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Of Copulas, Quantiles, Ranks and Spectra - An L1-Approach to Spectral Analysis

  • Holger Dette
  • Marc Hallin
  • Tobias Kley
  • Stanislav Volgushev

In this paper we present an alternative method for the spectral analysis of a strictly stationary time series {Yt}t2Z. We define a “new” spectrum as the Fourier transform of the differences between copulas of the pairs (Yt, Yt−k) and the independence copula. This object is called copula spectral density kernel and allows to separate marginal and serial aspects of a time series. We show that it is intrinsically related to the concept of quantile regression. Like in quantile regression, which provides more information about the conditional distribution than the classical location-scale model, the copula spectral density kernel is more informative than the spectral density obtained from the autocovariances. In particular the approach provides a complete description of the distributions of all pairs (Yt, Yt−k). Moreover, it inherits the robustness properties of classical quantile regression, because it does not require any distributional assumptions such as the existence of finite moments. In order to estimate the copula spectral density kernel we introduce rank-based Laplace periodograms which are calculated as bilinear forms of weighted L1-projections of the ranks of the observed time series onto a harmonic regression model. We establish the asymptotic distribution of those periodograms, and the consistency of adequately smoothed versions. The finite-sample properties of the new methodology, and its potential for applications are briefly investigated by simulations and a short empirical example.

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File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/104763/3/2011-038-DETTE_HALLIN_KLEY_VOLGUSHEV-ofcopulas.pdf
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Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number ECARES 2011-038.

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Length: 45 p.
Date of creation: Dec 2011
Date of revision:
Publication status: Published by:
Handle: RePEc:eca:wpaper:2013/104763
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  1. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-68, July.
  2. Hinich, Melvin J & Patterson, Douglas M, 1985. "Evidence of Nonlinearity in Daily Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(1), pages 69-77, January.
  3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  4. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(02), pages 186-199, June.
  5. Smith, Michael & Min, Aleksey & Almeida, Carlos & Czado, Claudia, 2010. "Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1467-1479.
  6. Li, Ta-Hsin, 2008. "Laplace Periodogram for Time Series Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 757-768, June.
  7. Yongmiao Hong, 2000. "Generalized spectral tests for serial dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 557-574.
  8. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
  9. Genest, Christian & Segers, Johan, 2010. "On the covariance of the asymptotic empirical copula process," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1837-1845, September.
  10. Abhyankar, A & Copeland, L S & Wong, W, 1997. "Uncovering Nonlinear Structure in Real-Time Stock-Market Indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 1-14, January.
  11. Chanda, K. C. & Puri, M. L. & Ruymgaart, F. H., 1990. "Asymptotic normality of L-statistics based on m(n)-decomposable time series," Journal of Multivariate Analysis, Elsevier, vol. 35(2), pages 260-275, November.
  12. Linton, O. & Whang, Yoon-Jae, 2007. "The quantilogram: With an application to evaluating directional predictability," Journal of Econometrics, Elsevier, vol. 141(1), pages 250-282, November.
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