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Journal ArticleDOI

Dynamic stochastic programmingfor asset-liability management

Giorgio Consigli, +1 more
- 01 Jun 1998 - 
- Vol. 81, pp 131-162
TLDR
The CALM model, designed to deal with uncertainty affecting both assets and liabilities (in the form of scenario dependent payments or borrowing costs) is presented, which is based on the current version of MSLiP.
Abstract
Multistage stochastic programming - in contrast to stochastic control - has found wideapplication in the formulation and solution of financial problems characterized by a largenumber of state variables and a generally low number of possible decision stages. Theliterature on the use of multistage recourse modelling to formalize complex portfolio optimizationproblems dates back to the early seventies, when the technique was first adopted tosolve a fixed income security portfolio problem. We present here the CALM model, whichhas been designed to deal with uncertainty affecting both assets (in either the portfolio orthe market) and liabilities (in the form of scenario dependent payments or borrowing costs).We consider as an instance a pension fund problem in which portfolio rebalancing is allowedover a long-term horizon at discrete time points and where liabilities refer to five differentclasses of pension contracts. The portfolio manager, given an initial wealth, seeks the maximizationof terminal wealth at the horizon, with investment returns modelled as discretestate random vectors. Decision vectors represent possible investments in the market andholding or selling assets in the portfolio, as well as borrowing decisions from a credit lineor deposits with a bank. Computational results are presented for a set of 10-stage portfolioproblems using different solution methods and libraries (OSL, CPLEX, OB1). The portfolioproblem, with an underlying vector data process which allows up to 2688 realizations at the10-year horizon, is solved on an IBM RS6000y590 for a set of twenty-four large-scale testproblems using the simplex and barrier methods provided by CPLEX (the latter for eitherlinear or quadratic objective), the predictorycorrector interior point method provided in OB1,the simplex method of OSL, the MSLiP-OSL code instantiating nested Benders decompositionwith subproblem solution using OSL simplex, and the current version of MSLiP.

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Citations
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Journal ArticleDOI

Scenarios for Multistage Stochastic Programs

TL;DR: The case when enough data paths can be generated according to an accepted parametric or nonparametric stochastic model when no assumptions on convexity with respect to the random parameters are required is discussed.
Journal ArticleDOI

A Heuristic for Moment-Matching Scenario Generation

TL;DR: This work presents an algorithm that produces a discrete joint distribution consistent with specified values of the first four marginal moments and correlations, constructed by decomposing the multivariate problem into univariate ones, and using an iterative procedure that combines simulation, Cholesky decomposition and various transformations to achieve the correct correlations.
Journal ArticleDOI

Scenario generation and stochastic programming models for asset liability management

TL;DR: The results show that the performance of the multi-stage stochastic program could be improved drastically by choosing an appropriate scenario generation method.
Journal ArticleDOI

An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty

TL;DR: In this article, an interval-parameter multi-stage stochastic linear programming (IMSLP) method has been developed for water resources decision making under uncertainty, where penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised water allocation targets are violated.
Journal ArticleDOI

Value-at-Risk in Portfolio Optimization: Properties and Computational Approach ⁄

TL;DR: In this article, the authors present a method of calculating the portfolio which gives the smallest value-at-risk among those, which yield at least some specifled expected return, using this approach, the complete mean-V@R ecient frontier may be calculated.
References
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Peter Kall
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