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Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets
Applied Economics and Finance
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| Title |
Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets
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| Creator |
Li, Longqing
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| Description |
Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.
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| Publisher |
Redfame Publishing
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| Contributor |
—
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| Date |
2018-07-13
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
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| Format |
application/pdf
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| Identifier |
http://redfame.com/journal/index.php/aef/article/view/3376
10.11114/aef.v5i5.3376 |
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| Source |
Applied Economics and Finance; Vol 5, No 5 (2018); 1-9
2332-7308 2332-7294 |
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| Language |
eng
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| Relation |
http://redfame.com/journal/index.php/aef/article/view/3376/3735
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| Rights |
Copyright (c) 2018 Applied Economics and Finance
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