Portfolio Optimization for Extreme Risks with Maximum Diversification: An Empirical Analysis

Heavy tailedness and interconnectedness widely exist in stock returns large insurance claims, which contributes to huge losses for financial institutions. Diversification ratio (DR) measures the degree of diversification using Value-at-Risk, is known capture extreme risks better than variance. The portfolio optimization strategy based on DR maximizes effect risks. In this paper, we empirically examine by more 350 S&P 500 stocks under assumption that are modeled with a flexible multivariate heavy-tailed model. This verified empirically. performance compared four benchmark strategies: equally weighted portfolio, minimum-variance risk index most diversified portfolio. comparison includes annualized return, modified Sharpe ratio, maximum drawdown, concentration, turnover, diversification. outperforms other strategies. particular, shows highest return maintains level during global crisis 2007–2009.