BFS 2002 

Contributed Talk 
Garud Iyengar, Donald Goldfarb
In this talk we show how to formulate and solve robust portfolio selection
problems. The objective of these robust formulations is to
systematically combat the sensitivity of the optimal portfolio to
statistical and modeling errors in
the estimates of the relevant market parameters. We introduce ``uncertainty
structures'' for the market parameters and show that the robust
portfolio selection problems corresponding to these uncertainty
structures can be reformulated as secondorder cone programs and,
therefore, the computational effort required to solve them is
comparable to that required for solving convex quadratic
programs. Moreover, we
show that these uncertainty structures correspond to confidence regions
associated with the statistical procedures used to estimate the market
parameters. We demonstrate a simple recipe for efficiently computing
robust portfolios given raw market data and a desired
level of confidence.
http://www.corc.ieor.columbia.edu/reports/techreports/tr200105.ps.gz