Multivariate extremes, aggregation and risk estimation
Höskuldur Ari Hauksson,
Ulrich Müller and
No P2, CeNDEF Workshop Papers, January 2001 from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance
We briefly introduce some basic facts about multivariate extreme value theory and present some new results regarding finite aggregates and multivariate extreme value distributions. Based on our results high frequency data can considerably improve quality of estimates of extreme movements in financial markets. Secondly we present an empirical exploration of what the tails really look like for four foreign exchange rates sampled at varying frequencies. Both temporal and spatial dependence is considered. In particular we estimate the spectral measure, which along with the tail index, completely determines the extreme value distribution. Lastly we apply our results to the problem of portfolio optimisation or risk minimization. We analyze how the expected shortfall and VaR scale with time horizon and find that this scaling is not by a factor of square root of time as is frequently used, but by a different power of time. We show that the accuracy of risk estimation can be drastically improved by using hourly or bihourly data.
References: Add references at CitEc
Citations: View citations in EconPapers (20) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Journal Article: Multivariate extremes, aggregation and risk estimation (2001)
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:ams:cdws01:p2
Access Statistics for this paper
More papers in CeNDEF Workshop Papers, January 2001 from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands. Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().