Long-run wavelet-based correlation for financial time series

The asset allocation decision often relies upon correlation estimates arising from short-run data. Short run correlation estimates may, however, be distorted by frictions. In this paper, we introduce a long-run wavelet-based correlation estimator, distinguishing between long-run common behavior and short-run singular events. Using generated data, we demonstrate a reduction in bias and error of up to 84.2% and 38.9%, respectively, relative to a traditional subsampled approach. Exploiting the wavelet decomposition into short- and long-run components, we develop a model to help understand the sources of any heterogeneity in correlation. The implication is that short-run correlation may be downward biased by frictions, the latter manifesting as serial- and cross-serial correlation in the raw time series. In an empirical application to G7 international equity markets, we present evidence of increasing correlations at longer run horizons. The significance for the asset allocation decision are examined using a minimum-variance framework, highlighting distinct optimal allocation weights at short- and long-run horizons. (C) 2018 Elsevier B.V. All rights reserved.