the tail mean-variance model and extended efficient frontier

In portfolio theory, it is well-known that the distributions of stock returns often have non-gaussian characteristics. therefore, we need nonsymmetric distributions for modeling and accurate analysis of actuarial data. for this purpose and optimal portfolio selection, we use the tail meanvariance (tmv) model, which focuses on the rare risks but high losses and usually happens in the tail of return distribution. the proposed tmv model is based on two risk measures the tail condition expectation (tce) and tail variance (tv) under generalized skewelliptical (gse) distribution. we first apply a convex optimization approach and obtain an explicit and easy solution for the tmv optimization problem, and then derive the tmv efficient frontier. finally, we provide a practical example of implementing a tmv optimal portfolio selection in the tehran stock exchange and show tce-tv efficient frontier.