A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series

DOI10.1016/j.csda.2019.106810OpenAlexW2966104599MaRDI QIDQ2008095

Nicole Barthel, Yarema Okhrin, Claudia Czado

Publication date: 22 November 2019

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

Full work available at URL: https://arxiv.org/abs/1802.09585

zbMATH Keywords

forecastingrealized volatilitytime-series modelingpartial correlation vineR-vine structure selection

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Uses Software

• RiskMetrics
• CopulaModel

Cites Work

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