Research Article

Auto insurance premiums in Ghana: An Autoregressive Distributed Lag model approach to risk exposure variables


Abstract

This study examined risk exposure and auto insurance premium determinants in Ghana. We analysed an existing data set of 23 434 policies (without claims = 84.1%, policies with claims = 15.9%; comprehensive policies = 48.0%, third-party policies = 52.0%) applying the Autoregressive Distributed Lag (ARDL) model, controlling for driver demographics, value of car, and car usage variables. Findings indicate policyholders’ age significantly determine premiums charges. Additionally, auto seating capacity significantly influenced third-party rather than comprehensive premiums, and auto’s cubic capacity had no significant impact on premium charges. Pricing system impact premiums were influenced by policyholders’ characteristics more than variables from the insured vehicle. These findings suggest that policyholders’ age (novice drivers) and vehicles with many occupants increases auto insurers risk exposure.

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