Volatility clustering in the presence of time-varying model parameters

Edoardo Otranto

Journal of Applied Statistics, 2013, vol. 40, issue 4, 901-915

Abstract: The volatility pattern of financial time series is often characterized by several peaks and abrupt changes, consistent with the time-varying coefficients of the underlying data-generating process. As a consequence, the model-based classification of the volatility of a set of assets could vary over a period of time. We propose a procedure to classify the unconditional volatility obtained from an extended family of Multiplicative Error Models with time-varying coefficients to verify if it changes in correspondence with different regimes or particular dates. The proposed procedure is experimented on 15 stock indices.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2012.759191 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:40:y:2013:i:4:p:901-915

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2012.759191

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().