Static vs stochastic optimization: A case study of FTSE Bursa Malaysia sectorial indices

Journal Article · Thu Jun 19 00:00:00 EDT 2014 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.4882529· OSTI ID:22311274

Mamat, Nur Jumaadzan Zaleha; Jaaman, Saiful Hafizah; Ahmad, Rokiah Rozita ^[1]

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1. School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)

Traditional portfolio optimization methods in the likes of Markowitz' mean-variance model and semi-variance model utilize static expected return and volatility risk from historical data to generate an optimal portfolio. The optimal portfolio may not truly be optimal in reality due to the fact that maximum and minimum values from the data may largely influence the expected return and volatility risk values. This paper considers distributions of assets' return and volatility risk to determine a more realistic optimized portfolio. For illustration purposes, the sectorial indices data in FTSE Bursa Malaysia is employed. The results show that stochastic optimization provides more stable information ratio.

• OSTI ID: 22311274
• Journal Information: AIP Conference Proceedings, Vol. 1602, Issue 1; Conference: 3. international conference on mathematical sciences, Kuala Lumpur (Malaysia), 17-19 Dec 2013; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICAL METHODS AND COMPUTING
ECONOMICS
MATHEMATICAL MODELS
MATHEMATICAL SOLUTIONS
OPTIMIZATION
STOCHASTIC PROCESSES
VOLATILITY