Decisions on ass et allocations are often determined by covariance estimates from historical market data. In this paper, we introduce a wavelet‐based portfolio algorithm, distinguishing between newly embedded news and long‐run information that has already been fully absorbed by the market. Exploiting the wavelet decomposition into short‐ and long‐run covariance regimes, we introduce an approach to focus on particular covariance components. Using generated data, we demonstrate that short‐run covariance regimes comprise the relevant information for periodical portfolio management. In an empirical application to US stocks and other international markets for weekly, monthly, quarterly, and yearly holding periods (and rebalancing), we present evidence that the application of wavelet‐based covariance estimates from short‐run information outperforms portfolio allocations that are based on covariance estimates from historical data.

中文翻译：

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用于应用投资组合管理的基于短期小波的协方差机制

资产配置决策通常由历史市场数据的协方差估计决定。在本文中，我们介绍了一种基于小波的投资组合算法，区分新嵌入的新闻和已经被市场完全吸收的长期信息。利用小波分解成短期和长期协方差机制，我们引入了一种专注于特定协方差分量的方法。使用生成的数据，我们证明短期协方差机制包含定期投资组合管理的相关信息。在对美国股票和其他国际市场的每周、每月、每季度和每年持有期（和再平衡）的实证应用中，