Journal of Operational Risk

Risk.net

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

Christian Hess

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.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here