Details
| Original language | English |
|---|---|
| Pages (from-to) | 380-398 |
| Number of pages | 19 |
| Journal | European Journal of Operational Research |
| Volume | 298 |
| Issue number | 1 |
| Early online date | 14 Jul 2021 |
| Publication status | Published - 1 Apr 2022 |
Abstract
Uncertainty requires suitable techniques for risk assessment. Combining stochastic approximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk.
Keywords
- Cyber risk, Risk management, Risk measures, Robustness, Simulation
ASJC Scopus subject areas
- Computer Science(all)
- Mathematics(all)
- Modelling and Simulation
- Decision Sciences(all)
- Management Science and Operations Research
- Decision Sciences(all)
- Information Systems and Management
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In: European Journal of Operational Research, Vol. 298, No. 1, 01.04.2022, p. 380-398.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Simulation methods for robust risk assessment and the distorted mix approach
AU - Kim, Sojung
AU - Weber, Stefan
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Uncertainty requires suitable techniques for risk assessment. Combining stochastic approximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk.
AB - Uncertainty requires suitable techniques for risk assessment. Combining stochastic approximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk.
KW - Cyber risk
KW - Risk management
KW - Risk measures
KW - Robustness
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85111811988&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2021.07.005
DO - 10.1016/j.ejor.2021.07.005
M3 - Article
AN - SCOPUS:85111811988
VL - 298
SP - 380
EP - 398
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 1
ER -