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