Regular Variation and Extremal Dependence of GARCH Residuals with Application to Market Risk Measures

Fabrizio Laurini and Jonathan Tawn

Econometric Reviews, 2009, vol. 28, issue 1-3, 146-169

Abstract: Stock returns exhibit heavy tails and volatility clustering. These features, motivating the use of GARCH models, make it difficult to predict times and sizes of losses that might occur. Estimation of losses, like the Value-at-Risk, often assume that returns, normalized by the level of volatility, are Gaussian. Often under ARMA-GARCH modeling, such scaled returns are heavy tailed and show extremal dependence, whose strength reduces when increasing extreme levels. We model heavy tails of scaled returns with generalized Pareto distributions, while extremal dependence can be reduced by declustering data.

Keywords: Declustering; Expected shortfalls; Extremal dependence; Generalized Pareto distribution; Regular variation; Value-at-Risk (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/07474930802387985 (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:emetrv:v:28:y:2009:i:1-3:p:146-169

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474930802387985

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().