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STATISTIC MODELING OF DEPENDENT RISKS IN HEALTH INSURANCE
Abstract
Solvency of health insurance company depends on tariff politics that should be adequate to value of risks insured which depends on number of claims received. Insured’s diseases can occur simultaneously or in various combinations so claims in health insurance are depended. Traditional actuarial methods based on calculation of proportion of insured with each disease among insured individuals don’t take into account possible dependencies between diseases occurrence. Multivariate probit models are the contemporary econometrical models, which allow evaluating relationships for any number of related dependent binary variables. They could be used for evaluation of dependent risk in health insurance. We presents model estimation results for diseases of lungs, heart and spine based on Individual Russia Longitudinal Monitoring Survey – Higher School of Economics (RLMS-HSE) data. It is showed that multivariate probit model provides a more correct risk assessment than calculated using traditional actuarial technique, based on official statistics of morbidity.
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