Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230230
Authors: 
Berger, Theo
Gençay, Ramazan
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 39 [Issue:] 4 [Pages:] 642-660
Abstract: 
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.
Subjects: 
portfolio management
short‐run trends
wavelet decomposition
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
Document Version: 
Published Version

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