Choosing solutions under risk and uncertainty requires the consideration of several factors. One of the main factors in … choosing a solution is modeling the decision maker's attitude to risk. The expected utility theory was the first approach that … allowed to correctly model various nuances of the attitude to risk. Further research in this area has led to the emergence of …

framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure … including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse … sensitivity analysis framework suitable for risk models. …

distribution, may contain more than one parameter. The coherency of the resulting risk measures is ensured by restricting the … exponentiated exponential and Gompertz distributions. Closed-form expressions for risk measures were derived for uniform … risk measures. We then propose a simple plug-in estimate of risk measures and conduct simulation studies to compare and …

distortion parameters can be influenced by political factors or users' risk aversion levels, we generate plots of the distortion … functions to examine how these parameters impact the tasks and users' attitudes toward risk. The coherent properties of the … resulting risk measures are explored, outlining the conditions under which the transmuted Kumaraswamy and transmuted truncated …

evaluation of the methodological and empirical advances in the measurement of the extreme market risk. This paper argues that a … sustain the rise of financial markets. Thereafter, this review identified the value at risk (VaR) and VaR-based alternative … expected shortfall (ES) as the principal measures of extreme market risk. The deficiencies in the standard modelling approaches …

incorporating time-series behaviors of asset returns on the basis of coherent risk minimization. Analyzing the dual form of our … robust optimization. For this optimization, we prove that the worst-case coherent risk measure can be decomposed into the … empirical risk measure and the penalty terms. Numerical results demonstrate that when the number of assets is small, linear …

When the uni-variate risk measure analysis is generalized into the multi-variate setting, many complex theoretical and … applied problems arise, and therefore the mathematical models used for risk quantification usually present model risk. As a … task, we propose a novel multi-variate risk measure, based on the notion of the Wasserstein barycenter. The proposed …

risks. This study aimed to measure and assess foreign exchange risk utilizing Neural Networks and ARMA-GARCH models. Data on … GBP. This research explores advanced methods for measuring and assessing foreign exchange risk using Neural Networks … for financial institutions, investors, and policymakers, equipping them with robust tools for risk management in currency …

This study evaluates the sensitivity and robustness of the systemic risk measure, Conditional Value-at-Risk (CoVaR … the vine copula and APARCH-DCC in assessing portfolio systemic risk. This advanced approach provides nuanced insights into … strengthening risk management practices. Future research could explore the sensitivity of the CoVaR to diferent weighting schemes …