Multi-objective stochastic programming for portfolio selection

Generally, in the portfolio selection problem the Decision Maker (DM) considers simultaneously conflicting objectives such as rate of return, liquidity and risk. Multi-objective programming techniques such as goal programming (GP) and compromise programming (CP) are used to choose the portfolio best satisfying the DM’s aspirations and preferences. In this article, we assume that the parameters associated with the objectives are random and normally distributed. We propose a chance constrained compromise programming model (CCCP) as a deterministic transformation to multi-objective stochastic programming portfolio model. CCCP is based on CP and chance constrained programming (CCP) models. The proposed program is illustrated by means of a portfolio selection problem from the Tunisian stock exchange market.     

Mean-variance portfolio selection for a non-life insurance company

We consider a collective insurance risk model with a compound Cox claim process, in which the evolution of a claim intensity is described by a stochastic differential equation driven by a Brownian motion. The insurer operates in a financial market consisting of a risk-free asset with a constant force of interest and a risky asset which price is driven by a Lévy noise. We investigate two optimization problems. The first one is the classical mean-variance portfolio selection. In this case the efficient frontier is derived. The second optimization problem, except the mean-variance terminal objective, includes also a running cost penalizing deviations of the insurer’s wealth from a specified profit-solvency target which is a random process. In order to find optimal strategies we apply techniques from the stochastic control theory.     

Determination of the composition of the insurance and investment portfolios of a casualty insurance company

This paper presents a model in which expected return is maximized subject to two chance constraints. The first chance constraint requires that the total return is greater than some lower bound with a stipulated probability. The second chance constraint is used to maintain the firm's liquidity. An evaluation of the solution is given. The influence of a premium change on the data requirements of the model is discussed.     

Mean-semivariance models for fuzzy portfolio selection

This paper discusses portfolio selection problem in fuzzy environment. In the paper, semivariance is originally presented for fuzzy variable, and three properties of the semivariance are proven. Based on the concept of semivariance of fuzzy variable, two fuzzy mean-semivariance models are proposed. To solve the new models in general cases, a fuzzy simulation based genetic algorithm is presented in the paper. In addition, two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the designed algorithm.     

MIN-MAD life: A multi-period optimization model for life insurance company investment decisions

This paper presents a model, called the MIN-MAD Life Model, for managing the investments of a life insurance company over a multiperiod planning horizon. The MIN-MAD Life Model is a linear programming under uncertainty model based on Markowitz portfolio theory. Given the insurance company's current position and its forecasts of possible future developments with their associated probabilities, the model helps determine the set of efficient investment decisions over the planning horizon subject to market constraints and to the insurance company's legal and policy constraints. The senior executives of the life insurance company need examine only the set of efficient investment decisions to determine their optimal investment decisions.     

A compromise solution for the multiobjective stochastic linear programming under partial uncertainty

This paper solves the multiobjective stochastic linear program with partially known probability. We address the case where the probability distribution is defined by crisp inequalities. We propose a chance constrained approach and a compromise programming approach to transform the multiobjective stochastic linear program with linear partial information on probability distribution into its equivalent uniobjective problem. The resulting program is then solved using the modified L-shaped method. We illustrate our results by an example.     

Annual intrinsic value of a company in a competitive insurance market

In this paper we analyze a measure of the insurance company’s value in an extended Lundberg model which includes the effect of competition on pricing. The extended model is designed to be an integral part of a multi-year controlled risk model of a company operating on both competitive insurance and financial markets, when insureds migrate in seeking for better rates and investors migrate in seeking for higher return on investments.     

An optimization heuristic for the berth scheduling problem

Berths are among the most important resources in a port. In this research we present an optimization-based approach for the berth scheduling problem, which is to determine the berthing time and space for each incoming ship. The neighborhood-search based heuristic treats the quay as a continuous space. In additional to basic physical requirements, this model takes several factors important in practice into consideration, including the first-come-first-served rule, clearance distance between ships, and possibility of ship shifting. Computational experience is provided.     

