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.