We study the propagation of uncertainty from a class of priors introduced by Arias-Nicolás et al. [(2016) Bayesian Analysis, 11(4), 1107–1136] to the premiums (both the collective and the Bayesian), for a wide family of premium principles (specifically, those that preserve the likelihood ratio order). The class under study reflects the prior uncertainty using distortion functions and fulfills some desirable requirements: elicitation is easy, the prior uncertainty can be measured by different metrics, and the range of quantities of interest is easily obtained from the extremal members of the class. We illustrate the methodology with several examples based on different claim counts models.

In this paper, we propose a model for the optimal premium pricing policy of an insurance company into a competitive environment using Dynamic Programming into a stochastic, discrete-time framework when the company is expected to drop part of the market. In our approach, the volume of business which is related to the past year experience, the average premium of the market, the company's premium which is a control function and a linear stochastic disturbance, have been considered. Consequently, maximizing the total expected linear discounted utility of the wealth over a finite time horizon, the optimal premium strategy is defined analytically and endogenously. Finally, considering two different strategies for the average premium of the market, the optimal premium policy for a company with an expected decreasing volume of business is derived and fully investigated. The results of this paper are further evaluated by using data from the Greek Automobile Insurance Industry.