<p>We present an optimal investment-reinsurance strategy for insurers operating under a dependent risk model that integrates a linear Gaussian stochastic factor framework. This work advances the mean-variance optimization literature by accounting for the dependency between claim number processes through common shocks and modeling the returns of risky assets using a linear Gaussian stochastic factor model. The insurer’s portfolio includes a risk-free asset and multiple risky assets. By applying linear-quadratic control theory, we solve the mean-variance optimization problem through a backward stochastic differential equation (BSDE) approach and derive explicit solutions for the efficient strategy and the efficient frontier. Additionally, numerical examples are provided to demonstrate the practical applicability and effectiveness of the proposed method in real-world settings.</p>