Challenges in Implementing Worst-Case Analysis
Jon Danielsson (),
Lerby Ergun and
Casper de Vries ()
Staff Working Papers from Bank of Canada
Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach. We find that if the return distribution has a heavy tail, the non-parametric worst-case analysis, i.e. the minimum of the sample, is always downwards biased and hence is overly conservative. Relying on semi-parametric EVT reduces the bias considerably in the case of relatively heavy tails. But for the less-heavy tails this relationship is reversed. Estimates for a large sample of US stock returns indicate that this pattern in the bias is indeed present in financial data. With respect to risk management, this induces an overly conservative capital allocation if the worst case is estimated incorrectly.
Keywords: Financial; stability (search for similar items in EconPapers)
JEL-codes: C01 C14 C58 (search for similar items in EconPapers)
Pages: 28 pages
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 (1) Track citations by RSS feed
Downloads: (external link)
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:bca:bocawp:18-47
Access Statistics for this paper
More papers in Staff Working Papers from Bank of Canada 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada. Contact information at EDIRC.
Bibliographic data for series maintained by ().