Implementation of mean-variance and tail optimization based portfolio choice on risky assets

Abstract:

An asset manager's goal is to provide a high return relative the risk taken, and thus faces the

challenge of how to choose an optimal portfolio. Many mathematical methods have been developed

to achieve a good balance between these attributes and using di erent risk measures. In this

thesis, we test the use of a relatively simple and common approach: the Markowitz mean-variance

method, and a more quantitatively demanding approach: the tail optimization method. Using a

ctive portfolio based on data provided by the Swedish fund management company Enter Fonder

we implement these approaches and compare the results. We analyze how each method weighs the

underlying assets in order to get an optimal portfolio.