The Mean–Variance Rule and Expected Utility: The Multi-Period Case
发现，使用短期数据构建的均值方差投资组合，在长期持有策略下仍能接近最优，解决了长期投资中因收益非正态分布而质疑均值方差框架的问题。
Portfolio managers often face the challenge of building long-term investment strategies using return data observed over much shorter horizons. This creates a “horizon mismatch” between a portfolio’s design and how it is ultimately held. While traditional mean–variance optimization is often dismissed as unrealistic for long horizons due to non-normal returns, we show that the horizon mismatch actually rescues the mean–variance framework. In fact, efficient mean–variance portfolios based on short-horizon data, with a buy-and-hold strategy for the long run, are actually optimal in the multi-period case, and they are located on the multi-period mean–variance efficient frontier. Thus, by employing the mean–variance rule, investors can still achieve near-optimal outcomes even over long investment horizons. This article offers a practitioner-focused perspective on why mean–variance optimization remains highly relevant—and how it can be safely applied across different investment horizons, even in the face of returns with skewed distributions.