Markowitz Theory in Portfolio Optimization for Heavy Tailed Assets

Authors

  • Wong Ghee Ching School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
  • Che Mohd Imran Che Taib School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia

DOI:

https://doi.org/10.46754/umtjur.v1i3.74

Keywords:

portfolio optimization, heavy-tailed, risk and returns, Markowitz model, backtesting

Abstract

This paper aims at solving an optimization problem in the presence of heavy tail behavior of financial assets. The question of minimizing risk subjected to a certain expected return or maximizing return for a given expected risk are two objective functions to be solved using Markowitz model. The Markowitz based strategies namely the mean variance portfolio, minimum variance portfolio and equally weighted portfolio are proposed in conjunction with mean and variance analysis of the portfolio. The historical prices of stocks traded at Bursa Malaysia are used for empirical analysis. We employed CAPM in order to investigate the performance of the Markowitz model which was benchmarked with risk adjusted KLSE Composite Index. We performed a backtesting study of portfolio optimization techniques defined under modern portfolio theory in order to find the optimal portfolio. Our findings showed that the mean variance portfolio outperformed the other two strategies in terms of performance of investment for heavy tailed assets.

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Additional Files

Published

2019-07-31

How to Cite

Wong Ghee Ching, & Che Mohd Imran Che Taib. (2019). Markowitz Theory in Portfolio Optimization for Heavy Tailed Assets . Universiti Malaysia Terengganu Journal of Undergraduate Research, 1(3), 1–14. https://doi.org/10.46754/umtjur.v1i3.74