We investigate multiperiod portfolio selection problems in a Black & Scholes type market where a basket of 1 riskfree and m risky securities are traded continuously. We look for the optimal allocation of wealth within the class of ’constant mix ’ portfolios. First, we consider the portfolio selection problem of a decision maker who invests money at predetermined points in time in order to obtain a target capital at the end of the time period under consideration. A second problem concerns a decision maker who invests some amount of money (the initial wealth or provision) in order to be able to fullfil a series of future consumptions or payment obligations. Several optimality criteria and their interpretation within Yaari’s dual theory of choice under risk are presented. For both selection problems, we propose accurate approximations based on the concept of comonotonicity, as studied in Dhaene, Denuit, Goovaerts, Kaas & Vyncke (2002 a,b). Our analytical approach avoids simulation, and hence reduces the computing effort drastically. 1

In sensitivity analysis one wants to know how the problem and the optimal solutions change under the variation of the input data. We consider the case when variation happens in the right hand side of the constraints and/or in the linear term of the objective function. We are interested to find the range of the parameter variation in Convex Quadratic Optimization (CQO) problems where the support set of a given primal optimal solution remains invariant. This question has been first raised in Linear Optimization (LO) and known as Type II (so called Support Set Invariancy) sensitivity analysis. We present computable auxiliary problems to identify the range of parameter variation in support set invariancy sensitivity analysis for CQO. It should be mentioned that all given auxiliary problems are LO problems and can be solved by an interior point method in polynomial time. We also highlight the differences between characteristics of support set invariancy sensitivity analysis for LO and CQO.

Abstract. We consider the global optimization of two problems arising from financial applications. The first problem originates from the portfolio selection problem when high-order moments are taken into account. The second issue we address is the problem of scenario generation. Both problems are non-convex, large-scale, and highly relevant in financial engineering. For the two problems we consider, we apply a new stochastic global optimization algorithm that has been developed specifically for this class of problems. The algorithm is an extension to the constrained case of the so called diffusion algorithm. We discuss how a financial planning model (of realistic size) can be solved to global optimality using a stochastic algorithm. Initial numerical results are given that show the feasibility of the proposed approach. 1

Downside loss averse preferences have seen a resurgence in the port-folio management literature. This is due to the increasing usage of derivatives in managing equity portfolios, and the increased usage of quantitative techniques for bond portfolio management. We employ the lower partial moment as a risk measure for downside loss aversion, and compare mean-variance (M-V) and mean-lower partial moment (M-LPM) optimal portfolios under non-normal as-set return distributions. When asset returns are nearly normally distributed, there is little difference between the optimal M-V and M-LPM portfolios. When asset returns are non-normal with large left tails, we document significant dif-ferences in M-V and M-LPM optimal portfolios. This observation is consistent with industry usage of M-V theory for equity portfolios, but not for fixed income portfolios.

The risk management of non-maturing account positions in a bank’s balance like savings deposits or certain types of loans is complicated by the embedded options that clients may exercise. In addition to the usual interest rate risk, there is also uncertainty in the timing and amount of future cash flows. Since the corresponding volume risk cannot directly be hedged, the account must be replicated by a portfolio of instruments with explicit maturities. This paper introduces a multistage stochastic programming model that determines an optimal replicating portfolio from scenarios for future outcomes of the relevant risk factors: Market rates, client rates and volume of the non-maturing account. The weights for the allocation of new tranches are frequently adjusted to latest observations of the latter. A case study based on data of a real deposit position demonstrates that the resulting dynamic portfolio provides a significantly higher margin at lower risk compared to a static benchmark.

Abstract—This paper considers robust mean-variance portfolio selection problems including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed models are transformed into the deterministic equivalent problems. Furthermore, since it is difficult to solve them analytically and efficiently due to nonlinear programming problems, the solution method is constructed introducing a parameter and doing the equivalent transformations. Index Terms—Portfolio selection problem, Robust optimization, Fuzzy optimization, Nonlinear programming

Abstract—This paper considers robust mean-variance portfolio selection problems including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed models are transformed into the deterministic equivalent problems. Furthermore, since it is difficult to solve them analytically and efficiently due to nonlinear programming problems, the solution method is constructed introducing a parameter and doing the equivalent transformations. Index Terms—Portfolio selection problem, Robust optimization, Fuzzy optimization, Nonlinear programming

The mathematics of the portfolio frontier:

Abstract not found

Abstract — We consider a financial decision problem involving dynamic investment decisions on a single risky instrument over multiple and discrete time periods. Investment returns are assumed stochastic and possibly dependent over time, and proportional transaction costs are considered in the model. In this setting, the investor’s goal is to determine investment policies that maximize the net profit while maintaining the associated risk under control. We propose approximations of the ensuing stochastic multistage optimization problem that are based on affine recourse strategies and that lead to efficiently solvable second order cone or semidefinite programs. I.