Fuzzy Risk Measure for Operational Risk

2018 ◽  
Vol 28 (1) ◽  
pp. 1-17
Author(s):  
Assem Tharwat ◽  
Ramadan ZeinEldin ◽  
Hamiden Khalifa ◽  
Ahmed Saleim
Keyword(s):  
Risk Measure ◽  
Operational Risk

Asymptotics for Operational Risk Quantified with Expected Shortfall

2009 ◽  
Vol 39 (2) ◽  
pp. 735-752 ◽  
Author(s):  
Francesca Biagini ◽  
Sascha Ulmer
Keyword(s):  
Confidence Level ◽  
Risk Measure ◽  
Operational Risk ◽  
Expected Shortfall ◽  
Dependence Structure ◽  
Practical Relevance ◽  
Convex Risk Measure ◽  
Multivariate Case ◽  
Univariate Case ◽  
Levy Copula

AbstractIn this paper we estimate operational risk by using the convex risk measure Expected Shortfall (ES) and provide an approximation as the confidence level converges to 100% in the univariate case. Then we extend this approach to the multivariate case, where we represent the dependence structure by using a Lévy copula as in Böcker and Klüppelberg (2006) and Böcker and Klüppelberg, C. (2008). We compare our results to the ones obtained in Böcker and Klüppelberg (2006) and (2008) for Operational VaR and discuss their practical relevance.

scholarly journals Practice Oriented and Monte Carlo Based Estimation of the Value-at-Risk for Operational Risk Measurement

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 50 ◽  
Author(s):  
Francesca Greselin ◽  
Fabio Piacenza ◽  
Ričardas Zitikis
Keyword(s):  
Monte Carlo ◽  
Value At Risk ◽  
Sampling Error ◽  
Risk Measure ◽  
Simulated Data ◽  
Operational Risk ◽  
Computational Effort ◽  
Risk Measurement ◽  
Sampling Errors ◽  
Eu Regulation

We explore the Monte Carlo steps required to reduce the sampling error of the estimated 99.9% quantile within an acceptable threshold. Our research is of primary interest to practitioners working in the area of operational risk measurement, where the annual loss distribution cannot be analytically determined in advance. Usually, the frequency and the severity distributions should be adequately combined and elaborated with Monte Carlo methods, in order to estimate the loss distributions and risk measures. Naturally, financial analysts and regulators are interested in mitigating sampling errors, as prescribed in EU Regulation 2018/959. In particular, the sampling error of the 99.9% quantile is of paramount importance, along the lines of EU Regulation 575/2013. The Monte Carlo error for the operational risk measure is here assessed on the basis of the binomial distribution. Our approach is then applied to realistic simulated data, yielding a comparable precision of the estimate with a much lower computational effort, when compared to bootstrap, Monte Carlo repetition, and two other methods based on numerical optimization.

Functional and operational risk as a criterion for assessing the durability of an autonomous energy system

2019 ◽  
Vol 12 (1) ◽  
pp. 56-62 ◽  
Author(s):  
A. O. Nedosekin ◽  
A. V. Smirnov ◽  
D. P. Makarenko ◽  
Z. I. Abdoulaeva
Keyword(s):  
Service Life ◽  
Structural Reliability ◽  
Residual Life ◽  
Fuzzy Random Variable ◽  
Operational Risk ◽  
Energy System ◽  
Technical System ◽  
Residual Service Life ◽  
Technological Parameters ◽  
Residual Service

The article presents new models and methods for estimating the residual service life of an autonomous energy system, using the functional operational risk criterion (FOR). The purpose of the article is to demonstrate a new method of durability evaluation using the fuzzy logic and soft computing framework. Durability in the article is understood as a complex property directly adjacent to the complex property of system resilience, as understood in the Western practice of assessing and ensuring the reliability of technical systems. Due to the lack of reliable homogeneous statistics on system equipment failures and recoveries, triangular fuzzy estimates of failure and recovery intensities are used as fuzzy functions of time based on incomplete data and expert estimates. The FOR in the model is the possibility for the system availability ratio to be below the standard level. An example of the evaluation of the FOR and the residual service life of a redundant cold supply system of a special facility is considered. The transition from the paradigm of structural reliability to the paradigm of functional reliability based on the continuous degradation of the technological parameters of an autonomous energy system is considered. In this case, the FOR can no longer be evaluated by the criterion of a sudden failure, nor is it possible to build a Markov’s chain on discrete states of the technical system. Assuming this, it is appropriate to predict the defi ning functional parameters of a technical system as fuzzy functions of a general form and to estimate the residual service life of the technical system as a fuzzy random variable. Then the FOR is estimated as the possibility for the residual life of the technical system to be below its warranty period, as determined by the supplier of the equipment.

Cyber attacks as a source of operational risk in electronic banking

2018 ◽  
Vol 24 (3) ◽  
pp. 629-640
Author(s):  
P.V. Revenkov ◽  
◽  
A.A. Berdyugin ◽  
Keyword(s):  
Operational Risk ◽  
Cyber Attacks ◽  
Electronic Banking
Sign in / Sign up

Export Citation Format

Share Document