 Model assessment for time series dynamics using copula spectral densities: A graphical toolStefan Birr, Tobias Kley and Stanislav Volgushev Journal of Multivariate Analysis, 2019, vol. 172, issue C, 122-146 Abstract: Finding parametric models that accurately describe the dependence structure of observed data is a central task in the analysis of time series. Classical frequency domain methods provide a popular set of tools for fitting and diagnostics of time series models, but their applicability is seriously impacted by the limitations of covariances as a measure of dependence. Motivated by recent developments of frequency domain methods that are based on copulas instead of covariances, we propose a novel graphical tool to assess the quality of time series models for describing dependencies that go beyond linearity. We provide a theoretical justification of our approach and show in simulations that it can successfully distinguish between subtle differences in time series dynamics, including non-linear dynamics which result from GARCH and EGARCH models. We also demonstrate the utility of the proposed tools through an application to modeling returns of the S&P 500 stock market index. Keywords: Bootstrap; Copula; Frequency domain; Spectral density; Time series (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:172:y:2019:i:c:p:122-146 Ordering information: This journal article can be ordered from DOI: 10.1016/j.jmva.2019.03.003 Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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