Model Uncertainty in Operational Risk Modeling Due to Data Truncation: A Single Risk Case
Daoping Yu and
Vytaras Brazauskas
Additional contact information
Daoping Yu: School of Computer Science and Mathematics, University of Central Missouri, Warrensburg, MO 64093, USA
Vytaras Brazauskas: Department of Mathematical Sciences, University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USA
Risks, 2017, vol. 5, issue 3, 1-17
Abstract:
Over the last decade, researchers, practitioners, and regulators have had intense debates about how to treat the data collection threshold in operational risk modeling. Several approaches have been employed to fit the loss severity distribution: the empirical approach, the “naive” approach, the shifted approach, and the truncated approach. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. The main objective of this paper is to understand the impact of model uncertainty on the value-at-risk (VaR) estimators. To accomplish that, we take the bank’s perspective and study a single risk. Under this simplified scenario, we can solve the problem analytically (when the underlying distribution is exponential) and show that it uncovers similar patterns among VaR estimates to those based on the simulation approach (when data follow a Lomax distribution). We demonstrate that for a fixed probability distribution, the choice of the truncated approach yields the lowest VaR estimates, which may be viewed as beneficial to the bank, whilst the “naive” and shifted approaches lead to higher estimates of VaR. The advantages and disadvantages of each approach and the probability distributions under study are further investigated using a real data set for legal losses in a business unit (Cruz 2002).
Keywords: asymptotics; data truncation; delta method; model validation; operational risk; VaR estimation (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.mdpi.com/2227-9091/5/3/49/pdf (application/pdf)
https://www.mdpi.com/2227-9091/5/3/49/ (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:gam:jrisks:v:5:y:2017:i:3:p:49-:d:111862
Access Statistics for this article
Risks is currently edited by Mr. Claude Zhang
More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().