This research aims to model the effective systematic risks of financial recovery in the insurance industry. This research is a type of applied research. The period of research is 11 years (1400-1390). For this purpose, the information on 14 systematic risks affecting the financial solvency of insurance companies was entered into dynamic, selective, and Bayesian averaging models. Based on the error rate, the Bayesian averaging model had the highest accuracy among the selected models. After estimating the model, 5 economic growth risks, inflation uncertainty, exchange rate, sanctions, and KOF index were selected; Also, based on the results of the TVPFAVAR model, it was assessed that the impact shock of the selected variables in the long-term period is stronger than the short-term period, which indicates that the elasticity of financial prosperity is greater than the changes in systematic risk variables compared to the short-term elasticity. Based on the results of economic growth and the KOF index, the positive effect and uncertainty variables of inflation, exchange rate, and sanctions hurt financial wealth in the general trend.