An algebraic geometry algorithm for scheduling in presence of setups and correlated demands

We study here a problem of schedulingn job types onm parallel machines, when setups are required and the demands for the products are correlated random variables. We model this problem as a chance constrained integer program.Methods of solution currently available—in integer programming and stochastic programming—are not sufficient to solve this model exactly. We develop and introduce here a new approach, based on a geometric interpretation of some recent results in Gröbner basis theory, to provide a solution method applicable to a general class of chance constrained integer programming problems.Out algorithm is conceptually simple and easy to implement. Starting from a (possibly) infeasible solution, we move from one lattice point to another in a monotone manner regularly querying a membership oracle for feasibility until the optimal solution is found. We illustrate this methodology by solving a problem based on a real system.Corresponding author.     

A set-covering model for optimizing selection of portfolio of microcontrollers in an automotive supplier company

The sourcing decisions of microcontrollers in automotive industries are complex to manage largely due to the increasing complexity of products requirements, multiple suppliers, and the nature of microcontroller pricing structures. This paper presents a set-covering model that allows the user to select the most economical microcontrollers that meet all the critical product requirements while minimizing the total cost. The optimization process is carried out in two phases. The first phase deals with the construction of a buildable combination matrix by mapping out the critical product requirements against the microcontroller specifications. In the second phase, the model makes an optimal assignment of microcontrollers to each feasible or buildable product by utilizing economies of scales offered by large microcontroller volumes. Lot size constraints are used to handle the step function in the microcontrollers pricing structure. A case study from Visteon Corporation is used to demonstrate the application of the model. Pilot implementation of the model shows a potential saving of nearly two millions over a 4-year planning horizon.     

Portfolio selection with a new definition of risk

In the field of portfolio selection, variance, semivariance and probability of an adverse outcome are three best-known mathematical definitions of risk. Lots of models were built to minimize risk based on these definitions. This paper gives a new definition of risk for portfolio selection and proposes a new type of model based on this definition. In addition, a hybrid intelligent algorithm is employed to solve the optimization problem in general cases. One numerical example is also presented for the sake of illustration.     

On the existence of solutions to the quadratic mixed-integer mean–variance portfolio selection problem

In the standard mean–variance portfolio selection approach, several operative features are not taken into account. Among these neglected aspects, one of particular interest is the finite divisibility of the (stock) assets, i.e. the obligation to buy/sell only integer quantities of asset lots whose number is pre-established. In order to consider such a feature, we deal with a suitably defined quadratic mixed-integer programming problem. In particular, we formulate this problem in terms of quantities of asset lots (instead of, as usual, in terms of capital per cent quotas). Secondly, we provide necessary and sufficient conditions for the existence of a non-empty mixed-integer feasible set of the considered programming problem. Thirdly, we present some rounding procedures for finding, in a finite number of steps, a feasible mixed-integer solution which is better than the one detected by the necessary and sufficient conditions in terms of the value assumed by the portfolio variance. Finally, we perform an extensive computational experiment by means of which we verify the goodness of our approach.     

Evaluation of the probability of bankruptcy for a model of insurance company

We study the problem of evaluation of the probability of ruin of an insurance company for infinitely many steps in the case where the company can invest its capital to bank deposits at any time. As a distribution of the amounts of claims to the insurance company, we use the gamma-distribution with the parameters n and α. __________ Translated from Ukrains’kyi Matematychnyi Zhurnal, Vol. 59, No. 4, pp. 447–457, April, 2007.     

Optimal asset allocation for a general portfolio of life insurance policies

Asset liability matching remains an important topic in life insurance research. The objective of this paper is to find an optimal asset allocation for a general portfolio of life insurance policies. Using a multi-asset model to investigate the optimal asset allocation of life insurance reserves, this study obtains formulae for the first two moments of the accumulated asset value. These formulae enable the analysis of portfolio problems and a first approximation of optimal investment strategies. This research provides a new perspective for solving both single-period and multiperiod asset allocation problems in application to life insurance policies. The authors obtain an efficient frontier in the case of single-period method; for the multiperiod method, the optimal asset allocation strategies can differ considerably for different portfolio structures.     

