Next Article in Journal
Interest Rates Term Structure under Ambiguity
Next Article in Special Issue
An Integrated Approach to Pricing Catastrophe Reinsurance
Previous Article in Journal
A Cointegrated Regime-Switching Model Approach with Jumps Applied to Natural Gas Futures Prices
Previous Article in Special Issue
Effects of Gainsharing Provisions on the Selection of a Discount Rate for a Defined Benefit Pension Plan
Article Menu
Issue 3 (September) cover image

Export Article

Open AccessFeature PaperArticle
Risks 2017, 5(3), 49; doi:10.3390/risks5030049

Model Uncertainty in Operational Risk Modeling Due to Data Truncation: A Single Risk Case

School of Computer Science and Mathematics, University of Central Missouri, Warrensburg, MO 64093, USA
Department of Mathematical Sciences, University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USA
Author to whom correspondence should be addressed.
Academic Editor: Albert Cohen
Received: 27 April 2017 / Revised: 15 August 2017 / Accepted: 1 September 2017 / Published: 13 September 2017
View Full-Text   |   Download PDF [450 KB, uploaded 14 September 2017]   |  


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). View Full-Text
Keywords: asymptotics; data truncation; delta method; model validation; operational risk; VaR estimation asymptotics; data truncation; delta method; model validation; operational risk; VaR estimation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yu, D.; Brazauskas, V. Model Uncertainty in Operational Risk Modeling Due to Data Truncation: A Single Risk Case. Risks 2017, 5, 49.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Risks EISSN 2227-9091 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top