*****  Note Special Date and Venue *****

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                                    Seminar


       Department of Systems Engineering and Engineering Management,

                    The Chinese University of Hong Kong

Title:
A Relative performance approach to robust portfolio selection
when there is model ambiguity

Speaker:
Andrew Lim
Industrial Engineering & Operations Research Department
University of California,
Berkeley

Date : June 21, 2006 (Wednesday)

Time : 4:30 p.m. - 5:30 p.m.

Venue : Room 602 (E-Service Lab), William M.W. Mong Engineering Building

(Engineering Building Complex Phase 2), CUHK

Abstract:
Recent interest in the topic of ``investment with model ambiguity" in the
finance, economics and decision theory communities has been motivated largely
by efforts to incorporate ``ambiguity aversion", as suggested by experiments
such as the Ellsberg Paradox, in the analysis of agent behavior. Closely
related work on ``robust portfolio selection" in the optimization community
has been driven by the observation that the solutions of classical optimal
portfolio selection problems (such as ``mean-variance optimization") are
sensitive to statistical errors that can arise during calibration, and that
the ``real world" performance of such portfolios can be poor if these errors
are ignored. The commonly used method for addressing these issues is some sort
of ``worst case" optimization which has led in turn to methodologies such as
``worst case mean-variance" and ``worst case utility maximization". While the
``worst case approach" has its axiomatic foundations in the work of Gilboa and
Schmeidler, it has also been criticized for being ``overly pessimistic".

In this talk, we propose and analyze an alternative measure of` ``robust
performance". This alternative measure differs from the typical ``worst case
expected utility" and ``worst case mean-variance" formulations in that the
``robust performance" of a (dynamic) portfolio is evaluated not only on the
basis of its performance when there is an adversarial opponent (``nature"),
but also by its performance relative to a fully informed ``benchmark investor"
who behaves optimally given complete knowledge of the otherwise ambiguous
model. This ``relative performance" approach has several important properties:
(i) decisions arising from this approach are less pessimistic than the
portfolios obtained from the typical ``worst case expected utility" and
``worst case mean-variance" formulations, (ii) the dynamic ``relative
performance" problem reduces to a convex static optimization problem under
reasonable choices of the benchmark portfolio, and (iii) the solution of the
``relative performance" problem coincides with that of a ``Bayesian" portfolio
choice problem with an appropriately chosen prior. The static problem is
interesting in its own right: it can be interpreted as a less pessimistic
alternative to the single period ``worst case mean-variance" problem.

Bio:
Andrew Lim is an Assistant Professor in the Department of Industrial
Engineering and Operations Research at the University of California (Berkeley.
He is the recipient of a National Science Foundation CAREER Award and has
research interests in stochastic control and optimization and applications.
　

Note : Cookies and drinks will be available at 4:15 pm.

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