A Bank Asset and Liability Management Model

Kusý, Martin; Ziemba, William T.

doi:10.1287/opre.34.3.356

Cited by 258 publications

(126 citation statements)

References 34 publications

(3 reference statements)

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“…Indeed, analogous stochastic programming models have appeared in investment planning as well as asset and liability management (e.g., Bradley and Crane 1972, Kusy and Ziemba 1986, Mulvey and Vladimirou 1989, Zenios 1993, Birge and Louveaux 1997, pp. 20-28, Mulvey et al 2000.…”

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confidence: 99%

Contingent Portfolio Programming for the Management of Risky Projects

Gustafsson

Salo

2005

Operations Research

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Methods for selecting a research and development (R&D) project portfolio have attracted considerable interest among practitioners and academics. This notwithstanding, the industrial uptake of these methods has remained limited, partly due to the difficulties of capturing all the relevant concerns in R&D portfolio management. Motivated by these difficulties, we develop Contingent Portfolio Programming (CPP) which extends earlier approaches in that it (i) uses states of nature to capture exogenous uncertainties, (ii) models resources through dynamic state variables, and (iii) provides guidance for the selection of an optimal project portfolio that is compatible with the decision maker's risk attitude. Although CPP is presented here in the context of R&D project portfolios, it is applicable to a variety of investment problems where the dynamics and interactions of investment opportunities must be accounted for. The selection of research and development (R&D) projects has attracted considerable interest in the literatures on technology management and operations research (OR). These projects involve many characteristics -such as uncertainties and interdependent resource constraints -that are potentially amenable to analysis by OR techniques. Indeed, there exists a variety of related methods, ranging from scoring methods such as value trees (e.g., Keeney and Raiffa, 1976) and the Analytic Hierarchy Process (AHP; Saaty 1980) to optimization models (see, e.g., Luenberger 1998, p. 106, Gear et al. 1971, Baker 1974, Baker and Freeland 1975, Jackson 1983, Fox et al. 1984, Schmidt and Freeland 1992, Ghasemzadeh et al. 1999 and dynamic programming methods such as decision trees and real options (Hespos and Strassmann 1965, Dixit and Pindyck 1994, Trigeorgis 1996. Yet, despite this plethora of methodological approaches, the industrial uptake of these methods has remained limited (see, e.g., Liberatore and Titus 1983), possibly due to the difficulties of capturing the full range of phenomena that are relevant to the problem of selecting and managing R&D projects.Building on the literatures on decision analysis, technology management, and portfolio optimization, we develop Contingent Portfolio Programming (CPP) as a modeling framework which accommodates most of the characteristics that are relevant to R&D project selection. In CPP, R&D projects are regarded as risky investment opportunities that consume and produce several resources over multiple time periods. The staged nature of R&D projects is captured through project-specific decision trees (cf. Cooper 1993;Gear and Lockett 1973) which support managerial flexibility by allowing the decision maker (DM) to take stepwise decisions on each project in view of most recent information (Trigeorgis 1996). Uncertainties, on the other hand, are modeled through a state tree in the spirit of stochastic programming (see, e.g., Birge and Louveaux 1997).The DM's risk attitude is captured through an objective function that is a combination of a mean-risk model (Markowitz 1959(Ma...

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confidence: 99%

Contingent Portfolio Programming for the Management of Risky Projects

Gustafsson

Salo

2005

Operations Research

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“…He reports that the solution times for such an algorithm are only "marginally greater" than the times required to solve the same LPs with each summation replaced by a single variable. For example, for an application in ÿnance, Kusy and Ziemba (1986) found the simple-recourse model's solution times to be ": : : 1.5-2 times that of a related deterministic model : : : . "…”

