Wavelet decomposition and applied portfolio management

Theo Berger

Abstract

ABSTRACT

In this paper, we decompose financial return series into their time and frequency domains in order to separate short-term noise from long-term trends. First, we investigate the dependence between US stocks at different time scales before and after the outbreak of financial crisis. Second, we set up a novel analysis and introduce the application of decomposed return series to a portfolio management setup. We then model portfolios that minimize the volatility of each particular time scale. As a result, portfolio compositions that minimize the short-run volatility of the first scales represent a promising choice, since they slightly outperform portfolio compositions that minimize the variance of the unfiltered return series.