Abstract
AbstractDecisions 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.
Highlights
Capital allocation between volatile stocks is at the center of portfolio formation decisions, and adequate estimates of the unknown covariance matrix describe crucial information for strategic portfolio allocations
As the results of the conducted simulation analysis do not differ markedly for different parametrizations of dynamic conditional correlation (DCC) parameters by each simulation run, in the remainder of this section we present the analysis of the average statistics of each memory scenario
This paper adds to the growing field of wavelet-based risk measurements and demonstrates a novel perspective on what is the relevant information for applied portfolio optimization
Summary
Capital allocation between volatile stocks is at the center of portfolio formation decisions, and adequate estimates of the unknown covariance matrix describe crucial information for strategic portfolio allocations. Historical data contain information about both short- and long-run information, and conclusions drawn from respective covariance estimates provide limited insight regarding periodical diversification opportunities. We introduce a wavelet-based approach to improve the future performance of portfolio allocations. Employing a multi-horizon nonparametric filter—the wavelet transformation—we develop a covariance estimator to distinguish between newly embedded information content of underlying historical market prices and news. Ramazan Gençay passed away after this manuscript had been completed. That has been fully absorbed by the market.. To assess the relevance of competing information components, we apply a simple mean-variance efficient portfolio allocation algorithm and study the out-of-sample performance of wavelet-based covariance estimates. That has been fully absorbed by the market. To assess the relevance of competing information components, we apply a simple mean-variance efficient portfolio allocation algorithm and study the out-of-sample performance of wavelet-based covariance estimates.
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