Can the single-loss approximation method compete with the standard monte carlo simulation technique?

Christian Hess

Journal of Operational Risk

Abstract: ABSTRACT In this paper we evaluate the single-loss approximation method for high-quantile loss estimation on the basis of SAS OpRisk Global Data. Due to its simplicity, the single-loss approximation method has become a popular tool for capital requirement calculation purposes in the financial services industry. As the single-loss approximation method requires some strict assumptions, the naive use of this method was criticized in a 2010 paper by Degen. Although we support this criticism, the single-loss approximation method yields astonishingly exact results for the underlying data set and our calibrated heavy-tailed lognormal loss severity model. We show in this paper that the value-at-risk (VaR) estimates by the single-loss approximation method are more accurate than the quantile estimates computed by a Monte Carlo simulation with one million losses. However, we find a significant 99.9%VaR underestimation for a medium-tailed gamma loss severity model.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-operational-risk/2 ... simulation-technique (text/html)

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:rsk:journ3:2160902

Access Statistics for this article

More articles in Journal of Operational Risk from Journal of Operational Risk
Bibliographic data for series maintained by Thomas Paine ().