  Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a
backtesting study of the portfolio optimization strategy based on the extreme
risk index (ERI). This method uses multivariate extreme value theory to
minimize the probability of large portfolio losses. With more than 400 stocks
to choose from, our study seems to be the first application of extreme value
techniques in portfolio management on a large scale. The primary aim of our
investigation is the potential of ERI in practice. The performance of this
strategy is benchmarked against the minimum variance portfolio and the equally
weighted portfolio. These fundamental strategies are important benchmarks for
large-scale applications. Our comparison includes annualized portfolio returns,
maximal drawdowns, transaction costs, portfolio concentration, and asset
diversity in the portfolio. In addition to that we study the impact of an
alternative tail index estimator. Our results show that the ERI strategy
significantly outperforms both the minimum-variance portfolio and the equally
weighted portfolio on assets with heavy tails.
