**SPEAKER: **Apostolos Fertis

**ABSTRACT:**

Coherent risks can be expressed as the worst-case expectation when the
probability distribution varies in some uncertainty set, according to
the representation theorem. Very often, randomness can be divided in two
stages, and there is additional information about the possible first
stage scenarios. Traditional coherent risks, such as the CVaR, fail to
make use of this information. In this talk, we introduce a new class of
risk measures, called robust risk measures, which combine the
uncertainty set of a traditional risk measure with the additional
information about the first stage scenarios. We state and prove a
representation theorem for the robust risk measures, which facilitates
their computation. We define and show how to compute the Robust CVaR,
the robust risk constructed based on CVaR. We compare the optimal-Robust
CVaR and optimal-CVaR portfolios under diverse scenarios constructed
using real New York Stock Exchange (NYSE) and NASDAQ data from 2005 to
2010.

Bio:

Apostolos Fertis completed his PhD at the Electrical Engineering and
Computer Science Department of the Massachusetts Institute of Technology
in 2009. Currently he is a researcher at the Institute for Operations
Research (IFOR) in Zurich.

In PhD thesis, under the supervision of Professor Dimitris Bertsimas,
he investigated the application of the robust optimization concept in
confronting the uncertainty in the samples used to produce statistical
estimates. In January 2010, he initiated the "Robust Risk Management"
research project at theIFOR. The project aspires to introduce a new idea
in uncertainty management by combining traditional risk management
techniques with robust optimization.