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For two-component mixtures of shifted distributions a general formula for finding the value of the shift parameter ã0,s, at which the cumulant coefficients γs ( p ã) of any order s ≥ 3 are equal to zero, is obtained. An algorithm for mathematical and computer modeling of two component mixtures of shifted distributions with zero cumulant coefficients is formulated. General formulas for two-component mixtures of shifted gamma-distributions with zero cumulant coefficients of any order are obtained and examples of mixtures with zero skewness and kurtosis coefficients are given. General formulas of two-component mixtures of shifted Student’s distributions with zero cumulant coefficients of any order are obtained and examples of mixtures with zero kurtosis coefficient and coefficient γ6 ( p, ã) are given. The research results provide the practical possibility of using two-component mixtures of shifted distributions for mathematical and computer modeling of non-Gaussian random variables with zero cumulant coefficients of any order. [ABSTRACT FROM AUTHOR]

The dependence of the extremes and zeros of the excess kurtosis γ4(p, ã ) on the weight coefficient P is researched. Formulas for finding the extrema points, the values of the minimums and maximums of the excess kurtosis are obtained. Conditions on the shift parameter ã under which the extrema points belong to the interval [0;1] are determined. Formulas for finding the zeros of the excess kurtosis are obtained and conditions on shift parameter under which the roots of the equation γ4(p, ã ) are real and belong to the interval [0;1] are determined. Examples of calculating extremes and zeros of the excess kurtosis of two-component mixtures of shifted non-Gaussian distributions are considered. The results of the research justify the possibility of practical application of two-component mixtures of shifted distributions for mathematical and computer modeling of an infinite number of non-Gaussian random variables with negative, positive and zero excess kurtosis. [ABSTRACT FROM AUTHOR]

Extensive high-speed railway (HSR) network resembled the intricate vascular system of the human body, crisscrossing mainlands. Seismic events, known for their unpredictability, pose a significant threat to both trains and bridges, given the HSR's extended operational duration. Therefore, ensuring the running safety of train-bridge coupled (TBC) system, primarily composed of simply supported beam bridges, is paramount. Traditional methods like the Monte Carlo method fall short in analyzing this intricate system efficiently. Instead, efficient algorithm like the new point estimate method combined with moment expansion approximation (NPEM-MEA) is applied to study random responses of numerical simulation TBC systems. Validation of the NPEM-MEA's feasibility is conducted using the Monte Carlo method. Comparative analysis confirms the accuracy and efficiency of the method, with a recommended truncation order of four to six for the NPEM-MEA. Additionally, the influences of seismic magnitude and epicentral distance are discussed based on the random dynamic responses in the TBC system. This methodology not only facilitates seismic safety assessments for TBC systems but also contributes to standard-setting for these systems under earthquake conditions. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for computing Value at Risk for financial assets that does not follow a normal distribution of return. A back-testing approach have been applied in order to select the best theoretical non-Gaussian distributions that can explain the behavior of the empirical data. In this study, Cauchy, Laplace, Logistic and Beta distributions have been considered. As benchmark, historical distribution, and Extreme Value Theory (EVT) method have been used. The experiment suggests differences in estimation of over 5 times between one method and another. [ABSTRACT FROM AUTHOR]

The estimation of higher-order co-moments of asset returns play an important role in higher-order moment portfolio selection. We improve the estimation of higher-order co-moments by using non-Gaussian components in the observed factor models and construct a portfolio selection method, labelled as Non-Gaussian Component (NGC) portfolio. We assume the non-normality of asset returns is driven by the independent non-Gaussian components in the observed factors. Through identifying and extracting those non-Gaussian components, the parameters in the portfolio objective function have been significantly decreased. We show that the non-Gaussian components can be estimated consistently by the independent component analysis and higher-order cumulant tests. Simulation studies confirm the good finite sample properties of our estimation procedure and further the performance of the NGC portfolio. Empirical results show that the NGC portfolio outperforms the benchmark portfolios, and only a few non-Gaussian components are needed to optimize the objective function. [ABSTRACT FROM AUTHOR]