Section: The Primal Boundmentioning

confidence: 99%

Restricted-Recourse Bounds for Stochastic Linear Programming

Morton

Wood

1999

Operations Research

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We consider the problem of bounding the expected value of a linear program (LP) containing random coe cients, with applications to solving two-stage stochastic programs. An upper bound for minimizations is derived from a restriction of an equivalent, penalty-based formulation of the primal stochastic LP, and a lower bound is obtained from a restriction of a reformulation of the dual. Our "restrictedrecourse bounds" are more general and more easily computed than most other bounds because random coe cients may appear anywhere in the LP, neither independence nor boundedness of the coe cients is needed, and the bound is computed by solving a single LP or nonlinear program. Analytical examples demonstrate that the new bounds can be stronger than complementary Jensen bounds. (An upper bound is "complementary" to a lower bound, and vice versa). In computational work, we apply the bounds to a two-stage stochastic program for semiconductor manufacturing with uncertain demand and production rates.T his paper develops new techniques for bounding the expected value of a stochastic linear program, which is a linear program (LP), some or all of whose coe cients are random. The random coe cients may be discretely or continuously distributed, may be independent or contain dependencies, and may occur anywhere in the objective function, right-hand side, or constraint matrix. Calculating (or estimating) the expected value of a stochastic LP is key to solving two-stage and multi-stage stochastic programs with recourse (Dantzig 1955).It is usually impossible to compute exactly the expected value of a stochastic LP unless the random coe cients are discretely distributed and the total number of realizations of the coe cients is small, or the problem has a very special structure. As a result, solution techniques for stochastic programming with recourse usually involve approximations that are based on Monte Carlo sampling (e.g., Ermoliev 1983, Dantzig and Glynn 1990, Higle and Sen 1991 or on deterministically valid bounds. This paper involves approximations of the latter type.When initial lower and upper bounds are insu ciently tight, they can often be improved within a sequentialapproximation algorithm that iteratively partitions the support of the random variables (e.g., Kall et al. 1988). We do not discuss the details of these algorithms here, but note that our restricted-recourse bounds can be incorporated within such algorithms just as well as other bounds can, and possibly better because of improved computational e ciency and fewer technical requirements.The problem of computing the expected value of a stochastic LP also arises as a stand-alone problem. For instance, the stochastic maximum-ow problem (e.g., Evans 1976) calculates the expected value of the maximum ow through a network whose arc capacities are nonnegative random variables. Another example is the stochastic PERT problem (Fulkerson 1962), which evaluates the expected length of a longest path in a directed acyclic network with stochastic arc lengths. Both of these prob...

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“…Although closed-form solutions to those problems can be derived under strong assumptions, such an approach cannot be easily generalized in the presence of market frictions, e.g., transaction costs. Moreover, it requires to overcome a heavy computational burden for actual numerical implementation (Brennan et al, 1997;Brandt, 1999), and therefore, alternative various stochastic programming models have been proposed for multi-period portfolio optimization (e.g., Kusy and Ziemba, 1986;Mulvey and Vladimirou, 1989;Dantzig and Infanger, 1993;Cariño et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Constant Rebalanced Portfolio Optimization Under Nonlinear Transaction Costs

Takano

Gotoh

2010

Asia-Pac Financ Markets

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In this paper, we study a multi-period portfolio optimization where conditional value-atrisk (CVaR) is controlled as well as expected return, and the so-called constant rebalancing strategy is employed under nonlinear transaction costs. In general, the optimization of this strategy itself is, however, difficult to attain a globally optimal solution because of the nonconvexity. In addition, nonlinearity of the transaction cost and CVaR functions makes things worse, and even a locally optimal solution may not be reached via a state-of-the-art nonlinear programming solver when the size of the problem is large. In order to provide a practical solution to the highly complex problem, we propose a local search algorithm where linear approximation problems and nonlinear equations are iteratively solved. Computational results are presented, showing that the proposed algorithm attains a good solution in practical time even when a revised version of an existing global optimization approach cannot return any feasible solutions.

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A Bank Asset and Liability Management Model

Cited by 258 publications

References 34 publications

Contingent Portfolio Programming for the Management of Risky Projects

Contingent Portfolio Programming for the Management of Risky Projects

Restricted-Recourse Bounds for Stochastic Linear Programming

Constant Rebalanced Portfolio Optimization Under Nonlinear Transaction Costs

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