Analysis of actuarial risk with generalized linear models
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2025.4.04Keywords:
actuarial risk, generalized linear models, simulation modeling, exponential family of distributions, Bayesian data analysis, Monte Carlo method for Markov chainsAbstract
The problem of applying generalized linear models to the analysis of actuarial risks in the context of premium charges to clients was considered. The Monte-Carlo method for Markov chains was applied. Two situations were considered for the computational experiment. For the first one, insurance indicators and the target variable were randomly assigned due to the problem of public data access. To create three datasets, charges were generated from normal, gamma, and Pareto distributions with dynamic variance, and noise was added to stimulate a non-stationary process. In the second situation, actual actuarial data from the Singa-pore Actuarial Society was used. Generalized Linear Models with normal dis-tribution and logarithmic link function, an exponential distribution and loga-rithmic link function, and Laplace distribution with identity link function were constructed. Based on the model-fitting quality metrics, conclusions were drawn about their structure.
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