How Optimal Are Risk-Based Portfolios?
研究了风险型投资规则（如逆波动率组合）在均值-方差框架下达到理论最优的充要条件，并用多个数据集验证了这些条件，解释了为何这类组合在样本外表现优于传统方法。
Since the early 2000s, interest in risk-based rules has grown as the asset management industry adopted these allocation methods to maximize diversification benefits, with the hope of achieving better out-of-sample performance compared to traditional methodologies (e.g., mean–variance). The theoretical optimality of risk-based portfolios is often an afterthought. In this article, the authors suggest that perhaps this theoretical optimality warrants increased attention, especially given these portfolios’ common acceptance. The authors provide the necessary and sufficient conditions for a class of risk-based rules (e.g., inverse volatility portfolios) to be considered theoretically optimal in a mean–variance framework, and they show that such conditions are met empirically using many common datasets. This finding supports the empirical observation that these portfolios outperform out-of-sample both mean–variance and equally weighted portfolios. The authors’ results suggest that risk-based rules have merits from the perspective of optimality, beyond their robust characteristics.