Count copula regression model using generalized beta distribution of the second kind

Abstract

Modelling claims severity for obtaining insurance premium is one of the major concerns of the insurance industry. There is a considerable amount of literature on the actuarial application of the copula model to calculate the pure premium. In this paper, we model claims severity for computing the pure premium in the collision market by means of the count copula model. Moreover, we apply a regression model using a generalized beta distribution of the second kind (GB2) to compute the premium for an average claim and the conditional computation for all coverage levels. Like many other researchers, we assume that the number of accidents is independent from the size of claims. For real data application, we use a portfolio of a major automobile insurer in Iran in 2007-2008, with a subsample of 59,547 policies available in their portfolio. We then proceed to compare the estimated premiums with the real premiums. The results demonstrate that there is strong positive dependency between the real premium and the estimated one.

Phytosomes as Innovative Delivery Systems for Phytochemicals: A Comprehensive Review of Literature

“…127 Also, some statistical studies are needed for better application in real world. 128 , 129 The main characteristics of phytosomes are (1) size and shape; (2) surface charge; (3) chemical composition; (4) lamellarity and stability; (5) encapsulation efficiency and (6) release behavior. The goal of this chapter is to provide a thorough summary and a systematic overview of all analytical instruments used to characterize phytosomes, including the latest papers.…”

Copula-based Modeling for IBNR Claim Loss Reserving

“…Moreover, the maximal value of τ is captured when θ goes to infinity. For more information, one can refer to [43][44][45][46].…”

Implementation of MCDM-Based Integrated Approach to Identifying the Uncertainty Factors on the Constructional Project

“…Artificial intelligence in the industry today: due to the spread of knowledge and more complex decisionmaking process, the use of information systems, especially artificial intelligence systems in decision making, has become more important [39][40][41][42]. e spread of knowledge in the field of technology and industry and the complexity of environmental decisions-in other words, human life-has attracted the attention of experts for the use of decision support systems in environmental affairs [43,44].…”

“…Increasing environmental concerns attract the attention of governments [32][33][34][35], customers, and organizations [36][37][38][39][40][41][42][43][44][45][46].…”