Vine-Kopula-basierte Modellierung und Vorhersage von multivariaten realisierten Volatilitätszeitreihen

• Antragstellerinnen / Antragsteller: Professorin Dr. Claudia Czado; Professor Dr. Yarema Okhrin
• Fachliche Zuordnung: Statistik und Ökonometrie
• Förderung: Förderung von 2015 bis 2021
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 263890942

• Erstellungsjahr: 2021

Zusammenfassung der Projektergebnisse

This project contributed to development of vine copula based models under different data structures occurring in econometrics and life sciences. These improved estimation and forecasting of realized volatility time series over standard approaches and as well provided first time development of vine based dependence models under right-censoring.

Projektbezogene Publikationen (Auswahl)

• (2018) Vine copula based likelihood estimation of dependence patterns in multivariate event time data. Computational Statistics & Data Analysis 117: 109-127
  Barthel, Nicole, Candida Geerdens, Matthias Killiches, Paul Janssen, and Claudia Czado
  
• (2019) Dependence modeling for recurrent event times subject to right-censoring with D-vine copulas. Biometrics 75.2: 439-451
  Barthel, Nicole, Candida Geerdens, Claudia Czado and Paul Janssen
  
• (2019) Modelling temporal dependence of realized variances with vines. Econometrics and Statistics 12: 198-216
  Czado, Claudia, Eugen Ivanov, and Yarema Okhrin
  
• (2020) A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series. Computational Statistics & Data Analysis 142
  Barthel, Nicole, Claudia Czado, and Yarema Okhrin