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Simulation-Based Optimal Portfolio Selection Strategy¡ªEvidence from Asian Markets


  • Longqing Li


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.

Suggested Citation

  • Longqing Li, 2018. "Simulation-Based Optimal Portfolio Selection Strategy¡ªEvidence from Asian Markets," Applied Economics and Finance, Redfame publishing, vol. 5(5), pages 1-9, September.
  • Handle: RePEc:rfa:aefjnl:v:5:y:2018:i:5:p:1-9

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    More about this item


    portfolio optimization; mean-CVaR; Monte Carlo; hyperbolic distribution;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General


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