Bayesian Extreme Value Mixture Modelling for Estimating VaR

Xin Zhao, Carl John Scarrott, Marco Reale and Les Oxley

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

Abstract: A new extreme value mixture modelling approach for estimating Value-at-Risk (VaR) is proposed, overcoming the key issues of determining the threshold which defines the distribution tail and accounts for uncertainty due to threshold choice. A two-stage approach is adopted: volatility estimation followed by conditional extremal modelling of the independent innovations. Bayesian inference is used to account for all uncertainties and enables inclusion of expert prior information, potentially overcoming the inherent sparsity of extremal data. Simulations show the reliability and flexibility of the proposed mixture model, followed by VaR forecasting for capturing returns during the current financial crisis.

Keywords: Extreme values; Bayesian; Threshold estimation; Value-at-Risk (search for similar items in EconPapers)
JEL-codes: C11 G12 G17 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2009-10-27
New Economics Papers: this item is included in nep-ecm and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://repec.canterbury.ac.nz/cbt/econwp/0915.pdf (application/pdf)

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:cbt:econwp:09/15

Access Statistics for this paper

More papers in Working Papers in Economics from University of Canterbury, Department of Economics and Finance Private Bag 4800, Christchurch, New Zealand. Contact information at EDIRC.
Bibliographic data for series maintained by Albert Yee ().