Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)

Abstract Portfolio insurance is a hedging strategy which is used to limit portfolio losses without having to sell off stock when stocks decline in value. Consequently, the minimization of the costs related to portfolio insurance is a very important investment strategy. On the one hand, a popular option to solve the static minimum-cost portfolio insurance problem is based on the use of linear programming (LP) methods. On the other hand, the static portfolio selection under transaction costs (PSTC) problem is usually approached by nonlinear programming (NLP) methods. In this article, we define and study the time-varying minimum-cost portfolio insurance under transaction costs (TV-MCPITC) problem in the form of a time-varying nonlinear programming (TV-NLP) problem. Using the Beetle Antennae Search (BAS) algorithm, we also provide an online solution to the static NLP problem. The online solution to a time-varying financial problem is a great technical analysis tool and along with fundamental analysis will enable the investors to make better decisions. To the best of our knowledge, an approach that incorporates modern meta-heuristic optimization techniques to provide a more realistic online solution to the TV-MCPITC problem is original. In this way, by presenting an online solution to a time-varying financial problem we highlight the limitations of static methods. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods.