English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In multivariate time series analysis, the coherence measures the lineardependency between two-time series at different frequencies. However, real dataapplications often exhibit nonlinear dependency in the frequency domain.Conventional coherence analysis fails to capture such dependency. The quantilecoherence, on the other hand, characterizes nonlinear dependency by definingthe coherence at a set of quantile levels based on trigonometric quantileregression. Although quantile coherence is a more powerful tool, its estimationremains challenging due to the high level of noise. This paper introduces a newestimation technique for quantile coherence. The proposed method issemi-parametric, which uses the parametric form of the spectrum of the vectorautoregressive (VAR) model as an approximation to the quantile spectral matrix,along with nonparametric smoothing across quantiles. For each fixed quantilelevel, we obtain the VAR parameters from the quantile periodograms, then, usingthe Durbin-Levinson algorithm, we calculate the preliminary estimate ofquantile coherence using the VAR parameters. Finally, we smooth the preliminaryestimate of quantile coherence across quantiles using a nonparametric smoother.Numerical results show that the proposed estimation method outperformsnonparametric methods. We show that quantile coherence-based bivariate timeseries clustering has advantages over the ordinary VAR coherence. Forapplications, the identified clusters of financial stocks by quantile coherencewith a market benchmark are shown to have an intriguing and more accuratestructure of diversified investment portfolios that may be used by investors tomake better decisions.

A semi-parametric estimation method for quantile coherence with an application to bivariate financial time series clustering