-

 

 

 




Optimization Online





 

Mean and covariance matrix adaptive estimation for a weakly stationary process. Application in stochastic optimization.

Vincent Guigues (vguigues***at***puc-rio.br)

Abstract: We introduce an adaptive algorithm to estimate the uncertain parameter of a stochastic optimization problem. The procedure estimates the one-step-ahead means, variances and covariances of a random process in a distribution-free and multidimensional framework when these means, variances and covariances are slowly varying on a given past interval. The quality of the approximate problem obtained when employing our estimation of the uncertain parameter is controlled in function of the number of components of the process and of the length of the largest past interval where the means, variances and covariances slowly vary. The procedure is finally applied to a portfolio selection model.

Keywords: Adaptive estimation; weakly stationary process; stochastic optimization; Value-at-Risk; portfolio management

Category 1: Stochastic Programming

Citation: Statistics & Decision, 26, 109-143, 2008

Download: [PDF]

Entry Submitted: 02/23/2011
Entry Accepted: 02/24/2011
Entry Last Modified: 02/24/2011

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Programming Society