Risk Modeling via Direct Utility Maximization Using Numerical Quadrature

An approach to risk modeling is developed which uses nonlinear programming and numerical integration to directly solve the expected utility maximization problem. The approach contrasts with earlier efforts in that, rather than using an empirical density function, a joint probability density function is explicitly specified. Comparisons are done showing that this approach yields more accurate solutions than the empirical density approach even when many points are sampled from the theoretical distribution.