Multistage mean-variance portfolio selection in cointegrated vector autoregressive systems

The problem of portfolio choice is an example of sequential decision making under uncertainty. Investors must consider their attitudes towards risk and reward in face of an unknown future, in order to make complex financial choices. Often, mathematical models of investor preferences and asset return dynamics aid in this process, resulting in a wide range of portfolio choice paradigms, one of which is considered in this thesis. Specifically, it is assumed that the investor operates so as to maximize his expected terminal wealth, subject to a risk (variance) constraint, in what is known as mean-variance optimal (MVO) portfolio selection, and that the log-prices of the assets evolve according a simple linear system known as a cointegrated vector autoregressive (VAR) process. While MVO portfolio choice remains the most popular formulation for single-stage asset allocation problems in both academia and industry, computational difficulties traditionally limit its use in a dynamic, multistage setting. Cointegration models are popular among industry practitioners as they encode the belief that the log-prices of many groups of assets are not WSS, yet move together in a coordinated fashion. Such systems exhibit temporary states of disequilibrium or relative asset mis-pricings that can be exploited for profit. Here, a set of multiperiod trading strategies are developed and studied. Both static and dynamic frameworks are considered, in which rebalancing is prohibited or allowed, respectively. Throughout this work, the relationship between the resulting portfolio weight vectors and the geometry of a cointegrated VAR process is demonstrated. In the static case, the performance of the MVO solution is analyzed in terms of the use of leverage, the correlation structure of the inter-stage portfolio returns, and the investment time horizon.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Rudoy, Melanie Beth

Advisor dc:contributor.advisor

• Charles E. Rohrs.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/46794

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/46794