A receding horizon control approach to portfolio optimization using a risk-minimax objective for wealth tracking

Srikanth Sridharan, Divakar Chitturi, Armando Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In this paper, we consider the problem of financial portfolio optimization. A hierarchical framework is used, and receding horizon control (RHC) ideas are exploited to pose and solve two relevant constrained optimization problems. We first present the classic problem of wealth maximization subject to risk constraints. We also formulate a new approach to portfolio optimization which attempts to minimize the peak risk over the prediction horizon, while trying to track a wealth objective. This approach is designed to assist investors that might be unable to precisely specify their risk tolerance. We compare this methodology with the classical approach. It is concluded that this approach may be particularly beneficial during downturns appreciably limiting losses during downturns while providing most of the upturn benefits.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Control Applications, CCA 2011
Pages1282-1287
Number of pages6
DOIs
StatePublished - Nov 7 2011
Event2011 20th IEEE International Conference on Control Applications, CCA 2011 - Denver, CO, United States
Duration: Sep 28 2011Sep 30 2011

Publication series

NameProceedings of the IEEE International Conference on Control Applications

Other

Other2011 20th IEEE International Conference on Control Applications, CCA 2011
Country/TerritoryUnited States
CityDenver, CO
Period9/28/119/30/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • General Mathematics

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