Results 1 to 10 of about 542,898 (181)
An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount [PDF]
Risks, 2021 This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of ...
George Tzougas, Himchan Jeong
doaj +3 more sources
On the accuracy of phase-type approximations of heavy-tailed risk models [PDF]
, 2014 Numerical evaluation of ruin probabilities in the classical risk model is an important problem. If claim sizes are heavy-tailed, then such evaluations are challenging. To overcome this, an attractive way is to approximate the claim sizes with a phase-type distribution.
Adan, Ivo J. B. F. +3 more
arxiv +15 more sources
Mathematics, 2023 Consider an insurance risk model with arbitrary dependence structures between the claim sizes. Suppose that the risky investment in the insurer can be established by the Cox–Ingersoll–Ross model.
Ming Cheng, Dingcheng Wang
doaj +1 more source
Bayesian optimal investment and reinsurance with dependent financial and insurance risks [PDF]
Statistics & Risk Modeling 39(1-2), 2022, 2021 Major events like natural catastrophes or the COVID-19 crisis have impact both on the financial market and on claim arrival intensities and claim sizes of insurers. Thus, when optimal investment and reinsurance strategies have to be determined it is important to consider models which reflect this dependence.
arxiv +1 more source
Deep Quantile and Deep Composite Model Regression [PDF]
Insurance: Mathematics and Economics, 2023, Volume 109, 2021 A main difficulty in actuarial claim size modeling is that there is no simple off-the-shelf distribution that simultaneously provides a good distributional model for the main body and the tail of the data. In particular, covariates may have different effects for small and for large claim sizes.
arxiv +1 more source
Ein neuer Ansatz zur Frequenzmodellierung im Versicherungswesen (A new Approach to frequency modeling in risk theory) [PDF]
arXiv, 2023 The collective risk model differentiates usually between claims frequencies (and their distribution) and claim sizes (and their distribution). For the claims frequencies typically classical discrete distributions are considered, such as Binomial-, Negative binomial- or Poisson distributions.
arxiv
Mixed fractional Risk Process [PDF]
Journal of Mathematical Analysis and Applications 504(1) (2021)
125379, 2020 In this paper, we introduce a risk process, namely, the mixed fractional risk process (MFRP) in which the number of claims in the associated claim process are modelled using the mixed fractional Poisson process (MFPP). The covariance structure of the MFRP is studied and its long-range dependence property has been established.
arxiv +1 more source
On some compound distributions with Borel summands [PDF]
Insurance Math. Econom., Vol. 62 (2015) 234-244, 2014 The generalized Poisson distribution is well known to be a compound Poisson distribution with Borel summands. As a generalization we present closed formulas for compound Bartlett and Delaporte distributions with Borel summands and a recursive structure for certain compound shifted Delaporte mixtures with Borel summands.
arxiv +1 more source
SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator [PDF]
, 2021 In this paper, we first introduce a simulator of cases estimates of incurred losses, called `SPLICE` (Synthetic Paid Loss and Incurred Cost Experience). In three modules, case estimates are simulated in continuous time, and a record is output for each individual claim.
arxiv +1 more source
Alternative modelling and inference methods for claim size distributions [PDF]
Ann. actuar. sci. 14 (2020) 1-19, 2019 The upper tail of a claim size distribution of a property line of business is frequently modelled by Pareto distribution. However, the upper tail does not need to be Pareto distributed, extraordinary shapes are possible. Here, the opportunities for the modelling of loss distributions are extended. The basic idea is the adjustment of a base distribution
arxiv +1 more source