations allow for including considerations of transaction costs and taxes while keeping the dimension of the problem low. In the area of credit risk estimation, we propose a model for estimating the survival probability distribution and the fair prices of credit risky bonds from market prices of similar credit risky securities. We address the issue of uncertainty in key parameters of the model, such as discount factors, by using robust optimization modeling. We also suggest a method for classification of credit risky bonds based on integer programming techniques.by Dessislava A. Pachamanova.Ph.D

A robust optimization approach to asset-liability management under time-varying investment opportunities

This paper presents an asset liability management model based on robust optimization techniques. The model explicitly takes into consideration the time-varying aspect of investment opportunities. The emphasis of the proposed approach is on computational tractability and practical appeal. Computational studies with real market data study the performance of robust-optimization-based strategies, and compare it to the performance of the classical stochastic programming approach

Portfolio construction and analytics

xxviii, 595 pages : illustrations ; 23 c

Robust portfolio allocation under discrete asset choice constraints

The mean-variance portfolio allocation model is very sensitive to estimation errors in the model parameters. Robust optimization is a technique used to incorporate the uncertainty introduced by estimation errors directly into portfolio allocation. Practitioners are often faced with complex constraints on the portfolio structure such as limits on the number of securities in the portfolio, which are modelled with discrete variables, and introduce discontinuities in the efficient frontier. This article investigates the size of discontinuities in the efficient frontiers obtained by the classical and robust mean-variance models under such discrete asset choice constraints, as well as the impact of portfolio size on the discontinuity being considered. In addition, we analyse the effects of applying discrete asset choice restrictions to the portfolio selection problem, as well as using estimated and true parameters in the computation of the classical and robust mean-variance investment strategies under discrete asset choice constraints. Computational experiments reveal reduction of the size of discontinuity when using robust optimization mean-variance models

Robust strategies for facility location under uncertainty

This paper considers a stochastic facility location problem in which multiple capacitated facilities serve customers with a single product, and a stockout probabilistic requirement is stated as a chance constraint. Customer demand is assumed to be uncertain and to follow either a normal or an ambiguous distribution. We study robust approximations to the problem in order to incorporate information about the random demand distribution in the best possible, computationally tractable way. We also discuss how a decision maker's risk preferences can be incorporated in the problem through robust optimization. Finally, we present numerical experiments that illustrate the performance of the different robust formulations. Robust optimization strategies for facility location appear to have better worst-case performance than nonrobust strategies. They also outperform nonrobust strategies in terms of realized average total cost when the actual demand distributions have higher expected values than the expected values used as input to the optimization models. © 2012 Elsevier B.V. All rights reserved