TY - JOUR
ID - 677085
TI - The Tail Mean-Variance Model and Extended Efficient Frontier
JO - Advances in Mathematical Finance and Applications
JA - AMFA
LA - en
SN - 2538-5569
AU - Jamshidi Eini, Esmat
AU - Khaloozadeh, Hamid
AD - Department of Systems and Control, K.N. Toosi University of Technology, Tehran, Iran
Y1 - 2021
PY - 2021
VL - 6
IS - 1
SP - 179
EP - 193
KW - Tail Mean-Variance criterion
KW - Optimal portfolio selection
KW - Efficient Frontier
KW - Skew-Elliptical Distributions
DO - 10.22034/amfa.2020.1892182.1365
N2 - In portfolio theory, it is well-known that the distributions of stock returns often have non-Gaussian characteristics. Therefore, we need non-symmetric distributions for modeling and accurate analysis of actuarial data. For this purpose and optimal portfolio selection, we use the Tail Mean-Variance (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 Skew-Elliptical (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.
UR - http://amfa.iau-arak.ac.ir/article_677085.html
L1 - http://amfa.iau-arak.ac.ir/article_677085_9a64e84f3965b191ca67680751babd24.pdf
ER -