Mean-Variance Portfolio Optimization Under Parametric Uncertainty | Научно-инновационный портал СФУ

Mean-Variance Portfolio Optimization Under Parametric Uncertainty

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Springer Science and Business Media Deutschland GmbH; 14 October 2020 through 17 October 2020; 14 October 2020 through 17 October 2020

Год издания: 2020

Идентификатор DOI: 10.1007/978-3-030-63319-6_74

Ключевые слова: efficient frontier, investment portfolio, markowitz model, mean-variance analysis, optimal solution

Аннотация: In this paper portfolio optimization problem is studied under premise that the asset classes in investment portfolio are selected but the values of asset returns and variances are random. The sensitivity of the Markowitz model to market uncertainties is described by ‘conservative’, ‘nominal’ and ‘optimistic’ formulations of the problem. An algorithm for defining the initial parameters to describe the conservative and optimistic formulation of the Markowitz problem is developed. The shift of the efficient frontier of portfolios is evaluated. The applicability of the developed model is demonstrated by considering an illustrative example via the Trading Organiser ‘Moscow Exchange’. The nominal, conservative and optimistic portfolio weight distributions are obtained. The difference in the obtained weight distributions and the shift of efficient frontiers confirm that the solution of the Markowitz problem is sensitive to changing market parameters. Mean – standard deviation diagrams for the nominal, optimistic and conservative weight distributions are constructed, the interval values of portfolio means and risk are assessed in real time. It is concluded that portfolios with a conservative weight distribution produce better results than nominal and optimistic portfolios: higher mean return values are obtained at a lower risk. The obtained results are of practical interest under conditions of financial market instability. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Издание

Журнал: Advances in Intelligent Systems and Computing

Выпуск журнала: Vol. 1295

Номера страниц: 797-812

ISSN журнала: 00253159

Авторы

  • Malakhova A.A. (Siberian Federal University, Krasnoyarsk, Russian Federation)
  • Starova O.V. (Siberian Federal University, Krasnoyarsk, Russian Federation)
  • Yarkova S.A. (Krasnoyarsk Institute of Railway Transport - Branch of the Irkutsk State Transport University, Krasnoyarsk, Russian Federation)
  • Danilova A.S. (Krasnoyarsk Institute of Railway Transport - Branch of the Irkutsk State Transport University, Krasnoyarsk, Russian Federation)
  • Zdanovich M.Y. (Siberian Federal University, Krasnoyarsk, Russian Federation, Krasnoyarsk Institute of Railway Transport - Branch of the Irkutsk State Transport University, Krasnoyarsk, Russian Federation)
  • Kravtsov D.I. (Siberian Federal University, Krasnoyarsk, Russian Federation)
  • Zyablikov D.V. (Siberian Federal University, Krasnoyarsk, Russian Federation)
  • Silhavy R.
  • Silhavy P.
  • Prokopova Z.

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