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Showing 2 results for Risk Measure
Roghayeh Ghorbani Gholi Abad, Gholam Reza Mohtashami Borzadaran, Mohammad Amini, Zahra Behdani,
Volume 18, Issue 2 (2-2025)
Abstract
Abstract: The use of tail risk measures has been noticed in recent decades, especially in the financial and banking industry. The most common ones are value at risk and expected shortfall. The tail Gini risk measure, a composite risk measure, was introduced recently. The primary purpose of this article is to find the relationship between the concepts of economic risks, especially the expected shortfall and the tail Gini risk measure, with the concepts of inequality indices in the economy and reliability. Examining the relationship between these concepts allows the researcher to use the concepts of one to investigate other concepts. As you will see below, the existing mathematical relationships between the tail risk measures and the mentioned indices have been obtained, and these relationships have been calculated for some distributions. Finally, real data from the Iranian Stock Exchange was used to familiarize the concept of this tail risk measure.
Seyed Jamal, Khorashadizadeh, Fatemeh Yousefzadeh, Sara Jomhoori,
Volume 19, Issue 2 (4-2025)
Abstract
Researchers develop generalized families of distributions to better model data in fields like risk management, economics, and insurance. In this paper, a new distribution, the Extended Exponential Log-Logistic Distribution, is introduced, which belongs to the class of heavy-tailed distributions. Some statistical properties of the model, including moments, moment-generating function, entropy, and economic inequality curves, are derived. Six estimation methods are proposed for estimating the model parameters, and the performance of these methods is evaluated using randomly generated datasets. Additionally, several insurance-related measures, including Value at Risk, Tail Value at Risk, Tail Variance, and Tail Variance Premium, are calculated. Finally, two real insurance datasets are employed, showing that the proposed model fits the data better than many existing related models.
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