Investment and reinsurance with incomplete information and biased beliefs

We explore how the insurer's biased beliefs, overconfidence and overextrapolation, influence the optimal reinsurance and asset allocation strategies with incomplete information. In our model setup, the two biased beliefs will impact the posterior mean and variance during the process of Bayesian learning. We show that overextrapolation always leads to underinvestment and overconfidence always leads to overinvestment. Overconfidence and overextrapolation on the risky asset influence reinsurance demand by the correlation coefficient between the risky asset's price and insurance claims. Specifically, when the correlation coefficient is negative, overextrapolation induces more reinsurance demand and overconfidence induces less reinsurance demand, and when the relative coefficient is positive, the opposite results hold. Moreover, these results have a certain stability, even if there is ambiguity in the claim model or the main model parameters change.