*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
TITLE: Mean-variance portfolio optimization when means and covariances are unknown
SPEAKER: Dr. Haipeng Xin
ABSTRACT:
Markowitz's celebrated mean-variance portfolio optimization theory
assumes that the means and covariances of the underlying asset returns
are known. In practice, they are unknown and have to be estimated from
historical data. Plugging the estimates into the efficient frontier
that assumes known parameters has led to portfolios that may perform
poorly and have counter-intuitive asset allocation weights; this has been
referred to as the ``Markowitz optimization enigma.'' After reviewing
different approaches in the literature to address these difficulties, we
explain the root cause of the enigma and propose a new approach to resolve
it. Not only is the new approach shown to provide substantial
improvements over previous methods, but it also allows flexible modeling
to incorporate dynamic features and fundamental analysis of the training
sample of historical data, as illustrated in simulation and empirical studies.
This is a joint work with Tze Leung Lai (Stanford University) and Zehao Chen
(Bosera Funds).
Short bio: Haipeng Xing graduated from the Department of Statistics at Stanford
University at 2005, and then jointed the Department of Statistics at Columbia
University. In 2008, he moved the Department of Applied Maths and Statistics at
SUNY, Stony Brook. His research interests include financial econometrics and
engineering, time series modeling and adaptive control, and change-point
problems.
