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Quantile Periodogram And Time-Dependent Variance

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  • Ta-Hsin Li

Abstract

type="main" xml:id="jtsa12065-abs-0001"> This article investigates the statistical properties of the recently introduced quantile periodogram for time series with time-dependent variance. The asymptotic distribution of the quantile periodogram is derived in the case where the time series consists of i.i.d. random variables multiplied by a time-dependent scale parameter. It is shown that the time-dependent variance is represented approximately additively in the mean of the asymptotic distribution of the quantile periodogram. It is also shown that the strength of the representation is proportional to the squared quantile of the i.i.d. random variables, so that a stronger characterization is expected at upper and lower quantile levels if the time series is centred at zero. These properties are further demonstrated by simulation results. The series of daily returns from the Dow Jones Industrial Average, which is known to exhibit heteroscedastic volatility, serves to motivate the investigation.

Suggested Citation

  • Ta-Hsin Li, 2014. "Quantile Periodogram And Time-Dependent Variance," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(4), pages 322-340, July.
    • Handle: RePEc:bla:jtsera:v:35:y:2014:i:4:p:322-340
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    File URL: http://hdl.handle.net/10.1111/jtsa.12065
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    References listed on IDEAS

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    1. Holger Dette & Marc Hallin & Tobias Kley & Stanislav Volgushev, 2011. "Of Copulas, Quantiles, Ranks and Spectra - An L1-Approach to Spectral Analysis," Working Papers ECARES ECARES 2011-038, ULB -- Universite Libre de Bruxelles.
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    8. Ta-Hsin Li, 2012. "Quantile Periodograms," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 765-776, June.
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    Citations

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    Cited by:

    1. Stefan Birr & Holger Dette & Marc Hallin & Tobias Kley & Stanislav Volgushev, 2016. "On Wigner-Ville Spectra and the Unicity of Time-Varying Quantile-Based Spectral Densities," Working Papers ECARES ECARES 2016-38, ULB -- Universite Libre de Bruxelles.
    2. Stefan Birr & Stanislav Volgushev & Tobias Kley & Holger Dette & Marc Hallin, 2017. "Quantile spectral analysis for locally stationary time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1619-1643, November.
    3. Jozef Baruník & Tobias Kley, 2019. "Quantile coherency: A general measure for dependence between cyclical economic variables," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 131-152.
    4. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    5. Ta-Hsin Li, 2019. "Quantile-Frequency Analysis and Spectral Divergence Metrics for Diagnostic Checks of Time Series With Nonlinear Dynamics," Papers 1908.02545, arXiv.org.
    6. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    7. Yaeji Lim & Hee-Seok Oh, 2022. "Quantile spectral analysis of long-memory processes," Empirical Economics, Springer, vol. 62(3), pages 1245-1266, March.
    8. Chen, Tianbo & Sun, Ying & Li, Ta-Hsin, 2021. "A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).

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