15 research outputs found
A Closed-Form Solution of the Multi-Period Portfolio Choice Problem for a Quadratic Utility Function
In the present paper, we derive a closed-form solution of the multi-period
portfolio choice problem for a quadratic utility function with and without a
riskless asset. All results are derived under weak conditions on the asset
returns. No assumption on the correlation structure between different time
points is needed and no assumption on the distribution is imposed. All
expressions are presented in terms of the conditional mean vectors and the
conditional covariance matrices. If the multivariate process of the asset
returns is independent it is shown that in the case without a riskless asset
the solution is presented as a sequence of optimal portfolio weights obtained
by solving the single-period Markowitz optimization problem. The process
dynamics are included only in the shape parameter of the utility function. If a
riskless asset is present then the multi-period optimal portfolio weights are
proportional to the single-period solutions multiplied by time-varying
constants which are depending on the process dynamics. Remarkably, in the case
of a portfolio selection with the tangency portfolio the multi-period solution
coincides with the sequence of the simple-period solutions. Finally, we compare
the suggested strategies with existing multi-period portfolio allocation
methods for real data.Comment: 38 pages, 9 figures, 3 tables, changes: VAR(1)-CCC-GARCH(1,1) process
dynamics and the analysis of increasing horizon are included in the
simulation study, under revision in Annals of Operations Researc
Power management in a hydro-thermal system under uncertainty by Lagrangian relaxation
We present a dynamic multistage stochastic programming model for the cost-optimal generation of electric power in a hydro-thermal system under uncertainty in load, inflow to reservoirs and prices for fuel and delivery contracts. The stochastic load process is approximated by a scenario tree obtained by adapting a SARIMA model to historical data, using empirical means and variances of simulated scenarios to construct an initial tree, and reducing it by a scenario deletion procedure based on a suitable probability distance. Our model involves many mixed-integer variables and individual power unit constraints, but relatively few coupling constraints. Hence we employstochastic Lagrangian relaxation that assigns stochastic multipliers to the coupling constraints. Solving the Lagrangian dual by a proximal bundle method leads to successive decomposition into single thermal and hydro unit subproblems that are solved by dynamic programming and a specialized descent algorithm, respectively. The optimal stochastic multipliers are used in Lagrangian heuristics to construct approximately optimal first stage decisions. Numerical results are presented for realistic data from a German power utility, with a time horizon of one week and scenario numbers ranging from 5 to 100. The corresponding optimization problems have up to 200,000 binary and 350,000 continuous variables, and more than 500,000 constraints