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Robust Optimization Approaches to Single Period Portfolio Allocation Problem

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Robustness Analysis in Decision Aiding, Optimization, and Analytics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 241))

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Abstract

Portfolio management is one of the fundamental problems in financial decision making. In a typical portfolio management problem, an investor is concerned with an optimal allocation of the capital among a number of available financial assets to maximize the return on the investment while minimizing the risk. This problem was formulated in the mean-variance portfolio management framework proposed by Markowitz in 1952. Since then, it has been widely studied by researchers and the practitioners. However, the solution is sensitive to model parameters due to data uncertainty. In this chapter, we review robust approaches to deal with data uncertainty for a single-period portfolio allocation problem. We first introduce the main ideas of robust optimization using symmetric and asymmetric uncertainty sets where the uncertain asset returns belong to. We then focus on data driven and distributionally robust optimization approaches.

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Authors and Affiliations

  1. Warwick Business School, The University of Warwick, Coventry, UK

    Nalân Gülpınar & Zhezhi Hu

Authors
  1. Nalân Gülpınar
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  2. Zhezhi Hu
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Correspondence to Nalân Gülpınar .

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Editors and Affiliations

  1. School of Production Engineering & Mgmt, Technical University of Crete, Chania, Greece

    Michael Doumpos

  2. School of Production Engineering & Mgmt, Technical University of Crete, Chania, Greece

    Constantin Zopounidis

  3. School of Production Engineering & Mgmt, Technical University of Crete, Chania, Greece

    Evangelos Grigoroudis

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Gülpınar, N., Hu, Z. (2016). Robust Optimization Approaches to Single Period Portfolio Allocation Problem. In: Doumpos, M., Zopounidis, C., Grigoroudis, E. (eds) Robustness Analysis in Decision Aiding, Optimization, and Analytics. International Series in Operations Research & Management Science, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-33121-8_12

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