May 8, 2024
Abouzar Bazyari

Abouzar Bazyari

Academic Rank: Assistant professor
Address:
Degree: Ph.D in -
Phone: -
Faculty: Faculty of Intelligent Systems and Data Science

Research

Title Ruin Probabilities for Two Risk Models with Asymptotically Independent and Dependent Classes
Type Article
Keywords
Classical risk model Dependence structure Fourier transform method Heavy-tailed distribution Ruin probability
Journal JIRSS-JOURNAL OF THE IRANIAN STATISTICAL SOCIETY
DOI
Researchers Abouzar Bazyari (First researcher)

Abstract

This paper presents two new insurance risk models for analyzing the ruin probabilities. Firstly, we restrict ourselves to the classical risk model contains heavy-tailed distribution of individual net losses and changeable premium income rates. Under certain technical assumptions, some asymptotic expansions and recursive formulas are obtained for the ruin probabilities. In the second risk model, we assume that the different classes of the portfolio business are dependent and compute the finite time ruin probability based on the discretization of the distribution function. We present some numerical examples in the portfolio of business and show that the value of ruin probability increases as dependence level increases. Moreover, the sensitivity of the results are investigated with respect to the parameters of Weibull and Exponential distributions.