Asymptotic behavior of the central path for a special class of degenerate SDP problems

This paper studies the asymptotic behavior of the central path (X(ν),S(ν),y(ν)) as ν↓0 for a class of degenerate semidefinite programming (SDP) problems, namely those that do not have strictly complementary primal-dual optimal solutions and whose “degenerate diagonal blocks” of the central path are assumed to satisfy We establish the convergence of the central path towards a primal-dual optimal solution, which is characterized as being the unique optimal solution of a certain log-barrier problem. A characterization of the class of SDP problems which satisfy our assumptions are also provided. It is shown that the re-parametrization t>0→(X(t⁴),S(t⁴),y(t⁴)) of the central path is analytic at t=0. The limiting behavior of the derivative of the central path is also investigated and it is shown that the order of convergence of the central path towards its limit point is Finally, we apply our results to the convex quadratically constrained convex programming (CQCCP) problem and characterize the class of CQCCP problems which can be formulated as SDPs satisfying the assumptions of this paper. In particular, we show that CQCCP problems with either a strictly convex objective function or at least one strictly convex constraint function lie in this class.This author was supported in part by CAPES and PRONEX-Otimização (FAPERJ/CNPq).This author was supported in part by FUNAPE/UFG, CAPES, PADCT-CNPq and PRONEX-Otimização (FAPERJ/CNPq).This author was supported in part by NSF Grants CCR-9902010, CCR-0203113 and INT-9910084 and ONR grant N00014-03-1-0401.Mathematics Subject Classification (1991): 90C20, 90C22, 90C25, 90C30, 90C33, 90C45, 90C51     

A branch-reduce-cut algorithm for the global optimization of probabilistically constrained linear programs

We consider probabilistically constrained linear programs with general distributions for the uncertain parameters. These problems involve non-convex feasible sets. We develop a branch-and-bound algorithm that searches for a global optimal solution to this problem by successively partitioning the non-convex feasible region and by using bounds on the objective function to fathom inferior partition elements. This basic algorithm is enhanced by domain reduction and cutting plane strategies to reduce the size of the partition elements and hence tighten bounds. The proposed branch-reduce-cut algorithm exploits the monotonicity properties inherent in the problem, and requires solving linear programming subproblems. We provide convergence proofs for the algorithm. Some illustrative numerical results involving problems with discrete distributions are presented.     

A unified method for a class of convex separable nonlinear knapsack problems

In this paper, a unified algorithm is proposed for solving a class of convex separable nonlinear knapsack problems, which are characterized by positive marginal cost (PMC) and increasing marginal loss–cost ratio (IMLCR). By taking advantage of these two characteristics, the proposed algorithm is applicable to the problem with equality or inequality constraints. In contrast to the methods based on Karush–Kuhn–Tucker (KKT) conditions, our approach has linear computation complexity. Numerical results are reported to demonstrate the efficacy of the proposed algorithm for different problems.     

On the effectiveness of scenario generation techniques in single-period portfolio optimization

In single-period portfolio selection problems the expected value of both the risk measure and the portfolio return have to be estimated. Historical data realizations, used as equally probable scenarios, are frequently used to this aim. Several other parametric and non-parametric methods can be applied. When dealing with scenario generation techniques practitioners are mainly concerned on how reliable and effective such methods are when embedded into portfolio selection models. In this paper we survey different techniques to generate scenarios for the rates of return. We also compare the techniques by providing in-sample and out-of-sample analysis of the portfolios obtained by using these techniques to generate the rates of return. Evidence on the computational burden required by the different techniques is also provided. As reference model we use the Worst Conditional Expectation model with transaction costs. Extensive computational results based on different historical data sets from London Stock Exchange Market (FTSE) are presented and some interesting financial conclusions are drawn.     

Optimization of production allocation and transportation of customer orders for a leading forest products company

A leading manufacturer of forest products with several production facilities located in geographical proximity to each other has recently acquired a number of new production plants in other regions/countries to increase its production capacity and expand its national and international markets. With the addition of this new capacity, the company wanted to know how to best allocate customer orders to its various mills to minimize the total cost of production and transportation. We developed mixed-integer programming models to jointly optimize production allocation and transportation of customer orders on a weekly basis. The models were run with real order files and the test results indicated the potential for significant cost savings over the company’s current practices. The company further customized the models, integrated them into their IT system and implemented them successfully. Besides the actual cost savings for the company, the whole process from the initial step of analyzing the problem, to developing, testing, customizing, integrating and finally implementing the models provided enhanced intelligence to sales staff.     

The optimal strategy for insurance company under the influence of terminal value

This paper considers a model of an insurance company which is allowed to invest a risky asset and to purchase proportional reinsurance. The objective is to find the policy which maximizes the expected total discounted dividend pay-out until the time of bankruptcy and the terminal value of the company under liquidity constraint. We find the solution of this problem via solving the problem with zero terminal value. We also analyze the influence of terminal value on the optimal policy.