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Long-run wavelet-based correlation for financial time series

Thomas Conlon (), John Cotter () and Ramazan Gencay

European Journal of Operational Research, 2018, vol. 271, issue 2, 676-696

Abstract: 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.

Keywords: Decision analysis; Long-run; Correlation; Wavelet; Portfolio allocation (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1016/j.ejor.2018.05.028

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