Volume 27 , Issue 2 , PP: 123-131, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmedia Musa M. Ibrahim 1 *
Doi: https://doi.org/10.54216/IJNS.270211
Ram Awadh (RA) distribution is flexible to handle skewedness and heavy tailed observations, which are frequent in financial risk management. With flexible structure, it has potential to be a reliable model in financial data modeling and decision-making process in the scenarios of indeterminacy. The new one parameter lifetime distribution is proposed and called as the neutrosophic RA distribution ( ) in this article. We obtain the raw and central moments of it and investigate some important statistical properties such as the coefficient of variation, skewness, kurtosis and index of dispersion. Moreover, some reliability properties such as the hazard rate function mean residual life function, and stochastic orderings of the distribution are considered. The method of maximum likelihood estimation (MLE) is utilized for parameter estimation. A comprehensive simulation study is carried out to evaluate the behavior of the distribution and its statistical properties. Finally, a real-world dataset of economic sector is utilized to illustrate its practical importance.
Skewed distribution , Financial model , Neutrosophic probability , Estimation
[1] H. Shahirinia, E. S. Soofi, and D. C. Yu, "Probability distributions of outputs of stochastic economic dispatch," International Journal of Electrical Power & Energy Systems, vol. 81, pp. 308–316, 2016.
[2] M. F. Steel, "Model averaging and its use in economics," Journal of Economic Literature, vol. 58, no. 3, pp. 644–719, 2020.
[3] R. B. Myerson and E. Zambrano, Probability Models for Economic Decisions. Cambridge, MA: MIT Press, 2019.
[4] F. D’Acunto, U. Malmendier, and M. Weber, "What do the data tell us about inflation expectations?" in Handbook of Economic Expectations, pp. 133–161, 2023.
[5] P. Stockhammar and L.-E. Öller, "On the probability distribution of economic growth," Journal of Applied Statistics, vol. 38, no. 9, pp. 2023–2041, 2011.
[6] T. Angelidis, A. Benos, and S. Degiannakis, "The use of GARCH models in VaR estimation," Statistical Methodology, vol. 1, no. 1–2, pp. 105–128, 2004.
[7] H. P. Kumar and S. B. Patil, "Volatility forecasting – A performance measure of GARCH techniques with different distribution models," International Journal of Soft Computing, Mathematics and Control (IJSCMC), vol. 5, no. 2/3, 2016.
[8] S. T. Rachev, M. Hoechstoetter, F. J. Fabozzi, and S. M. Focardi, Probability and Statistics for Finance. Hoboken, NJ: Wiley, 2010.
[9] S. Mittnik and S. T. Rachev, "Modeling asset returns with alternative stable distributions," Econometric Reviews, vol. 12, no. 3, pp. 261–330, 1993.
[10] F. Longin, "The choice of the distribution of asset returns: How extreme value theory can help?" Journal of Banking & Finance, vol. 29, no. 4, pp. 1017–1035, 2005.
[11] R. Thakur, S. C. Malik, and M. Raj, "Neutrosophic Laplace distribution with application in financial data analysis," Neutrosophic Sets and Systems, vol. 57, pp. 224, 2024.
[12] R. A. K. Sherwani et al., "Neutrosophic normal probability distribution a spine of parametric neutrosophic statistical tests: properties and applications," in Neutrosophic Operational Research: Methods and Applications, pp. 153–169, 2021.
[13] M.-I. Bolos, I.-A. Bradea, and C. Delcea, "Modeling the performance indicators of financial assets with neutrosophic fuzzy numbers," Symmetry, vol. 11, no. 8, p. 1021, 2019.
[14] Z. Khan et al., "On statistical development of neutrosophic gamma distribution with applications to complex data analysis," Complexity, vol. 2021, p. 3701236, 2021.
[15] A. K. Verma and S. K. Gupta, "A study on the impact of economic policies on stock market volatility," Journal of Economic Dynamics, vol. 10, no. 3, pp. 150–165, 2023.
[16] M. Lathamaheswari et al., "Neutrosophic perspective of neutrosophic probability distributions and its application," Collected Papers, vol. X: On Neutrosophics, Plithogenics, Hypersoft Set, Hypergraphs, and other topics, p. 267, 2022.
[17] F. S. Al-Duais, "Neutrosophic log-gamma distribution and its applications to industrial growth," Neutrosophic Sets and Systems, vol. 72, no. 1, p. 22, 2024.
[18] A. Alvaracín Jarrín et al., "Neutrosophic statistics applied in social science," Neutrosophic Sets and Systems, vol. 44, no. 1, p. 1, 2021.
[19] J. K. Patel and R. T. Singh, "Statistical methods for analyzing financial data: A review," Journal of Financial Studies, vol. 5, no. 1, pp. 78–92, 2021.
[20] V. Christianto and F. Smarandache, "A review of seven applications of neutrosophic logic: in cultural psychology, economics theorizing, conflict resolution, philosophy of science, etc.," J, vol. 2, no. 2, pp. 128–137, 2019.
[21] K. K. Shukla, "Ram Awadh distribution with properties and applications," Biom Biostat Int J, vol. 7, no. 6, pp. 515–523, 2018.
[22] B. Patel and C. D. Mehta, "Statistical analysis of financial time series using machine learning techniques," Journal of Financial Analytics, vol. 12, no. 1, pp. 45–60, 2023.
[23] L. M. Chen and R. A. Smith, "Exploring the relationship between economic indicators and stock market performance," Journal of Economic Perspectives, vol. 15, no. 2, pp. 100–115, 2022.
[24] World Bank, "GDP per capita (current US$) – Saudi Arabia," World Bank Data, 2024.
