Premium
Short‐run wavelet‐based covariance regimes for applied portfolio management
Author(s) -
Berger Theo,
Gençay Ramazan
Publication year - 2020
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2650
Subject(s) - covariance , portfolio , wavelet , econometrics , computer science , project portfolio management , portfolio optimization , economics , financial economics , mathematics , statistics , artificial intelligence , management , project management
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.