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Asset liability management using stochastic programming
This chapter sets out to explain an important financial planning model
called asset liability management (ALM); in particular, it discusses why in
practice, optimum planning models are used. The ability to build an integrated
approach that combines liability models with that of asset allocation
decisions has proved to be desirable and more efficient in that it can lead to
better ALM decisions. The role of uncertainty and quantification of risk in
these planning models is considered
A mixed integer linear programming model for optimal sovereign debt issuance
Copyright @ 2011, Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in the European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version is available at the link below.Governments borrow funds to finance the excess of cash payments or interest payments over receipts, usually by issuing fixed income debt and index-linked debt. The goal of this work is to propose a stochastic optimization-based approach to determine the composition of the portfolio issued over a series of government auctions for the fixed income debt, to minimize the cost of servicing debt while controlling risk and maintaining market liquidity. We show that this debt issuance problem can be modeled as a mixed integer linear programming problem with a receding horizon. The stochastic model for the interest rates is calibrated using a Kalman filter and the future interest rates are represented using a recombining trinomial lattice for the purpose of scenario-based optimization. The use of a latent factor interest rate model and a recombining lattice provides us with a realistic, yet very tractable scenario generator and allows us to do a multi-stage stochastic optimization involving integer variables on an ordinary desktop in a matter of seconds. This, in turn, facilitates frequent re-calibration of the interest rate model and re-optimization of the issuance throughout the budgetary year allows us to respond to the changes in the interest rate environment. We successfully demonstrate the utility of our approach by out-of-sample back-testing on the UK debt issuance data
Theory and Applications of Robust Optimization
In this paper we survey the primary research, both theoretical and applied,
in the area of Robust Optimization (RO). Our focus is on the computational
attractiveness of RO approaches, as well as the modeling power and broad
applicability of the methodology. In addition to surveying prominent
theoretical results of RO, we also present some recent results linking RO to
adaptable models for multi-stage decision-making problems. Finally, we
highlight applications of RO across a wide spectrum of domains, including
finance, statistics, learning, and various areas of engineering.Comment: 50 page
An approximate dynamic programming approach to food security of communities following hazards
Food security can be threatened by extreme natural hazard events for
households of all social classes within a community. To address food security
issues following a natural disaster, the recovery of several elements of the
built environment within a community, including its building portfolio, must be
considered. Building portfolio restoration is one of the most challenging
elements of recovery owing to the complexity and dimensionality of the problem.
This study introduces a stochastic scheduling algorithm for the identification
of optimal building portfolio recovery strategies. The proposed approach
provides a computationally tractable formulation to manage multi-state,
large-scale infrastructure systems. A testbed community modeled after Gilroy,
California, is used to illustrate how the proposed approach can be implemented
efficiently and accurately to find the near-optimal decisions related to
building recovery following a severe earthquake.Comment: As opposed to the preemptive scheduling problem, which was addressed
in multiple works by us, we deal with a non-preemptive stochastic scheduling
problem in this work. Submitted to 13th International Conference on
Applications of Statistics and Probability in Civil Engineering, ICASP13
Seoul, South Korea, May 26-30, 201
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Software tools for stochastic programming: A Stochastic Programming Integrated Environment (SPInE)
SP models combine the paradigm of dynamic linear programming with
modelling of random parameters, providing optimal decisions which hedge
against future uncertainties. Advances in hardware as well as software
techniques and solution methods have made SP a viable optimisation tool.
We identify a growing need for modelling systems which support the creation
and investigation of SP problems. Our SPInE system integrates a number of
components which include a flexible modelling tool (based on stochastic
extensions of the algebraic modelling languages AMPL and MPL), stochastic
solvers, as well as special purpose scenario generators and database tools.
We introduce an asset/liability management model and illustrate how SPInE
can be used to create and process this model as a multistage SP application
A Dual Method For Backward Stochastic Differential Equations with Application to Risk Valuation
We propose a numerical recipe for risk evaluation defined by a backward
stochastic differential equation. Using dual representation of the risk
measure, we convert the risk valuation to a stochastic control problem where
the control is a certain Radon-Nikodym derivative process. By exploring the
maximum principle, we show that a piecewise-constant dual control provides a
good approximation on a short interval. A dynamic programming algorithm extends
the approximation to a finite time horizon. Finally, we illustrate the
application of the procedure to financial risk management in conjunction with
nested simulation and on an multidimensional portfolio valuation problem
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
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