Testing for independence in heavy-tailed time series using the codifference function

DOI10.1016/J.CSDA.2009.07.009zbMath1454.62033OpenAlexW2083102692MaRDI QIDQ961960

Dedi Rosadi

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.07.009

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (8)

Subordinated continuous-time AR processes and their application to modeling behavior of mechanical system ⋮ The modified Yule-Walker method for \(\alpha\)-stable time series models ⋮ Cross-codifference for bidimensional VAR(1) time series with infinite variance ⋮ Spatio‐Temporal Dependence Measures for Bivariate AR(1) Models with α‐Stable Noise ⋮ Multivariate \(\alpha\)-stable distributions: VAR(1) processes, measures of dependence and their estimations ⋮ Bootstrap testing multiple changes in persistence for a heavy-tailed sequence ⋮ Codifference as a practical tool to measure interdependence ⋮ Asymptotic behavior of the cross-dependence measures for bidimensional AR(1) model with $\alpha $-stable noise

Uses Software

• STABLE
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Cites Work

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• Estimating the codifference function of linear time series models with infinite variance
• Rates of convergence for the empirical distribution function and the empirical characteristic function of a broad class of linear processes
• Time series: theory and methods
• Limit theory for the sample covariance and correlation functions of moving averages
• The asymptotic null distribution of the Box-Pierce \(\mathcal Q\)-statistic for random variables with infinite variance. An application to German stock returns
• Testing the stable Paretian assumption
• Diagnostic checking in linear processes with infinite variance
• Approximation Theorems of Mathematical Statistics
• Detecting dependence in heavy-tailed time series using Portmanteau-type dependence tests
• Simple consistent estimators of stable distribution parameters
• On a measure of lack of fit in time series models
• INFINITE VARIANCE STABLE ARMA PROCESSES
• Advanced Calculus with Applications in Statistics
• Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

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