• Medientyp: E-Book
  • Titel: Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns
  • Beteiligte: Goh, Joel [Verfasser:in]; Lim, Kian-Guan [Sonstige Person, Familie und Körperschaft]; Sim, Melvyn [Sonstige Person, Familie und Körperschaft]; Zhang, Weina [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [S.l.]: SSRN, [2017]
  • Umfang: 1 Online-Ressource (31 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.1394922
  • Identifikator:
  • Entstehung:
  • Anmerkungen: In: European Journal of Operational Research, 2012, Vol 221, 397-406
    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 22, 2012 erstellt
  • Beschreibung: We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a newly-defined Partitioned Value-at-Risk (PVaR) risk measure by using half-space statistical information. Using simulated data, the PVaR approach always generates better risk-return tradeoffs in the optimal portfolios when compared to traditional Markowitz mean-variance approach. When using real financial data, our approach also outperforms the Markowitz approach in the risk-return tradeoff. Given that the PVaR measure is also a robust risk measure, our new approach can be very useful for optimal portfolio allocations when asset return distributions are asymmetrical
  • Zugangsstatus: Freier Zugang