Time varying Hierarchical Archimedean Copulae
AbstractThere is increasing demand for models of time-varying and non-Gaussian dependencies for mul- tivariate time-series. Available models suffer from the curse of dimensionality or restrictive assumptions on the parameters and the distribution. A promising class of models are the hierarchical Archimedean copulae (HAC) that allow for non-exchangeable and non-Gaussian dependency structures with a small number of parameters. In this paper we develop a novel adaptive estimation technique of the parameters and of the structure of HAC for time-series. The approach relies on a local change point detection procedure and a locally constant HAC approximation. Typical applications are in the financial area but also recently in the spatial analysis of weather parameters. We analyse the time varying dependency structure of stock indices and exchange rates. We find that for stock indices the copula parameter changes dynam- ically but the hierarchical structure is constant over time. Interestingly in our exchange rate example both structure and parameters vary dynamically.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2010-018.
Length: 32 pages
Date of creation: Feb 2010
Date of revision:
copula; multivariate distribution; Archimedean copula; adaptive estimation;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
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- Francis X. Diebold & Kamil Yilmaz, 2011.
"On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms,"
KoÃ§ University-TUSIAD Economic Research Forum Working Papers
1124, Koc University-TUSIAD Economic Research Forum.
- Francis X. Diebold & Kamil Yılmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," PIER Working Paper Archive 11-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Francis X. Diebold & Kamil Yilmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," NBER Working Papers 17490, National Bureau of Economic Research, Inc.
- Francis X. Diebold & Kamil Yilmaz, 2011. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Working Papers 11-45, Federal Reserve Bank of Philadelphia.
- Fengler, Matthias & Okhrin, Ostap, 2012.
Economics Working Paper Series
1214, University of St. Gallen, School of Economics and Political Science.
- Nikolaus Hautsch & Julia Schuamburg & Melanie Schienle, 2012. "Modeling Time-Varying Dependencies between Positive-Valued High-Frequency Time Series," SFB 649 Discussion Papers SFB649DP2012-054, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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