Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data
Cathy Ning (),
Dinghai Xu and
Tony Wirjanto ()
No 6, Working Papers from Ryerson University, Department of Economics
Volatility clustering is a well-known stylized feature of financial asset returns. In this paper, we investigate the asymmetric pattern of volatility clustering on both the stock and foreign exchange rate markets. To this end, we employ copula-based semi-parametric univariate time-series models that accommodate the clusters of both large and small volatilities in the analysis. Using daily realized volatilities of the individual company stocks, stock indices and foreign exchange rates constructed from high frequency data, we find that volatility clustering is strongly asymmetric in the sense that clusters of large volatilities tend to be much stronger than those of small volatilities. In addition, the asymmetric pattern of volatility clusters continues to be visible even when the clusters are allowed to be changing over time, and the volatility clusters themselves remain persistent even after forty days.
Keywords: Volatility clustering; Copulas; Realized volatility; High-frequency data. (search for similar items in EconPapers)
JEL-codes: C51 G32 (search for similar items in EconPapers)
Pages: 33 pages
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Working Paper: Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rye:wpaper:wp006
Access Statistics for this paper
More papers in Working Papers from Ryerson University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Doosoo Kim ().