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

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.
Authors:
; ;  [1]
  1. School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)
Publication Date:
OSTI Identifier:
22311274
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1602; Journal 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)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; ECONOMICS; MATHEMATICAL MODELS; MATHEMATICAL SOLUTIONS; OPTIMIZATION; STOCHASTIC PROCESSES; VOLATILITY