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Short-run wavelet-based covariance regimes for applied portfolio management

  • Decisions on asset 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.

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Author:Theo BergerORCiDGND, Ramazan Gençay
DOI original:https://doi.org/10.1002/for.2650
Parent Title (English):Journal of Forecasting
Document Type:Article
Year of Completion:2020
Publishing Institution:Hochschule Hannover
Release Date:2024/06/17
Tag:portfolio management; short-run trends; wavelet decomposition
GND Keyword:Portfoliomanagement; Aktie
First Page:642
Last Page:660
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:330 Wirtschaft
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International