• Medientyp: E-Book
  • Titel: Convergence of the Embedded Mean-Variance Optimal Points with Discrete Sampling
  • Beteiligte: Dang, Duy-Minh [VerfasserIn]; Forsyth, Peter [Sonstige Person, Familie und Körperschaft]; Li, Yuying [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [S.l.]: SSRN, [2017]
  • Umfang: 1 Online-Ressource (28 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.2346912
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 18, 2015 erstellt
  • Beschreibung: A numerical technique based on the embedding technique proposed in [21, 33] for dynamic mean-variance (MV) optimization problems may yield spurious points, i.e. points which are not on the efficient frontier. In [27], it is shown that spurious points can be eliminated by examining the left upper convex hull of the solution of the embedded problem. However, any numerical algorithm will generate only a discrete sampling of the solution set of the embedded problem. In this paper, we formally establish that, under mild assumptions, every limit point of a suitably defined sequence of upper convex hulls of the sampled solution of the embedded problem is on the original MV efficient frontier. For illustration, we discuss an MV asset-liability problem under jump diffusions, which is solved using a numerical Hamilton-Jacobi-Bellman partial differential equation approach
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