Of copulas, quantiles, ranks and spectra: an \(L_{1}\)-approach to spectral analysis
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Publication:2348726
DOI10.3150/13-BEJ587zbMath1337.62286arXiv1111.7205OpenAlexW1591244523MaRDI QIDQ2348726
Stanislav Volgushev, Marc Hallin, Tobias Kley, Dette, Holger
Publication date: 15 June 2015
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1111.7205
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Inference from stochastic processes and spectral analysis (62M15)
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Cites Work
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- The quantilogram: with an application to evaluating directional predictability
- \(U\)-processes, \(U\)-quantile processes and generalized linear statistics of dependent data
- An introduction to copulas.
- On the covariance of the asymptotic empirical copula process
- A bootstrap test for time series linearity
- Rates of convergence for empirical processes of stationary mixing sequences
- Limiting distributions for \(L_1\) regression estimators under general conditions
- Least absolute deviation estimation for all-pass time series models
- Vines -- a new graphical model for dependent random variables.
- A Fourier analysis of extreme events
- A CENTRAL LIMIT THEOREM FOR MIXING TRIANGULAR ARRAYS OF VARIABLES WHOSE DEPENDENCE IS ALLOWED TO GROW WITH THE SAMPLE SIZE
- MIXING PROPERTIES OF A GENERAL CLASS OF GARCH(1,1) MODELS WITHOUT MOMENT ASSUMPTIONS ON THE OBSERVED PROCESS
- Statistical Modeling of Temporal Dependence in Financial Data via a Copula Function
- Laplace Periodogram for Time Series Analysis
- Regression Quantiles
- Generalized Spectral Tests for Serial Dependence
- Hypothesis Testing in Time Series via the Empirical Characteristic Function: A Generalized Spectral Density Approach
- MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
- Quantile Periodograms
- Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence
- Towards a Unified Approach for Proving Geometric Ergodicity and Mixing Properties of Nonlinear Autoregressive Processes
- Quantile Autoregression
- Convergence of stochastic processes
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