***** 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.

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

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***** ALL ARE WELCOME *****

Host : Prof. Xunyu Zhou

Tel : 2609 8238

Email : xyzhou@se.cuhk.edu.hk

For more information please

refer to http://www.se.cuhk.edu.hk/~seg5810/

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