Cart (Loading....) | Create Account
Close category search window

A dynamic programming approach to two-stage mean-variance portfolio selection in cointegrated vector autoregressive systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Rudoy, M.B. ; Digital Signal Process. Group, Massachusetts Inst. of Technol., Cambridge, MA, USA ; Rohrs, C.E.

In this paper we study the problem of optimal portfolio construction when the trading horizon consists of two consecutive decision intervals and rebalancing is permitted. It is assumed that the log-prices of the underlying assets are non-stationary, and specifically follow a discrete-time cointegrated vector autoregressive model. We extend the classical Markowitz mean-variance optimization approach to a multi-period setting, in which the new objective is to maximize the total expected return, subject to a constraint on the total allowable risk. In contrast to traditional approaches, we adopt a definition for risk which takes into account the non-zero correlations between the inter-stage returns. This portfolio optimization problem amounts to not only determining the relative proportions of the assets to hold during each stage, but also requires one to determine the degree of portfolio leverage to assume. Due to a fixed constraint on the standard deviation of the total return, the leverage decision is equivalent to deciding how to optimally partition the allowed variance, and thus variance can be viewed as a shared resource between the stages. We derive the optimal portfolio weights and variance scheduling scheme for a trading strategy based on a dynamic programming approach, which is utilized in order to make the problem computationally tractable. The performance of this method is compared to other trading strategies using both Monte Carlo simulations and real data, and promising results are obtained.

Published in:

Decision and Control, 2008. CDC 2008. 47th IEEE Conference on

Date of Conference:

9-11 Dec. 2008

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.